From 383355f744373f3264c48f87627b867e257a114d Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 13 May 2025 12:37:05 -0700 Subject: [PATCH 01/79] predict_single_sample_DLBCLone --- DESCRIPTION | 2 +- NAMESPACE | 1 + R/umap.R | 1402 ++++++++++++++-------- man/construct_reduced_winning_version.Rd | 10 +- man/make_and_annotate_umap.Rd | 2 +- man/predict_single_sample_DLBCLone.Rd | 85 ++ 6 files changed, 990 insertions(+), 512 deletions(-) create mode 100644 man/predict_single_sample_DLBCLone.Rd diff --git a/DESCRIPTION b/DESCRIPTION index b2c3098..77e5dc7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,7 +15,7 @@ Description: Collection of functions and helpers to classify different B-cell ly License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.2 Depends: R (>= 3.5.0) LazyData: true diff --git a/NAMESPACE b/NAMESPACE index 93e5d72..cc67d3d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,6 +10,7 @@ export(construct_reduced_winning_version) export(make_and_annotate_umap) export(massage_matrix_for_clustering) export(predict_single_sample) +export(predict_single_sample_DLBCLone) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) import(GAMBLR.data) diff --git a/R/umap.R b/R/umap.R index e35114b..15b4725 100644 --- a/R/umap.R +++ b/R/umap.R @@ -11,76 +11,104 @@ #' @returns a list of data frames with the predictions and the UMAP input #' @export #' -predict_single_sample = function(test_df, - train_df, - train_metadata, - best_params, - truth_classes = c("EZB","MCD","ST2","N1","BN2"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE){ - train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) - train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - - if(nrow(test_df)>1){ - message("Warning: you have supplied more than one sample to test with. Will proceed with all") - } - combined_mutation_status_df = bind_rows(test_df,train_df) - if(any(rownames(test_df) %in% train_metadata_use$sample_id)){ - stop("one or more samples overlap with your training data!") - } - placeholder_meta = data.frame(sample_id = rownames(test_df)) - train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) - - outs = make_and_annotate_umap(df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - init="random", - n_epochs = 1500, - seed=best_params$seed, - metadata=train_metadata_use, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = best_params$na_option) - - test_coords = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df), - is.na(lymphgen)) %>% select(sample_id,V1,V2) %>% +predict_single_sample = function( + test_df, + train_df, + train_metadata, + best_params, + truth_classes = c("EZB","MCD","ST2","N1","BN2"), + drop_unlabeled_from_training=TRUE, + make_plot = TRUE +){ + train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) + train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) + + if(nrow(test_df)>1){ + message("Warning: you have supplied more than one sample to test with. Will proceed with all") + } + + combined_mutation_status_df = bind_rows(test_df,train_df) + + if(any(test_df$sample_id %in% train_metadata_use$sample_id)){ + stop("one or more samples overlap with your training data!") + } + + placeholder_meta = data.frame(sample_id = test_df$sample_id) + train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) + + outs = make_and_annotate_umap( + df=combined_mutation_status_df, + min_dist = 0, + n_neighbors = 55, + init="spca", + n_epochs = 1500, + seed=best_params$seed, + metadata=train_metadata_use, + ret_model=TRUE, + metric="cosine", + join_column="sample_id", + na_vals = best_params$na_option + ) + + train_coords = dplyr::filter( + outs$df, + sample_id %in% train_df$sample_id, + ) %>% + select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - pred = weighted_knn_predict_with_conf( - train_coords = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df), - !is.na(lymphgen)) %>% select(V1,V2), - train_labels = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df) - !is.na(lymphgen)) %>% pull(lymphgen), - test_coords = test_coords, - k=best_params$k, - conf_threshold = best_params$threshold, - na_label="Other", - use_weights = best_params$use_w, - ignore_top = FALSE) - pred$sample_id = rownames(test_coords) - - if(drop_unlabeled_from_training){ - pred = dplyr::filter(pred,sample_id %in% rownames(test_df)) - } - #print(head(outs$df)) - anno_out = left_join(pred,outs$df,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - if(make_plot){ + #View(train_coords) - pp = ggplot(outs$df,aes(x=V1,y=V2,colour=lymphgen,label=sample_id)) + - geom_point() + - geom_point(data = anno_out,aes(colour=predicted_label)) + - geom_label_repel(data = anno_out, - aes(x=V1,y=V2,label=label), - nudge_x=1,nudge_y=1,colour="black") + - scale_colour_manual(values=get_gambl_colours()) + - ggtitle(paste("k:",best_params$k,"seed:",best_params$seed)) - print(pp) - } - return(list(prediction = pred, umap_input = outs$features, model=outs$model)) + train_labels = dplyr::filter( + outs$df, + sample_id %in% train_df$sample_id + ) %>% + pull(lymphgen) + + test_coords = dplyr::filter( + outs$df, + sample_id %in% test_df$sample_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = FALSE + ) + + pred$sample_id = test_df$sample_id + + if(drop_unlabeled_from_training){ + pred = dplyr::filter(pred,sample_id %in% test_df$sample_id) + } + + #print(head(outs$df)) + anno_out = left_join(pred,outs$df,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + + if(make_plot){ + pp = ggplot(outs$df,aes(x=V1,y=V2,colour=lymphgen,label=sample_id)) + + geom_point() + + geom_point(data = anno_out,aes(colour=predicted_label)) + + geom_label_repel( + data = anno_out, + aes(x=V1,y=V2,label=label), + nudge_x=1, + nudge_y=1, + colour="black" + ) + + scale_colour_manual(values=get_gambl_colours()) + + ggtitle(paste("k:",best_params$k,"seed:",best_params$seed)) + print(pp) + } + + return(list(prediction = pred, umap_input = outs$features, model=outs$model)) } #' Plot the result of a lymphgen classification @@ -113,82 +141,129 @@ predict_single_sample = function(test_df, #' classes = c("MCD","EZB","BN2","ST2","N1","A53","Other"), #' annotate_accuracy=TRUE,label_offset = 1) #' -DLBCLone_train_test_plot = function(test_df, - train_df, - predictions_df, - other_df, - details, - annotate_accuracy = FALSE, - classes = c("BN2","ST2","MCD","EZB","N1"), - label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other"){ - - title = paste0("N_class:",details$num_classes," N_feats:",details$num_features," k=",details$k," threshold=",details$threshold," bacc=",round(details$accuracy,3)) - if("BN2" %in% classes){ - print(details) - acc_df = data.frame(lymphgen = c("N1","BN2","EZB","MCD","ST2","Other","A53"), - accuracy = c(details$N1_bacc, - details$BN2_bacc, - details$EZB_bacc, - details$MCD_bacc, - details$ST2_bacc, - details$Other_bacc, - details$A53_bacc)) - }else if("C1" %in% classes){ - acc_df = data.frame(lymphgen = c("C1","C2","C3","C4","C5"), - accuracy = c(details$C1_bacc, - details$C2_bacc, - details$C3_bacc, - details$C4_bacc, - details$C5_bacc)) - }else{ - stop("no labels to add?") - } - # Add the predicted labels for Other (unclassified) cases, if provided - if(!missing(other_df)){ - in_df = bind_rows(train_df, - test_df, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label), - mutate(other_df,dataset=title3,lymphgen=predicted_label) - ) - }else{ - in_df = bind_rows(train_df, - test_df, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label) - ) - } - - pp = ggplot(in_df) + - geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + - scale_colour_manual(values=get_gambl_colours()) + - facet_wrap(~dataset,ncol=1) + - theme_Morons() + ggtitle(title) - if(annotate_accuracy){ - #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label %in% classes) %>% - group_by(predicted_label) %>% - summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% - mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% - mutate(lymphgen=predicted_label) - centroids = left_join(centroids,acc_df) %>% - mutate(label=paste(lymphgen,":",round(accuracy,3))) +DLBCLone_train_test_plot = function( + test_df, + train_df, + predictions_df, + other_df, + details, + annotate_accuracy = FALSE, + classes = c("BN2","ST2","MCD","EZB","N1"), + label_offset = 2, + title1="GAMBL", + title2="predicted_class_for_HighConf", + title3 ="predicted_class_for_Other" +){ + + title = paste0("N_class:",details$num_classes," N_feats:",details$num_features," k=",details$k," threshold=",details$threshold," bacc=",round(details$accuracy,3)) + if("BN2" %in% classes){ + print(details) + acc_df = data.frame( + lymphgen = c( + "N1", + "BN2", + "EZB", + "MCD", + "ST2", + "Other", + "A53"), + accuracy = c( + details$N1_bacc, + details$BN2_bacc, + details$EZB_bacc, + details$MCD_bacc, + details$ST2_bacc, + details$Other_bacc, + details$A53_bacc + ) + ) + }else if("C1" %in% classes){ + acc_df = data.frame( + lymphgen = c( + "C1", + "C2", + "C3", + "C4", + "C5" + ), + accuracy = c( + details$C1_bacc, + details$C2_bacc, + details$C3_bacc, + details$C4_bacc, + details$C5_bacc + ) + ) + }else{ + stop("no labels to add?") + } + # Add the predicted labels for Other (unclassified) cases, if provided + if(!missing(other_df)){ + in_df = bind_rows( + train_df, + test_df, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label), + mutate(other_df,dataset=title3,lymphgen=predicted_label) + ) + }else{ + in_df = bind_rows( + train_df, + test_df, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + ) + } + + pp = ggplot(in_df) + + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + + scale_colour_manual(values=get_gambl_colours()) + + facet_wrap(~dataset,ncol=1) + + theme_Morons() + ggtitle(title) + if(annotate_accuracy){ + #add labels and set nudge direction based on what quadrant each group sits in + centroids = filter(predictions_df,predicted_label %in% classes) %>% + group_by(predicted_label) %>% + summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% + mutate(lymphgen=predicted_label) + centroids = left_join(centroids,acc_df) %>% + mutate(label=paste(lymphgen,":",round(accuracy,3))) centroids$dataset = title2 - pp = pp + geom_label_repel(data=filter(centroids,nudge_y < 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label),fill="white",size=5,nudge_y = -1 * label_offset , nudge_x = -1 * label_offset) + - geom_label_repel(data=filter(centroids,nudge_y < 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label),size=5,nudge_y = -1 * label_offset , nudge_x = 1 * label_offset) + - geom_label_repel(data=filter(centroids,nudge_y > 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label),size=5,nudge_y = 1 * label_offset , nudge_x = -1 * label_offset) + - geom_label_repel(data=filter(centroids,nudge_y > 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label),fill="white",size=5,nudge_y = 1 * label_offset , nudge_x = 1 * label_offset) - } - pp + pp = pp + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = -1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = -1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = 1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset + ) + } + pp } - #' Run UMAP and attach result to metadata #' #' @param df Feature matrix with one row per sample and one column per mutation @@ -225,66 +300,82 @@ DLBCLone_train_test_plot = function(test_df, #' metric="cosine") #' #' -make_and_annotate_umap = function(df, - metadata, - umap_out, - n_neighbors=55, - min_dist=0, - metric="cosine", - n_epochs=1500, - init="spca", - ret_model=TRUE, - na_vals = "drop", - join_column="sample_id", - seed=42){ - original_n = nrow(df) - if(na_vals == "to_zero"){ - df[is.na(df)] = 0 - }else if(na_vals == "drop"){ - df <- df[, colSums(is.na(df)) == 0] - } - rs = rowSums(df,na.rm=TRUE) - df = df[rs>0,] - if(missing(df)){ - stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") - } - if(missing(metadata)){ - stop("metadata is required and should contain a column sample_id that matches the row names of your mutation data frame") - } - - df= df[rownames(df) %in% metadata[[join_column]],] - - if(missing(umap_out)){ - umap_out = umap2(df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1) # possibly add rng_type = "deterministic" -#IMPORTANT: n_threads must not be changed because it will break reproducibility - - }else{ - umap_out = umap_transform(X=df, - model=umap_out$model) - ret_model = FALSE - } - if(ret_model){ - umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) - }else{ - umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) - } - umap_df = left_join(umap_df,metadata) - - results = list() - results[["df"]]=umap_df - results[["features"]] = df - if(ret_model){ - results[["model"]]= umap_out - } - return(results) +make_and_annotate_umap = function( + df, + metadata, + umap_out, + n_neighbors = 55, + min_dist = 0, + metric = "cosine", + n_epochs = 1500, + init = "spca", + ret_model = TRUE, + na_vals = "drop", + join_column = "sample_id", + seed = 12345 +){ + df = df %>% column_to_rownames(var = "sample_id") + original_n = nrow(df) + + if (na_vals == "to_zero") { + df[is.na(df)] = 0 + } else if (na_vals == "drop") { + df <- df[, colSums(is.na(df)) == 0] + } + + rs = rowSums(df, na.rm = TRUE) + df = df[rs > 0, ] + + if (missing(df)) { + stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") + } + + if (ret_model && missing(metadata)) { + stop("metadata is required and should contain a column sample_id that matches the row names of your mutation data frame") + } + + # Keep only samples in metadata for training mode + if (ret_model) { + df = df[rownames(df) %in% metadata[[join_column]], ] + } + + if (missing(umap_out)) { + umap_out = umap2( + df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs = n_epochs, + init = init, + seed = seed, + n_threads = 1 + ) + } else { + umap_out = umap_transform( + X = df, + model = umap_out + ) + ret_model = FALSE + } + + if (ret_model) { + umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) + umap_df = left_join(umap_df, metadata, by = join_column) + } else { + umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) + # No metadata join for projection-only mode + } + + results = list() + results[["df"]] = umap_df + results[["features"]] = df + + if (ret_model) { + results[["model"]] = umap_out + } + + return(results) } #' Optimize parameters for classifying samples using UMAP and k-nearest neighbor @@ -311,221 +402,254 @@ make_and_annotate_umap = function(df, #' umap_out = lymphgen_A53_all_feat_gambl, # force use existing UMAP fit #' eval_group = NULL, # use all samples for evaluating accuracy #' truth_classes = c("MCD","EZB","BN2","ST2","N1","A53","Other")) - -DLBCLone_optimize_params = function(combined_mutation_status_df, - metadata_df, - umap_out, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - eval_group = "Lacy", - min_k=3, - max_k=30, - verbose = FALSE, - seed = 12345){ - na_opt = c("drop") - num_class = length(truth_classes) - weights_opt = c(TRUE,FALSE) - threshs = seq(0,0.95,0.05) - #ks = seq(5,25,1) - ks = seq(min_k,max_k,1) - results <- data.frame() - best_params <- data.frame() - #best so far: - threshold = 0.3 - k = 6 - use_w = TRUE - best_acc = 0 - best_fit = NULL - - best_pred = NULL - other_pred = NULL - for(na_option in na_opt){ - if(missing(umap_out)){ - outs = make_and_annotate_umap(df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option) - }else{ - #project onto existing model instead of re-running UMAP - outs = make_and_annotate_umap(df=combined_mutation_status_df, - umap_out = umap_out, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option) - } - ignore_top = FALSE - if(is.null(eval_group)){ - ignore_top = TRUE - } - for(use_w in weights_opt){ - for(k in ks){ +DLBCLone_optimize_params = function( + combined_mutation_status_df, + metadata_df, + umap_out, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), + eval_group = "Lacy", + min_k=3, + max_k=30, + verbose = FALSE, + seed = 12345 +){ + na_opt = c("drop") + num_class = length(truth_classes) + weights_opt = c(TRUE,FALSE) + threshs = seq(0,0.95,0.05) + ks = seq(min_k,max_k,1) + results <- data.frame() + best_params <- data.frame() + #best so far: + threshold = 0.3 + k = 6 + use_w = TRUE + best_acc = 0 + best_fit = NULL + best_pred = NULL + other_pred = NULL + + for(na_option in na_opt){ + if(missing(umap_out)){ + outs = make_and_annotate_umap( + df=combined_mutation_status_df, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + init = "spca", + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) + }else{ + #project onto existing model instead of re-running UMAP + outs = make_and_annotate_umap( + df=combined_mutation_status_df, + umap_out = umap_out, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + init = "spca", + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) + } + + ignore_top = FALSE + if(is.null(eval_group)){ + ignore_top = TRUE + } + + for(use_w in weights_opt){ + for(k in ks){ - for (threshold in threshs){ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - - }else{ - - test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) - train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - } - if(verbose){ - print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) - } + for (threshold in threshs){ + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + }else{ + test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) + train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + } - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=k, - conf_threshold =threshold, - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose) - - if(is.null(eval_group)){ - xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) - train_d = filter(outs$df,lymphgen %in% truth_classes) - }else{ - xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) - train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) - } - if("Other" %in% truth_classes){ - xx_d$lymphgen = factor(xx_d$lymphgen) - }else{ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - }else{ - test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) - train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - } - n_other = nrow(test_coords) - - if(!"Other" %in% truth_classes && n_other > 0){ - pred_other = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=k, - conf_threshold =threshold, - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose) - - if(!is.null(eval_group)){ - xx_o = bind_cols(filter(outs$df,dataset == eval_group,lymphgen =="Other") ,pred_other) - }else{ - xx_o = bind_cols(filter(outs$df,lymphgen == "Other") ,pred_other) - } - } - xx_d$lymphgen = factor(xx_d$lymphgen,levels = c(unique(xx_d$lymphgen),"Other")) - - } - - true_factor = factor(xx_d$lymphgen,levels = levels(xx_d$lymphgen)) - if(verbose){ - print("true_factor") - print(levels(true_factor )) - print("===") - print(levels(xx_d$predicted_label)) - } - - conf_matrix <- confusionMatrix(xx_d$predicted_label, true_factor) - - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class - sn <- conf_matrix$byClass[, "Sensitivity"] # one per class - if(verbose){ - print(bal_acc) - } - - overall_balanced_accuracy <- mean(bal_acc, na.rm = TRUE) - row <- data.frame(k = k, - threshold = threshold, - use_weights = use_w, - accuracy = overall_balanced_accuracy, - N1_sn = unname(sn["Class: N1"]), - BN2_sn= unname(sn["Class: BN2"]), - ST2_sn = unname(sn["Class: ST2"]), - N1_bacc = unname(bal_acc["Class: N1"]), - BN2_bacc = unname(bal_acc["Class: BN2"]), - MCD_bacc = unname(bal_acc["Class: MCD"]), - EZB_bacc = unname(bal_acc["Class: EZB"]), - A53_bacc = unname(bal_acc["Class: A53"]), - Other_bacc = unname(bal_acc["Class: Other"]), - C1_bacc = unname(bal_acc["Class: C1"]), - C2_bacc = unname(bal_acc["Class: C2"]), - C3_bacc = unname(bal_acc["Class: C3"]), - C4_bacc = unname(bal_acc["Class: C4"]), - C5_bacc = unname(bal_acc["Class: C5"]), - ST2_bacc = unname(bal_acc["Class: ST2"]), na_option= na_option) - if(overall_balanced_accuracy > best_acc){ - best_acc = overall_balanced_accuracy - print(paste("best accuracy:",best_acc,"k:",k,"threshold:",threshold,"na:",na_option,"Balanced accuracy:",overall_balanced_accuracy)) - - best_fit = outs - best_pred = xx_d - if(!"Other" %in% truth_classes && n_other > 0){ - other_pred = pred_other - } + if(verbose){ + print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) + } + + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=k, + conf_threshold =threshold, + na_label="Other", + use_weights = use_w, + ignore_top = ignore_top, + verbose = verbose + ) - best_params = row - } - results <- rbind(results, row) + if(is.null(eval_group)){ + xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) + train_d = filter(outs$df,lymphgen %in% truth_classes) + }else{ + xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) + train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) + } + + if("Other" %in% truth_classes){ + xx_d$lymphgen = factor(xx_d$lymphgen) + }else{ + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + }else{ + test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) + train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) + } + + n_other = nrow(test_coords) + + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=k, + conf_threshold =threshold, + na_label="Other", + use_weights = use_w, + ignore_top = ignore_top, + verbose = verbose + ) + + if(!is.null(eval_group)){ + xx_o = bind_cols(filter(outs$df,dataset == eval_group,lymphgen =="Other") ,pred_other) + }else{ + xx_o = bind_cols(filter(outs$df,lymphgen == "Other") ,pred_other) + } + } + + xx_d$lymphgen = factor(xx_d$lymphgen,levels = c(unique(xx_d$lymphgen),"Other")) + + } + + true_factor = factor(xx_d$lymphgen,levels = levels(xx_d$lymphgen)) + + if(verbose){ + print("true_factor") + print(levels(true_factor )) + print("===") + print(levels(xx_d$predicted_label)) + } + + conf_matrix <- confusionMatrix(xx_d$predicted_label, true_factor) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class + sn <- conf_matrix$byClass[, "Sensitivity"] # one per class + + if(verbose){ + print(bal_acc) + } + + overall_balanced_accuracy <- mean(bal_acc, na.rm = TRUE) + row <- data.frame( + k = k, + threshold = threshold, + use_weights = use_w, + accuracy = overall_balanced_accuracy, + N1_sn = unname(sn["Class: N1"]), + BN2_sn= unname(sn["Class: BN2"]), + ST2_sn = unname(sn["Class: ST2"]), + N1_bacc = unname(bal_acc["Class: N1"]), + BN2_bacc = unname(bal_acc["Class: BN2"]), + MCD_bacc = unname(bal_acc["Class: MCD"]), + EZB_bacc = unname(bal_acc["Class: EZB"]), + A53_bacc = unname(bal_acc["Class: A53"]), + Other_bacc = unname(bal_acc["Class: Other"]), + C1_bacc = unname(bal_acc["Class: C1"]), + C2_bacc = unname(bal_acc["Class: C2"]), + C3_bacc = unname(bal_acc["Class: C3"]), + C4_bacc = unname(bal_acc["Class: C4"]), + C5_bacc = unname(bal_acc["Class: C5"]), + ST2_bacc = unname(bal_acc["Class: ST2"]), + na_option= na_option + ) + + if(overall_balanced_accuracy > best_acc){ + best_acc = overall_balanced_accuracy + print(paste( + "best accuracy:", + best_acc, + "k:", + k, + "threshold:", + threshold, + "na:", + na_option, + "Balanced accuracy:", + overall_balanced_accuracy + )) + + best_fit = outs + best_pred = xx_d + + if(!"Other" %in% truth_classes && n_other > 0){ + other_pred = pred_other + } + + best_params = row + + } + results <- rbind(results, row) + } + } } - } } - } - best_params$num_classes = num_class - best_params$num_features = ncol(best_fit$features) - best_params$seed = seed - - test_coords = outs$df %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=best_params$k, - conf_threshold =best_params$threshold, - na_label="Other", - use_weights = best_params$use_weights, - ignore_top = ignore_top, - verbose = verbose) - xx_d = bind_cols(outs$df,pred) - to_ret = list(params=results, - best_params = best_params, - model=best_fit$model, - features=best_fit$features, - df=outs$df, - predictions=xx_d) - if(!"Other" %in% truth_classes && n_other > 0){ - to_ret[["predictions_other"]] = xx_o - to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) - } - return(to_ret) -} + best_params$num_classes = num_class + best_params$num_features = ncol(best_fit$features) + best_params$seed = seed + + test_coords = outs$df %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=best_params$k, + conf_threshold =best_params$threshold, + na_label="Other", + use_weights = best_params$use_weights, + ignore_top = ignore_top, + verbose = verbose + ) + xx_d = bind_cols(outs$df,pred) + to_ret = list( + params=results, + best_params = best_params, + model=best_fit$model, + features=best_fit$features, + df=outs$df, + predictions=xx_d + ) + + if(!"Other" %in% truth_classes && n_other > 0){ + to_ret[["predictions_other"]] = xx_o + to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) + } + return(to_ret) +} #' Weighted k-nearest neighbor with confidence estimate #' @@ -553,114 +677,380 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, #' @returns data frame with labels and confidence values for rows in test_coords #' @export #' -weighted_knn_predict_with_conf <- function(train_coords, - train_labels, - test_coords, - k, - epsilon = 1e-5, - conf_threshold = NULL, - na_label = "Other", - verbose = FALSE, - use_weights = TRUE, - ignore_top = FALSE, - track_neighbors = FALSE) { - if (nrow(train_coords)==0 || nrow(test_coords) == 0) { - print("train_coords:") - print(nrow(train_coords)) - print("test:") - print(nrow(test_coords)) - stop("train_coords and test_coords must be data frames with at least one row") - } +weighted_knn_predict_with_conf <- function( + train_coords, + train_labels, + test_coords, + k, + epsilon = 1e-5, + conf_threshold = NULL, + na_label = "Other", + verbose = FALSE, + use_weights = TRUE, + ignore_top = FALSE, + track_neighbors = FALSE +) { + if (nrow(train_coords)==0 || nrow(test_coords) == 0) { + print("train_coords:") + print(nrow(train_coords)) + print("test:") + print(nrow(test_coords)) + stop("train_coords and test_coords must be data frames with at least one row") + } + + nn <- get.knnx(train_coords, test_coords, k) + all_neighbors = data.frame() + preds <- character(nrow(test_coords)) + confs <- numeric(nrow(test_coords)) + + train_labels = as.character(train_labels) - nn <- get.knnx(train_coords, test_coords, k) - all_neighbors = data.frame() - preds <- character(nrow(test_coords)) - confs <- numeric(nrow(test_coords)) - - train_labels = as.character(train_labels) - for (i in 1:nrow(test_coords)) { - neighbors <- nn$nn.index[i, ] - distances <- nn$nn.dist[i, ] - if(ignore_top){ - #ignore a neighbor if it has identical V1 and V2 - distances <- nn$nn.dist[i, ] - if(distances[1] == 0){ - neighbors = neighbors[-1] - distances = distances[-1] - } + for (i in 1:nrow(test_coords)) { + neighbors <- nn$nn.index[i, ] + distances <- nn$nn.dist[i, ] + if(ignore_top){ + #ignore a neighbor if it has identical V1 and V2 + distances <- nn$nn.dist[i, ] + if(distances[1] == 0){ + neighbors = neighbors[-1] + distances = distances[-1] + } + + } + distances = distances + epsilon + weights <- 1 / distances + + if(use_weights){ + weights <- 1 / distances + }else{ + weights <- rep(1,length(distances)) + } + + + neighbor_labels <- train_labels[neighbors] + + if(verbose){ + print("neighbors:") + print(neighbors) + print("distances:") + print(distances) + print("weights:") + print(weights) + print("labels:") + print(neighbor_labels) + } + # Remove NAs (just in case) + valid <- !is.na(neighbor_labels) + neighbor_labels <- neighbor_labels[valid] + weights <- weights[valid] + + if (length(neighbor_labels) == 0) { + preds[i] <- na_label + confs[i] <- NA + next + } + + weighted_votes <- tapply(weights, neighbor_labels, sum) + + if(track_neighbors){ + # Create a data frame to store neighbors, distances, and weights + neighbor_info <- data.frame( + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + #weight = paste(round(weights, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = paste(weighted_votes,collapse=",") + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } + + if (length(weighted_votes) == 0) { + preds[i] <- na_label + confs[i] <- NA + } else { + predicted_label <- names(which.max(weighted_votes)) + total_weight <- sum(weighted_votes) + pred_weight <- weighted_votes[predicted_label] + confidence <- pred_weight / total_weight + # Confidence thresholding + if (!is.null(conf_threshold) && confidence < conf_threshold) { + preds[i] <- na_label + confs[i] <- confidence + } else { + preds[i] <- predicted_label + confs[i] <- confidence + } + } } - distances = distances + epsilon - weights <- 1 / distances - if(use_weights){ - weights <- 1 / distances - }else{ - weights <- rep(1,length(distances)) + + to_return = data.frame( + predicted_label = factor(preds), + confidence = confs) + + if(track_neighbors){ + print(dim(to_return)) + print(dim(all_neighbors)) + to_return = bind_cols(to_return,all_neighbors) } + + return(to_return) +} - neighbor_labels <- train_labels[neighbors] +#' Predict class for a single sample without using umap_transform and plot result of classification +#' +#' @param seed Random seed for reproducibility +#' @param test_df Data frame containing the mutation status of the test sample +#' @param train_df Data frame containing the mutation status of the training samples +#' @param train_metadata Metadata for training samples with truth labels in lymphgen column +#' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful +#' for reproducibility and for using the same UMAP model on different datasets. +#' @param best_params Data frame from DLBCLone_optimize_params with the best parameters +#' @param plot_metadata Data frame containing the training data with UMAP coordinates from DLBCLone_optimize_params +#' @param predictions_df Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params +#' @param other_df Data frame containing the predictions for samples in the "Other" class +#' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") +#' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data +#' @param make_plot Set to TRUE to plot the UMAP projection and predictions +#' @param annotate_accuracy Set to true to add labels with accuracy values +#' @param classes Vector of classes that were used in the training and testing +#' @param label_offset Length of the label offset for the accuracy labels +#' @param title1 additional argument +#' @param title2 additional argument +#' @param title3 additional argument +#' +#' @returns a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +#' @export +#' +#' @examples +#' predict_single_sample_DLBCLone( +#' seed = 1234, +#' test_df = test_df, +#' train_df = train_df, +#' train_metadata = train_metadata, +#' umap_out = umap_out, +#' best_params = best_params, +#' plot_metadata = plot_metadata, +#' predictions_df = predictions_df, +#' annotate_accuracy = TRUE +#' ) +#' +predict_single_sample_DLBCLone <- function( + seed, + test_df, + train_df, + train_metadata, + umap_out, + best_params, + plot_metadata, + predictions_df, + other_df, + truth_classes = c("EZB","MCD","ST2","N1","BN2"), + drop_unlabeled_from_training=TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + classes = c("BN2","ST2","MCD","EZB","N1"), + label_offset = 2, + title1="GAMBL", + title2="predicted_class_for_HighConf", + title3 ="predicted_class_for_Other" +){ + set.seed(seed) + + train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) + train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - if(verbose){ - print("neighbors:") - print(neighbors) - print("distances:") - print(distances) - print("weights:") - print(weights) - print("labels:") - print(neighbor_labels) + if(nrow(test_df)>1){ + message("Warning: you have supplied more than one sample to test with. Will proceed with all") } - # Remove NAs (just in case) - valid <- !is.na(neighbor_labels) - neighbor_labels <- neighbor_labels[valid] - weights <- weights[valid] - if (length(neighbor_labels) == 0) { - preds[i] <- na_label - confs[i] <- NA - next + + #combined_mutation_status_df = bind_rows(test_df,train_df) + + if(any(test_df$sample_id %in% train_metadata_use$sample_id)){ + stop("one or more samples overlap with your training data!") } + + placeholder_meta = data.frame(sample_id = test_df$sample_id) + train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) - weighted_votes <- tapply(weights, neighbor_labels, sum) - if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - neighbor_info <- data.frame( - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = paste(weighted_votes,collapse=",") - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - } - if (length(weighted_votes) == 0) { - preds[i] <- na_label - confs[i] <- NA - } else { - predicted_label <- names(which.max(weighted_votes)) - total_weight <- sum(weighted_votes) - pred_weight <- weighted_votes[predicted_label] - confidence <- pred_weight / total_weight - - # Confidence thresholding - if (!is.null(conf_threshold) && confidence < conf_threshold) { - preds[i] <- na_label - confs[i] <- confidence - } else { - preds[i] <- predicted_label - confs[i] <- confidence - } + #project test data onto existing model instead of re-running UMAP + test_projection = make_and_annotate_umap( + df = test_df, + umap_out = umap_out$model, + ret_model = FALSE, # now projecting onto existing model + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + + train_coords = dplyr::filter( + umap_out$df, + sample_id %in% train_df$sample_id, + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + train_labels = dplyr::filter( + umap_out$df, + sample_id %in% train_df$sample_id + ) %>% + pull(lymphgen) + + test_coords = dplyr::filter( + test_projection$df, + sample_id %in% test_df$sample_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = FALSE + ) + + pred$sample_id = test_df$sample_id + + if(drop_unlabeled_from_training){ + pred = dplyr::filter(pred,sample_id %in% test_df$sample_id) } - } - to_return = data.frame( - predicted_label = factor(preds), - confidence = confs) - if(track_neighbors){ - print(dim(to_return)) - print(dim(all_neighbors)) - to_return = bind_cols(to_return,all_neighbors) - } - return(to_return) -} + + anno_umap <- select(test_projection$df, sample_id, V1, V2) + anno_out = left_join(pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + anno_out = anno_out %>% + mutate( + V1 = as.numeric(V1), + V2 = as.numeric(V2), + label = as.character(label) + ) + + if(make_plot){ + title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) + if("BN2" %in% classes){ + print(best_params) + acc_df = data.frame( + lymphgen = c( + "N1", + "BN2", + "EZB", + "MCD", + "ST2", + "Other", + "A53" + ), + accuracy = c( + best_params$N1_bacc, + best_params$BN2_bacc, + best_params$EZB_bacc, + best_params$MCD_bacc, + best_params$ST2_bacc, + best_params$Other_bacc, + best_params$A53_bacc + ) + ) + }else if("C1" %in% classes){ + acc_df = data.frame( + lymphgen = c( + "C1", + "C2", + "C3", + "C4", + "C5" + ), + accuracy = c( + best_params$C1_bacc, + best_params$C2_bacc, + best_params$C3_bacc, + best_params$C4_bacc, + best_params$C5_bacc + ) + ) + }else{ + stop("no labels to add?") + } + # Add the predicted labels for Other (unclassified) cases, if provided + if(!missing(other_df)){ + in_df = bind_rows( + plot_metadata, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label), + mutate(other_df,dataset=title3,lymphgen=predicted_label) + ) + }else{ + in_df = bind_rows( + plot_metadata, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + ) + } + + pp = ggplot(in_df) + + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + + scale_colour_manual(values=get_gambl_colours()) + + facet_wrap(~dataset,ncol=1) + + theme_Morons() + ggtitle(title) + + if(annotate_accuracy){ + #add labels and set nudge direction based on what quadrant each group sits in + centroids = filter(predictions_df,predicted_label %in% classes) %>% + group_by(predicted_label) %>% + summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% + mutate(lymphgen=predicted_label) + centroids = left_join(centroids,acc_df) %>% + mutate(label=paste(lymphgen,":",round(accuracy,3))) + + pp = pp + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = -1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = -1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = 1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data = anno_out, + aes(x=V1,y=V2,label=label), + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset, + colour="red" + ) + } + } + return(list( + prediction = pred, + umap_input = umap_out$features, + model=umap_out$model, + plot = pp + )) +} diff --git a/man/construct_reduced_winning_version.Rd b/man/construct_reduced_winning_version.Rd index 6b66577..5e0772a 100644 --- a/man/construct_reduced_winning_version.Rd +++ b/man/construct_reduced_winning_version.Rd @@ -5,17 +5,19 @@ \title{Construct reduced 21-dimension feature vector for DLBCLass} \usage{ construct_reduced_winning_version( - mutations = "inst/extdata/DLBCL.699.fullGSM.Sep_23_2022.tsv", - fisher_test_result = "inst/extdata/fisher_exact_5x2.Sep_23_2022.combined.tsv", + mutations_file = "inst/extdata/DLBCL.699.fullGSM.Sep_23_2022.tsv", + fisher_test_result_file = "inst/extdata/fisher_exact_5x2.Sep_23_2022.combined.tsv", + include_cn = TRUE, + mutation_data, add_missing_features = FALSE ) } \arguments{ -\item{fisher_test_result}{Fisher's test result from DLBCLass github repository} - \item{add_missing_features}{Set to TRUE to fill in missing features with zeroes} \item{data}{Data frame of mutation status from DLBCLass supplement} + +\item{fisher_test_result}{Fisher's test result from DLBCLass github repository} } \value{ a list of data frames with the full mutation features, collapsed diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 38a5d9e..595586b 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -16,7 +16,7 @@ make_and_annotate_umap( ret_model = TRUE, na_vals = "drop", join_column = "sample_id", - seed = 42 + seed = 12345 ) } \arguments{ diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd new file mode 100644 index 0000000..6b890a8 --- /dev/null +++ b/man/predict_single_sample_DLBCLone.Rd @@ -0,0 +1,85 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{predict_single_sample_DLBCLone} +\alias{predict_single_sample_DLBCLone} +\title{Predict class for a single sample without using umap_transform and plot result of classification} +\usage{ +predict_single_sample_DLBCLone( + seed, + test_df, + train_df, + train_metadata, + umap_out, + best_params, + plot_metadata, + predictions_df, + other_df, + truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), + drop_unlabeled_from_training = TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + classes = c("BN2", "ST2", "MCD", "EZB", "N1"), + label_offset = 2, + title1 = "GAMBL", + title2 = "predicted_class_for_HighConf", + title3 = "predicted_class_for_Other" +) +} +\arguments{ +\item{seed}{Random seed for reproducibility} + +\item{test_df}{Data frame containing the mutation status of the test sample} + +\item{train_df}{Data frame containing the mutation status of the training samples} + +\item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} + +\item{umap_out}{UMAP output from a previous run. The function will use this model to project the data, useful +for reproducibility and for using the same UMAP model on different datasets.} + +\item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} + +\item{plot_metadata}{Data frame containing the training data with UMAP coordinates from DLBCLone_optimize_params} + +\item{predictions_df}{Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params} + +\item{other_df}{Data frame containing the predictions for samples in the "Other" class} + +\item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} + +\item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} + +\item{make_plot}{Set to TRUE to plot the UMAP projection and predictions} + +\item{annotate_accuracy}{Set to true to add labels with accuracy values} + +\item{classes}{Vector of classes that were used in the training and testing} + +\item{label_offset}{Length of the label offset for the accuracy labels} + +\item{title1}{additional argument} + +\item{title2}{additional argument} + +\item{title3}{additional argument} +} +\value{ +a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +} +\description{ +Predict class for a single sample without using umap_transform and plot result of classification +} +\examples{ +predict_single_sample_DLBCLone( + seed = 1234, + test_df = test_df, + train_df = train_df, + train_metadata = train_metadata, + umap_out = umap_out, + best_params = best_params, + plot_metadata = plot_metadata, + predictions_df = predictions_df, + annotate_accuracy = TRUE +) + +} From cb006606081cb7e4e7678aa913656d833dcb680c Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 13 May 2025 14:57:43 -0700 Subject: [PATCH 02/79] quick_optimize_fix --- R/umap.R | 12 ++++-------- man/predict_single_sample_DLBCLone.Rd | 6 +----- 2 files changed, 5 insertions(+), 13 deletions(-) diff --git a/R/umap.R b/R/umap.R index 15b4725..2d699cb 100644 --- a/R/umap.R +++ b/R/umap.R @@ -428,7 +428,7 @@ DLBCLone_optimize_params = function( best_fit = NULL best_pred = NULL other_pred = NULL - + for(na_option in na_opt){ if(missing(umap_out)){ outs = make_and_annotate_umap( @@ -454,7 +454,6 @@ DLBCLone_optimize_params = function( init = "spca", seed=seed, metadata=metadata_df, - ret_model=T, metric="cosine", join_column="sample_id", na_vals = na_option) @@ -807,7 +806,6 @@ weighted_knn_predict_with_conf <- function( #' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful #' for reproducibility and for using the same UMAP model on different datasets. #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters -#' @param plot_metadata Data frame containing the training data with UMAP coordinates from DLBCLone_optimize_params #' @param predictions_df Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params #' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") @@ -830,8 +828,7 @@ weighted_knn_predict_with_conf <- function( #' train_df = train_df, #' train_metadata = train_metadata, #' umap_out = umap_out, -#' best_params = best_params, -#' plot_metadata = plot_metadata, +#' best_params = best_params #' predictions_df = predictions_df, #' annotate_accuracy = TRUE #' ) @@ -843,7 +840,6 @@ predict_single_sample_DLBCLone <- function( train_metadata, umap_out, best_params, - plot_metadata, predictions_df, other_df, truth_classes = c("EZB","MCD","ST2","N1","BN2"), @@ -980,13 +976,13 @@ predict_single_sample_DLBCLone <- function( # Add the predicted labels for Other (unclassified) cases, if provided if(!missing(other_df)){ in_df = bind_rows( - plot_metadata, + umap_out$df, mutate(predictions_df,dataset=title2,lymphgen=predicted_label), mutate(other_df,dataset=title3,lymphgen=predicted_label) ) }else{ in_df = bind_rows( - plot_metadata, + umap_out$df, mutate(predictions_df,dataset=title2,lymphgen=predicted_label) ) } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 6b890a8..3f61b37 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -11,7 +11,6 @@ predict_single_sample_DLBCLone( train_metadata, umap_out, best_params, - plot_metadata, predictions_df, other_df, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), @@ -39,8 +38,6 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} -\item{plot_metadata}{Data frame containing the training data with UMAP coordinates from DLBCLone_optimize_params} - \item{predictions_df}{Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} @@ -76,8 +73,7 @@ predict_single_sample_DLBCLone( train_df = train_df, train_metadata = train_metadata, umap_out = umap_out, - best_params = best_params, - plot_metadata = plot_metadata, + best_params = best_params predictions_df = predictions_df, annotate_accuracy = TRUE ) From 3246e243552b92385620acbe7544ebf73b074003 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 13 May 2025 23:22:21 -0700 Subject: [PATCH 03/79] fixed predict_single_sample umap projections --- R/umap.R | 54 ++++++++++++++++++--------- man/DLBCLone_optimize_params.Rd | 2 +- man/make_and_annotate_umap.Rd | 8 ++-- man/predict_single_sample_DLBCLone.Rd | 3 -- 4 files changed, 42 insertions(+), 25 deletions(-) diff --git a/R/umap.R b/R/umap.R index 2d699cb..9393161 100644 --- a/R/umap.R +++ b/R/umap.R @@ -304,15 +304,15 @@ make_and_annotate_umap = function( df, metadata, umap_out, - n_neighbors = 55, - min_dist = 0, + n_neighbors = 10, + min_dist = 1, metric = "cosine", - n_epochs = 1500, + n_epochs = 200, init = "spca", ret_model = TRUE, na_vals = "drop", join_column = "sample_id", - seed = 12345 + seed = 1234 ){ df = df %>% column_to_rownames(var = "sample_id") original_n = nrow(df) @@ -411,7 +411,7 @@ DLBCLone_optimize_params = function( min_k=3, max_k=30, verbose = FALSE, - seed = 12345 + seed = 1234 ){ na_opt = c("drop") num_class = length(truth_classes) @@ -433,9 +433,9 @@ DLBCLone_optimize_params = function( if(missing(umap_out)){ outs = make_and_annotate_umap( df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, + min_dist = 1, + n_neighbors = 10, + n_epochs = 200, init = "spca", seed=seed, metadata=metadata_df, @@ -448,9 +448,9 @@ DLBCLone_optimize_params = function( outs = make_and_annotate_umap( df=combined_mutation_status_df, umap_out = umap_out, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, + min_dist = 1, + n_neighbors = 10, + n_epochs = 200, init = "spca", seed=seed, metadata=metadata_df, @@ -812,7 +812,6 @@ weighted_knn_predict_with_conf <- function( #' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data #' @param make_plot Set to TRUE to plot the UMAP projection and predictions #' @param annotate_accuracy Set to true to add labels with accuracy values -#' @param classes Vector of classes that were used in the training and testing #' @param label_offset Length of the label offset for the accuracy labels #' @param title1 additional argument #' @param title2 additional argument @@ -846,7 +845,6 @@ predict_single_sample_DLBCLone <- function( drop_unlabeled_from_training=TRUE, make_plot = TRUE, annotate_accuracy = FALSE, - classes = c("BN2","ST2","MCD","EZB","N1"), label_offset = 2, title1="GAMBL", title2="predicted_class_for_HighConf", @@ -870,6 +868,28 @@ predict_single_sample_DLBCLone <- function( placeholder_meta = data.frame(sample_id = test_df$sample_id) train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) + trained_features <- colnames(umap_out$features) + + train_df <- train_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + + #project train data onto existing model instead of re-running UMAP + train_projection <- make_and_annotate_umap( + df = train_df, + umap_out = umap_out$model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + + test_df <- test_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + #project test data onto existing model instead of re-running UMAP test_projection = make_and_annotate_umap( df = test_df, @@ -881,7 +901,7 @@ predict_single_sample_DLBCLone <- function( ) train_coords = dplyr::filter( - umap_out$df, + train_projection$df, sample_id %in% train_df$sample_id, ) %>% select(sample_id,V1,V2) %>% @@ -931,7 +951,7 @@ predict_single_sample_DLBCLone <- function( if(make_plot){ title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) - if("BN2" %in% classes){ + if("BN2" %in% truth_classes){ print(best_params) acc_df = data.frame( lymphgen = c( @@ -953,7 +973,7 @@ predict_single_sample_DLBCLone <- function( best_params$A53_bacc ) ) - }else if("C1" %in% classes){ + }else if("C1" %in% truth_classes){ acc_df = data.frame( lymphgen = c( "C1", @@ -995,7 +1015,7 @@ predict_single_sample_DLBCLone <- function( if(annotate_accuracy){ #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label %in% classes) %>% + centroids = filter(predictions_df,predicted_label %in% truth_classes) %>% group_by(predicted_label) %>% summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 95599bf..cf40c9d 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -13,7 +13,7 @@ DLBCLone_optimize_params( min_k = 3, max_k = 30, verbose = FALSE, - seed = 12345 + seed = 1234 ) } \arguments{ diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 595586b..3ee63f3 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -8,15 +8,15 @@ make_and_annotate_umap( df, metadata, umap_out, - n_neighbors = 55, - min_dist = 0, + n_neighbors = 10, + min_dist = 1, metric = "cosine", - n_epochs = 1500, + n_epochs = 200, init = "spca", ret_model = TRUE, na_vals = "drop", join_column = "sample_id", - seed = 12345 + seed = 1234 ) } \arguments{ diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 3f61b37..0f08187 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -17,7 +17,6 @@ predict_single_sample_DLBCLone( drop_unlabeled_from_training = TRUE, make_plot = TRUE, annotate_accuracy = FALSE, - classes = c("BN2", "ST2", "MCD", "EZB", "N1"), label_offset = 2, title1 = "GAMBL", title2 = "predicted_class_for_HighConf", @@ -50,8 +49,6 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{annotate_accuracy}{Set to true to add labels with accuracy values} -\item{classes}{Vector of classes that were used in the training and testing} - \item{label_offset}{Length of the label offset for the accuracy labels} \item{title1}{additional argument} From 3f32683d6de5c1944bdd790c0f65c75dea1038fe Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 14 May 2025 10:33:42 -0700 Subject: [PATCH 04/79] response to ryans review --- R/umap.R | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/R/umap.R b/R/umap.R index 9393161..007b2a6 100644 --- a/R/umap.R +++ b/R/umap.R @@ -870,13 +870,13 @@ predict_single_sample_DLBCLone <- function( trained_features <- colnames(umap_out$features) - train_df <- train_df %>% + train_df = train_df %>% column_to_rownames("sample_id") %>% select(all_of(trained_features)) %>% rownames_to_column("sample_id") #project train data onto existing model instead of re-running UMAP - train_projection <- make_and_annotate_umap( + train_projection = make_and_annotate_umap( df = train_df, umap_out = umap_out$model, ret_model = FALSE, @@ -885,7 +885,7 @@ predict_single_sample_DLBCLone <- function( na_vals = best_params$na_option ) - test_df <- test_df %>% + test_df = test_df %>% column_to_rownames("sample_id") %>% select(all_of(trained_features)) %>% rownames_to_column("sample_id") @@ -908,9 +908,17 @@ predict_single_sample_DLBCLone <- function( column_to_rownames("sample_id") train_labels = dplyr::filter( - umap_out$df, + train_projection$df, sample_id %in% train_df$sample_id ) %>% + left_join( + umap_out$df %>% select( + sample_id, + cohort, + lymphgen + ), + by = "sample_id" + ) %>% pull(lymphgen) test_coords = dplyr::filter( @@ -933,10 +941,6 @@ predict_single_sample_DLBCLone <- function( pred$sample_id = test_df$sample_id - if(drop_unlabeled_from_training){ - pred = dplyr::filter(pred,sample_id %in% test_df$sample_id) - } - anno_umap <- select(test_projection$df, sample_id, V1, V2) anno_out = left_join(pred,anno_umap,by="sample_id") %>% @@ -1070,3 +1074,4 @@ predict_single_sample_DLBCLone <- function( plot = pp )) } + \ No newline at end of file From b0b9f73f779f99a0523b3d16f7273a699c639ff8 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 14 May 2025 14:16:12 -0700 Subject: [PATCH 05/79] ignore_top fix --- R/umap.R | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/R/umap.R b/R/umap.R index 007b2a6..283f93d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -928,6 +928,11 @@ predict_single_sample_DLBCLone <- function( select(sample_id,V1,V2) %>% column_to_rownames("sample_id") + if(any(rownames(test_coords) %in% rownames(train_coords)) && ignore_top == FALSE){ + warning("Some test samples also appear in the training set. Matched training samples will be excluded. Consider setting ignore_top = TRUE to avoid inaccurate high confidence.") + train_coords = train_coords[!rownames(train_coords) %in% rownames(test_coords),] + } + pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, From 2f44d636282641eb80543ed298860888a2ffe6c0 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 14 May 2025 14:39:25 -0700 Subject: [PATCH 06/79] adding ignore_top param --- R/umap.R | 8 +++++--- man/predict_single_sample_DLBCLone.Rd | 5 +++++ 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/R/umap.R b/R/umap.R index 283f93d..74e87ea 100644 --- a/R/umap.R +++ b/R/umap.R @@ -808,6 +808,9 @@ weighted_knn_predict_with_conf <- function( #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters #' @param predictions_df Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params #' @param other_df Data frame containing the predictions for samples in the "Other" class +#' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with +#' distance = 0. This is usually only relevant when re-classifying labeled +#' samples to estimate overall accuracy #' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") #' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data #' @param make_plot Set to TRUE to plot the UMAP projection and predictions @@ -841,6 +844,7 @@ predict_single_sample_DLBCLone <- function( best_params, predictions_df, other_df, + ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","N1","BN2"), drop_unlabeled_from_training=TRUE, make_plot = TRUE, @@ -858,8 +862,6 @@ predict_single_sample_DLBCLone <- function( if(nrow(test_df)>1){ message("Warning: you have supplied more than one sample to test with. Will proceed with all") } - - #combined_mutation_status_df = bind_rows(test_df,train_df) if(any(test_df$sample_id %in% train_metadata_use$sample_id)){ stop("one or more samples overlap with your training data!") @@ -941,7 +943,7 @@ predict_single_sample_DLBCLone <- function( conf_threshold = best_params$threshold, na_label = "Other", use_weights = best_params$use_w, - ignore_top = FALSE + ignore_top = ignore_top ) pred$sample_id = test_df$sample_id diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 0f08187..fef1612 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -13,6 +13,7 @@ predict_single_sample_DLBCLone( best_params, predictions_df, other_df, + ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), drop_unlabeled_from_training = TRUE, make_plot = TRUE, @@ -41,6 +42,10 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} +\item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with +distance = 0. This is usually only relevant when re-classifying labeled +samples to estimate overall accuracy} + \item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} \item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} From a1157cd4828678ffd0e64d13689155939f0158cb Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 15 May 2025 10:02:23 -0700 Subject: [PATCH 07/79] removed redundant code --- R/umap.R | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/R/umap.R b/R/umap.R index 74e87ea..787be7c 100644 --- a/R/umap.R +++ b/R/umap.R @@ -858,14 +858,6 @@ predict_single_sample_DLBCLone <- function( train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - - if(nrow(test_df)>1){ - message("Warning: you have supplied more than one sample to test with. Will proceed with all") - } - - if(any(test_df$sample_id %in% train_metadata_use$sample_id)){ - stop("one or more samples overlap with your training data!") - } placeholder_meta = data.frame(sample_id = test_df$sample_id) train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) @@ -945,8 +937,8 @@ predict_single_sample_DLBCLone <- function( use_weights = best_params$use_w, ignore_top = ignore_top ) - - pred$sample_id = test_df$sample_id + + pred$sample_id = test_df$sample_id anno_umap <- select(test_projection$df, sample_id, V1, V2) From 5392a3913a11cdbc1baf3826f99848276d47f0f4 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 15 May 2025 11:32:52 -0700 Subject: [PATCH 08/79] removal of predictions_df param now that we are projecting training data on optimized UMAP. predictions_df param is redundant as a result --- R/umap.R | 66 ++++++++++++++++----------- man/predict_single_sample_DLBCLone.Rd | 3 -- 2 files changed, 40 insertions(+), 29 deletions(-) diff --git a/R/umap.R b/R/umap.R index 787be7c..351d5a4 100644 --- a/R/umap.R +++ b/R/umap.R @@ -806,7 +806,6 @@ weighted_knn_predict_with_conf <- function( #' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful #' for reproducibility and for using the same UMAP model on different datasets. #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters -#' @param predictions_df Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params #' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled @@ -842,7 +841,6 @@ predict_single_sample_DLBCLone <- function( train_metadata, umap_out, best_params, - predictions_df, other_df, ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","N1","BN2"), @@ -879,21 +877,6 @@ predict_single_sample_DLBCLone <- function( na_vals = best_params$na_option ) - test_df = test_df %>% - column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% - rownames_to_column("sample_id") - - #project test data onto existing model instead of re-running UMAP - test_projection = make_and_annotate_umap( - df = test_df, - umap_out = umap_out$model, - ret_model = FALSE, # now projecting onto existing model - seed = seed, - join_column = "sample_id", - na_vals = best_params$na_option - ) - train_coords = dplyr::filter( train_projection$df, sample_id %in% train_df$sample_id, @@ -901,7 +884,7 @@ predict_single_sample_DLBCLone <- function( select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - train_labels = dplyr::filter( + train_df_proj = dplyr::filter( train_projection$df, sample_id %in% train_df$sample_id ) %>% @@ -912,9 +895,26 @@ predict_single_sample_DLBCLone <- function( lymphgen ), by = "sample_id" - ) %>% + ) + + train_labels <- train_df_proj %>% pull(lymphgen) + test_df = test_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + + #project test data onto existing model instead of re-running UMAP + test_projection = make_and_annotate_umap( + df = test_df, + umap_out = umap_out$model, + ret_model = FALSE, # now projecting onto existing model + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + test_coords = dplyr::filter( test_projection$df, sample_id %in% test_df$sample_id @@ -927,7 +927,19 @@ predict_single_sample_DLBCLone <- function( train_coords = train_coords[!rownames(train_coords) %in% rownames(test_coords),] } - pred = weighted_knn_predict_with_conf( + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top + ) + train_pred$sample_id <- rownames(train_coords) + + test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, @@ -938,11 +950,11 @@ predict_single_sample_DLBCLone <- function( ignore_top = ignore_top ) - pred$sample_id = test_df$sample_id + test_pred$sample_id = test_df$sample_id anno_umap <- select(test_projection$df, sample_id, V1, V2) - anno_out = left_join(pred,anno_umap,by="sample_id") %>% + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% mutate(label = paste(sample_id,predicted_label,round(confidence,3))) anno_out = anno_out %>% @@ -952,6 +964,8 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) + predictions_df = left_join(train_pred, train_df_proj %>% select(sample_id, V1, V2), by = "sample_id") + if(make_plot){ title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) if("BN2" %in% truth_classes){ @@ -996,16 +1010,17 @@ predict_single_sample_DLBCLone <- function( }else{ stop("no labels to add?") } + # Add the predicted labels for Other (unclassified) cases, if provided if(!missing(other_df)){ in_df = bind_rows( - umap_out$df, + train_df_proj, mutate(predictions_df,dataset=title2,lymphgen=predicted_label), mutate(other_df,dataset=title3,lymphgen=predicted_label) ) }else{ in_df = bind_rows( - umap_out$df, + train_df_proj, mutate(predictions_df,dataset=title2,lymphgen=predicted_label) ) } @@ -1067,10 +1082,9 @@ predict_single_sample_DLBCLone <- function( } } return(list( - prediction = pred, + prediction = test_pred, umap_input = umap_out$features, model=umap_out$model, plot = pp )) } - \ No newline at end of file diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index fef1612..2e9c72b 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -11,7 +11,6 @@ predict_single_sample_DLBCLone( train_metadata, umap_out, best_params, - predictions_df, other_df, ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), @@ -38,8 +37,6 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} -\item{predictions_df}{Data frame containing the predictions with UMAP coordinates from DLBCLone_optimize_params} - \item{other_df}{Data frame containing the predictions for samples in the "Other" class} \item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with From 1fa88e1722eeeb92a834fbbd12fd15839cedc621 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 15 May 2025 14:44:24 -0700 Subject: [PATCH 09/79] preserve rownames in knn --- R/umap.R | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/R/umap.R b/R/umap.R index 351d5a4..e90f6ac 100644 --- a/R/umap.R +++ b/R/umap.R @@ -784,8 +784,9 @@ weighted_knn_predict_with_conf <- function( } to_return = data.frame( - predicted_label = factor(preds), - confidence = confs) + predicted_label = factor(preds), + confidence = confs + ) if(track_neighbors){ print(dim(to_return)) @@ -793,6 +794,8 @@ weighted_knn_predict_with_conf <- function( to_return = bind_cols(to_return,all_neighbors) } + rownames(to_return) <- rownames(test_coords) + return(to_return) } @@ -937,7 +940,7 @@ predict_single_sample_DLBCLone <- function( use_weights = best_params$use_w, ignore_top = ignore_top ) - train_pred$sample_id <- rownames(train_coords) + train_pred <- rownames_to_column(train_pred, var = "sample_id") test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -949,8 +952,7 @@ predict_single_sample_DLBCLone <- function( use_weights = best_params$use_w, ignore_top = ignore_top ) - - test_pred$sample_id = test_df$sample_id + test_pred <- rownames_to_column(test_pred, var = "sample_id") anno_umap <- select(test_projection$df, sample_id, V1, V2) @@ -964,7 +966,9 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) - predictions_df = left_join(train_pred, train_df_proj %>% select(sample_id, V1, V2), by = "sample_id") + predictions_train_df <- left_join(train_pred, train_projection$df %>% select(sample_id, V1, V2), by = "sample_id") + predictions_test_df <- left_join(test_pred, test_projection$df %>% select(sample_id, V1, V2), by = "sample_id") + predictions_df <- bind_rows(predictions_train_df, predictions_test_df) if(make_plot){ title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) From f6f59f194c5e75b9270e327b539a7a6a04db2778 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 15 May 2025 20:51:13 -0700 Subject: [PATCH 10/79] multiple test samples fix --- R/umap.R | 89 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 46 insertions(+), 43 deletions(-) diff --git a/R/umap.R b/R/umap.R index e90f6ac..652f75d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -856,23 +856,46 @@ predict_single_sample_DLBCLone <- function( title3 ="predicted_class_for_Other" ){ set.seed(seed) + + if(ignore_top){ + # Allow overlapping samples: rename test duplicates temporarily + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + test_df <- test_df %>% + mutate(sample_id = ifelse( + sample_id %in% dupes, + paste0(sample_id, "_test"), + sample_id + )) + } + } else { + # Drop overlaps to prevent rowname collisions + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + warning(paste("Removing", length(dupes), "overlapping samples from train_df to avoid duplicated rownames. \n Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) + train_df <- train_df %>% filter(!sample_id %in% dupes) + train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) + } + } - train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) - train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - - placeholder_meta = data.frame(sample_id = test_df$sample_id) - train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) - - trained_features <- colnames(umap_out$features) + trained_features = colnames(umap_out$features) train_df = train_df %>% column_to_rownames("sample_id") %>% select(all_of(trained_features)) %>% rownames_to_column("sample_id") + train_id <- train_df$sample_id + + test_df = test_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + test_id <- test_df$sample_id + + combined_df <- bind_rows(train_df, test_df) - #project train data onto existing model instead of re-running UMAP - train_projection = make_and_annotate_umap( - df = train_df, + projection <- make_and_annotate_umap( + df = combined_df, umap_out = umap_out$model, ret_model = FALSE, seed = seed, @@ -881,15 +904,15 @@ predict_single_sample_DLBCLone <- function( ) train_coords = dplyr::filter( - train_projection$df, - sample_id %in% train_df$sample_id, + projection$df, + sample_id %in% train_id ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") train_df_proj = dplyr::filter( - train_projection$df, - sample_id %in% train_df$sample_id + projection$df, + sample_id %in% train_id ) %>% left_join( umap_out$df %>% select( @@ -900,36 +923,16 @@ predict_single_sample_DLBCLone <- function( by = "sample_id" ) - train_labels <- train_df_proj %>% + train_labels = train_df_proj %>% pull(lymphgen) - test_df = test_df %>% - column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% - rownames_to_column("sample_id") - - #project test data onto existing model instead of re-running UMAP - test_projection = make_and_annotate_umap( - df = test_df, - umap_out = umap_out$model, - ret_model = FALSE, # now projecting onto existing model - seed = seed, - join_column = "sample_id", - na_vals = best_params$na_option - ) - test_coords = dplyr::filter( - test_projection$df, - sample_id %in% test_df$sample_id + projection$df, + sample_id %in% test_id ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - if(any(rownames(test_coords) %in% rownames(train_coords)) && ignore_top == FALSE){ - warning("Some test samples also appear in the training set. Matched training samples will be excluded. Consider setting ignore_top = TRUE to avoid inaccurate high confidence.") - train_coords = train_coords[!rownames(train_coords) %in% rownames(test_coords),] - } - train_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, @@ -940,7 +943,7 @@ predict_single_sample_DLBCLone <- function( use_weights = best_params$use_w, ignore_top = ignore_top ) - train_pred <- rownames_to_column(train_pred, var = "sample_id") + train_pred = rownames_to_column(train_pred, var = "sample_id") test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -952,9 +955,9 @@ predict_single_sample_DLBCLone <- function( use_weights = best_params$use_w, ignore_top = ignore_top ) - test_pred <- rownames_to_column(test_pred, var = "sample_id") + test_pred = rownames_to_column(test_pred, var = "sample_id") - anno_umap <- select(test_projection$df, sample_id, V1, V2) + anno_umap = select(projection$df, sample_id, V1, V2) anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% mutate(label = paste(sample_id,predicted_label,round(confidence,3))) @@ -966,9 +969,9 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) - predictions_train_df <- left_join(train_pred, train_projection$df %>% select(sample_id, V1, V2), by = "sample_id") - predictions_test_df <- left_join(test_pred, test_projection$df %>% select(sample_id, V1, V2), by = "sample_id") - predictions_df <- bind_rows(predictions_train_df, predictions_test_df) + predictions_train_df = left_join(train_pred, projection$df %>% select(sample_id, V1, V2), by = "sample_id") + predictions_test_df = left_join(test_pred, projection$df %>% select(sample_id, V1, V2), by = "sample_id") + predictions_df = bind_rows(predictions_train_df, predictions_test_df) if(make_plot){ title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) From bd1ead70cdd8da2884bf286439d77d15c015c2cd Mon Sep 17 00:00:00 2001 From: rdmorin Date: Mon, 19 May 2025 11:04:22 -0700 Subject: [PATCH 11/79] clean up, add new function --- NAMESPACE | 2 +- R/dlbclass.R | 123 +++--- R/umap.R | 511 +++++++++++++++-------- man/DLBCLone_optimize_params.Rd | 40 +- man/DLBCLone_train_test_plot.Rd | 11 +- man/construct_reduced_winning_version.Rd | 10 +- man/make_and_annotate_umap.Rd | 5 +- man/optimize_outgroup.Rd | 47 +++ man/predict_single_sample.Rd | 37 -- man/weighted_knn_predict_with_conf.Rd | 5 +- 10 files changed, 504 insertions(+), 287 deletions(-) create mode 100644 man/optimize_outgroup.Rd delete mode 100644 man/predict_single_sample.Rd diff --git a/NAMESPACE b/NAMESPACE index 93e5d72..927de7f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,7 +9,7 @@ export(complete_missing_from_matrix) export(construct_reduced_winning_version) export(make_and_annotate_umap) export(massage_matrix_for_clustering) -export(predict_single_sample) +export(optimize_outgroup) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) import(GAMBLR.data) diff --git a/R/dlbclass.R b/R/dlbclass.R index 38ee54d..1be5596 100644 --- a/R/dlbclass.R +++ b/R/dlbclass.R @@ -28,13 +28,16 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB stop("Please provide either a mutations file or a mutation_data data frame.") } }else{ - mutation_data = read.table(mutations,sep="\t",row.names = 1,header=1) + mutation_data = read.table(mutations_file,sep="\t",row.names = 1,header=1) } # Transpose data if 'MYD88' is in row names if ("MYD88" %in% rownames(mutation_data)) { - mutation_data <- t(mutation_data) %>% as.data.frame() %>% column_to_rownames("sample_id") + mutation_data <- t(mutation_data) %>% as.data.frame() + #rownames(mutation_data) <- NULL + #print(head(mutation_data)) + #mutation_data = mutation_data %>% column_to_rownames("sample_id") } mutation_data = mutation_data %>% dplyr::select(-any_of(c("PLOIDY","PURITY","COO")),-ends_with("CCF")) #print(colnames(mutation_data)) @@ -72,34 +75,72 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB #mutation_data <- mutation_data[, !colnames(mutation_data) %in% genes_to_drop] # Aggregate specific features into vectors - print(grep("BCL6",colnames(mutation_data),value=T)) + + genes = c("BCL6","BCL2") BCL6_ALT <- rowSums(select(mutation_data, any_of(c("BCL6_SV","SV.BCL6", "BCL6"))), na.rm = TRUE) - NOTCH2_vec <- rowSums(select(mutation_data, any_of(c("NOTCH2","NOTCH2HOTSPOT", "SPEN", "DTX1"))), na.rm = TRUE) - M88O_vec <- rowSums(select(mutation_data, any_of(c("MYD88","MYD88.OTHER", "TNFAIP3", "TNIP1", "BCL10", "NFKBIE"))), na.rm = TRUE) + NOTCH2_genes <- c("NOTCH2","NOTCH2HOTSPOT", "SPEN", "DTX1") + genes = c(genes,NOTCH2_genes) + NOTCH2_vec <- rowSums(select(mutation_data, any_of(NOTCH2_genes)), na.rm = TRUE) - CD70_vec <- rowSums(select(mutation_data, any_of(c("CD70", "FAS", "CD58", "B2M", "FADD", "HLA.B","HLA-B"))), na.rm = TRUE) + MYD88_genes = c("MYD88","MYD88.OTHER", "TNFAIP3", "TNIP1", "BCL10", "NFKBIE") + genes = c(genes,MYD88_genes) + M88O_vec <- rowSums(select(mutation_data, any_of(MYD88_genes)), na.rm = TRUE) + CD70_genes = c("CD70", "FAS", "CD58", "B2M", "FADD", "HLA.B","HLA-B") + CD70_vec <- rowSums(select(mutation_data, any_of(CD70_genes)), na.rm = TRUE) + genes = c(genes,CD70_genes) # Additional vectors for other clusters #BCL2_combined <- rowSums(mutation_data[, c("BCL2", "SV.BCL2")], na.rm = TRUE) BCL2_combined <- rowSums(select(mutation_data, (starts_with("BCL2"))), na.rm = TRUE) + HIST_genes = c("HIST1H2AC","HIST1H2ACHOTSPOT", + "HIST1H1E", "HIST1H1EHOTSPOT","HIST1H1B", + "HIST1H2AM","HIST1H2AMHOTSPOT", "HIST1H1C", + "HIST1H1CHOTSPOT","HIST1H1D", "HIST1H1DHOTSPOT", + "HIST1H2BC","HIST1H2BCHOTSPOT") + genes = c(genes,HIST_genes) + SGK1_genes <- c("SGK1", "TET2", "SGK1HOTSPOT","NFKBIA","NFKBIAHOTSPOT", + "STAT3","STAT3HOTSPOT", "PTPN6", "BRAF","BRAFHOTSPOT", "KRAS", "KRASHOTSPOT", + "CD83","CD83HOTSPOT", "SF3B1","SF3B1HOTSPOT", "CD274", "MEF2C","MEF2CHOTSPOT", "KLHL6","KLHL6HOTSPOT", "CXCR4","CXCR4HOTSPOT", "PTEN", + "RAC2", "SESN3", "SOCS1","SOCS1HOTSPOT", "METAP1D") + DUSP2_genes <- c("DUSP2", "DUSP2HOTSPOT", "ZFP36L1","ZFP36L1HOTSPOT", "CRIP1", + "ACTB", "LTB", "YY1", "PABPC1","PABPC1HOTSPOT") - #CREBBP_vec <- rowSums(mutation_data[, c("CREBBP", "EZH2", "KMT2D", "EP300")], na.rm = TRUE) + TBL1XR1_genes <- c("TBL1XR1", "PIM1", "PIM1HOTSPOT", + "PRDM1","PRDM1HOTSPOT", + "ETV6","ETV6HOTSPOT", "ZC3H12A", + "BTG1", "BTG1HOTSPOT", "BTG2", "BTG2HOTSPOT", + "IGLL5", "IGLL5HOTSPOT", + "TMSB4X","TMSB4XHOTSPOT", + "GRHPR","GRHPRHOTSPOT","HLA.C","HLA-C","HLA-CHOTSPOT", + "MYD88", "TOX", "LYN", + "POU2F2","POU2F2HOTSPOT", + "IKZF3","IKZF3HOTSPOT", + "HLA.A", "ZFP36L1","HLA-A","HLA-AHOTSPOT", + "CARD11", "CARD11HOTSPOT","SF3B1", + "HLA.B","HLA-B","HLA-BHOTSPOT", "IRF2BP2", "OSBPL10", "ATP2A2", "PIM2", "IRF4", "BCL11A", + "METAP1D", "ETS1", "CCDC27") + CREBBP_genes <- c("CREBBP", "EZH2", "KMT2D", "EP300") + genes = c(genes,SGK1_genes,DUSP2_genes,TBL1XR1_genes,CREBBP_genes) CREBBP_vec <- rowSums(select(mutation_data, starts_with("CREBBP"), starts_with("EZH2"), starts_with("KMT2D"), starts_with("EP300")), na.rm = TRUE) - + PTEN_genes = c("PTEN") + C1_genes = c("UBE2A", "TMEM30A", "ZEB2", "GNAI2", "X5P.AMP", "POU2F2", "IKZF3", + "EBF1", "LYN", "BCL7A", "CXCR4", "CCDC27", "TUBGCP5", "SMG7", "RHOA", "BTG2") + TP53_genes = c("TP53") + GNA13_genes = c("GNA13", "TNFRSF14", "MAP2K1", "MEF2B", "IRF8", "HVCN1", + "GNAI2", "MEF2C", "SOCS1", "EEF1A1", "RAC2", + "POU2AF1") + genes = c(genes,PTEN_genes,C1_genes,TP53_genes,GNA13_genes) if(include_cn){ - C1_vec4 <- rowSums(select(mutation_data, any_of(c("UBE2A", "TMEM30A", "ZEB2", "GNAI2", "X5P.AMP", "POU2F2", "IKZF3", "X3Q28.DEL", - "EBF1", "LYN", "BCL7A", "CXCR4", "CCDC27", "TUBGCP5", "SMG7", "RHOA", "BTG2"))), na.rm = TRUE) - TP53_biallelic <- rowSums(mutation_data[, c("TP53", "X17P.DEL")], na.rm = TRUE) + C1_vec4 <- rowSums(select(mutation_data, any_of(C1_genes)), na.rm = TRUE) + TP53_biallelic <- rowSums(mutation_data[, TP53_genes,drop=FALSE], na.rm = TRUE) X21Q_AMP <- mutation_data[, "X21Q.AMP"] - GNA13_vec <- rowSums(mutation_data[, c("GNA13", "TNFRSF14", "MAP2K1", "MEF2B", "IRF8", "HVCN1", - "GNAI2", "MEF2C", "SOCS1", "EEF1A1", "RAC2", "X12Q.AMP", - "POU2AF1", "X6Q14.1.DEL")], na.rm = TRUE) + GNA13_vec <- rowSums(mutation_data[, GNA13_genes], na.rm = TRUE) Sum_C2_ARM <- rowSums(mutation_data[, c("X17P.DEL", "X21Q.AMP", "X11Q.AMP", "X6P.AMP", "X11P.AMP", "X6Q.DEL", "X7P.AMP", "X13Q.AMP", "X7Q.AMP", "X3Q.AMP", "X5P.AMP", "X18P.AMP", "X3P.AMP", "X19Q.AMP", "X9Q.AMP", "X12P.AMP", "X12Q.AMP")], na.rm = TRUE) @@ -109,7 +150,7 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB "X18Q23.DEL", "X19P13.3.DEL", "X13Q34.DEL", "X7Q22.1.AMP", "X10Q23.31.DEL", "X9P24.1.AMP", "X3Q28.AMP", "X11Q23.3.AMP", "X17Q24.3.AMP", "X3Q28.DEL", "X13Q14.2.DEL", "X18Q21.32.AMP", "X19Q13.32.DEL", "X6P21.1.AMP", "X18Q22.2.AMP", "EP300", "ZNF423", "CD274")], na.rm = TRUE) - PTEN <- rowSums(mutation_data[, c("PTEN", "X10Q23.31.DEL", "X13Q14.2.DEL")], na.rm = TRUE) + PTEN <- rowSums(mutation_data[, PTEN_genes, drop=FALSE], na.rm = TRUE) Sum_C5_CNA <- rowSums(mutation_data[, c("X18Q.AMP", "X3Q.AMP", "X3P.AMP", "X19Q13.42.AMP", "X6Q21.DEL", "X18P.AMP", "X19Q.AMP", "X8Q12.1.DEL", "X6Q14.1.DEL", "X19P13.2.DEL", "X9P21.3.DEL", "X18Q21.32.AMP", "X18Q22.2.AMP", "X1Q42.12.DEL", "X1Q32.1.AMP", "X6P21.33.DEL")], na.rm = TRUE) @@ -131,7 +172,7 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB starts_with("BTG2")), na.rm = TRUE) TP53_biallelic <- rowSums(mutation_data[, c("TP53"),drop=FALSE], na.rm = TRUE) - PTEN <- rowSums(mutation_data[, c("PTEN"),drop=FALSE], na.rm = TRUE) + PTEN <- rowSums(select(mutation_data,any_of(PTEN_genes))) #GNA13_vec <- rowSums(mutation_data[, c("GNA13", "TNFRSF14", "MAP2K1", "MEF2B", "IRF8", "HVCN1", # "GNAI2", "MEF2C", "SOCS1", "EEF1A1", "RAC2", # "POU2AF1")], na.rm = TRUE) @@ -149,40 +190,12 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB SV_MYC <- select(mutation_data, any_of(c("SV.MYC", "MYC","MYC_SV"))) %>% rowSums(na.rm = TRUE) - Hist_comp <- rowSums(select(mutation_data, any_of(c("HIST1H2AC","HIST1H2ACHOTSPOT", - "HIST1H1E", "HIST1H1EHOTSPOT","HIST1H1B", - "HIST1H2AM","HIST1H2AMHOTSPOT", "HIST1H1C", - "HIST1H1CHOTSPOT","HIST1H1D", "HIST1H1DHOTSPOT", - "HIST1H2BC","HIST1H2BCHOTSPOT"))), na.rm = TRUE) - SGK1_vec <- rowSums(select(mutation_data, any_of(c("SGK1", "TET2", "SGK1HOTSPOT","NFKBIA","NFKBIAHOTSPOT", - "STAT3","STAT3HOTSPOT", "PTPN6", "BRAF","BRAFHOTSPOT", "KRAS", "KRASHOTSPOT", - "CD83","CD83HOTSPOT", "SF3B1","SF3B1HOTSPOT", "CD274", "MEF2C","MEF2CHOTSPOT", "KLHL6","KLHL6HOTSPOT", "CXCR4","CXCR4HOTSPOT", "PTEN", - "RAC2", "SESN3", "SOCS1","SOCS1HOTSPOT", "METAP1D"))), na.rm = TRUE) - #SGK1_vec <- rowSums(mutation_data[, c("SGK1", "TET2", "NFKBIA", "STAT3", "PTPN6", "BRAF", "KRAS", - # "CD83", "SF3B1", "CD274", "MEF2C", "KLHL6", "CXCR4", "PTEN", - # "RAC2", "SESN3", "SOCS1", "METAP1D")], na.rm = TRUE) - #DUSP2_vec <- rowSums(mutation_data[, c("DUSP2", "ZFP36L1", "CRIP1", "ACTB", "LTB", "YY1", "PABPC1")], na.rm = TRUE) - DUSP2_vec <- rowSums(select(mutation_data, any_of(c("DUSP2", "DUSP2HOTSPOT", "ZFP36L1","ZFP36L1HOTSPOT", "CRIP1", "ACTB", "LTB", "YY1", "PABPC1","PABPC1HOTSPOT"))), na.rm = TRUE) + Hist_comp <- rowSums(select(mutation_data, any_of(HIST_genes)), na.rm = TRUE) + SGK1_vec <- rowSums(select(mutation_data, any_of(SGK1_genes)), na.rm = TRUE) + DUSP2_vec <- rowSums(select(mutation_data, any_of(DUSP2_genes)), na.rm = TRUE) + - #TBL1XR1_vec <- rowSums(mutation_data[, c("TBL1XR1", "PIM1", "PRDM1", "ETV6", "ZC3H12A", "BTG1", "BTG2", - # "IGLL5", "TMSB4X", "GRHPR", "HLA.C", "MYD88", "TOX", "LYN", - # "POU2F2", "IKZF3", "HLA.A", "ZFP36L1", "CARD11", "SF3B1", - # "HLA.B", "IRF2BP2", "OSBPL10", "ATP2A2", "PIM2", "IRF4", "BCL11A", - # "METAP1D", "ETS1", "CCDC27")], na.rm = TRUE) - TBL1XR1_vec <- rowSums(select(mutation_data, any_of(c("TBL1XR1", "PIM1", "PIM1HOTSPOT", - "PRDM1","PRDM1HOTSPOT", - "ETV6","ETV6HOTSPOT", "ZC3H12A", - "BTG1", "BTG1HOTSPOT", "BTG2", "BTG2HOTSPOT", - "IGLL5", "IGLL5HOTSPOT", - "TMSB4X","TMSB4XHOTSPOT", - "GRHPR","GRHPRHOTSPOT","HLA.C","HLA-C","HLA-CHOTSPOT", - "MYD88", "TOX", "LYN", - "POU2F2","POU2F2HOTSPOT", - "IKZF3","IKZF3HOTSPOT", - "HLA.A", "ZFP36L1","HLA-A","HLA-AHOTSPOT", - "CARD11", "CARD11HOTSPOT","SF3B1", - "HLA.B","HLA-B","HLA-BHOTSPOT", "IRF2BP2", "OSBPL10", "ATP2A2", "PIM2", "IRF4", "BCL11A", - "METAP1D", "ETS1", "CCDC27"))), na.rm = TRUE) + TBL1XR1_vec <- rowSums(select(mutation_data, any_of(TBL1XR1_genes)), na.rm = TRUE) MYD88_L265P_CD79B <- rowSums(select(mutation_data, any_of(c("MYD88.L265P","MYD88HOTSPOT", "CD79B","CD79BHOTSPOT"))), na.rm = TRUE) @@ -220,9 +233,6 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB C1_vec4 = C1_vec4, CD70_vec = CD70_vec, TP53_biallelic = TP53_biallelic, - #X21Q_AMP = X21Q_AMP, - #Sum_C2_ARM = Sum_C2_ARM, - #Sum_C2_FOCAL = Sum_C2_FOCAL, BCL2_combined = BCL2_combined, CREBBP_vec = CREBBP_vec, GNA13_vec = GNA13_vec, @@ -231,10 +241,8 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB Hist_comp = Hist_comp, SGK1_vec = SGK1_vec, DUSP2_vec = DUSP2_vec, - #CN_2P16_1_AMP = CN_2P16_1_AMP, TBL1XR1_vec = TBL1XR1_vec, MYD88_L265P_CD79B = MYD88_L265P_CD79B - #Sum_C5_CNA = Sum_C5_CNA ) } @@ -252,5 +260,10 @@ construct_reduced_winning_version <- function(mutations_file = "inst/extdata/DLB return(list(reduced=reduced_data, full=full_data, no_cn=data_no_cn, - feature_names=list(ssm_features=ssm,hotspot_features=hotspot,cnv_features=cnv,sv_features=sv))) + ssm_genes = genes, + feature_names=list(ssm_features=ssm, + hotspot_features=hotspot, + cnv_features=cnv, + sv_features=sv + ))) } diff --git a/R/umap.R b/R/umap.R index e35114b..0fddc1f 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,89 +1,83 @@ -#' Predict class for a single sample without using umap_transform +#' Optimize the threshold for classifying samples as "Other" #' -#' @param test_df Data frame containing the mutation status of the test sample -#' @param train_df Data frame containing the mutation status of the training samples -#' @param best_params Data frame from DLBCLone_optimize_params with the best parameters -#' @param train_metadata Metadata for training samples with truth labels in lymphgen column -#' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") -#' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data -#' @param make_plot Set to TRUE to plot the UMAP projection and predictions +#' Performs a post-hoc evaluation of the classification of a sample as one of +#' the main classes vs the outgroup/unclassified label "Other" and returns the +#' optimal threshold for classifying a sample as "Other" based on the ground +#' truth provided in the true_labels vector. It evaluates the performance +#' of the classifier using a range of thresholds and returns the best threshold +#' based on the specified metric (balanced accuracy or accuracy). +#' This function is not generally meant to be called directly but rather is +#' a helper function used by DLBCLone_optimize_params. +#' +#' @param predicted_labels Vector of predicted labels for the samples +#' @param true_labels Vector of true labels for the samples +#' @param other_score Vector of scores for the "Other" class for each sample () +#' @param all_classes Vector of classes to use for training and testing. +#' Default: c("MCD","EZB","BN2","N1","ST2","Other") +#' @param maximize Metric to use for optimization. +#' Either "accuracy" (actual accuracy across all samples) or "balanced_accuracy" +#' (the mean of the balanced accuracy values across all classes). +#' Default: "balanced_accuracy" +#' @param exclude_other_for_accuracy Set to TRUE to exclude the +#' "Other" class from the 'lymphgen' column when calculating accuracy metrics +#' (passed to DLBCLone_optimize_params). Default: FALSE #' #' @returns a list of data frames with the predictions and the UMAP input #' @export #' -predict_single_sample = function(test_df, - train_df, - train_metadata, - best_params, - truth_classes = c("EZB","MCD","ST2","N1","BN2"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE){ - train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) - train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - - if(nrow(test_df)>1){ - message("Warning: you have supplied more than one sample to test with. Will proceed with all") - } - combined_mutation_status_df = bind_rows(test_df,train_df) - if(any(rownames(test_df) %in% train_metadata_use$sample_id)){ - stop("one or more samples overlap with your training data!") - } - placeholder_meta = data.frame(sample_id = rownames(test_df)) - train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) - - outs = make_and_annotate_umap(df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - init="random", - n_epochs = 1500, - seed=best_params$seed, - metadata=train_metadata_use, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = best_params$na_option) - - test_coords = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df), - is.na(lymphgen)) %>% select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - pred = weighted_knn_predict_with_conf( - train_coords = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df), - !is.na(lymphgen)) %>% select(V1,V2), - train_labels = dplyr::filter(outs$df, - #!sample_id %in% rownames(test_df) - !is.na(lymphgen)) %>% pull(lymphgen), - test_coords = test_coords, - k=best_params$k, - conf_threshold = best_params$threshold, - na_label="Other", - use_weights = best_params$use_w, - ignore_top = FALSE) - pred$sample_id = rownames(test_coords) - - if(drop_unlabeled_from_training){ - pred = dplyr::filter(pred,sample_id %in% rownames(test_df)) +optimize_outgroup <- function(predicted_labels, + true_labels, + other_score, + all_classes = c("MCD", + "EZB", + "BN2", + "N1", + "ST2", + "Other"), + maximize ="balanced_accuracy", + exclude_other_for_accuracy = FALSE){ + + rel_thresholds = seq(1,10,0.1) + sens_df = data.frame() + acc_df = data.frame() + predictions = data.frame(predicted_label=as.character(predicted_labels), + true_label=as.character(true_labels)) + + for(threshold in rel_thresholds){ + predictions_new = mutate(predictions, + predicted_label = ifelse(other_score < threshold, + predicted_label, + "Other")) + + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + } + bal_acc$threshold = threshold + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sens_df = bind_rows(sens_df,sn) } - #print(head(outs$df)) - anno_out = left_join(pred,outs$df,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - if(make_plot){ - - pp = ggplot(outs$df,aes(x=V1,y=V2,colour=lymphgen,label=sample_id)) + - geom_point() + - geom_point(data = anno_out,aes(colour=predicted_label)) + - geom_label_repel(data = anno_out, - aes(x=V1,y=V2,label=label), - nudge_x=1,nudge_y=1,colour="black") + - scale_colour_manual(values=get_gambl_colours()) + - ggtitle(paste("k:",best_params$k,"seed:",best_params$seed)) - print(pp) + if(maximize %in% c("balanced_accuracy","accuracy")){ + best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) + }else{ + best = slice_head(arrange(sens_df,desc(average_sensitivity)),n=1) + } - return(list(prediction = pred, umap_input = outs$features, model=outs$model)) + + return(best) } -#' Plot the result of a lymphgen classification + +#' Plot the result of a DLBCLone classification #' #' @param test_df Data frame containing the test data with UMAP coordinates #' @param train_df Data frame containing the training data with UMAP coordinates @@ -119,32 +113,39 @@ DLBCLone_train_test_plot = function(test_df, other_df, details, annotate_accuracy = FALSE, + classes = c("BN2","ST2","MCD","EZB","N1"), label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other"){ - - title = paste0("N_class:",details$num_classes," N_feats:",details$num_features," k=",details$k," threshold=",details$threshold," bacc=",round(details$accuracy,3)) - if("BN2" %in% classes){ - print(details) - acc_df = data.frame(lymphgen = c("N1","BN2","EZB","MCD","ST2","Other","A53"), - accuracy = c(details$N1_bacc, - details$BN2_bacc, - details$EZB_bacc, - details$MCD_bacc, - details$ST2_bacc, - details$Other_bacc, - details$A53_bacc)) - }else if("C1" %in% classes){ - acc_df = data.frame(lymphgen = c("C1","C2","C3","C4","C5"), - accuracy = c(details$C1_bacc, - details$C2_bacc, - details$C3_bacc, - details$C4_bacc, - details$C5_bacc)) - }else{ - stop("no labels to add?") + title1="Original Class", + title2="DLBCLone Predicted Class", + title3 ="DLBCLone Predicted Class (Other)",base_size = 1){ + title = "" + if(!missing(details)){ + title = paste0("N_class:",details$num_classes," N_feats:",details$num_features," k=",details$k," threshold=",details$threshold," bacc=",round(details$accuracy,3)) + + } + if(annotate_accuracy){ + if("BN2" %in% classes){ + #print(details) + acc_df = data.frame(lymphgen = classes, + accuracy = c( + details$BN2_bacc, + details$EZB_bacc, + details$MCD_bacc, + details$ST2_bacc, + details$Other_bacc, + details$A53_bacc)) + }else if("C1" %in% classes){ + acc_df = data.frame(lymphgen = c("C1","C2","C3","C4","C5"), + accuracy = c(details$C1_bacc, + details$C2_bacc, + details$C3_bacc, + details$C4_bacc, + details$C5_bacc)) + }else{ + stop("no labels to add?") + } + } # Add the predicted labels for Other (unclassified) cases, if provided if(!missing(other_df)){ @@ -153,18 +154,20 @@ DLBCLone_train_test_plot = function(test_df, mutate(predictions_df,dataset=title2,lymphgen=predicted_label), mutate(other_df,dataset=title3,lymphgen=predicted_label) ) + in_df = mutate(in_df,dataset = factor(dataset,levels=unique(c(unique(train_df$dataset),title1,title2,title3)))) }else{ in_df = bind_rows(train_df, test_df, mutate(predictions_df,dataset=title2,lymphgen=predicted_label) ) + in_df = mutate(in_df,dataset = factor(dataset,levels=unique(c(unique(train_df$dataset),title1,title2)))) } pp = ggplot(in_df) + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + scale_colour_manual(values=get_gambl_colours()) + facet_wrap(~dataset,ncol=1) + - theme_Morons() + ggtitle(title) + theme_Morons(base_size=base_size) + ggtitle(title) if(annotate_accuracy){ #add labels and set nudge direction based on what quadrant each group sits in centroids = filter(predictions_df,predicted_label %in% classes) %>% @@ -172,6 +175,7 @@ DLBCLone_train_test_plot = function(test_df, summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% mutate(lymphgen=predicted_label) + #print(centroids) centroids = left_join(centroids,acc_df) %>% mutate(label=paste(lymphgen,":",round(accuracy,3))) @@ -185,7 +189,7 @@ DLBCLone_train_test_plot = function(test_df, geom_label_repel(data=filter(centroids,nudge_y > 0, nudge_x > 0), aes(x=mean_V1,y=mean_V2,label=label),fill="white",size=5,nudge_y = 1 * label_offset , nudge_x = 1 * label_offset) } - pp + pp + guides(colour = guide_legend(nrow = 1)) } @@ -236,7 +240,10 @@ make_and_annotate_umap = function(df, ret_model=TRUE, na_vals = "drop", join_column="sample_id", - seed=42){ + seed=12345, + target_column, + target_metric="euclidean", + target_weight=0.5){ original_n = nrow(df) if(na_vals == "to_zero"){ df[is.na(df)] = 0 @@ -251,20 +258,42 @@ make_and_annotate_umap = function(df, if(missing(metadata)){ stop("metadata is required and should contain a column sample_id that matches the row names of your mutation data frame") } - - df= df[rownames(df) %in% metadata[[join_column]],] - + keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] + df= df[keep_rows,] + metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) + message(paste("kept",nrow(metadata),"rows of the data")) if(missing(umap_out)){ - umap_out = umap2(df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1) # possibly add rng_type = "deterministic" -#IMPORTANT: n_threads must not be changed because it will break reproducibility + if(missing(target_column)){ + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1) # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must not be changed because it will break reproducibility + }else{ + #supervised + metadata[[target_column]] = factor(metadata[[target_column]]) + print(table(metadata[[target_column]])) + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + y = metadata[[target_column]], + target_metric = target_metric, + target_weight = target_weight + ) # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must not be changed because it will break reproducibility + } + }else{ umap_out = umap_transform(X=df, @@ -289,18 +318,31 @@ make_and_annotate_umap = function(df, #' Optimize parameters for classifying samples using UMAP and k-nearest neighbor #' -#' @param combined_mutation_status_df Data frame with one row per sample and one column per mutation -#' @param metadata_df Data frame of metadata with one row per sample and three required columns: sample_id, dataset and lymphgen -#' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other") -#' @param eval_group Specify whether certain rows will be evaluated and held out from training rather than using all samples. -#' @param umap_out additional argument -#' @param min_k additional argument -#' @param max_k additional argument -#' @param verbose additional argument -#' @param seed additional argument +#' @param combined_mutation_status_df Data frame with one row per sample and +#' one column per mutation +#' @param metadata_df Data frame of metadata with one row per sample and +#' three required columns: sample_id, dataset and lymphgen +#' @param truth_classes Vector of classes to use for training and testing. +#' Default: c("EZB","MCD","ST2","N1","BN2","Other") +#' @param eval_group Specify whether certain rows will be evaluated and +#' held out from training rather than using all samples. +#' @param umap_out The output of a previous run of make_and_annotate_umap. +#' If provided, the function will use this model to project the data +#' instead of re-running UMAP. +#' @param min_k Starting k for knn (Default: 3) +#' @param max_k Ending k for knn (Default: 33) +#' @param verbose Whether to print verbose outputs to console +#' @param seed Random seed to use for reproducibility (default: 12345) +#' @param maximize Metric to use for optimization. Either "sensitivity" +#' (average sensitivity across all classes), "accuracy" +#' (actual accuracy across all samples) or "balanced_accuracy" (the mean of the +#' balanced accuracy values across all classes). Default: "balanced_accuracy" #' #' @returns List of data frames with the results of the parameter optimization -#' including the best model, the associated knn parameters and the annotated UMAP output +#' including the best model, the associated knn parameters and the annotated +#' UMAP output as a data frame. The list also includes the predictions for the +#' "Other" class if it was included in the training and testing. +#' #' @export #' #' @examples @@ -315,27 +357,38 @@ make_and_annotate_umap = function(df, DLBCLone_optimize_params = function(combined_mutation_status_df, metadata_df, umap_out, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), + truth_classes = c("EZB", + "MCD", + "ST2", + "N1", + "BN2", + "Other"), + optimize_for_other = TRUE, eval_group = "Lacy", min_k=3, - max_k=30, + max_k=33, verbose = FALSE, - seed = 12345){ + seed = 12345, + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE + ) { + if(optimize_for_other){ + exclude_other_for_accuracy = FALSE + }else{ + exclude_other_for_accuracy = TRUE + } na_opt = c("drop") num_class = length(truth_classes) weights_opt = c(TRUE,FALSE) - threshs = seq(0,0.95,0.05) - #ks = seq(5,25,1) - ks = seq(min_k,max_k,1) + threshs = seq(0,0.9,0.1) + ks = seq(min_k,max_k,2) results <- data.frame() best_params <- data.frame() - #best so far: - threshold = 0.3 - k = 6 + use_w = TRUE best_acc = 0 best_fit = NULL - + this_accuracy = 0 best_pred = NULL other_pred = NULL for(na_option in na_opt){ @@ -370,7 +423,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, } for(use_w in weights_opt){ for(k in ks){ - + message(paste("K:",k)) for (threshold in threshs){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) @@ -440,8 +493,9 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, xx_d$lymphgen = factor(xx_d$lymphgen,levels = c(unique(xx_d$lymphgen),"Other")) } - - true_factor = factor(xx_d$lymphgen,levels = levels(xx_d$lymphgen)) + true_factor = xx_d$lymphgen + pred_factor = factor(xx_d$predicted_label,levels = levels(xx_d$lymphgen)) + if(verbose){ print("true_factor") print(levels(true_factor )) @@ -449,7 +503,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, print(levels(xx_d$predicted_label)) } - conf_matrix <- confusionMatrix(xx_d$predicted_label, true_factor) + conf_matrix <- confusionMatrix(pred_factor, true_factor) bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class sn <- conf_matrix$byClass[, "Sensitivity"] # one per class @@ -457,15 +511,49 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, print(bal_acc) } - overall_balanced_accuracy <- mean(bal_acc, na.rm = TRUE) + overall_accuracy <- conf_matrix$overall[["Accuracy"]] + if(exclude_other_for_accuracy){ + mean_balanced_accuracy = mean(bal_acc[!names(bal_acc) == "Class: Other"], na.rm = TRUE) + }else{ + mean_balanced_accuracy = mean(bal_acc) + } + + overall_sensitivity<- mean(sn[!names(sn) == "Class: Other"], na.rm = TRUE) + + if(optimize_for_other){ + optimized_accuracy_and_thresh = optimize_outgroup(pred_factor, + true_factor, + xx_d$other_score, + all_classes = truth_classes, + maximize = maximize, + exclude_other_for_accuracy = exclude_other_for_accuracy) + out_opt_thresh = optimized_accuracy_and_thresh$threshold + optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 + out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + }else{ + out_opt_acc = 0 + out_opt_thresh = 0 + } + if(maximize == "sensitivity"){ + this_accuracy = overall_sensitivity + }else if(maximize == "balanced_accuracy"){ + this_accuracy =mean_balanced_accuracy + }else{ + this_accuracy = overall_accuracy + } + if(out_opt_acc > this_accuracy){ + this_accuracy = out_opt_acc + } row <- data.frame(k = k, threshold = threshold, use_weights = use_w, - accuracy = overall_balanced_accuracy, + optimized_accuracy = this_accuracy, + overall_accuracy = overall_accuracy, + mean_balanced_accuracy = mean_balanced_accuracy, + sensitivity = overall_sensitivity, N1_sn = unname(sn["Class: N1"]), BN2_sn= unname(sn["Class: BN2"]), ST2_sn = unname(sn["Class: ST2"]), - N1_bacc = unname(bal_acc["Class: N1"]), BN2_bacc = unname(bal_acc["Class: BN2"]), MCD_bacc = unname(bal_acc["Class: MCD"]), EZB_bacc = unname(bal_acc["Class: EZB"]), @@ -476,10 +564,16 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, C3_bacc = unname(bal_acc["Class: C3"]), C4_bacc = unname(bal_acc["Class: C4"]), C5_bacc = unname(bal_acc["Class: C5"]), - ST2_bacc = unname(bal_acc["Class: ST2"]), na_option= na_option) - if(overall_balanced_accuracy > best_acc){ - best_acc = overall_balanced_accuracy - print(paste("best accuracy:",best_acc,"k:",k,"threshold:",threshold,"na:",na_option,"Balanced accuracy:",overall_balanced_accuracy)) + ST2_bacc = unname(bal_acc["Class: ST2"]), + threshold_outgroup = out_opt_thresh, + accuracy_out = out_opt_acc, + na_option= na_option) + + + if(this_accuracy > best_acc){ + best_acc = this_accuracy + + message(paste("best accuracy:",best_acc,"k:",k,"threshold:",threshold,"Other_thresh:",out_opt_thresh,"na:",na_option,"Balanced accuracy:",overall_accuracy, "sensitivity:",overall_sensitivity)) best_fit = outs best_pred = xx_d @@ -510,7 +604,8 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, na_label="Other", use_weights = best_params$use_weights, ignore_top = ignore_top, - verbose = verbose) + verbose = verbose, + track_neighbors = TRUE) xx_d = bind_cols(outs$df,pred) to_ret = list(params=results, best_params = best_params, @@ -557,13 +652,14 @@ weighted_knn_predict_with_conf <- function(train_coords, train_labels, test_coords, k, - epsilon = 1e-5, + epsilon = 0.1, conf_threshold = NULL, na_label = "Other", verbose = FALSE, use_weights = TRUE, ignore_top = FALSE, - track_neighbors = FALSE) { + track_neighbors = TRUE, + separate_other = TRUE) { #big change here. Other is considered separately for optimization if (nrow(train_coords)==0 || nrow(test_coords) == 0) { print("train_coords:") print(nrow(train_coords)) @@ -571,14 +667,18 @@ weighted_knn_predict_with_conf <- function(train_coords, print(nrow(test_coords)) stop("train_coords and test_coords must be data frames with at least one row") } - - nn <- get.knnx(train_coords, test_coords, k) + # get the 100 nearest neighbors + nn <- get.knnx(train_coords, test_coords, 100) all_neighbors = data.frame() preds <- character(nrow(test_coords)) confs <- numeric(nrow(test_coords)) train_labels = as.character(train_labels) for (i in 1:nrow(test_coords)) { + if(verbose){ + print(paste("index:",i)) + print(test_coords[i,]) + } neighbors <- nn$nn.index[i, ] distances <- nn$nn.dist[i, ] if(ignore_top){ @@ -613,29 +713,66 @@ weighted_knn_predict_with_conf <- function(train_coords, } # Remove NAs (just in case) valid <- !is.na(neighbor_labels) + #num_other_neighbors = sum(neighbor_labels == "Other") + other_dists = distances[neighbor_labels == "Other"] + if(separate_other){ + valid = valid & neighbor_labels != "Other" + } neighbor_labels <- neighbor_labels[valid] weights <- weights[valid] + distances <- distances[valid] + neighbors <- neighbors[valid] + #now take the first k neighbors + if(length(neighbor_labels) > k){ + neighbor_labels = neighbor_labels[1:k] + weights = weights[1:k] + distances = distances[1:k] + neighbors = neighbors[1:k] + } + + others_closer = which(other_dists < max(distances)) + + others_distances = other_dists[others_closer] + if(use_weights){ + others_weights = 1 / others_distances + }else{ + others_weights = rep(1,length(others_closer)) + } + neighbors_other = length(others_closer) + other_weighted_votes = sum(others_weights) + #other_weighted_votes = neighbors_other + # print(paste("other weighted votes:",other_weighted_votes)) + #} if (length(neighbor_labels) == 0) { - preds[i] <- na_label - confs[i] <- NA - next + preds[i] <- "Other" + confs[i] <- 1 + if(track_neighbors){ + + rel_other = 10 + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = "", + neighbors_other = neighbors_other, + other_weighted_votes = 0, + total_w = 1, + pred_w = 2 + + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + next; + } } weighted_votes <- tapply(weights, neighbor_labels, sum) - if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - neighbor_info <- data.frame( - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = paste(weighted_votes,collapse=",") - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - } + if (length(weighted_votes) == 0) { preds[i] <- na_label confs[i] <- NA + total_weight <- 0 + pred_weight <- 0 } else { predicted_label <- names(which.max(weighted_votes)) total_weight <- sum(weighted_votes) @@ -651,13 +788,47 @@ weighted_knn_predict_with_conf <- function(train_coords, confs[i] <- confidence } } + + if(track_neighbors){ + # Create a data frame to store neighbors, distances, and weights + if(separate_other){ + rel_other = other_weighted_votes / pred_weight + }else{ + rel_other = 0 + } + #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + #weight = paste(round(weights, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = paste(weighted_votes,collapse=","), + neighbors_other = neighbors_other, + other_weighted_votes = other_weighted_votes, + total_w = total_weight, + pred_w = pred_weight + + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } + } to_return = data.frame( - predicted_label = factor(preds), + predicted_label = preds, confidence = confs) if(track_neighbors){ - print(dim(to_return)) - print(dim(all_neighbors)) + #print(dim(to_return)) + #print(dim(all_neighbors)) + #check for any missing points + if(!nrow(to_return)==nrow(all_neighbors)){ + print("mismatch in row number for to_return and all_neighbors") + print(nrow(to_return)) + print(nrow(all_neighbors)) + print(head(to_return)) + print(head(all_neighbors)) + stop("") + } to_return = bind_cols(to_return,all_neighbors) } return(to_return) diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 95599bf..26fa168 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -9,35 +9,51 @@ DLBCLone_optimize_params( metadata_df, umap_out, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), + optimize_for_other = TRUE, eval_group = "Lacy", min_k = 3, - max_k = 30, + max_k = 33, verbose = FALSE, - seed = 12345 + seed = 12345, + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE ) } \arguments{ -\item{combined_mutation_status_df}{Data frame with one row per sample and one column per mutation} +\item{combined_mutation_status_df}{Data frame with one row per sample and +one column per mutation} -\item{metadata_df}{Data frame of metadata with one row per sample and three required columns: sample_id, dataset and lymphgen} +\item{metadata_df}{Data frame of metadata with one row per sample and +three required columns: sample_id, dataset and lymphgen} -\item{umap_out}{additional argument} +\item{umap_out}{The output of a previous run of make_and_annotate_umap. +If provided, the function will use this model to project the data +instead of re-running UMAP.} -\item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other")} +\item{truth_classes}{Vector of classes to use for training and testing. +Default: c("EZB","MCD","ST2","N1","BN2","Other")} -\item{eval_group}{Specify whether certain rows will be evaluated and held out from training rather than using all samples.} +\item{eval_group}{Specify whether certain rows will be evaluated and +held out from training rather than using all samples.} -\item{min_k}{additional argument} +\item{min_k}{Starting k for knn (Default: 3)} -\item{max_k}{additional argument} +\item{max_k}{Ending k for knn (Default: 33)} -\item{verbose}{additional argument} +\item{verbose}{Whether to print verbose outputs to console} -\item{seed}{additional argument} +\item{seed}{Random seed to use for reproducibility (default: 12345)} + +\item{maximize}{Metric to use for optimization. Either "sensitivity" +(average sensitivity across all classes), "accuracy" +(actual accuracy across all samples) or "balanced_accuracy" (the mean of the +balanced accuracy values across all classes). Default: "balanced_accuracy"} } \value{ List of data frames with the results of the parameter optimization -including the best model, the associated knn parameters and the annotated UMAP output +including the best model, the associated knn parameters and the annotated +UMAP output as a data frame. The list also includes the predictions for the +"Other" class if it was included in the training and testing. } \description{ Optimize parameters for classifying samples using UMAP and k-nearest neighbor diff --git a/man/DLBCLone_train_test_plot.Rd b/man/DLBCLone_train_test_plot.Rd index b394ffe..e29273e 100644 --- a/man/DLBCLone_train_test_plot.Rd +++ b/man/DLBCLone_train_test_plot.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/umap.R \name{DLBCLone_train_test_plot} \alias{DLBCLone_train_test_plot} -\title{Plot the result of a lymphgen classification} +\title{Plot the result of a DLBCLone classification} \usage{ DLBCLone_train_test_plot( test_df, @@ -13,9 +13,10 @@ DLBCLone_train_test_plot( annotate_accuracy = FALSE, classes = c("BN2", "ST2", "MCD", "EZB", "N1"), label_offset = 2, - title1 = "GAMBL", - title2 = "predicted_class_for_HighConf", - title3 = "predicted_class_for_Other" + title1 = "Original Class", + title2 = "DLBCLone Predicted Class", + title3 = "DLBCLone Predicted Class (Other)", + base_size = 1 ) } \arguments{ @@ -45,7 +46,7 @@ DLBCLone_train_test_plot( a ggplot object } \description{ -Plot the result of a lymphgen classification +Plot the result of a DLBCLone classification } \examples{ #add the dataset name to the metadata if it's not already there (required for the plot work) diff --git a/man/construct_reduced_winning_version.Rd b/man/construct_reduced_winning_version.Rd index 6b66577..5e0772a 100644 --- a/man/construct_reduced_winning_version.Rd +++ b/man/construct_reduced_winning_version.Rd @@ -5,17 +5,19 @@ \title{Construct reduced 21-dimension feature vector for DLBCLass} \usage{ construct_reduced_winning_version( - mutations = "inst/extdata/DLBCL.699.fullGSM.Sep_23_2022.tsv", - fisher_test_result = "inst/extdata/fisher_exact_5x2.Sep_23_2022.combined.tsv", + mutations_file = "inst/extdata/DLBCL.699.fullGSM.Sep_23_2022.tsv", + fisher_test_result_file = "inst/extdata/fisher_exact_5x2.Sep_23_2022.combined.tsv", + include_cn = TRUE, + mutation_data, add_missing_features = FALSE ) } \arguments{ -\item{fisher_test_result}{Fisher's test result from DLBCLass github repository} - \item{add_missing_features}{Set to TRUE to fill in missing features with zeroes} \item{data}{Data frame of mutation status from DLBCLass supplement} + +\item{fisher_test_result}{Fisher's test result from DLBCLass github repository} } \value{ a list of data frames with the full mutation features, collapsed diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 38a5d9e..bd94e15 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -16,7 +16,10 @@ make_and_annotate_umap( ret_model = TRUE, na_vals = "drop", join_column = "sample_id", - seed = 42 + seed = 12345, + target_column, + target_metric = "euclidean", + target_weight = 0.5 ) } \arguments{ diff --git a/man/optimize_outgroup.Rd b/man/optimize_outgroup.Rd new file mode 100644 index 0000000..3b2637a --- /dev/null +++ b/man/optimize_outgroup.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{optimize_outgroup} +\alias{optimize_outgroup} +\title{Optimize the threshold for classifying samples as "Other"} +\usage{ +optimize_outgroup( + predicted_labels, + true_labels, + other_score, + all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE +) +} +\arguments{ +\item{predicted_labels}{Vector of predicted labels for the samples} + +\item{true_labels}{Vector of true labels for the samples} + +\item{other_score}{Vector of scores for the "Other" class for each sample ()} + +\item{all_classes}{Vector of classes to use for training and testing. +Default: c("MCD","EZB","BN2","N1","ST2","Other")} + +\item{maximize}{Metric to use for optimization. +Either "accuracy" (actual accuracy across all samples) or "balanced_accuracy" +(the mean of the balanced accuracy values across all classes). +Default: "balanced_accuracy"} + +\item{exclude_other_for_accuracy}{Set to TRUE to exclude the +"Other" class from the 'lymphgen' column when calculating accuracy metrics +(passed to DLBCLone_optimize_params). Default: FALSE} +} +\value{ +a list of data frames with the predictions and the UMAP input +} +\description{ +Performs a post-hoc evaluation of the classification of a sample as one of +the main classes vs the outgroup/unclassified label "Other" and returns the +optimal threshold for classifying a sample as "Other" based on the ground +truth provided in the true_labels vector. It evaluates the performance +of the classifier using a range of thresholds and returns the best threshold +based on the specified metric (balanced accuracy or accuracy). +This function is not generally meant to be called directly but rather is +a helper function used by DLBCLone_optimize_params. +} diff --git a/man/predict_single_sample.Rd b/man/predict_single_sample.Rd deleted file mode 100644 index e429880..0000000 --- a/man/predict_single_sample.Rd +++ /dev/null @@ -1,37 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/umap.R -\name{predict_single_sample} -\alias{predict_single_sample} -\title{Predict class for a single sample without using umap_transform} -\usage{ -predict_single_sample( - test_df, - train_df, - train_metadata, - best_params, - truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), - drop_unlabeled_from_training = TRUE, - make_plot = TRUE -) -} -\arguments{ -\item{test_df}{Data frame containing the mutation status of the test sample} - -\item{train_df}{Data frame containing the mutation status of the training samples} - -\item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} - -\item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} - -\item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} - -\item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} - -\item{make_plot}{Set to TRUE to plot the UMAP projection and predictions} -} -\value{ -a list of data frames with the predictions and the UMAP input -} -\description{ -Predict class for a single sample without using umap_transform -} diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index 21821ef..e4eaf13 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -9,13 +9,14 @@ weighted_knn_predict_with_conf( train_labels, test_coords, k, - epsilon = 1e-05, + epsilon = 0.1, conf_threshold = NULL, na_label = "Other", verbose = FALSE, use_weights = TRUE, ignore_top = FALSE, - track_neighbors = FALSE + track_neighbors = TRUE, + separate_other = TRUE ) } \arguments{ From e686f153707c042820058d68158c8be26029cbf3 Mon Sep 17 00:00:00 2001 From: rdmorin Date: Mon, 19 May 2025 11:38:51 -0700 Subject: [PATCH 12/79] update docs and add optimized output --- R/umap.R | 56 +++++++++++++++++++++++++++------ man/DLBCLone_optimize_params.Rd | 45 ++++++++++++++++++++------ 2 files changed, 81 insertions(+), 20 deletions(-) diff --git a/R/umap.R b/R/umap.R index 0fddc1f..fc61353 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,3 +1,6 @@ + + + #' Optimize the threshold for classifying samples as "Other" #' #' Performs a post-hoc evaluation of the classification of a sample as one of @@ -324,13 +327,23 @@ make_and_annotate_umap = function(df, #' three required columns: sample_id, dataset and lymphgen #' @param truth_classes Vector of classes to use for training and testing. #' Default: c("EZB","MCD","ST2","N1","BN2","Other") -#' @param eval_group Specify whether certain rows will be evaluated and -#' held out from training rather than using all samples. +#' @param eval_group If desired, use this to specify which rows will be +#' evaluated and held out from training rather than using all samples. +#' NOTE: this parameter will probably become deprecated! #' @param umap_out The output of a previous run of make_and_annotate_umap. #' If provided, the function will use this model to project the data #' instead of re-running UMAP. #' @param min_k Starting k for knn (Default: 3) #' @param max_k Ending k for knn (Default: 33) +#' @param optimize_for_other Set to TRUE to optimize the threshold for +#' classifying samples as "Other" based on the relative proportion of +#' samples near the sample in UMAP space with the "Other" label. Rather than +#' treating Other as just another class, this will optimize the threshold for +#' a separate score that considers how many Other and non-Other samples are +#' in the neighborhood of the sample in question. This parameter will NOT change +#' the value in predicted_label. Instead, the predicted_label_optimized column +#' will contain the optimized label. Default: FALSE +#' #' @param verbose Whether to print verbose outputs to console #' @param seed Random seed to use for reproducibility (default: 12345) #' @param maximize Metric to use for optimization. Either "sensitivity" @@ -347,12 +360,27 @@ make_and_annotate_umap = function(df, #' #' @examples #' -#' lymphgen_A53_DLBCLone = DLBCLone_optimize_params( -#' lgen_feat_status, #our binary feature matrix -#' a53_meta, #our metadata -#' umap_out = lymphgen_A53_all_feat_gambl, # force use existing UMAP fit -#' eval_group = NULL, # use all samples for evaluating accuracy -#' truth_classes = c("MCD","EZB","BN2","ST2","N1","A53","Other")) +#' +#' # Aim to maximize classification of samples into non-Other class +#' lymphgen_lyseq_no_other = +#' GAMBLR.predict::DLBCLone_optimize_params( +#' dlbcl_status_combined_lyseq, +#' dlbcl_meta_lyseq_train, +#' min_k = 5,max_k=23, +#' optimize_for_other = F, +#' truth_classes = c("MCD","EZB","ST2","BN2")) +#' +#' # Aim to maximize balanced accuracy while allowing samples to be +#' # unclassified (assigned to "Other") +#' +#' DLBCLone_lymphgen_lyseq_prime_opt = +#' GAMBLR.predict::DLBCLone_optimize_params( +#' dlbcl_status_combined_lyseq_prime, +#' dlbcl_meta_lyseq_train, +#' min_k = 5,max_k=23, +#' optimize_for_other = T, +#' truth_classes = c("MCD","EZB","ST2","BN2","Other")) +#' DLBCLone_optimize_params = function(combined_mutation_status_df, metadata_df, @@ -363,8 +391,8 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, "N1", "BN2", "Other"), - optimize_for_other = TRUE, - eval_group = "Lacy", + optimize_for_other = FALSE, + eval_group = NULL, min_k=3, max_k=33, verbose = FALSE, @@ -606,6 +634,14 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, ignore_top = ignore_top, verbose = verbose, track_neighbors = TRUE) + if(optimize_for_other){ + + pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, + "Other", + predicted_label)) + }else{ + pred = mutate(pred,predicted_label_optimized = predicted_label) + } xx_d = bind_cols(outs$df,pred) to_ret = list(params=results, best_params = best_params, diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 26fa168..76ea427 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -9,8 +9,8 @@ DLBCLone_optimize_params( metadata_df, umap_out, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), - optimize_for_other = TRUE, - eval_group = "Lacy", + optimize_for_other = FALSE, + eval_group = NULL, min_k = 3, max_k = 33, verbose = FALSE, @@ -33,8 +33,18 @@ instead of re-running UMAP.} \item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other")} -\item{eval_group}{Specify whether certain rows will be evaluated and -held out from training rather than using all samples.} +\item{optimize_for_other}{Set to TRUE to optimize the threshold for +classifying samples as "Other" based on the relative proportion of +samples near the sample in UMAP space with the "Other" label. Rather than +treating Other as just another class, this will optimize the threshold for +a separate score that considers how many Other and non-Other samples are +in the neighborhood of the sample in question. This parameter will NOT change +the value in predicted_label. Instead, the predicted_label_optimized column +will contain the optimized label. Default: FALSE} + +\item{eval_group}{If desired, use this to specify which rows will be +evaluated and held out from training rather than using all samples. +NOTE: this parameter will probably become deprecated!} \item{min_k}{Starting k for knn (Default: 3)} @@ -60,10 +70,25 @@ Optimize parameters for classifying samples using UMAP and k-nearest neighbor } \examples{ -lymphgen_A53_DLBCLone = DLBCLone_optimize_params( - lgen_feat_status, #our binary feature matrix - a53_meta, #our metadata - umap_out = lymphgen_A53_all_feat_gambl, # force use existing UMAP fit - eval_group = NULL, # use all samples for evaluating accuracy - truth_classes = c("MCD","EZB","BN2","ST2","N1","A53","Other")) + +# Aim to maximize classification of samples into non-Other class +lymphgen_lyseq_no_other = +GAMBLR.predict::DLBCLone_optimize_params( + dlbcl_status_combined_lyseq, + dlbcl_meta_lyseq_train, + min_k = 5,max_k=23, + optimize_for_other = F, + truth_classes = c("MCD","EZB","ST2","BN2")) + +# Aim to maximize balanced accuracy while allowing samples to be +# unclassified (assigned to "Other") + +DLBCLone_lymphgen_lyseq_prime_opt = +GAMBLR.predict::DLBCLone_optimize_params( + dlbcl_status_combined_lyseq_prime, + dlbcl_meta_lyseq_train, + min_k = 5,max_k=23, + optimize_for_other = T, + truth_classes = c("MCD","EZB","ST2","BN2","Other")) + } From 42a282dfa2264d2cd2ae1a656cd424c38669a6d1 Mon Sep 17 00:00:00 2001 From: rdmorin Date: Wed, 21 May 2025 11:47:14 -0700 Subject: [PATCH 13/79] add functionality and incorporate single sample function --- NAMESPACE | 4 + R/umap.R | 675 +++++++++++++++++++++++++- man/assemble_genetic_features.Rd | 46 ++ man/make_and_annotate_umap.Rd | 3 +- man/make_neighborhood_plot.Rd | 33 ++ man/predict_single_sample_DLBCLone.Rd | 81 ++++ man/weighted_knn_predict_with_conf.Rd | 3 +- 7 files changed, 827 insertions(+), 18 deletions(-) create mode 100644 man/assemble_genetic_features.Rd create mode 100644 man/make_neighborhood_plot.Rd create mode 100644 man/predict_single_sample_DLBCLone.Rd diff --git a/NAMESPACE b/NAMESPACE index 927de7f..66e8f5f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,6 +2,7 @@ export(DLBCLone_optimize_params) export(DLBCLone_train_test_plot) +export(assemble_genetic_features) export(classify_bl) export(classify_dlbcl) export(classify_fl) @@ -10,14 +11,17 @@ export(construct_reduced_winning_version) export(make_and_annotate_umap) export(massage_matrix_for_clustering) export(optimize_outgroup) +export(predict_single_sample_DLBCLone) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) import(GAMBLR.data) import(GAMBLR.helpers) import(dplyr) +import(ggplot2) import(randomForest, except = c("combine")) import(readr) import(tibble) import(tidyr) import(tidyselect) import(uwot) +importFrom(rlang,sym) diff --git a/R/umap.R b/R/umap.R index fc61353..698d2ab 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,5 +1,225 @@ +#' Assemble genetic features for UMAP input +#' +#' This function assembles a matrix of genetic features for each sample, including mutation status, +#' aSHM counts, and structural variant status for BCL2, BCL6, and MYC. It supports both genome and capture sequencing types. +#' +#' @param these_samples_metadata Data frame with sample metadata, must include seq_type and sample_id. +#' @param metadata_columns Columns in metadata to use for SV status (default: c("bcl2_ba","bcl6_ba","myc_ba")). +#' @param genes Vector of gene symbols to include. +#' @param synon_genes Vector of gene symbols for synonymous mutations. +#' @param maf_with_synon MAF data frame including synonymous mutations. +#' @param hotspot_genes Vector of hotspot genes. +#' @param sv_value Value to assign for SV presence (default: 3). +#' @param synon_value Value to assign for synonymous mutations (default: 1). +#' @param coding_value Value to assign for coding mutations (default: 2). +#' +#' @return Matrix of assembled features for each sample. +#' @export +assemble_genetic_features <- function(these_samples_metadata, + metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), + genes, + synon_genes, + maf_with_synon, + hotspot_genes, + genome_build = "grch37", + sv_value = 3, + synon_value = 1, + coding_value = 2, + include_ashm = TRUE){ + if(include_ashm){ + #TODO: ensure this supports both genome builds correctly + some_regions = GAMBLR.utils::create_bed_data( + GAMBLR.data::grch37_ashm_regions, + fix_names = "concat", + concat_cols = c("gene","region"), + sep="-") + #make the aSHM count matrix and combine if necessary + if ("genome" %in% these_samples_metadata$seq_type){ + ashm_matrix_genome <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "genome", + these_samples_metadata = these_samples_metadata + ) + + colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) + } + if ("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix_cap <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "capture", + these_samples_metadata = these_samples_metadata + ) + colnames(ashm_matrix_cap) = gsub("-.+","",colnames(ashm_matrix_cap)) + } + if ("genome" %in% these_samples_metadata$seq_type && "capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = bind_rows(ashm_matrix_genome, ashm_matrix_cap) + }else if("genome" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_genome + }else if("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_cap + }else{ + stop("no eligible seq_type provided in these_samples_metadata") + } +} + + + status_with_silent = get_coding_ssm_status( + these_samples_metadata = these_samples_metadata, + maf_data = maf_with_synon, + include_hotspots = TRUE, + genes_of_interest = "MYD88", + include_silent_genes = synon_genes[synon_genes %in% genes], + gene_symbols = genes + ) + status_with_silent = status_with_silent %>% column_to_rownames("sample_id") + + status_without_silent = get_coding_ssm_status( + these_samples_metadata = these_samples_metadata, + maf_data = maf_with_synon, + include_hotspots = TRUE, + genes_of_interest = "MYD88", + include_silent = FALSE, + gene_symbols = genes + ) + + status_without_silent = status_without_silent %>% column_to_rownames("sample_id") + + if(any(! colnames(status_with_silent) %in% colnames(status_without_silent))){ + print(colnames(status_with_silent)[!colnames(status_with_silent) %in% colnames(status_without_silent)]) + stop("some columns are missing from the status_without_silent matrix") + } + if(include_ashm){ + ashm_matrix = select(ashm_matrix, any_of(colnames(status_with_silent))) %>% select(any_of(synon_genes)) + + #fill in gaps from aSHM (other non-coding variants in the genes) + + missing = status_with_silent[rownames(ashm_matrix), + colnames(ashm_matrix)]==0 & + ashm_matrix[rownames(ashm_matrix), + colnames(ashm_matrix)] == synon_value + + status_with_silent[rownames(missing), + colnames(missing)] = synon_value + + } + + + status_combined = status_with_silent + status_without_silent + if(coding_value == 1){ + status_combined[status_combined > 1] = 1 + } + #TODO: generalize this to use the column names provided in metadata_columns + bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) + bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) + myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) + + status_combined[,"BCL2_SV"] = 0 + status_combined[bcl2_id,"BCL2_SV"] = sv_value + + status_combined[,"MYC_SV"] = 0 + status_combined[myc_id,"MYC_SV"] = sv_value + + status_combined[,"BCL6_SV"] = 0 + status_combined[bcl6_id,"BCL6_SV"] = sv_value + return(status_combined) + +} + +old_assemble_genetic_features <- function(these_samples_metadata, + metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), + genes, + synon_genes, + maf_with_synon, + hotspot_genes, + sv_value = 3, + synon_value = 1, + coding_value = 2){ + #TODO: ensure this supports both genome builds correctly + some_regions = GAMBLR.utils::create_bed_data( + GAMBLR.data::grch37_ashm_regions, + fix_names = "concat", + concat_cols = c("gene","region"), + sep="-") + #make the aSHM count matrix and combine if necessary + if ("genome" %in% these_samples_metadata$seq_type){ + ashm_matrix_genome <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "genome", + these_samples_metadata = these_samples_metadata + ) + + colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) + } + if ("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix_cap <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "capture", + these_samples_metadata = these_samples_metadata + ) + colnames(ashm_matrix_cap) = gsub("-.+","",colnames(ashm_matrix_cap)) + } + if ("genome" %in% these_samples_metadata$seq_type && "capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = bind_rows(ashm_matrix_genome, ashm_matrix_cap) + }else if("genome" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_genome + }else if("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_cap + }else{ + stop("no eligible seq_type provided in these_samples_metadata") + } + + status_with_silent = get_coding_ssm_status( + these_samples_metadata = these_samples_metadata, + maf_data = maf_with_synon, + include_hotspots = TRUE, + genes_of_interest = "MYD88", + include_silent_genes = synon_genes, + gene_symbols = genes + ) + status_with_silent = status_with_silent %>% column_to_rownames("sample_id") + + status_without_silent = get_coding_ssm_status( + these_samples_metadata = these_samples_metadata, + maf_data = maf_with_synon, + include_hotspots = TRUE, + genes_of_interest = "MYD88", + include_silent = FALSE, + gene_symbols = genes + ) + status_without_silent = status_without_silent %>% column_to_rownames("sample_id") + + status_combined = status_with_silent + status_without_silent + if(coding_value == 1){ + status_combined[status_combined > 1] = 1 + } + + #fill in gaps from aSHM (other non-coding variants in the genes) + + missing = status_with_silent[rownames(ashm_matrix), + colnames(ashm_matrix)]==0 & + ashm_matrix[rownames(ashm_matrix), + colnames(ashm_matrix)] == synon_value + + status_with_silent[rownames(missing), + colnames(missing)] = synon_value + + bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) + bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) + myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) + + status_combined[,"BCL2_SV"] = 0 + status_combined[bcl2_id,"BCL2_SV"] = sv_value + + status_combined[,"MYC_SV"] = 0 + status_combined[myc_id,"MYC_SV"] = sv_value + + status_combined[,"BCL6_SV"] = 0 + status_combined[bcl6_id,"BCL6_SV"] = sv_value + return(status_combined) + +} #' Optimize the threshold for classifying samples as "Other" #' @@ -246,7 +466,18 @@ make_and_annotate_umap = function(df, seed=12345, target_column, target_metric="euclidean", - target_weight=0.5){ + target_weight=0.5, + calc_dispersion = FALSE){ + + # Function to compute mean (or median) pairwise distance within a group + pairwise_dispersion <- function(df_group) { + coords <- as.matrix(df_group[, c("V1", "V2")]) + dists <- dist(coords) # Euclidean pairwise distances + return(median(dists)) + } + if("sample_id" %in% colnames(df)){ + df = df %>% column_to_rownames(var = "sample_id") + } original_n = nrow(df) if(na_vals == "to_zero"){ df[is.na(df)] = 0 @@ -258,13 +489,13 @@ make_and_annotate_umap = function(df, if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } - if(missing(metadata)){ - stop("metadata is required and should contain a column sample_id that matches the row names of your mutation data frame") + if(!missing(metadata)){ + keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] + df= df[keep_rows,] + metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) + message(paste("kept",nrow(metadata),"rows of the data that match the metadata provided")) } - keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] - df= df[keep_rows,] - metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) - message(paste("kept",nrow(metadata),"rows of the data")) + if(missing(umap_out)){ if(missing(target_column)){ umap_out = umap2(df %>% as.matrix(), @@ -279,6 +510,9 @@ make_and_annotate_umap = function(df, #IMPORTANT: n_threads must not be changed because it will break reproducibility }else{ #supervised + if(missing(metadata)){ + stop("metadata must be provided for supervised UMAP") + } metadata[[target_column]] = factor(metadata[[target_column]]) print(table(metadata[[target_column]])) umap_out = umap2(df %>% as.matrix(), @@ -300,19 +534,36 @@ make_and_annotate_umap = function(df, }else{ umap_out = umap_transform(X=df, - model=umap_out$model) + model=umap_out$model,seed=seed) ret_model = FALSE } + + if(ret_model){ umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) }else{ umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) } - umap_df = left_join(umap_df,metadata) - + if(!missing(metadata)){ + umap_df = left_join(umap_df,metadata,by=join_column) + } results = list() + if(calc_dispersion){ + print("calculating pairwise dispersion") + dispersion_df <- umap_df %>% + group_by(lymphgen) %>% + summarise( + n = n(), + mean_pairwise_distance = pairwise_dispersion(cur_data()) + ) %>% + arrange(mean_pairwise_distance) + results[["dispersion"]] = dispersion_df + } + + results[["df"]]=umap_df results[["features"]] = df + if(ret_model){ results[["model"]]= umap_out } @@ -695,7 +946,8 @@ weighted_knn_predict_with_conf <- function(train_coords, use_weights = TRUE, ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE) { #big change here. Other is considered separately for optimization + separate_other = TRUE, + max_neighbors = 500) { #big change here. Other is considered separately for optimization if (nrow(train_coords)==0 || nrow(test_coords) == 0) { print("train_coords:") print(nrow(train_coords)) @@ -704,7 +956,7 @@ weighted_knn_predict_with_conf <- function(train_coords, stop("train_coords and test_coords must be data frames with at least one row") } # get the 100 nearest neighbors - nn <- get.knnx(train_coords, test_coords, 100) + nn <- get.knnx(train_coords, test_coords, max_neighbors) all_neighbors = data.frame() preds <- character(nrow(test_coords)) confs <- numeric(nrow(test_coords)) @@ -726,6 +978,7 @@ weighted_knn_predict_with_conf <- function(train_coords, } } + distances = distances + epsilon weights <- 1 / distances if(use_weights){ @@ -758,6 +1011,13 @@ weighted_knn_predict_with_conf <- function(train_coords, weights <- weights[valid] distances <- distances[valid] neighbors <- neighbors[valid] + #number of neighbours should be, at least, k - 1. If less than that, warn the user + if(length(neighbors) < k-1){ + print(paste("Warning: number of neighbors is less than k-1.")) + print(paste("i:", i,"k:",k)) + print(paste("num_neighbors:",length(neighbors))) + print(table(valid)) + } #now take the first k neighbors if(length(neighbor_labels) > k){ neighbor_labels = neighbor_labels[1:k] @@ -776,6 +1036,7 @@ weighted_knn_predict_with_conf <- function(train_coords, } neighbors_other = length(others_closer) other_weighted_votes = sum(others_weights) + mean_other_dist = mean(others_distances) #other_weighted_votes = neighbors_other # print(paste("other weighted votes:",other_weighted_votes)) #} @@ -787,12 +1048,14 @@ weighted_knn_predict_with_conf <- function(train_coords, rel_other = 10 neighbor_info <- data.frame( other_score = rel_other, + neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), neighbor = paste(neighbors,collapse=","), distance = paste(round(distances, 3),collapse=","), label = paste(neighbor_labels,collapse=","), weighted_votes = "", neighbors_other = neighbors_other, other_weighted_votes = 0, + mean_other_dist = mean_other_dist, total_w = 1, pred_w = 2 @@ -810,6 +1073,7 @@ weighted_knn_predict_with_conf <- function(train_coords, total_weight <- 0 pred_weight <- 0 } else { + #print(weighted_votes) predicted_label <- names(which.max(weighted_votes)) total_weight <- sum(weighted_votes) pred_weight <- weighted_votes[predicted_label] @@ -835,10 +1099,11 @@ weighted_knn_predict_with_conf <- function(train_coords, #rel_other = ifelse(rel_other==0,0,log(rel_other)) neighbor_info <- data.frame( other_score = rel_other, - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), + neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + vote_labels = paste(names(weighted_votes),collapse=","), weighted_votes = paste(weighted_votes,collapse=","), neighbors_other = neighbors_other, other_weighted_votes = other_weighted_votes, @@ -866,8 +1131,386 @@ weighted_knn_predict_with_conf <- function(train_coords, stop("") } to_return = bind_cols(to_return,all_neighbors) + if(nrow(to_return ) != nrow(test_coords)){ + print("mismatch in row number for to_return and test_coords") + print(nrow(to_return)) + print(nrow(test_coords)) + print(head(to_return)) + print(head(test_coords)) + stop("") + } + rownames(to_return) <- rownames(test_coords) } return(to_return) } +#' @title Make Neighborhood Plot +#' @description +#' Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. +#' +#' @param single_sample_prediction_output A list containing prediction results and annotation data frames. +#' Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations). +#' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. +#' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. +#' +#' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. +#' +#' @details +#' The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. +#' +#' @import dplyr +#' @import ggplot2 +#' @importFrom rlang sym +#' +#' @examples +#' # Assuming 'output' is the result of DLBCLone_predict_single_sample on sample_id "SAMPLE123": +#' make_neighborhood_plot(output, "SAMPLE123") +make_neighborhood_plot <- function(single_sample_prediction_output, + this_sample_id, + prediction_in_title = TRUE){ + #extract the sample_id for all the nearest neighbors with non-Other labels + my_neighbours = filter(single_sample_prediction_output$prediction, + sample_id == this_sample_id) %>% + pull(neighbor_id) %>% strsplit(.,",") %>% unlist() + #set up links connecting each neighbor to the sample's point + links_df = filter(single_sample_prediction_output$anno_df,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) + my_x = filter(single_sample_prediction_output$anno_df, + sample_id==this_sample_id) %>% pull(V1) + my_y = filter(single_sample_prediction_output$anno_df, + sample_id==this_sample_id) %>% pull(V2) + if(prediction_in_title){ + title = paste(this_sample_id, + pull(single_sample_prediction_output$prediction, + !!sym("predicted_label"))) + if(single_sample_prediction_output$prediction$predicted_label_optimized == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ + title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") + } + + }else{ + title = this_sample_id + } + + + + pp=ggplot(mutate(single_sample_prediction_output$anno_df,group=lymphgen), + aes(x=V1,y=V2,colour=group)) + + geom_point(alpha=0.8,size=0.5) + + geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=0.5)+ + scale_colour_manual(values=get_gambl_colours()) + + ggtitle(title)+ + theme_minimal() + return(pp) +} + + +#' Predict class for a single sample without using umap_transform and plot result of classification +#' +#' @param seed Random seed for reproducibility +#' @param test_df Data frame containing the mutation status of the test sample +#' @param train_df Data frame containing the mutation status of the training samples +#' @param train_metadata Metadata for training samples with truth labels in lymphgen column +#' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful +#' for reproducibility and for using the same UMAP model on different datasets. +#' @param best_params Data frame from DLBCLone_optimize_params with the best parameters +#' @param other_df Data frame containing the predictions for samples in the "Other" class +#' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with +#' distance = 0. This is usually only relevant when re-classifying labeled +#' samples to estimate overall accuracy +#' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") +#' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data +#' @param make_plot Set to TRUE to plot the UMAP projection and predictions +#' @param annotate_accuracy Set to true to add labels with accuracy values +#' @param label_offset Length of the label offset for the accuracy labels +#' @param title1 additional argument +#' @param title2 additional argument +#' @param title3 additional argument +#' +#' @returns a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +#' @export +#' +#' @examples +#' predict_single_sample_DLBCLone( +#' seed = 1234, +#' test_df = test_df, +#' train_df = train_df, +#' train_metadata = train_metadata, +#' umap_out = umap_out, +#' best_params = best_params +#' predictions_df = predictions_df, +#' annotate_accuracy = TRUE +#' ) +#' +predict_single_sample_DLBCLone <- function( + test_df, + train_df, + train_metadata, + umap_out, + best_params, + other_df, + ignore_top = FALSE, + truth_classes = c("EZB","MCD","ST2","N1","BN2"), + drop_unlabeled_from_training=TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + label_offset = 2, + title1="GAMBL", + title2="predicted_class_for_HighConf", + title3 ="predicted_class_for_Other", + seed = 12345, + max_neighbors = 500 +){ + set.seed(seed) + + if(ignore_top){ + # Allow overlapping samples: rename test duplicates temporarily + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + test_df <- test_df %>% + mutate(sample_id = ifelse( + sample_id %in% dupes, + paste0(sample_id, "_test"), + sample_id + )) + } + } else { + # Drop overlaps to prevent rowname collisions + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + warning(paste("Removing", length(dupes), + "overlapping samples from train_df to avoid duplicated rownames.\n", + "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) + train_df <- train_df %>% filter(!sample_id %in% dupes) + train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) + } + } + + trained_features = colnames(umap_out$features) + + train_df = train_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + train_id <- train_df$sample_id + + test_df = test_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + test_id <- test_df$sample_id + + combined_df <- bind_rows(train_df, test_df) + + projection <- make_and_annotate_umap( + df = combined_df, + umap_out = umap_out, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + + train_coords = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + train_df_proj = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% + select(sample_id,V1,V2) %>% + left_join( #Join to the incoming metadata rather than trusting the metadata in the projection + train_metadata %>% select( + sample_id, + lymphgen + ), + by = "sample_id" + ) + + train_labels = train_df_proj %>% + pull(lymphgen) + + test_coords = dplyr::filter( + projection$df, + sample_id %in% test_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + predict_training = FALSE + if(predict_training){ + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top + ) + + train_pred = rownames_to_column(train_pred, var = "sample_id") + } + + + test_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top, + max_neighbors = max_neighbors + ) + + test_pred = rownames_to_column(test_pred, var = "sample_id") + + anno_umap = select(projection$df, sample_id, V1, V2) + + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + anno_out = anno_out %>% + mutate( + V1 = as.numeric(V1), + V2 = as.numeric(V2), + label = as.character(label) + ) + if(predict_training){ + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + }else{ + predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% + select(sample_id, V1, V2) + } + + predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + predictions_df = bind_rows(predictions_train_df %>% select(sample_id, V1, V2), + predictions_test_df %>% select(sample_id, V1, V2)) + if(make_plot){ + title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) + if("BN2" %in% truth_classes){ + print(best_params) + acc_df = data.frame( + lymphgen = c( + #"N1", + "BN2", + "EZB", + "MCD", + "ST2", + "Other", + "A53" + ), + accuracy = c( + #best_params$N1_bacc, + best_params$BN2_bacc, + best_params$EZB_bacc, + best_params$MCD_bacc, + best_params$ST2_bacc, + best_params$Other_bacc, + best_params$A53_bacc + ) + ) + }else if("C1" %in% truth_classes){ + acc_df = data.frame( + lymphgen = c( + "C1", + "C2", + "C3", + "C4", + "C5" + ), + accuracy = c( + best_params$C1_bacc, + best_params$C2_bacc, + best_params$C3_bacc, + best_params$C4_bacc, + best_params$C5_bacc + ) + ) + }else{ + stop("no labels to add?") + } + + # Add the predicted labels for Other (unclassified) cases, if provided + if(!missing(other_df)){ + in_df = bind_rows( + train_df_proj, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label), + mutate(other_df,dataset=title3,lymphgen=predicted_label) + ) + }else{ + in_df = bind_rows( + train_df_proj, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + ) + } + + pp = ggplot(in_df) + + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + + scale_colour_manual(values=get_gambl_colours()) + + facet_wrap(~dataset,ncol=1) + + theme_Morons() + ggtitle(title) + + if(annotate_accuracy){ + #add labels and set nudge direction based on what quadrant each group sits in + centroids = filter(predictions_df,predicted_label %in% truth_classes) %>% + group_by(predicted_label) %>% + summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% + mutate(lymphgen=predicted_label) + centroids = left_join(centroids,acc_df) %>% + mutate(label=paste(lymphgen,":",round(accuracy,3))) + + pp = pp + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = -1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = -1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = 1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data = anno_out, + aes(x=V1,y=V2,label=label), + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset, + colour="red" + ) + } + } + return(list( + prediction = test_pred, + umap_input = umap_out$features, + model=umap_out$model, + plot = pp, + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") + )) +} \ No newline at end of file diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd new file mode 100644 index 0000000..a494ea4 --- /dev/null +++ b/man/assemble_genetic_features.Rd @@ -0,0 +1,46 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{assemble_genetic_features} +\alias{assemble_genetic_features} +\title{Assemble genetic features for UMAP input} +\usage{ +assemble_genetic_features( + these_samples_metadata, + metadata_columns = c("bcl2_ba", "bcl6_ba", "myc_ba"), + genes, + synon_genes, + maf_with_synon, + hotspot_genes, + genome_build = "grch37", + sv_value = 3, + synon_value = 1, + coding_value = 2, + include_ashm = TRUE +) +} +\arguments{ +\item{these_samples_metadata}{Data frame with sample metadata, must include seq_type and sample_id.} + +\item{metadata_columns}{Columns in metadata to use for SV status (default: c("bcl2_ba","bcl6_ba","myc_ba")).} + +\item{genes}{Vector of gene symbols to include.} + +\item{synon_genes}{Vector of gene symbols for synonymous mutations.} + +\item{maf_with_synon}{MAF data frame including synonymous mutations.} + +\item{hotspot_genes}{Vector of hotspot genes.} + +\item{sv_value}{Value to assign for SV presence (default: 3).} + +\item{synon_value}{Value to assign for synonymous mutations (default: 1).} + +\item{coding_value}{Value to assign for coding mutations (default: 2).} +} +\value{ +Matrix of assembled features for each sample. +} +\description{ +This function assembles a matrix of genetic features for each sample, including mutation status, +aSHM counts, and structural variant status for BCL2, BCL6, and MYC. It supports both genome and capture sequencing types. +} diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index bd94e15..2eaaaea 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -19,7 +19,8 @@ make_and_annotate_umap( seed = 12345, target_column, target_metric = "euclidean", - target_weight = 0.5 + target_weight = 0.5, + calc_dispersion = FALSE ) } \arguments{ diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd new file mode 100644 index 0000000..08fc3d7 --- /dev/null +++ b/man/make_neighborhood_plot.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{make_neighborhood_plot} +\alias{make_neighborhood_plot} +\title{Make Neighborhood Plot} +\usage{ +make_neighborhood_plot( + single_sample_prediction_output, + this_sample_id, + prediction_in_title = TRUE +) +} +\arguments{ +\item{single_sample_prediction_output}{A list containing prediction results and annotation data frames. +Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations).} + +\item{this_sample_id}{Character. The sample ID for which the neighborhood plot will be generated.} + +\item{prediction_in_title}{Logical. If \code{TRUE}, includes the predicted label in the plot title.} +} +\value{ +A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. +} +\description{ +Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. +} +\details{ +The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. +} +\examples{ +# Assuming 'output' is the result of DLBCLone_predict_single_sample on sample_id "SAMPLE123": +make_neighborhood_plot(output, "SAMPLE123") +} diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd new file mode 100644 index 0000000..d53db50 --- /dev/null +++ b/man/predict_single_sample_DLBCLone.Rd @@ -0,0 +1,81 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{predict_single_sample_DLBCLone} +\alias{predict_single_sample_DLBCLone} +\title{Predict class for a single sample without using umap_transform and plot result of classification} +\usage{ +predict_single_sample_DLBCLone( + test_df, + train_df, + train_metadata, + umap_out, + best_params, + other_df, + ignore_top = FALSE, + truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), + drop_unlabeled_from_training = TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + label_offset = 2, + title1 = "GAMBL", + title2 = "predicted_class_for_HighConf", + title3 = "predicted_class_for_Other", + seed = 12345, + max_neighbors = 500 +) +} +\arguments{ +\item{test_df}{Data frame containing the mutation status of the test sample} + +\item{train_df}{Data frame containing the mutation status of the training samples} + +\item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} + +\item{umap_out}{UMAP output from a previous run. The function will use this model to project the data, useful +for reproducibility and for using the same UMAP model on different datasets.} + +\item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} + +\item{other_df}{Data frame containing the predictions for samples in the "Other" class} + +\item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with +distance = 0. This is usually only relevant when re-classifying labeled +samples to estimate overall accuracy} + +\item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} + +\item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} + +\item{make_plot}{Set to TRUE to plot the UMAP projection and predictions} + +\item{annotate_accuracy}{Set to true to add labels with accuracy values} + +\item{label_offset}{Length of the label offset for the accuracy labels} + +\item{title1}{additional argument} + +\item{title2}{additional argument} + +\item{title3}{additional argument} + +\item{seed}{Random seed for reproducibility} +} +\value{ +a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +} +\description{ +Predict class for a single sample without using umap_transform and plot result of classification +} +\examples{ +predict_single_sample_DLBCLone( + seed = 1234, + test_df = test_df, + train_df = train_df, + train_metadata = train_metadata, + umap_out = umap_out, + best_params = best_params + predictions_df = predictions_df, + annotate_accuracy = TRUE +) + +} diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index e4eaf13..74d7971 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -16,7 +16,8 @@ weighted_knn_predict_with_conf( use_weights = TRUE, ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE + separate_other = TRUE, + max_neighbors = 500 ) } \arguments{ From ea7b48a3070a901a2e2567d2c2c5a567c9cc52fd Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 21 May 2025 13:52:01 -0700 Subject: [PATCH 14/79] vectorized k updates to KNN, and optimize fx's --- DESCRIPTION | 2 +- R/umap.R | 551 ++++++++++++++------------ man/weighted_knn_predict_with_conf.Rd | 3 +- 3 files changed, 290 insertions(+), 266 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index b2c3098..77e5dc7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,7 +15,7 @@ Description: Collection of functions and helpers to classify different B-cell ly License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.2 Depends: R (>= 3.5.0) LazyData: true diff --git a/R/umap.R b/R/umap.R index 698d2ab..adacc3e 100644 --- a/R/umap.R +++ b/R/umap.R @@ -701,35 +701,68 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, ignore_top = TRUE } for(use_w in weights_opt){ - for(k in ks){ - message(paste("K:",k)) - for (threshold in threshs){ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + }else{ + test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) + train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + } - }else{ + pred_all = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=ks, + conf_threshold = -1, # Will threshold later + na_label="Other", + use_weights = use_w, + ignore_top = ignore_top, + verbose = verbose, + track_neighbors = optimize_for_other + ) - test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) - train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - } - if(verbose){ - print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) - } - - pred = weighted_knn_predict_with_conf( + if(!"Other" %in% truth_classes){ + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + }else{ + test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) + train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) + } + n_other = nrow(test_coords) + + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other_all = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, - k=k, - conf_threshold =threshold, + k=ks, + conf_threshold = -1, # Will threshold later na_label="Other", use_weights = use_w, ignore_top = ignore_top, - verbose = verbose) + verbose = verbose + ) + } + } + + for(k in ks){ + message(paste("K:",k)) + for (threshold in threshs){ + + if(verbose){ + print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) + } + k_index <- which(ks == k) + pred <- pred_all[[k_index]] %>% + mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) + if(is.null(eval_group)){ xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) train_d = filter(outs$df,lymphgen %in% truth_classes) @@ -737,44 +770,46 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) } - if("Other" %in% truth_classes){ - xx_d$lymphgen = factor(xx_d$lymphgen) - }else{ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - }else{ - test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) - train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - } - n_other = nrow(test_coords) - - if(!"Other" %in% truth_classes && n_other > 0){ - pred_other = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=k, - conf_threshold =threshold, - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose) - - if(!is.null(eval_group)){ - xx_o = bind_cols(filter(outs$df,dataset == eval_group,lymphgen =="Other") ,pred_other) - }else{ - xx_o = bind_cols(filter(outs$df,lymphgen == "Other") ,pred_other) - } + + # Always set true label factor levels to truth_classes + xx_d$lymphgen = factor(xx_d$lymphgen, levels = truth_classes) + + # Set predicted labels to allow "Other" + pred_levels = union(truth_classes, "Other") + xx_d$predicted_label = factor(xx_d$predicted_label, levels = pred_levels) + + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other <- pred_other_all[[k_index]] %>% + mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) + + if(!is.null(eval_group)){ + xx_o = bind_cols( + filter(outs$df, dataset == eval_group, lymphgen == "Other"), + pred_other + ) + } else { + xx_o = bind_cols( + filter(outs$df, lymphgen == "Other"), + pred_other + ) } - xx_d$lymphgen = factor(xx_d$lymphgen,levels = c(unique(xx_d$lymphgen),"Other")) + xx_o$lymphgen = factor("Other", levels = pred_levels) + xx_o$predicted_label = factor(xx_o$predicted_label, levels = pred_levels) + xx_d = bind_rows(xx_d, xx_o) } + true_factor = xx_d$lymphgen - pred_factor = factor(xx_d$predicted_label,levels = levels(xx_d$lymphgen)) - + pred_factor = xx_d$predicted_label + + if (exclude_other_for_accuracy) { + keep_idx = which(true_factor != "Other") + true_factor = true_factor[keep_idx] + pred_factor = pred_factor[keep_idx] + } + + + if(verbose){ print("true_factor") print(levels(true_factor )) @@ -800,19 +835,24 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, overall_sensitivity<- mean(sn[!names(sn) == "Class: Other"], na.rm = TRUE) if(optimize_for_other){ - optimized_accuracy_and_thresh = optimize_outgroup(pred_factor, - true_factor, - xx_d$other_score, - all_classes = truth_classes, - maximize = maximize, - exclude_other_for_accuracy = exclude_other_for_accuracy) + + optimized_accuracy_and_thresh = optimize_outgroup( + pred_factor, + true_factor, + xx_d$other_score, + all_classes = truth_classes, + maximize = maximize, + exclude_other_for_accuracy = exclude_other_for_accuracy + ) out_opt_thresh = optimized_accuracy_and_thresh$threshold optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 - out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + }else{ out_opt_acc = 0 out_opt_thresh = 0 } + if(maximize == "sensitivity"){ this_accuracy = overall_sensitivity }else if(maximize == "balanced_accuracy"){ @@ -820,6 +860,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, }else{ this_accuracy = overall_accuracy } + if(out_opt_acc > this_accuracy){ this_accuracy = out_opt_acc } @@ -847,8 +888,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, threshold_outgroup = out_opt_thresh, accuracy_out = out_opt_acc, na_option= na_option) - - + if(this_accuracy > best_acc){ best_acc = this_accuracy @@ -884,9 +924,11 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, use_weights = best_params$use_weights, ignore_top = ignore_top, verbose = verbose, - track_neighbors = TRUE) + track_neighbors = optimize_for_other + ) + pred = pred[[1]] + if(optimize_for_other){ - pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, "Other", predicted_label)) @@ -935,213 +977,196 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, #' @returns data frame with labels and confidence values for rows in test_coords #' @export #' -weighted_knn_predict_with_conf <- function(train_coords, - train_labels, - test_coords, - k, - epsilon = 0.1, - conf_threshold = NULL, - na_label = "Other", - verbose = FALSE, - use_weights = TRUE, - ignore_top = FALSE, - track_neighbors = TRUE, - separate_other = TRUE, - max_neighbors = 500) { #big change here. Other is considered separately for optimization - if (nrow(train_coords)==0 || nrow(test_coords) == 0) { - print("train_coords:") - print(nrow(train_coords)) - print("test:") - print(nrow(test_coords)) - stop("train_coords and test_coords must be data frames with at least one row") - } - # get the 100 nearest neighbors - nn <- get.knnx(train_coords, test_coords, max_neighbors) - all_neighbors = data.frame() - preds <- character(nrow(test_coords)) - confs <- numeric(nrow(test_coords)) - - train_labels = as.character(train_labels) - for (i in 1:nrow(test_coords)) { - if(verbose){ - print(paste("index:",i)) - print(test_coords[i,]) +weighted_knn_predict_with_conf <- function( + train_coords, + train_labels, + test_coords, + k, # a vector + epsilon = 0.1, + conf_threshold = NULL, + na_label = "Other", + verbose = FALSE, + use_weights = TRUE, + ignore_top = FALSE, + track_neighbors = TRUE, + separate_other = TRUE #big change here. Other is considered separately for optimization +) { + if (nrow(train_coords)==0 || nrow(test_coords) == 0) { + print("train_coords:") + print(nrow(train_coords)) + print("test:") + print(nrow(test_coords)) + stop("train_coords and test_coords must be data frames with at least one row") } - neighbors <- nn$nn.index[i, ] - distances <- nn$nn.dist[i, ] - if(ignore_top){ - #ignore a neighbor if it has identical V1 and V2 - distances <- nn$nn.dist[i, ] - if(distances[1] == 0){ - neighbors = neighbors[-1] - distances = distances[-1] - } - } - - distances = distances + epsilon - weights <- 1 / distances - if(use_weights){ - weights <- 1 / distances - }else{ - weights <- rep(1,length(distances)) - } + train_labels = as.character(train_labels) + max_k <- max(k) + nn <- get.knnx(train_coords, test_coords, max_k) + results_list <- list() - neighbor_labels <- train_labels[neighbors] + for (curr_k in k) { + if (verbose) cat("Processing k =", curr_k, "\n") - if(verbose){ - print("neighbors:") - print(neighbors) - print("distances:") - print(distances) - print("weights:") - print(weights) - print("labels:") - print(neighbor_labels) - } - # Remove NAs (just in case) - valid <- !is.na(neighbor_labels) - #num_other_neighbors = sum(neighbor_labels == "Other") - other_dists = distances[neighbor_labels == "Other"] - if(separate_other){ - valid = valid & neighbor_labels != "Other" - } - neighbor_labels <- neighbor_labels[valid] - weights <- weights[valid] - distances <- distances[valid] - neighbors <- neighbors[valid] - #number of neighbours should be, at least, k - 1. If less than that, warn the user - if(length(neighbors) < k-1){ - print(paste("Warning: number of neighbors is less than k-1.")) - print(paste("i:", i,"k:",k)) - print(paste("num_neighbors:",length(neighbors))) - print(table(valid)) - } - #now take the first k neighbors - if(length(neighbor_labels) > k){ - neighbor_labels = neighbor_labels[1:k] - weights = weights[1:k] - distances = distances[1:k] - neighbors = neighbors[1:k] - } + preds <- character(nrow(test_coords)) + confs <- numeric(nrow(test_coords)) + all_neighbors <- data.frame() - others_closer = which(other_dists < max(distances)) - - others_distances = other_dists[others_closer] - if(use_weights){ - others_weights = 1 / others_distances - }else{ - others_weights = rep(1,length(others_closer)) - } - neighbors_other = length(others_closer) - other_weighted_votes = sum(others_weights) - mean_other_dist = mean(others_distances) - #other_weighted_votes = neighbors_other - # print(paste("other weighted votes:",other_weighted_votes)) - #} - if (length(neighbor_labels) == 0) { - preds[i] <- "Other" - confs[i] <- 1 - if(track_neighbors){ - - rel_other = 10 - neighbor_info <- data.frame( - other_score = rel_other, - neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = "", - neighbors_other = neighbors_other, - other_weighted_votes = 0, - mean_other_dist = mean_other_dist, - total_w = 1, - pred_w = 2 - - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - next; - } - } + for (i in 1:nrow(test_coords)){ + if(verbose){ + print(paste("index:",i)) + print(test_coords[i,]) + } + neighbors <- nn$nn.index[i, ] + distances <- nn$nn.dist[i, ] + if(ignore_top){ + #ignore a neighbor if it has identical V1 and V2 + distances <- nn$nn.dist[i, ] + if(distances[1] == 0){ + neighbors = neighbors[-1] + distances = distances[-1] + } + } - weighted_votes <- tapply(weights, neighbor_labels, sum) - - if (length(weighted_votes) == 0) { - preds[i] <- na_label - confs[i] <- NA - total_weight <- 0 - pred_weight <- 0 - } else { - #print(weighted_votes) - predicted_label <- names(which.max(weighted_votes)) - total_weight <- sum(weighted_votes) - pred_weight <- weighted_votes[predicted_label] - confidence <- pred_weight / total_weight - - # Confidence thresholding - if (!is.null(conf_threshold) && confidence < conf_threshold) { - preds[i] <- na_label - confs[i] <- confidence - } else { - preds[i] <- predicted_label - confs[i] <- confidence - } - } + distances = distances + epsilon + weights <- 1 / distances + if(use_weights){ + weights <- 1 / distances + }else{ + weights <- rep(1,length(distances)) + } - if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - if(separate_other){ - rel_other = other_weighted_votes / pred_weight - }else{ - rel_other = 0 - } - #rel_other = ifelse(rel_other==0,0,log(rel_other)) - neighbor_info <- data.frame( - other_score = rel_other, - neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - vote_labels = paste(names(weighted_votes),collapse=","), - weighted_votes = paste(weighted_votes,collapse=","), - neighbors_other = neighbors_other, - other_weighted_votes = other_weighted_votes, - total_w = total_weight, - pred_w = pred_weight - - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - } + neighbor_labels <- train_labels[neighbors] + + if(verbose){ + print("neighbors:") + print(neighbors) + print("distances:") + print(distances) + print("weights:") + print(weights) + print("labels:") + print(neighbor_labels) + } + + # Remove NAs (just in case) + valid <- !is.na(neighbor_labels) + # num_other_neighbors = sum(neighbor_labels == "Other") + other_dists = distances[neighbor_labels == "Other"] + if(separate_other){ + valid = valid & neighbor_labels != "Other" + } + + neighbor_labels <- neighbor_labels[valid] + weights <- weights[valid] + distances <- distances[valid] + neighbors <- neighbors[valid] + + #now take the first k neighbors + if(length(neighbor_labels) > curr_k){ + neighbor_labels = neighbor_labels[1:curr_k] + weights = weights[1:curr_k] + distances = distances[1:curr_k] + neighbors = neighbors[1:curr_k] + } + + others_closer = which(other_dists < max(distances)) + others_distances = other_dists[others_closer] + if(use_weights){ + others_weights = 1 / others_distances + }else{ + others_weights = rep(1,length(others_closer)) + } + neighbors_other = length(others_closer) + other_weighted_votes = sum(others_weights) + + if (length(neighbor_labels) == 0) { + preds[i] <- "Other" + confs[i] <- 1 + if(track_neighbors){ + rel_other = 10 + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = "", + neighbors_other = neighbors_other, + other_weighted_votes = 0, + total_w = 1, + pred_w = 2 + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + next; + } + } + + weighted_votes <- tapply(weights, neighbor_labels, sum) - } - to_return = data.frame( - predicted_label = preds, - confidence = confs) - if(track_neighbors){ - #print(dim(to_return)) - #print(dim(all_neighbors)) - #check for any missing points - if(!nrow(to_return)==nrow(all_neighbors)){ - print("mismatch in row number for to_return and all_neighbors") - print(nrow(to_return)) - print(nrow(all_neighbors)) - print(head(to_return)) - print(head(all_neighbors)) - stop("") - } - to_return = bind_cols(to_return,all_neighbors) - if(nrow(to_return ) != nrow(test_coords)){ - print("mismatch in row number for to_return and test_coords") - print(nrow(to_return)) - print(nrow(test_coords)) - print(head(to_return)) - print(head(test_coords)) - stop("") + if (length(weighted_votes) == 0) { + preds[i] <- na_label + confs[i] <- NA + total_weight <- 0 + pred_weight <- 0 + } else { + predicted_label <- names(which.max(weighted_votes)) + total_weight <- sum(weighted_votes) + pred_weight <- weighted_votes[predicted_label] + confidence <- pred_weight / total_weight + + # Confidence thresholding + if (!is.null(conf_threshold) && confidence < conf_threshold) { + preds[i] <- na_label + confs[i] <- confidence + } else { + preds[i] <- predicted_label + confs[i] <- confidence + } + } + + if(track_neighbors){ + # Create a data frame to store neighbors, distances, and weights + if(separate_other){ + rel_other = other_weighted_votes / pred_weight + }else{ + rel_other = 0 + } + #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + #weight = paste(round(weights, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = paste(weighted_votes,collapse=","), + neighbors_other = neighbors_other, + other_weighted_votes = other_weighted_votes, + total_w = total_weight, + pred_w = pred_weight + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } + } + + to_return = data.frame( + predicted_label = preds, + confidence = confs + ) + + if(track_neighbors){ + if(!nrow(to_return)==nrow(all_neighbors)){ + print("mismatch in row number for to_return and all_neighbors") + print(nrow(to_return)) + print(nrow(all_neighbors)) + print(head(to_return)) + print(head(all_neighbors)) + stop("") + } + to_return = bind_cols(to_return,all_neighbors) + } + results_list[[paste0("k_", curr_k)]] <- to_return } - rownames(to_return) <- rownames(test_coords) - } - return(to_return) + return(results_list) } #' @title Make Neighborhood Plot @@ -1513,4 +1538,4 @@ predict_single_sample_DLBCLone <- function( df = predictions_df, anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") )) -} \ No newline at end of file +} diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index 74d7971..e4eaf13 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -16,8 +16,7 @@ weighted_knn_predict_with_conf( use_weights = TRUE, ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE, - max_neighbors = 500 + separate_other = TRUE ) } \arguments{ From ccc811e3de7edf9516b5dfeb2f2740e4d303777f Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 22 May 2025 10:07:17 -0700 Subject: [PATCH 15/79] indent --- R/umap.R | 312 +++++++++++++++++++++++++++---------------------------- 1 file changed, 156 insertions(+), 156 deletions(-) diff --git a/R/umap.R b/R/umap.R index adacc3e..cc6e70b 100644 --- a/R/umap.R +++ b/R/umap.R @@ -978,67 +978,67 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, #' @export #' weighted_knn_predict_with_conf <- function( - train_coords, - train_labels, - test_coords, - k, # a vector - epsilon = 0.1, - conf_threshold = NULL, - na_label = "Other", - verbose = FALSE, - use_weights = TRUE, - ignore_top = FALSE, - track_neighbors = TRUE, - separate_other = TRUE #big change here. Other is considered separately for optimization + train_coords, + train_labels, + test_coords, + k, # a vector + epsilon = 0.1, + conf_threshold = NULL, + na_label = "Other", + verbose = FALSE, + use_weights = TRUE, + ignore_top = FALSE, + track_neighbors = TRUE, + separate_other = TRUE #big change here. Other is considered separately for optimization ) { - if (nrow(train_coords)==0 || nrow(test_coords) == 0) { - print("train_coords:") - print(nrow(train_coords)) - print("test:") - print(nrow(test_coords)) - stop("train_coords and test_coords must be data frames with at least one row") - } + if (nrow(train_coords)==0 || nrow(test_coords) == 0) { + print("train_coords:") + print(nrow(train_coords)) + print("test:") + print(nrow(test_coords)) + stop("train_coords and test_coords must be data frames with at least one row") + } - train_labels = as.character(train_labels) - max_k <- max(k) - nn <- get.knnx(train_coords, test_coords, max_k) + train_labels = as.character(train_labels) + max_k <- max(k) + nn <- get.knnx(train_coords, test_coords, max_k) - results_list <- list() + results_list <- list() - for (curr_k in k) { - if (verbose) cat("Processing k =", curr_k, "\n") + for (curr_k in k) { + if (verbose) cat("Processing k =", curr_k, "\n") - preds <- character(nrow(test_coords)) - confs <- numeric(nrow(test_coords)) - all_neighbors <- data.frame() + preds <- character(nrow(test_coords)) + confs <- numeric(nrow(test_coords)) + all_neighbors <- data.frame() - for (i in 1:nrow(test_coords)){ - if(verbose){ - print(paste("index:",i)) - print(test_coords[i,]) - } - neighbors <- nn$nn.index[i, ] - distances <- nn$nn.dist[i, ] - if(ignore_top){ - #ignore a neighbor if it has identical V1 and V2 - distances <- nn$nn.dist[i, ] - if(distances[1] == 0){ - neighbors = neighbors[-1] - distances = distances[-1] - } - } + for (i in 1:nrow(test_coords)){ + if(verbose){ + print(paste("index:",i)) + print(test_coords[i,]) + } + neighbors <- nn$nn.index[i, ] + distances <- nn$nn.dist[i, ] + if(ignore_top){ + #ignore a neighbor if it has identical V1 and V2 + distances <- nn$nn.dist[i, ] + if(distances[1] == 0){ + neighbors = neighbors[-1] + distances = distances[-1] + } + } - distances = distances + epsilon - weights <- 1 / distances - if(use_weights){ - weights <- 1 / distances - }else{ - weights <- rep(1,length(distances)) - } + distances = distances + epsilon + weights <- 1 / distances + if(use_weights){ + weights <- 1 / distances + }else{ + weights <- rep(1,length(distances)) + } - neighbor_labels <- train_labels[neighbors] + neighbor_labels <- train_labels[neighbors] - if(verbose){ + if(verbose){ print("neighbors:") print(neighbors) print("distances:") @@ -1047,126 +1047,126 @@ weighted_knn_predict_with_conf <- function( print(weights) print("labels:") print(neighbor_labels) - } + } - # Remove NAs (just in case) - valid <- !is.na(neighbor_labels) - # num_other_neighbors = sum(neighbor_labels == "Other") - other_dists = distances[neighbor_labels == "Other"] - if(separate_other){ - valid = valid & neighbor_labels != "Other" - } + # Remove NAs (just in case) + valid <- !is.na(neighbor_labels) + # num_other_neighbors = sum(neighbor_labels == "Other") + other_dists = distances[neighbor_labels == "Other"] + if(separate_other){ + valid = valid & neighbor_labels != "Other" + } - neighbor_labels <- neighbor_labels[valid] - weights <- weights[valid] - distances <- distances[valid] - neighbors <- neighbors[valid] - - #now take the first k neighbors - if(length(neighbor_labels) > curr_k){ - neighbor_labels = neighbor_labels[1:curr_k] - weights = weights[1:curr_k] - distances = distances[1:curr_k] - neighbors = neighbors[1:curr_k] - } + neighbor_labels <- neighbor_labels[valid] + weights <- weights[valid] + distances <- distances[valid] + neighbors <- neighbors[valid] + + #now take the first k neighbors + if(length(neighbor_labels) > curr_k){ + neighbor_labels = neighbor_labels[1:curr_k] + weights = weights[1:curr_k] + distances = distances[1:curr_k] + neighbors = neighbors[1:curr_k] + } - others_closer = which(other_dists < max(distances)) - others_distances = other_dists[others_closer] - if(use_weights){ - others_weights = 1 / others_distances - }else{ - others_weights = rep(1,length(others_closer)) - } - neighbors_other = length(others_closer) - other_weighted_votes = sum(others_weights) - - if (length(neighbor_labels) == 0) { - preds[i] <- "Other" - confs[i] <- 1 - if(track_neighbors){ - rel_other = 10 - neighbor_info <- data.frame( - other_score = rel_other, - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = "", - neighbors_other = neighbors_other, - other_weighted_votes = 0, - total_w = 1, - pred_w = 2 - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - next; - } - } + others_closer = which(other_dists < max(distances)) + others_distances = other_dists[others_closer] + if(use_weights){ + others_weights = 1 / others_distances + }else{ + others_weights = rep(1,length(others_closer)) + } + neighbors_other = length(others_closer) + other_weighted_votes = sum(others_weights) - weighted_votes <- tapply(weights, neighbor_labels, sum) - - if (length(weighted_votes) == 0) { - preds[i] <- na_label - confs[i] <- NA - total_weight <- 0 - pred_weight <- 0 - } else { - predicted_label <- names(which.max(weighted_votes)) - total_weight <- sum(weighted_votes) - pred_weight <- weighted_votes[predicted_label] - confidence <- pred_weight / total_weight - - # Confidence thresholding - if (!is.null(conf_threshold) && confidence < conf_threshold) { - preds[i] <- na_label - confs[i] <- confidence - } else { - preds[i] <- predicted_label - confs[i] <- confidence - } - } + if (length(neighbor_labels) == 0) { + preds[i] <- "Other" + confs[i] <- 1 + if(track_neighbors){ + rel_other = 10 + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = "", + neighbors_other = neighbors_other, + other_weighted_votes = 0, + total_w = 1, + pred_w = 2 + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + next; + } + } - if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - if(separate_other){ - rel_other = other_weighted_votes / pred_weight - }else{ - rel_other = 0 - } - #rel_other = ifelse(rel_other==0,0,log(rel_other)) - neighbor_info <- data.frame( - other_score = rel_other, - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = paste(weighted_votes,collapse=","), - neighbors_other = neighbors_other, - other_weighted_votes = other_weighted_votes, - total_w = total_weight, - pred_w = pred_weight - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - } + weighted_votes <- tapply(weights, neighbor_labels, sum) + + if (length(weighted_votes) == 0) { + preds[i] <- na_label + confs[i] <- NA + total_weight <- 0 + pred_weight <- 0 + } else { + predicted_label <- names(which.max(weighted_votes)) + total_weight <- sum(weighted_votes) + pred_weight <- weighted_votes[predicted_label] + confidence <- pred_weight / total_weight + + # Confidence thresholding + if (!is.null(conf_threshold) && confidence < conf_threshold) { + preds[i] <- na_label + confs[i] <- confidence + } else { + preds[i] <- predicted_label + confs[i] <- confidence } + } - to_return = data.frame( - predicted_label = preds, - confidence = confs + if(track_neighbors){ + # Create a data frame to store neighbors, distances, and weights + if(separate_other){ + rel_other = other_weighted_votes / pred_weight + }else{ + rel_other = 0 + } + #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + #weight = paste(round(weights, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = paste(weighted_votes,collapse=","), + neighbors_other = neighbors_other, + other_weighted_votes = other_weighted_votes, + total_w = total_weight, + pred_w = pred_weight ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } + } - if(track_neighbors){ - if(!nrow(to_return)==nrow(all_neighbors)){ + to_return = data.frame( + predicted_label = preds, + confidence = confs + ) + + if(track_neighbors){ + if(!nrow(to_return)==nrow(all_neighbors)){ print("mismatch in row number for to_return and all_neighbors") print(nrow(to_return)) print(nrow(all_neighbors)) print(head(to_return)) print(head(all_neighbors)) stop("") - } - to_return = bind_cols(to_return,all_neighbors) - } - results_list[[paste0("k_", curr_k)]] <- to_return + } + to_return = bind_cols(to_return,all_neighbors) } - return(results_list) + results_list[[paste0("k_", curr_k)]] <- to_return + } + return(results_list) } #' @title Make Neighborhood Plot From 83fb7286e9526239fd99fd999d7791f43486f606 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 22 May 2025 10:53:56 -0700 Subject: [PATCH 16/79] update back to vectorized k PR for optimize, and KNN, and rdmorin PR --- NAMESPACE | 2 +- R/umap.R | 1476 ++++++++++++++----------- man/DLBCLone_optimize_params.Rd | 4 +- man/make_and_annotate_umap.Rd | 12 +- man/predict_single_sample_DLBCLone.Rd | 9 +- 5 files changed, 846 insertions(+), 657 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index a2acb07..66e8f5f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,7 +10,7 @@ export(complete_missing_from_matrix) export(construct_reduced_winning_version) export(make_and_annotate_umap) export(massage_matrix_for_clustering) -export(predict_single_sample) +export(optimize_outgroup) export(predict_single_sample_DLBCLone) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) diff --git a/R/umap.R b/R/umap.R index a81d264..d9428af 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,5 +1,3 @@ - - #' Assemble genetic features for UMAP input #' #' This function assembles a matrix of genetic features for each sample, including mutation status, @@ -248,104 +246,55 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @returns a list of data frames with the predictions and the UMAP input #' @export #' -predict_single_sample = function( - test_df, - train_df, - train_metadata, - best_params, - truth_classes = c("EZB","MCD","ST2","N1","BN2"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE -){ - train_metadata_use = filter(train_metadata,lymphgen %in% truth_classes) - train_metadata_notuse = filter(train_metadata,!lymphgen %in% truth_classes) - - if(nrow(test_df)>1){ - message("Warning: you have supplied more than one sample to test with. Will proceed with all") - } - - combined_mutation_status_df = bind_rows(test_df,train_df) +optimize_outgroup <- function(predicted_labels, + true_labels, + other_score, + all_classes = c("MCD", + "EZB", + "BN2", + "N1", + "ST2", + "Other"), + maximize ="balanced_accuracy", + exclude_other_for_accuracy = FALSE){ - if(any(test_df$sample_id %in% train_metadata_use$sample_id)){ - stop("one or more samples overlap with your training data!") - } - - placeholder_meta = data.frame(sample_id = test_df$sample_id) - train_metadata_use= bind_rows(placeholder_meta,train_metadata_use) - - outs = make_and_annotate_umap( - df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - init="spca", - n_epochs = 1500, - seed=best_params$seed, - metadata=train_metadata_use, - ret_model=TRUE, - metric="cosine", - join_column="sample_id", - na_vals = best_params$na_option - ) - - train_coords = dplyr::filter( - outs$df, - sample_id %in% train_df$sample_id, - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - #View(train_coords) - - train_labels = dplyr::filter( - outs$df, - sample_id %in% train_df$sample_id - ) %>% - pull(lymphgen) - - test_coords = dplyr::filter( - outs$df, - sample_id %in% test_df$sample_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k = best_params$k, - conf_threshold = best_params$threshold, - na_label = "Other", - use_weights = best_params$use_w, - ignore_top = FALSE - ) - - pred$sample_id = test_df$sample_id + rel_thresholds = seq(1,10,0.1) + sens_df = data.frame() + acc_df = data.frame() + predictions = data.frame(predicted_label=as.character(predicted_labels), + true_label=as.character(true_labels)) + + for(threshold in rel_thresholds){ + predictions_new = mutate(predictions, + predicted_label = ifelse(other_score < threshold, + predicted_label, + "Other")) + + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + } + bal_acc$threshold = threshold + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sens_df = bind_rows(sens_df,sn) + } + if(maximize %in% c("balanced_accuracy","accuracy")){ + best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) + }else{ + best = slice_head(arrange(sens_df,desc(average_sensitivity)),n=1) - if(drop_unlabeled_from_training){ - pred = dplyr::filter(pred,sample_id %in% test_df$sample_id) - } - - #print(head(outs$df)) - anno_out = left_join(pred,outs$df,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + } - if(make_plot){ - pp = ggplot(outs$df,aes(x=V1,y=V2,colour=lymphgen,label=sample_id)) + - geom_point() + - geom_point(data = anno_out,aes(colour=predicted_label)) + - geom_label_repel( - data = anno_out, - aes(x=V1,y=V2,label=label), - nudge_x=1, - nudge_y=1, - colour="black" - ) + - scale_colour_manual(values=get_gambl_colours()) + - ggtitle(paste("k:",best_params$k,"seed:",best_params$seed)) - print(pp) - } - - return(list(prediction = pred, umap_input = outs$features, model=outs$model)) + return(best) } @@ -379,129 +328,92 @@ predict_single_sample = function( #' classes = c("MCD","EZB","BN2","ST2","N1","A53","Other"), #' annotate_accuracy=TRUE,label_offset = 1) #' -DLBCLone_train_test_plot = function( - test_df, - train_df, - predictions_df, - other_df, - details, - annotate_accuracy = FALSE, - classes = c("BN2","ST2","MCD","EZB","N1"), - label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other" -){ - +DLBCLone_train_test_plot = function(test_df, + train_df, + predictions_df, + other_df, + details, + annotate_accuracy = FALSE, + + classes = c("BN2","ST2","MCD","EZB","N1"), + label_offset = 2, + title1="Original Class", + title2="DLBCLone Predicted Class", + title3 ="DLBCLone Predicted Class (Other)",base_size = 1){ + title = "" + if(!missing(details)){ title = paste0("N_class:",details$num_classes," N_feats:",details$num_features," k=",details$k," threshold=",details$threshold," bacc=",round(details$accuracy,3)) + + } + if(annotate_accuracy){ if("BN2" %in% classes){ - print(details) - acc_df = data.frame( - lymphgen = c( - "N1", - "BN2", - "EZB", - "MCD", - "ST2", - "Other", - "A53"), - accuracy = c( - details$N1_bacc, - details$BN2_bacc, - details$EZB_bacc, - details$MCD_bacc, - details$ST2_bacc, - details$Other_bacc, - details$A53_bacc - ) - ) + #print(details) + acc_df = data.frame(lymphgen = classes, + accuracy = c( + details$BN2_bacc, + details$EZB_bacc, + details$MCD_bacc, + details$ST2_bacc, + details$Other_bacc, + details$A53_bacc)) }else if("C1" %in% classes){ - acc_df = data.frame( - lymphgen = c( - "C1", - "C2", - "C3", - "C4", - "C5" - ), - accuracy = c( - details$C1_bacc, - details$C2_bacc, - details$C3_bacc, - details$C4_bacc, - details$C5_bacc - ) - ) - }else{ - stop("no labels to add?") - } - # Add the predicted labels for Other (unclassified) cases, if provided - if(!missing(other_df)){ - in_df = bind_rows( - train_df, - test_df, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label), - mutate(other_df,dataset=title3,lymphgen=predicted_label) - ) + acc_df = data.frame(lymphgen = c("C1","C2","C3","C4","C5"), + accuracy = c(details$C1_bacc, + details$C2_bacc, + details$C3_bacc, + details$C4_bacc, + details$C5_bacc)) }else{ - in_df = bind_rows( - train_df, - test_df, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label) - ) + stop("no labels to add?") } + + } + # Add the predicted labels for Other (unclassified) cases, if provided + if(!missing(other_df)){ + in_df = bind_rows(train_df, + test_df, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label), + mutate(other_df,dataset=title3,lymphgen=predicted_label) + ) + in_df = mutate(in_df,dataset = factor(dataset,levels=unique(c(unique(train_df$dataset),title1,title2,title3)))) + }else{ + in_df = bind_rows(train_df, + test_df, + mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + ) + in_df = mutate(in_df,dataset = factor(dataset,levels=unique(c(unique(train_df$dataset),title1,title2)))) + } - pp = ggplot(in_df) + - geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + - scale_colour_manual(values=get_gambl_colours()) + - facet_wrap(~dataset,ncol=1) + - theme_Morons() + ggtitle(title) - if(annotate_accuracy){ - #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label %in% classes) %>% - group_by(predicted_label) %>% - summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% - mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% - mutate(lymphgen=predicted_label) - centroids = left_join(centroids,acc_df) %>% - mutate(label=paste(lymphgen,":",round(accuracy,3))) + pp = ggplot(in_df) + + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + + scale_colour_manual(values=get_gambl_colours()) + + facet_wrap(~dataset,ncol=1) + + theme_Morons(base_size=base_size) + ggtitle(title) + if(annotate_accuracy){ + #add labels and set nudge direction based on what quadrant each group sits in + centroids = filter(predictions_df,predicted_label %in% classes) %>% + group_by(predicted_label) %>% + summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% + mutate(lymphgen=predicted_label) + #print(centroids) + centroids = left_join(centroids,acc_df) %>% + mutate(label=paste(lymphgen,":",round(accuracy,3))) centroids$dataset = title2 - pp = pp + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = -1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = -1 * label_offset, - nudge_x = 1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = 1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = 1 * label_offset, - nudge_x = 1 * label_offset - ) - } - pp + pp = pp + geom_label_repel(data=filter(centroids,nudge_y < 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label),fill="white",size=5,nudge_y = -1 * label_offset , nudge_x = -1 * label_offset) + + geom_label_repel(data=filter(centroids,nudge_y < 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label),size=5,nudge_y = -1 * label_offset , nudge_x = 1 * label_offset) + + geom_label_repel(data=filter(centroids,nudge_y > 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label),size=5,nudge_y = 1 * label_offset , nudge_x = -1 * label_offset) + + geom_label_repel(data=filter(centroids,nudge_y > 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label),fill="white",size=5,nudge_y = 1 * label_offset , nudge_x = 1 * label_offset) + } + pp + guides(colour = guide_legend(nrow = 1)) } + #' Run UMAP and attach result to metadata #' #' @param df Feature matrix with one row per sample and one column per mutation @@ -538,82 +450,122 @@ DLBCLone_train_test_plot = function( #' metric="cosine") #' #' -make_and_annotate_umap = function( - df, - metadata, - umap_out, - n_neighbors = 10, - min_dist = 1, - metric = "cosine", - n_epochs = 200, - init = "spca", - ret_model = TRUE, - na_vals = "drop", - join_column = "sample_id", - seed = 1234 -){ +make_and_annotate_umap = function(df, + metadata, + umap_out, + n_neighbors=55, + min_dist=0, + metric="cosine", + n_epochs=1500, + init="spca", + ret_model=TRUE, + na_vals = "drop", + join_column="sample_id", + seed=12345, + target_column, + target_metric="euclidean", + target_weight=0.5, + calc_dispersion = FALSE){ + + # Function to compute mean (or median) pairwise distance within a group + pairwise_dispersion <- function(df_group) { + coords <- as.matrix(df_group[, c("V1", "V2")]) + dists <- dist(coords) # Euclidean pairwise distances + return(median(dists)) + } + if("sample_id" %in% colnames(df)){ df = df %>% column_to_rownames(var = "sample_id") - original_n = nrow(df) - - if (na_vals == "to_zero") { - df[is.na(df)] = 0 - } else if (na_vals == "drop") { - df <- df[, colSums(is.na(df)) == 0] - } - - rs = rowSums(df, na.rm = TRUE) - df = df[rs > 0, ] - - if (missing(df)) { - stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") - } - - if (ret_model && missing(metadata)) { - stop("metadata is required and should contain a column sample_id that matches the row names of your mutation data frame") - } - - # Keep only samples in metadata for training mode - if (ret_model) { - df = df[rownames(df) %in% metadata[[join_column]], ] - } - - if (missing(umap_out)) { - umap_out = umap2( - df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs = n_epochs, - init = init, - seed = seed, - n_threads = 1 - ) - } else { - umap_out = umap_transform( - X = df, - model = umap_out - ) - ret_model = FALSE - } - - if (ret_model) { - umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) - umap_df = left_join(umap_df, metadata, by = join_column) - } else { - umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) - # No metadata join for projection-only mode + } + original_n = nrow(df) + if(na_vals == "to_zero"){ + df[is.na(df)] = 0 + }else if(na_vals == "drop"){ + df <- df[, colSums(is.na(df)) == 0] + } + rs = rowSums(df,na.rm=TRUE) + df = df[rs>0,] + if(missing(df)){ + stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") + } + if(!missing(metadata)){ + keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] + df= df[keep_rows,] + metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) + message(paste("kept",nrow(metadata),"rows of the data that match the metadata provided")) + } + + if(missing(umap_out)){ + if(missing(target_column)){ + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1) # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must not be changed because it will break reproducibility + }else{ + #supervised + if(missing(metadata)){ + stop("metadata must be provided for supervised UMAP") + } + metadata[[target_column]] = factor(metadata[[target_column]]) + print(table(metadata[[target_column]])) + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + y = metadata[[target_column]], + target_metric = target_metric, + target_weight = target_weight + ) # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must not be changed because it will break reproducibility } + - results = list() - results[["df"]] = umap_df - results[["features"]] = df - - if (ret_model) { - results[["model"]] = umap_out - } + }else{ + umap_out = umap_transform(X=df, + model=umap_out$model,seed=seed) + ret_model = FALSE + } + + + if(ret_model){ + umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) + }else{ + umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) + } + if(!missing(metadata)){ + umap_df = left_join(umap_df,metadata,by=join_column) + } + results = list() + if(calc_dispersion){ + print("calculating pairwise dispersion") + dispersion_df <- umap_df %>% + group_by(lymphgen) %>% + summarise( + n = n(), + mean_pairwise_distance = pairwise_dispersion(cur_data()) + ) %>% + arrange(mean_pairwise_distance) + results[["dispersion"]] = dispersion_df + } - return(results) + + results[["df"]]=umap_df + results[["features"]] = df + + if(ret_model){ + results[["model"]]= umap_out + } + return(results) } #' Optimize parameters for classifying samples using UMAP and k-nearest neighbor @@ -657,260 +609,346 @@ make_and_annotate_umap = function( #' #' @examples #' -#' lymphgen_A53_DLBCLone = DLBCLone_optimize_params( -#' lgen_feat_status, #our binary feature matrix -#' a53_meta, #our metadata -#' umap_out = lymphgen_A53_all_feat_gambl, # force use existing UMAP fit -#' eval_group = NULL, # use all samples for evaluating accuracy -#' truth_classes = c("MCD","EZB","BN2","ST2","N1","A53","Other")) -DLBCLone_optimize_params = function( - combined_mutation_status_df, - metadata_df, - umap_out, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - eval_group = "Lacy", - min_k=3, - max_k=30, - verbose = FALSE, - seed = 1234 -){ - na_opt = c("drop") - num_class = length(truth_classes) - weights_opt = c(TRUE,FALSE) - threshs = seq(0,0.95,0.05) - ks = seq(min_k,max_k,1) - results <- data.frame() - best_params <- data.frame() - #best so far: - threshold = 0.3 - k = 6 - use_w = TRUE - best_acc = 0 - best_fit = NULL - best_pred = NULL - other_pred = NULL - - for(na_option in na_opt){ - if(missing(umap_out)){ - outs = make_and_annotate_umap( - df=combined_mutation_status_df, - min_dist = 1, - n_neighbors = 10, - n_epochs = 200, - init = "spca", - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option) +#' +#' # Aim to maximize classification of samples into non-Other class +#' lymphgen_lyseq_no_other = +#' GAMBLR.predict::DLBCLone_optimize_params( +#' dlbcl_status_combined_lyseq, +#' dlbcl_meta_lyseq_train, +#' min_k = 5,max_k=23, +#' optimize_for_other = F, +#' truth_classes = c("MCD","EZB","ST2","BN2")) +#' +#' # Aim to maximize balanced accuracy while allowing samples to be +#' # unclassified (assigned to "Other") +#' +#' DLBCLone_lymphgen_lyseq_prime_opt = +#' GAMBLR.predict::DLBCLone_optimize_params( +#' dlbcl_status_combined_lyseq_prime, +#' dlbcl_meta_lyseq_train, +#' min_k = 5,max_k=23, +#' optimize_for_other = T, +#' truth_classes = c("MCD","EZB","ST2","BN2","Other")) +#' + +DLBCLone_optimize_params = function(combined_mutation_status_df, + metadata_df, + umap_out, + truth_classes = c("EZB", + "MCD", + "ST2", + "N1", + "BN2", + "Other"), + optimize_for_other = FALSE, + eval_group = NULL, + min_k=3, + max_k=33, + verbose = FALSE, + seed = 12345, + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE + ) { + if(optimize_for_other){ + exclude_other_for_accuracy = FALSE + }else{ + exclude_other_for_accuracy = TRUE + } + na_opt = c("drop") + num_class = length(truth_classes) + weights_opt = c(TRUE,FALSE) + threshs = seq(0,0.9,0.1) + ks = seq(min_k,max_k,2) + results <- data.frame() + best_params <- data.frame() + + use_w = TRUE + best_acc = 0 + best_fit = NULL + this_accuracy = 0 + best_pred = NULL + other_pred = NULL + for(na_option in na_opt){ + if(missing(umap_out)){ + outs = make_and_annotate_umap(df=combined_mutation_status_df, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) + }else{ + #project onto existing model instead of re-running UMAP + outs = make_and_annotate_umap(df=combined_mutation_status_df, + umap_out = umap_out, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) + } + ignore_top = FALSE + if(is.null(eval_group)){ + ignore_top = TRUE + } + for(use_w in weights_opt){ + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + }else{ + test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) + train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + } + + pred_all = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=ks, + conf_threshold = -1, # Will threshold later + na_label="Other", + use_weights = use_w, + ignore_top = ignore_top, + verbose = verbose, + track_neighbors = optimize_for_other + ) + + if(!"Other" %in% truth_classes){ + if(is.null(eval_group)){ + test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) }else{ - #project onto existing model instead of re-running UMAP - outs = make_and_annotate_umap( - df=combined_mutation_status_df, - umap_out = umap_out, - min_dist = 1, - n_neighbors = 10, - n_epochs = 200, - init = "spca", - seed=seed, - metadata=metadata_df, - metric="cosine", - join_column="sample_id", - na_vals = na_option) + test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) + train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) + train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) } - - ignore_top = FALSE - if(is.null(eval_group)){ - ignore_top = TRUE + n_other = nrow(test_coords) + + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other_all = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=ks, + conf_threshold = -1, # Will threshold later + na_label="Other", + use_weights = use_w, + ignore_top = ignore_top, + verbose = verbose + ) } - - for(use_w in weights_opt){ - for(k in ks){ - - for (threshold in threshs){ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - }else{ - test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) - train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - } - - if(verbose){ - print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) - } - - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=k, - conf_threshold =threshold, - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose - ) - - if(is.null(eval_group)){ - xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) - train_d = filter(outs$df,lymphgen %in% truth_classes) - }else{ - xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) - train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) - } - - if("Other" %in% truth_classes){ - xx_d$lymphgen = factor(xx_d$lymphgen) - }else{ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - }else{ - test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) - train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) - train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - } - - n_other = nrow(test_coords) - - if(!"Other" %in% truth_classes && n_other > 0){ - pred_other = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=k, - conf_threshold =threshold, - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose - ) - - if(!is.null(eval_group)){ - xx_o = bind_cols(filter(outs$df,dataset == eval_group,lymphgen =="Other") ,pred_other) - }else{ - xx_o = bind_cols(filter(outs$df,lymphgen == "Other") ,pred_other) - } - } - - xx_d$lymphgen = factor(xx_d$lymphgen,levels = c(unique(xx_d$lymphgen),"Other")) - - } - - true_factor = factor(xx_d$lymphgen,levels = levels(xx_d$lymphgen)) - - if(verbose){ - print("true_factor") - print(levels(true_factor )) - print("===") - print(levels(xx_d$predicted_label)) - } + } + + for(k in ks){ + message(paste("K:",k)) + for (threshold in threshs){ - conf_matrix <- confusionMatrix(xx_d$predicted_label, true_factor) + if(verbose){ + print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) + } - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class - sn <- conf_matrix$byClass[, "Sensitivity"] # one per class + k_index <- which(ks == k) + pred <- pred_all[[k_index]] %>% + mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) - if(verbose){ - print(bal_acc) - } - - overall_balanced_accuracy <- mean(bal_acc, na.rm = TRUE) - row <- data.frame( - k = k, - threshold = threshold, - use_weights = use_w, - accuracy = overall_balanced_accuracy, - N1_sn = unname(sn["Class: N1"]), - BN2_sn= unname(sn["Class: BN2"]), - ST2_sn = unname(sn["Class: ST2"]), - N1_bacc = unname(bal_acc["Class: N1"]), - BN2_bacc = unname(bal_acc["Class: BN2"]), - MCD_bacc = unname(bal_acc["Class: MCD"]), - EZB_bacc = unname(bal_acc["Class: EZB"]), - A53_bacc = unname(bal_acc["Class: A53"]), - Other_bacc = unname(bal_acc["Class: Other"]), - C1_bacc = unname(bal_acc["Class: C1"]), - C2_bacc = unname(bal_acc["Class: C2"]), - C3_bacc = unname(bal_acc["Class: C3"]), - C4_bacc = unname(bal_acc["Class: C4"]), - C5_bacc = unname(bal_acc["Class: C5"]), - ST2_bacc = unname(bal_acc["Class: ST2"]), - na_option= na_option - ) + if(is.null(eval_group)){ + xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) + train_d = filter(outs$df,lymphgen %in% truth_classes) + }else{ + xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) + train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) + } - if(overall_balanced_accuracy > best_acc){ - best_acc = overall_balanced_accuracy - print(paste( - "best accuracy:", - best_acc, - "k:", - k, - "threshold:", - threshold, - "na:", - na_option, - "Balanced accuracy:", - overall_balanced_accuracy - )) - - best_fit = outs - best_pred = xx_d - - if(!"Other" %in% truth_classes && n_other > 0){ - other_pred = pred_other - } + # Always set true label factor levels to truth_classes + xx_d$lymphgen = factor(xx_d$lymphgen, levels = truth_classes) + + # Set predicted labels to allow "Other" + pred_levels = union(truth_classes, "Other") + xx_d$predicted_label = factor(xx_d$predicted_label, levels = pred_levels) + + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other <- pred_other_all[[k_index]] %>% + mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) + + if(!is.null(eval_group)){ + xx_o = bind_cols( + filter(outs$df, dataset == eval_group, lymphgen == "Other"), + pred_other + ) + } else { + xx_o = bind_cols( + filter(outs$df, lymphgen == "Other"), + pred_other + ) + } + + xx_o$lymphgen = factor("Other", levels = pred_levels) + xx_o$predicted_label = factor(xx_o$predicted_label, levels = pred_levels) + xx_d = bind_rows(xx_d, xx_o) + } + + true_factor = xx_d$lymphgen + pred_factor = xx_d$predicted_label + + if (exclude_other_for_accuracy) { + keep_idx = which(true_factor != "Other") + true_factor = true_factor[keep_idx] + pred_factor = pred_factor[keep_idx] + } + + + + if(verbose){ + print("true_factor") + print(levels(true_factor )) + print("===") + print(levels(xx_d$predicted_label)) + } - best_params = row + conf_matrix <- confusionMatrix(pred_factor, true_factor) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class + sn <- conf_matrix$byClass[, "Sensitivity"] # one per class + if(verbose){ + print(bal_acc) + } + + overall_accuracy <- conf_matrix$overall[["Accuracy"]] + if(exclude_other_for_accuracy){ + mean_balanced_accuracy = mean(bal_acc[!names(bal_acc) == "Class: Other"], na.rm = TRUE) + }else{ + mean_balanced_accuracy = mean(bal_acc) + } - } - results <- rbind(results, row) - } + overall_sensitivity<- mean(sn[!names(sn) == "Class: Other"], na.rm = TRUE) + + if(optimize_for_other){ + + optimized_accuracy_and_thresh = optimize_outgroup( + pred_factor, + true_factor, + xx_d$other_score, + all_classes = truth_classes, + maximize = maximize, + exclude_other_for_accuracy = exclude_other_for_accuracy + ) + out_opt_thresh = optimized_accuracy_and_thresh$threshold + optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 + out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + + }else{ + out_opt_acc = 0 + out_opt_thresh = 0 + } + + if(maximize == "sensitivity"){ + this_accuracy = overall_sensitivity + }else if(maximize == "balanced_accuracy"){ + this_accuracy =mean_balanced_accuracy + }else{ + this_accuracy = overall_accuracy + } + + if(out_opt_acc > this_accuracy){ + this_accuracy = out_opt_acc + } + row <- data.frame(k = k, + threshold = threshold, + use_weights = use_w, + optimized_accuracy = this_accuracy, + overall_accuracy = overall_accuracy, + mean_balanced_accuracy = mean_balanced_accuracy, + sensitivity = overall_sensitivity, + N1_sn = unname(sn["Class: N1"]), + BN2_sn= unname(sn["Class: BN2"]), + ST2_sn = unname(sn["Class: ST2"]), + BN2_bacc = unname(bal_acc["Class: BN2"]), + MCD_bacc = unname(bal_acc["Class: MCD"]), + EZB_bacc = unname(bal_acc["Class: EZB"]), + A53_bacc = unname(bal_acc["Class: A53"]), + Other_bacc = unname(bal_acc["Class: Other"]), + C1_bacc = unname(bal_acc["Class: C1"]), + C2_bacc = unname(bal_acc["Class: C2"]), + C3_bacc = unname(bal_acc["Class: C3"]), + C4_bacc = unname(bal_acc["Class: C4"]), + C5_bacc = unname(bal_acc["Class: C5"]), + ST2_bacc = unname(bal_acc["Class: ST2"]), + threshold_outgroup = out_opt_thresh, + accuracy_out = out_opt_acc, + na_option= na_option) + + if(this_accuracy > best_acc){ + best_acc = this_accuracy + + message(paste("best accuracy:",best_acc,"k:",k,"threshold:",threshold,"Other_thresh:",out_opt_thresh,"na:",na_option,"Balanced accuracy:",overall_accuracy, "sensitivity:",overall_sensitivity)) + + best_fit = outs + best_pred = xx_d + if(!"Other" %in% truth_classes && n_other > 0){ + other_pred = pred_other } + + best_params = row + } + results <- rbind(results, row) } + } } - best_params$num_classes = num_class - best_params$num_features = ncol(best_fit$features) - best_params$seed = seed - - test_coords = outs$df %>% select(V1,V2) - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=best_params$k, - conf_threshold =best_params$threshold, - na_label="Other", - use_weights = best_params$use_weights, - ignore_top = ignore_top, - verbose = verbose - ) - xx_d = bind_cols(outs$df,pred) - to_ret = list( - params=results, - best_params = best_params, - model=best_fit$model, - features=best_fit$features, - df=outs$df, - predictions=xx_d - ) - - if(!"Other" %in% truth_classes && n_other > 0){ - to_ret[["predictions_other"]] = xx_o - to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) - } - + } + best_params$num_classes = num_class + best_params$num_features = ncol(best_fit$features) + best_params$seed = seed + + test_coords = outs$df %>% select(V1,V2) + train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=best_params$k, + conf_threshold =best_params$threshold, + na_label="Other", + use_weights = best_params$use_weights, + ignore_top = ignore_top, + verbose = verbose, + track_neighbors = optimize_for_other + ) + pred = pred[[1]] + + if(optimize_for_other){ + pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, + "Other", + predicted_label)) + }else{ + pred = mutate(pred,predicted_label_optimized = predicted_label) + } + xx_d = bind_cols(outs$df,pred) + to_ret = list(params=results, + best_params = best_params, + model=best_fit$model, + features=best_fit$features, + df=outs$df, + predictions=xx_d) + if(!"Other" %in% truth_classes && n_other > 0){ + to_ret[["predictions_other"]] = xx_o + to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) + } return(to_ret) - } + + #' Weighted k-nearest neighbor with confidence estimate #' #' @param train_coords Data frame containing coordinates for samples with known @@ -938,129 +976,256 @@ DLBCLone_optimize_params = function( #' @export #' weighted_knn_predict_with_conf <- function( - train_coords, - train_labels, - test_coords, - k, - epsilon = 1e-5, - conf_threshold = NULL, - na_label = "Other", - verbose = FALSE, - use_weights = TRUE, - ignore_top = FALSE, - track_neighbors = FALSE -) { - if (nrow(train_coords)==0 || nrow(test_coords) == 0) { - print("train_coords:") - print(nrow(train_coords)) - print("test:") - print(nrow(test_coords)) - stop("train_coords and test_coords must be data frames with at least one row") - } - - nn <- get.knnx(train_coords, test_coords, k) - all_neighbors = data.frame() - preds <- character(nrow(test_coords)) - confs <- numeric(nrow(test_coords)) - - train_labels = as.character(train_labels) - - for (i in 1:nrow(test_coords)) { - neighbors <- nn$nn.index[i, ] - distances <- nn$nn.dist[i, ] - if(ignore_top){ - #ignore a neighbor if it has identical V1 and V2 - distances <- nn$nn.dist[i, ] - if(distances[1] == 0){ - neighbors = neighbors[-1] - distances = distances[-1] - } + train_coords, + train_labels, + test_coords, + k, # a vector + epsilon = 0.1, + conf_threshold = NULL, + na_label = "Other", + verbose = FALSE, + use_weights = TRUE, + ignore_top = FALSE, + track_neighbors = TRUE, + separate_other = TRUE #big change here. Other is considered separately for optimization +) { + if (nrow(train_coords)==0 || nrow(test_coords) == 0) { + print("train_coords:") + print(nrow(train_coords)) + print("test:") + print(nrow(test_coords)) + stop("train_coords and test_coords must be data frames with at least one row") + } - } - distances = distances + epsilon - weights <- 1 / distances - - if(use_weights){ - weights <- 1 / distances - }else{ - weights <- rep(1,length(distances)) - } + train_labels = as.character(train_labels) + max_k <- max(k) + nn <- get.knnx(train_coords, test_coords, max_k) + results_list <- list() - neighbor_labels <- train_labels[neighbors] + for (curr_k in k) { + if (verbose) cat("Processing k =", curr_k, "\n") - if(verbose){ - print("neighbors:") - print(neighbors) - print("distances:") - print(distances) - print("weights:") - print(weights) - print("labels:") - print(neighbor_labels) - } - # Remove NAs (just in case) - valid <- !is.na(neighbor_labels) - neighbor_labels <- neighbor_labels[valid] - weights <- weights[valid] - - if (length(neighbor_labels) == 0) { - preds[i] <- na_label - confs[i] <- NA - next - } - - weighted_votes <- tapply(weights, neighbor_labels, sum) - + preds <- character(nrow(test_coords)) + confs <- numeric(nrow(test_coords)) + all_neighbors <- data.frame() + + for (i in 1:nrow(test_coords)){ + if(verbose){ + print(paste("index:",i)) + print(test_coords[i,]) + } + neighbors <- nn$nn.index[i, ] + distances <- nn$nn.dist[i, ] + if(ignore_top){ + #ignore a neighbor if it has identical V1 and V2 + distances <- nn$nn.dist[i, ] + if(distances[1] == 0){ + neighbors = neighbors[-1] + distances = distances[-1] + } + } + + distances = distances + epsilon + weights <- 1 / distances + if(use_weights){ + weights <- 1 / distances + }else{ + weights <- rep(1,length(distances)) + } + + neighbor_labels <- train_labels[neighbors] + + if(verbose){ + print("neighbors:") + print(neighbors) + print("distances:") + print(distances) + print("weights:") + print(weights) + print("labels:") + print(neighbor_labels) + } + + # Remove NAs (just in case) + valid <- !is.na(neighbor_labels) + # num_other_neighbors = sum(neighbor_labels == "Other") + other_dists = distances[neighbor_labels == "Other"] + if(separate_other){ + valid = valid & neighbor_labels != "Other" + } + + neighbor_labels <- neighbor_labels[valid] + weights <- weights[valid] + distances <- distances[valid] + neighbors <- neighbors[valid] + + #now take the first k neighbors + if(length(neighbor_labels) > curr_k){ + neighbor_labels = neighbor_labels[1:curr_k] + weights = weights[1:curr_k] + distances = distances[1:curr_k] + neighbors = neighbors[1:curr_k] + } + + others_closer = which(other_dists < max(distances)) + others_distances = other_dists[others_closer] + if(use_weights){ + others_weights = 1 / others_distances + }else{ + others_weights = rep(1,length(others_closer)) + } + neighbors_other = length(others_closer) + other_weighted_votes = sum(others_weights) + + if (length(neighbor_labels) == 0) { + preds[i] <- "Other" + confs[i] <- 1 if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - neighbor_info <- data.frame( - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = paste(weighted_votes,collapse=",") - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) + rel_other = 10 + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = "", + neighbors_other = neighbors_other, + other_weighted_votes = 0, + total_w = 1, + pred_w = 2 + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + next; } + } + + weighted_votes <- tapply(weights, neighbor_labels, sum) - if (length(weighted_votes) == 0) { - preds[i] <- na_label - confs[i] <- NA + if (length(weighted_votes) == 0) { + preds[i] <- na_label + confs[i] <- NA + total_weight <- 0 + pred_weight <- 0 + } else { + predicted_label <- names(which.max(weighted_votes)) + total_weight <- sum(weighted_votes) + pred_weight <- weighted_votes[predicted_label] + confidence <- pred_weight / total_weight + + # Confidence thresholding + if (!is.null(conf_threshold) && confidence < conf_threshold) { + preds[i] <- na_label + confs[i] <- confidence } else { - predicted_label <- names(which.max(weighted_votes)) - total_weight <- sum(weighted_votes) - pred_weight <- weighted_votes[predicted_label] - confidence <- pred_weight / total_weight - - # Confidence thresholding - if (!is.null(conf_threshold) && confidence < conf_threshold) { - preds[i] <- na_label - confs[i] <- confidence - } else { - preds[i] <- predicted_label - confs[i] <- confidence - } + preds[i] <- predicted_label + confs[i] <- confidence + } + } + + if(track_neighbors){ + # Create a data frame to store neighbors, distances, and weights + if(separate_other){ + rel_other = other_weighted_votes / pred_weight + }else{ + rel_other = 0 } + #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( + other_score = rel_other, + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + #weight = paste(round(weights, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + weighted_votes = paste(weighted_votes,collapse=","), + neighbors_other = neighbors_other, + other_weighted_votes = other_weighted_votes, + total_w = total_weight, + pred_w = pred_weight + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } } - + to_return = data.frame( - predicted_label = factor(preds), - confidence = confs + predicted_label = preds, + confidence = confs ) - + if(track_neighbors){ - print(dim(to_return)) - print(dim(all_neighbors)) - to_return = bind_cols(to_return,all_neighbors) + if(!nrow(to_return)==nrow(all_neighbors)){ + print("mismatch in row number for to_return and all_neighbors") + print(nrow(to_return)) + print(nrow(all_neighbors)) + print(head(to_return)) + print(head(all_neighbors)) + stop("") + } + to_return = bind_cols(to_return,all_neighbors) } - - rownames(to_return) <- rownames(test_coords) + results_list[[paste0("k_", curr_k)]] <- to_return + } + return(results_list) +} - return(to_return) +#' @title Make Neighborhood Plot +#' @description +#' Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. +#' +#' @param single_sample_prediction_output A list containing prediction results and annotation data frames. +#' Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations). +#' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. +#' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. +#' +#' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. +#' +#' @details +#' The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. +#' +#' @import dplyr +#' @import ggplot2 +#' @importFrom rlang sym +#' +#' @examples +#' # Assuming 'output' is the result of DLBCLone_predict_single_sample on sample_id "SAMPLE123": +#' make_neighborhood_plot(output, "SAMPLE123") +make_neighborhood_plot <- function(single_sample_prediction_output, + this_sample_id, + prediction_in_title = TRUE){ + #extract the sample_id for all the nearest neighbors with non-Other labels + my_neighbours = filter(single_sample_prediction_output$prediction, + sample_id == this_sample_id) %>% + pull(neighbor_id) %>% strsplit(.,",") %>% unlist() + #set up links connecting each neighbor to the sample's point + links_df = filter(single_sample_prediction_output$anno_df,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) + my_x = filter(single_sample_prediction_output$anno_df, + sample_id==this_sample_id) %>% pull(V1) + my_y = filter(single_sample_prediction_output$anno_df, + sample_id==this_sample_id) %>% pull(V2) + if(prediction_in_title){ + title = paste(this_sample_id, + pull(single_sample_prediction_output$prediction, + !!sym("predicted_label"))) + if(single_sample_prediction_output$prediction$predicted_label_optimized == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ + title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") + } + }else{ + title = this_sample_id + } + + + + pp=ggplot(mutate(single_sample_prediction_output$anno_df,group=lymphgen), + aes(x=V1,y=V2,colour=group)) + + geom_point(alpha=0.8,size=0.5) + + geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=0.5)+ + scale_colour_manual(values=get_gambl_colours()) + + ggtitle(title)+ + theme_minimal() + return(pp) } + #' Predict class for a single sample without using umap_transform and plot result of classification #' #' @param seed Random seed for reproducibility @@ -1099,7 +1264,6 @@ weighted_knn_predict_with_conf <- function( #' ) #' predict_single_sample_DLBCLone <- function( - seed, test_df, train_df, train_metadata, @@ -1114,7 +1278,9 @@ predict_single_sample_DLBCLone <- function( label_offset = 2, title1="GAMBL", title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other" + title3 ="predicted_class_for_Other", + seed = 12345, + max_neighbors = 500 ){ set.seed(seed) @@ -1133,7 +1299,9 @@ predict_single_sample_DLBCLone <- function( # Drop overlaps to prevent rowname collisions dupes <- intersect(train_df$sample_id, test_df$sample_id) if(length(dupes) > 0){ - warning(paste("Removing", length(dupes), "overlapping samples from train_df to avoid duplicated rownames. \n Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) + warning(paste("Removing", length(dupes), + "overlapping samples from train_df to avoid duplicated rownames.\n", + "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) train_df <- train_df %>% filter(!sample_id %in% dupes) train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) } @@ -1143,7 +1311,7 @@ predict_single_sample_DLBCLone <- function( train_df = train_df %>% column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% + select(all_of(trained_features)) %>% rownames_to_column("sample_id") train_id <- train_df$sample_id @@ -1157,7 +1325,7 @@ predict_single_sample_DLBCLone <- function( projection <- make_and_annotate_umap( df = combined_df, - umap_out = umap_out$model, + umap_out = umap_out, ret_model = FALSE, seed = seed, join_column = "sample_id", @@ -1175,15 +1343,15 @@ predict_single_sample_DLBCLone <- function( projection$df, sample_id %in% train_id ) %>% - left_join( - umap_out$df %>% select( + select(sample_id,V1,V2) %>% + left_join( #Join to the incoming metadata rather than trusting the metadata in the projection + train_metadata %>% select( sample_id, - cohort, lymphgen ), by = "sample_id" ) - + train_labels = train_df_proj %>% pull(lymphgen) @@ -1193,8 +1361,9 @@ predict_single_sample_DLBCLone <- function( ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - - train_pred = weighted_knn_predict_with_conf( + predict_training = FALSE + if(predict_training){ + train_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, test_coords = train_coords, # <- predicitng training on self @@ -1203,9 +1372,12 @@ predict_single_sample_DLBCLone <- function( na_label = "Other", use_weights = best_params$use_w, ignore_top = ignore_top - ) - train_pred = rownames_to_column(train_pred, var = "sample_id") + ) + train_pred = rownames_to_column(train_pred, var = "sample_id") + } + + test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, @@ -1214,8 +1386,10 @@ predict_single_sample_DLBCLone <- function( conf_threshold = best_params$threshold, na_label = "Other", use_weights = best_params$use_w, - ignore_top = ignore_top + ignore_top = ignore_top, + max_neighbors = max_neighbors ) + test_pred = rownames_to_column(test_pred, var = "sample_id") anno_umap = select(projection$df, sample_id, V1, V2) @@ -1228,10 +1402,16 @@ predict_single_sample_DLBCLone <- function( V2 = as.numeric(V2), label = as.character(label) ) - - predictions_train_df = left_join(train_pred, projection$df %>% select(sample_id, V1, V2), by = "sample_id") - predictions_test_df = left_join(test_pred, projection$df %>% select(sample_id, V1, V2), by = "sample_id") - predictions_df = bind_rows(predictions_train_df, predictions_test_df) + if(predict_training){ + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + }else{ + predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% + select(sample_id, V1, V2) + } + + predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + predictions_df = bind_rows(predictions_train_df %>% select(sample_id, V1, V2), + predictions_test_df %>% select(sample_id, V1, V2)) if(make_plot){ title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) @@ -1239,7 +1419,7 @@ predict_single_sample_DLBCLone <- function( print(best_params) acc_df = data.frame( lymphgen = c( - "N1", + #"N1", "BN2", "EZB", "MCD", @@ -1248,7 +1428,7 @@ predict_single_sample_DLBCLone <- function( "A53" ), accuracy = c( - best_params$N1_bacc, + #best_params$N1_bacc, best_params$BN2_bacc, best_params$EZB_bacc, best_params$MCD_bacc, @@ -1352,6 +1532,8 @@ predict_single_sample_DLBCLone <- function( prediction = test_pred, umap_input = umap_out$features, model=umap_out$model, - plot = pp + plot = pp, + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") )) } diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index c4d1fca..76ea427 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -14,7 +14,9 @@ DLBCLone_optimize_params( min_k = 3, max_k = 33, verbose = FALSE, - seed = 1234 + seed = 12345, + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE ) } \arguments{ diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 3ee63f3..2eaaaea 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -8,15 +8,19 @@ make_and_annotate_umap( df, metadata, umap_out, - n_neighbors = 10, - min_dist = 1, + n_neighbors = 55, + min_dist = 0, metric = "cosine", - n_epochs = 200, + n_epochs = 1500, init = "spca", ret_model = TRUE, na_vals = "drop", join_column = "sample_id", - seed = 1234 + seed = 12345, + target_column, + target_metric = "euclidean", + target_weight = 0.5, + calc_dispersion = FALSE ) } \arguments{ diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 2e9c72b..d53db50 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -5,7 +5,6 @@ \title{Predict class for a single sample without using umap_transform and plot result of classification} \usage{ predict_single_sample_DLBCLone( - seed, test_df, train_df, train_metadata, @@ -20,12 +19,12 @@ predict_single_sample_DLBCLone( label_offset = 2, title1 = "GAMBL", title2 = "predicted_class_for_HighConf", - title3 = "predicted_class_for_Other" + title3 = "predicted_class_for_Other", + seed = 12345, + max_neighbors = 500 ) } \arguments{ -\item{seed}{Random seed for reproducibility} - \item{test_df}{Data frame containing the mutation status of the test sample} \item{train_df}{Data frame containing the mutation status of the training samples} @@ -58,6 +57,8 @@ samples to estimate overall accuracy} \item{title2}{additional argument} \item{title3}{additional argument} + +\item{seed}{Random seed for reproducibility} } \value{ a list of data frames with the predictions, the UMAP input, the model, and a ggplot object From 9d9084ff4ebb1cee9f90152ea52838f1949df0f8 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 22 May 2025 15:35:02 -0700 Subject: [PATCH 17/79] updated predict_single_sample_DLBCLone + KNN sample_id rowname preservation --- R/umap.R | 518 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 265 insertions(+), 253 deletions(-) diff --git a/R/umap.R b/R/umap.R index d9428af..f943447 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1007,7 +1007,11 @@ weighted_knn_predict_with_conf <- function( if (verbose) cat("Processing k =", curr_k, "\n") preds <- character(nrow(test_coords)) + names(preds) <- rownames(test_coords) # *IMPORTANT* preserve sample_id + confs <- numeric(nrow(test_coords)) + names(confs) <- rownames(test_coords) # *IMPORTANT* preserve sample_id + all_neighbors <- data.frame() for (i in 1:nrow(test_coords)){ @@ -1264,276 +1268,284 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' ) #' predict_single_sample_DLBCLone <- function( - test_df, - train_df, - train_metadata, - umap_out, - best_params, - other_df, - ignore_top = FALSE, - truth_classes = c("EZB","MCD","ST2","N1","BN2"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE, - annotate_accuracy = FALSE, - label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other", - seed = 12345, - max_neighbors = 500 + test_df, + train_df, + train_metadata, + umap_out, + best_params, + other_df, + ignore_top = FALSE, + truth_classes = c("EZB","MCD","ST2","N1","BN2"), + drop_unlabeled_from_training=TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + label_offset = 2, + title1="GAMBL", + title2="predicted_class_for_HighConf", + title3 ="predicted_class_for_Other", + seed = 12345, + max_neighbors = 500 # NEW unused fx ){ - set.seed(seed) - - if(ignore_top){ - # Allow overlapping samples: rename test duplicates temporarily - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - test_df <- test_df %>% - mutate(sample_id = ifelse( - sample_id %in% dupes, - paste0(sample_id, "_test"), - sample_id - )) - } - } else { - # Drop overlaps to prevent rowname collisions - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - warning(paste("Removing", length(dupes), - "overlapping samples from train_df to avoid duplicated rownames.\n", - "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) - train_df <- train_df %>% filter(!sample_id %in% dupes) - train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) - } + if(ignore_top){ + # Allow overlapping samples: rename test duplicates temporarily + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + test_df <- test_df %>% + mutate(sample_id = ifelse( + sample_id %in% dupes, + paste0(sample_id, "_test"), + sample_id + )) + } + } else { + # Drop overlaps to prevent rowname collisions + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + warning(paste("Removing", length(dupes), + "overlapping samples from train_df to avoid duplicated rownames.\n", + "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) + train_df <- train_df %>% filter(!sample_id %in% dupes) + train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) } + } - trained_features = colnames(umap_out$features) + trained_features = colnames(umap_out$features) - train_df = train_df %>% - column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% - rownames_to_column("sample_id") - train_id <- train_df$sample_id + train_df = train_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + train_id <- train_df$sample_id - test_df = test_df %>% - column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% - rownames_to_column("sample_id") - test_id <- test_df$sample_id + test_df = test_df %>% + column_to_rownames("sample_id") %>% + select(all_of(trained_features)) %>% + rownames_to_column("sample_id") + test_id <- test_df$sample_id - combined_df <- bind_rows(train_df, test_df) - - projection <- make_and_annotate_umap( - df = combined_df, - umap_out = umap_out, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = best_params$na_option - ) + combined_df <- bind_rows(train_df, test_df) - train_coords = dplyr::filter( - projection$df, - sample_id %in% train_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - - train_df_proj = dplyr::filter( - projection$df, - sample_id %in% train_id - ) %>% - select(sample_id,V1,V2) %>% - left_join( #Join to the incoming metadata rather than trusting the metadata in the projection - train_metadata %>% select( - sample_id, - lymphgen - ), - by = "sample_id" - ) + # Make dummy metadata for test samples + test_metadata <- data.frame( + sample_id = test_df$sample_id, + lymphgen = NA, # or any placeholder? + cohort = "Test-Sample" # or any placeholder + ) - train_labels = train_df_proj %>% - pull(lymphgen) - - test_coords = dplyr::filter( - projection$df, - sample_id %in% test_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - predict_training = FALSE - if(predict_training){ - train_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = train_coords, # <- predicitng training on self - k = best_params$k, - conf_threshold = best_params$threshold, - na_label = "Other", - use_weights = best_params$use_w, - ignore_top = ignore_top - ) + combined_metadata <- bind_rows(train_metadata, test_metadata) - train_pred = rownames_to_column(train_pred, var = "sample_id") - } - - - test_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k = best_params$k, - conf_threshold = best_params$threshold, - na_label = "Other", - use_weights = best_params$use_w, - ignore_top = ignore_top, - max_neighbors = max_neighbors - ) + projection <- make_and_annotate_umap( + df = combined_df, + metadata = combined_metadata, + umap_out = umap_out, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) - test_pred = rownames_to_column(test_pred, var = "sample_id") + train_coords = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") - anno_umap = select(projection$df, sample_id, V1, V2) + train_labels = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% + select(sample_id,V1,V2) %>% + left_join( + combined_metadata %>% select( + sample_id, + lymphgen + ), + by = "sample_id" + ) %>% + pull(lymphgen) + + test_coords = dplyr::filter( + projection$df, + sample_id %in% test_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + #predict_training = FALSE # potential problem? + #if(predict_training){ + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top + ) + train_pred <- as.data.frame(train_pred) + prefix <- paste0("k_", best_params$k, ".") + colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) + train_pred = rownames_to_column(train_pred, var = "sample_id") + #} + + test_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top#, + #max_neighbors = max_neighbors # NEW FEATURE that isnt used? + ) + test_pred <- as.data.frame(test_pred) + prefix <- paste0("k_", best_params$k, ".") + colnames(test_pred) <- sub(prefix, "", colnames(test_pred)) + test_pred = rownames_to_column(test_pred, var = "sample_id") - anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - anno_out = anno_out %>% + anno_umap = select(projection$df, sample_id, V1, V2) + + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + + anno_out = anno_out %>% mutate( - V1 = as.numeric(V1), - V2 = as.numeric(V2), - label = as.character(label) + V1 = as.numeric(V1), + V2 = as.numeric(V2), + label = as.character(label) ) - if(predict_training){ - predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + + #if(predict_training){ + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + #}else{ + #predictions_train_df = filter(projection$df, sample_id %in% train_id) + #} + predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + predictions_df = bind_rows(predictions_train_df, predictions_test_df) + + if(make_plot){ + title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) + if("BN2" %in% truth_classes){ + print(best_params) + acc_df = data.frame( + lymphgen = c( + #"N1", + "BN2", + "EZB", + "MCD", + "ST2", + "Other", + "A53" + ), + accuracy = c( + #best_params$N1_bacc, + best_params$BN2_bacc, + best_params$EZB_bacc, + best_params$MCD_bacc, + best_params$ST2_bacc, + best_params$Other_bacc, + best_params$A53_bacc + ) + ) + }else if("C1" %in% truth_classes){ + acc_df = data.frame( + lymphgen = c( + "C1", + "C2", + "C3", + "C4", + "C5" + ), + accuracy = c( + best_params$C1_bacc, + best_params$C2_bacc, + best_params$C3_bacc, + best_params$C4_bacc, + best_params$C5_bacc + ) + ) }else{ - predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% - select(sample_id, V1, V2) + stop("no labels to add?") } - - predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") - predictions_df = bind_rows(predictions_train_df %>% select(sample_id, V1, V2), - predictions_test_df %>% select(sample_id, V1, V2)) - - if(make_plot){ - title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) - if("BN2" %in% truth_classes){ - print(best_params) - acc_df = data.frame( - lymphgen = c( - #"N1", - "BN2", - "EZB", - "MCD", - "ST2", - "Other", - "A53" - ), - accuracy = c( - #best_params$N1_bacc, - best_params$BN2_bacc, - best_params$EZB_bacc, - best_params$MCD_bacc, - best_params$ST2_bacc, - best_params$Other_bacc, - best_params$A53_bacc - ) - ) - }else if("C1" %in% truth_classes){ - acc_df = data.frame( - lymphgen = c( - "C1", - "C2", - "C3", - "C4", - "C5" - ), - accuracy = c( - best_params$C1_bacc, - best_params$C2_bacc, - best_params$C3_bacc, - best_params$C4_bacc, - best_params$C5_bacc - ) - ) - }else{ - stop("no labels to add?") - } - # Add the predicted labels for Other (unclassified) cases, if provided - if(!missing(other_df)){ - in_df = bind_rows( - train_df_proj, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label), - mutate(other_df,dataset=title3,lymphgen=predicted_label) - ) - }else{ - in_df = bind_rows( - train_df_proj, - mutate(predictions_df,dataset=title2,lymphgen=predicted_label) - ) - } + # Add the predicted labels for Other (unclassified) cases, if provided + if(!missing(other_df)){ + in_df = bind_rows( + mutate(predictions_df,dataset=title2,lymphgen=predicted_label), + mutate(other_df,dataset=title3,lymphgen=predicted_label) + ) + }else{ + in_df = bind_rows( + mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + ) + } - pp = ggplot(in_df) + - geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + - scale_colour_manual(values=get_gambl_colours()) + - facet_wrap(~dataset,ncol=1) + - theme_Morons() + ggtitle(title) - - if(annotate_accuracy){ - #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label %in% truth_classes) %>% - group_by(predicted_label) %>% - summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% - mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% - mutate(lymphgen=predicted_label) - centroids = left_join(centroids,acc_df) %>% - mutate(label=paste(lymphgen,":",round(accuracy,3))) - - pp = pp + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = -1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = -1 * label_offset, - nudge_x = 1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = 1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = 1 * label_offset, - nudge_x = 1 * label_offset - ) + - geom_label_repel( - data = anno_out, - aes(x=V1,y=V2,label=label), - nudge_y = 1 * label_offset, - nudge_x = 1 * label_offset, - colour="red" - ) - } + pp = ggplot(in_df) + + geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + + scale_colour_manual(values=get_gambl_colours()) + + facet_wrap(~dataset,ncol=1) + + theme_Morons() + ggtitle(title) + + if(annotate_accuracy){ + #add labels and set nudge direction based on what quadrant each group sits in + centroids = filter(predictions_df,predicted_label %in% truth_classes) %>% + group_by(predicted_label) %>% + summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% + mutate(lymphgen=predicted_label) + centroids = left_join(centroids,acc_df) %>% + mutate(label=paste(lymphgen,":",round(accuracy,3))) + + pp = pp + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = -1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y < 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = -1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x < 0), + aes(x=mean_V1,y=mean_V2,label=label), + size=5, + nudge_y = 1 * label_offset, + nudge_x = -1 * label_offset + ) + + geom_label_repel( + data=filter(centroids,nudge_y > 0, nudge_x > 0), + aes(x=mean_V1,y=mean_V2,label=label), + fill="white", + size=5, + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset + ) + + geom_label_repel( + data = anno_out, + aes(x=V1,y=V2,label=label), + nudge_y = 1 * label_offset, + nudge_x = 1 * label_offset, + colour="red" + ) } - return(list( - prediction = test_pred, - umap_input = umap_out$features, - model=umap_out$model, - plot = pp, - df = predictions_df, - anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") - )) + } + + return(list( + prediction = test_pred, + umap_input = umap_out$features, + model=umap_out$model, + plot = pp, + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") + )) } From 8d682002518bd6207b02952f8c327a057ef4774f Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 23 May 2025 11:06:10 -0700 Subject: [PATCH 18/79] arg predict_training addition + maybe resolved duplicate sample_id '_test' labeling issue --- R/umap.R | 83 ++++++++++++++++++--------- man/predict_single_sample_DLBCLone.Rd | 10 +++- 2 files changed, 65 insertions(+), 28 deletions(-) diff --git a/R/umap.R b/R/umap.R index f943447..9403de6 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1240,6 +1240,9 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' for reproducibility and for using the same UMAP model on different datasets. #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters #' @param other_df Data frame containing the predictions for samples in the "Other" class +#' @param predict_training Set to TRUE to predict the projected training samples once stored as train_prediction. Use +#' train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run +#' @param train_prediction Data frame containing the projected train predictions from train samples #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled #' samples to estimate overall accuracy @@ -1274,6 +1277,8 @@ predict_single_sample_DLBCLone <- function( umap_out, best_params, other_df, + predict_training = FALSE, # store train prediction? + train_prediction = NULL, # stored train prediction ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","N1","BN2"), drop_unlabeled_from_training=TRUE, @@ -1283,19 +1288,14 @@ predict_single_sample_DLBCLone <- function( title1="GAMBL", title2="predicted_class_for_HighConf", title3 ="predicted_class_for_Other", - seed = 12345, - max_neighbors = 500 # NEW unused fx + seed = 12345 ){ if(ignore_top){ - # Allow overlapping samples: rename test duplicates temporarily + # Allow overlapping samples: rename test sample ID's to be: paste0(sample_id,"_test") dupes <- intersect(train_df$sample_id, test_df$sample_id) if(length(dupes) > 0){ test_df <- test_df %>% - mutate(sample_id = ifelse( - sample_id %in% dupes, - paste0(sample_id, "_test"), - sample_id - )) + mutate(sample_id = paste0(sample_id, "_test")) } } else { # Drop overlaps to prevent rowname collisions @@ -1328,10 +1328,14 @@ predict_single_sample_DLBCLone <- function( # Make dummy metadata for test samples test_metadata <- data.frame( sample_id = test_df$sample_id, - lymphgen = NA, # or any placeholder? - cohort = "Test-Sample" # or any placeholder + cohort = "Test-Sample", # or any placeholder + lymphgen = NA # or any placeholder? ) + # Ensure train_metadata has matching columns + train_metadata <- train_metadata %>% + select(sample_id, cohort, lymphgen) + combined_metadata <- bind_rows(train_metadata, test_metadata) projection <- make_and_annotate_umap( @@ -1372,8 +1376,9 @@ predict_single_sample_DLBCLone <- function( select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - #predict_training = FALSE # potential problem? - #if(predict_training){ + if(predict_training && !is.null(train_prediction)){ + train_pred = train_prediction + }else if(predict_training && is.null(train_prediction)){ train_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, @@ -1388,7 +1393,22 @@ predict_single_sample_DLBCLone <- function( prefix <- paste0("k_", best_params$k, ".") colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) train_pred = rownames_to_column(train_pred, var = "sample_id") - #} + }else{ + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = best_params$k, + conf_threshold = best_params$threshold, + na_label = "Other", + use_weights = best_params$use_w, + ignore_top = ignore_top + ) + train_pred <- as.data.frame(train_pred) + prefix <- paste0("k_", best_params$k, ".") + colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) + train_pred = rownames_to_column(train_pred, var = "sample_id") + } test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -1398,8 +1418,7 @@ predict_single_sample_DLBCLone <- function( conf_threshold = best_params$threshold, na_label = "Other", use_weights = best_params$use_w, - ignore_top = ignore_top#, - #max_neighbors = max_neighbors # NEW FEATURE that isnt used? + ignore_top = ignore_top, ) test_pred <- as.data.frame(test_pred) prefix <- paste0("k_", best_params$k, ".") @@ -1408,26 +1427,33 @@ predict_single_sample_DLBCLone <- function( anno_umap = select(projection$df, sample_id, V1, V2) - anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - + anno_out = left_join(test_pred,anno_umap,by="sample_id") + + if(ignore_top && length(dupes) > 0){ # remove "_test" from plotted test labels + anno_out$sample_id <- sub("_test$", "", anno_out$sample_id) + } + anno_out = anno_out %>% mutate( + label = as.character(paste( + sample_id, + predicted_label, + round(confidence,3) + )), V1 = as.numeric(V1), - V2 = as.numeric(V2), - label = as.character(label) + V2 = as.numeric(V2) ) - #if(predict_training){ - predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") - #}else{ - #predictions_train_df = filter(projection$df, sample_id %in% train_id) - #} + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") predictions_df = bind_rows(predictions_train_df, predictions_test_df) if(make_plot){ - title = paste0("N_class:", best_params$num_classes," N_feats:",best_params$num_features," k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3)) + title = paste0( + "N_class:", best_params$num_classes," N_feats:",best_params$num_features, + " k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3) + ) + if("BN2" %in% truth_classes){ print(best_params) acc_df = data.frame( @@ -1540,8 +1566,13 @@ predict_single_sample_DLBCLone <- function( } } + if(ignore_top && length(dupes) > 0){ # remove "_test" for returned prediction + test_pred$sample_id <- sub("_test$", "", test_pred$sample_id) + } + return(list( prediction = test_pred, + train_prediction = train_pred, umap_input = umap_out$features, model=umap_out$model, plot = pp, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index d53db50..6bfebfe 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -11,6 +11,8 @@ predict_single_sample_DLBCLone( umap_out, best_params, other_df, + predict_training = FALSE, + train_prediction = NULL, ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), drop_unlabeled_from_training = TRUE, @@ -20,8 +22,7 @@ predict_single_sample_DLBCLone( title1 = "GAMBL", title2 = "predicted_class_for_HighConf", title3 = "predicted_class_for_Other", - seed = 12345, - max_neighbors = 500 + seed = 12345 ) } \arguments{ @@ -38,6 +39,11 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} +\item{predict_training}{Set to TRUE to predict the projected training samples once stored as train_prediction. Use +train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run} + +\item{train_prediction}{Data frame containing the projected train predictions from train samples} + \item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with distance = 0. This is usually only relevant when re-classifying labeled samples to estimate overall accuracy} From b409266d9b5b4a9751cf371199cfda87ce9d9136 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 23 May 2025 11:18:00 -0700 Subject: [PATCH 19/79] better naming: train_prediction -> stored_train_prediction --- R/umap.R | 12 ++++++------ man/predict_single_sample_DLBCLone.Rd | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/R/umap.R b/R/umap.R index 9403de6..ec1f65d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1242,7 +1242,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param predict_training Set to TRUE to predict the projected training samples once stored as train_prediction. Use #' train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run -#' @param train_prediction Data frame containing the projected train predictions from train samples +#' @param stored_train_prediction Data frame containing the projected train predictions from train samples #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled #' samples to estimate overall accuracy @@ -1277,8 +1277,8 @@ predict_single_sample_DLBCLone <- function( umap_out, best_params, other_df, - predict_training = FALSE, # store train prediction? - train_prediction = NULL, # stored train prediction + predict_training = FALSE, + stored_train_prediction = NULL, ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","N1","BN2"), drop_unlabeled_from_training=TRUE, @@ -1376,9 +1376,9 @@ predict_single_sample_DLBCLone <- function( select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - if(predict_training && !is.null(train_prediction)){ - train_pred = train_prediction - }else if(predict_training && is.null(train_prediction)){ + if(predict_training && !is.null(stored_train_prediction)){ + train_pred = stored_train_prediction + }else if(predict_training && is.null(stored_train_prediction)){ train_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 6bfebfe..033bedc 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -12,7 +12,7 @@ predict_single_sample_DLBCLone( best_params, other_df, predict_training = FALSE, - train_prediction = NULL, + stored_train_prediction = NULL, ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), drop_unlabeled_from_training = TRUE, @@ -42,7 +42,7 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{predict_training}{Set to TRUE to predict the projected training samples once stored as train_prediction. Use train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run} -\item{train_prediction}{Data frame containing the projected train predictions from train samples} +\item{stored_train_prediction}{Data frame containing the projected train predictions from train samples} \item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with distance = 0. This is usually only relevant when re-classifying labeled From 9adde3128e1d75a74003efa79ae34ceeff020806 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 23 May 2025 11:20:08 -0700 Subject: [PATCH 20/79] fix --- R/umap.R | 4 ++-- man/predict_single_sample_DLBCLone.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/umap.R b/R/umap.R index ec1f65d..52b1641 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1240,8 +1240,8 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' for reproducibility and for using the same UMAP model on different datasets. #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters #' @param other_df Data frame containing the predictions for samples in the "Other" class -#' @param predict_training Set to TRUE to predict the projected training samples once stored as train_prediction. Use -#' train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run +#' @param predict_training Set to TRUE to predict the projected training samples once stored as stored_train_prediction. Use +#' stored_train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run #' @param stored_train_prediction Data frame containing the projected train predictions from train samples #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 033bedc..7860ab6 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -39,8 +39,8 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} -\item{predict_training}{Set to TRUE to predict the projected training samples once stored as train_prediction. Use -train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run} +\item{predict_training}{Set to TRUE to predict the projected training samples once stored as stored_train_prediction. Use +stored_train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run} \item{stored_train_prediction}{Data frame containing the projected train predictions from train samples} From 754128d4e61b0f0ff155d2ae02a8d0280753061f Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 26 May 2025 09:31:42 -0700 Subject: [PATCH 21/79] removing redundant else if statement --- R/umap.R | 17 +---------------- 1 file changed, 1 insertion(+), 16 deletions(-) diff --git a/R/umap.R b/R/umap.R index 52b1641..1324166 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1328,7 +1328,7 @@ predict_single_sample_DLBCLone <- function( # Make dummy metadata for test samples test_metadata <- data.frame( sample_id = test_df$sample_id, - cohort = "Test-Sample", # or any placeholder + cohort = "Test-Sample", lymphgen = NA # or any placeholder? ) @@ -1378,21 +1378,6 @@ predict_single_sample_DLBCLone <- function( if(predict_training && !is.null(stored_train_prediction)){ train_pred = stored_train_prediction - }else if(predict_training && is.null(stored_train_prediction)){ - train_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = train_coords, # <- predicitng training on self - k = best_params$k, - conf_threshold = best_params$threshold, - na_label = "Other", - use_weights = best_params$use_w, - ignore_top = ignore_top - ) - train_pred <- as.data.frame(train_pred) - prefix <- paste0("k_", best_params$k, ".") - colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) - train_pred = rownames_to_column(train_pred, var = "sample_id") }else{ train_pred = weighted_knn_predict_with_conf( train_coords = train_coords, From 6a0a8f86d298e2276cac18bc40db89a805d13e59 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 26 May 2025 11:31:36 -0700 Subject: [PATCH 22/79] N1_bacc restored --- R/umap.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/umap.R b/R/umap.R index 1324166..7c61378 100644 --- a/R/umap.R +++ b/R/umap.R @@ -872,6 +872,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, N1_sn = unname(sn["Class: N1"]), BN2_sn= unname(sn["Class: BN2"]), ST2_sn = unname(sn["Class: ST2"]), + N1_bacc = unname(bal_acc["Class: N1"]), BN2_bacc = unname(bal_acc["Class: BN2"]), MCD_bacc = unname(bal_acc["Class: MCD"]), EZB_bacc = unname(bal_acc["Class: EZB"]), @@ -1443,7 +1444,7 @@ predict_single_sample_DLBCLone <- function( print(best_params) acc_df = data.frame( lymphgen = c( - #"N1", + "N1", "BN2", "EZB", "MCD", @@ -1452,7 +1453,7 @@ predict_single_sample_DLBCLone <- function( "A53" ), accuracy = c( - #best_params$N1_bacc, + best_params$N1_bacc, best_params$BN2_bacc, best_params$EZB_bacc, best_params$MCD_bacc, From bc0df0354d79bcc7db012909ca55f4926e85a936 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 26 May 2025 15:38:40 -0700 Subject: [PATCH 23/79] tsv file results --- ...ass_accuracy_0.494optimize_for_other=T.tsv | 7 + ...edictions_acc0.494optimize_for_other=T.tsv | 2221 +++++++++++++++++ 2 files changed, 2228 insertions(+) create mode 100644 temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv create mode 100644 temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv diff --git a/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv b/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv new file mode 100644 index 0000000..32c13e6 --- /dev/null +++ b/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv @@ -0,0 +1,7 @@ +true_label acc n +BN2 0.7370517928286853 251 +EZB 0.9238683127572016 486 +MCD 0.9406392694063926 219 +N1 0.6521739130434783 23 +Other 0.1297640653357532 1102 +ST2 0.7122302158273381 139 diff --git a/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv b/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv new file mode 100644 index 0000000..3e98a2e --- /dev/null +++ b/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv @@ -0,0 +1,2221 @@ +sample_id predicted_label confidence other_score neighbor distance label weighted_votes neighbors_other other_weighted_votes total_w pred_w true_label correct fold +00-14595_tumorC EZB 1 0 1,1279,369,1055,1120,316,1937,1563,428,429,1022 0.12,0.135,0.142,0.174,0.174,0.178,0.179,0.187,0.2,0.201,0.202 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.7949626152367 0 0 65.79496261523674 65.79496261523674 EZB TRUE 1 +00-15201_tumorA EZB 0.6130037775316853 1.1286077267377064 1400,781,2134,816,2138 0.13,0.179,0.26,0.266,0.284 EZB,MCD,MCD,EZB,EZB 14.9604944506319,9.44472946311243 4 16.884529632799723 24.40522391374431 14.960494450631884 Other FALSE 2 +00-15201_tumorB N1 1 1.9687166381307977 697,3,2077 0.205,0.218,0.257 N1,N1,N1 13.3344064177855 5 26.251667774292322 13.334406417785459 13.334406417785459 N1 TRUE 3 +FL1015T2 EZB 0.8499435544092154 0.09667623902957859 365,96,399,166,1244,578,341,340,1042,105 0.139,0.198,0.206,0.211,0.212,0.216,0.219,0.222,0.255,0.261 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 7.198612170868,40.7740833206617 1 3.9418850253202393 47.97269549152965 40.77408332066165 EZB TRUE 4 +00-23442_tumorB EZB 1 0 5,368,250,102,780,118,170,448,433,385,1468 0.107,0.179,0.18,0.191,0.196,0.206,0.22,0.25,0.25,0.264,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6005982431752 0 0 55.60059824317517 55.60059824317517 EZB TRUE 5 +00-23442_tumorA EZB 1 0.24214940990432238 1991,6,79,1142,1118,367,1872,1293,1788 0.129,0.185,0.194,0.211,0.221,0.221,0.249,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.905731658932 2 10.631747012627901 43.90573165893197 43.90573165893197 EZB TRUE 6 +00-26427_tumorA N1 1 11.893018813930084 697 0.275 N1 3.63020689217569 8 43.1741188671041 3.6302068921756865 3.6302068921756865 Other FALSE 7 +00-26427_tumorC MCD 0.7834380758276059 0.5815062648348855 952,1890,1398,285,2030,1194 0.13,0.15,0.158,0.188,0.193,0.227 BN2,MCD,MCD,MCD,MCD,MCD 7.71031054808198,27.8929497089898 3 16.219925000501938 35.60326025707173 27.892949708989754 Other FALSE 8 +FL1002T2 EZB 1 0 364,9,1017,728,1295,168,1511,227,86,1041,137 0.116,0.134,0.202,0.224,0.23,0.244,0.273,0.281,0.285,0.291,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.5486925905079 0 0 51.54869259050787 51.54869259050787 EZB TRUE 9 +FL1006T2 EZB 1 0 277,128,116,275,602,10,503,731,712,691,28 0.123,0.208,0.218,0.227,0.229,0.241,0.25,0.262,0.273,0.28,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8497107554635 0 0 48.84971075546353 48.84971075546353 EZB TRUE 10 +01-12047_tumorB EZB 0.9004251219844818 0.1071014953973078 343,11,143,575,16,308,139,1533,1058,218 0.171,0.171,0.179,0.202,0.204,0.204,0.222,0.232,0.238,0.244 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.89482439827688,44.2623977427741 1 4.740568988121527 49.15722214105098 44.2623977427741 EZB TRUE 11 +01-12047_tumorC EZB 1 0 1111,1055,429,428,1110,369,1563,372,1022,12,299 0.114,0.12,0.132,0.161,0.168,0.178,0.18,0.181,0.191,0.192,0.197 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 69.0500951119995 0 0 69.05009511199954 69.05009511199954 EZB TRUE 12 +01-14774_tumorB BN2 0.7012543932969935 0 1086,590,13,1076,932,157,960,412,1722,1806 0.137,0.162,0.167,0.175,0.191,0.205,0.229,0.247,0.248,0.28 BN2,BN2,BN2,EZB,EZB,BN2,ST2,BN2,BN2,BN2 35.9673410195553,10.9547179951214,4.367945530539 0 0 51.290004545215695 35.96734101955527 BN2 TRUE 13 +01-14774_tumorA BN2 0.7442303180346492 0 1806,590,1086,157,932,13,865,1076,945,1444,960 0.188,0.259,0.26,0.264,0.265,0.285,0.286,0.288,0.291,0.298,0.304 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2,ST2 30.6348967960438,7.23849877950875,3.28979820153505 0 0 41.163193777087606 30.634896796043805 Other FALSE 14 +01-16433_tumorB ST2 0.3065584318286099 0.9775683190820227 476,15,1185,2121,636,1190,826,1183 0.115,0.173,0.218,0.285,0.311,0.33,0.356,0.366 MCD,EZB,ST2,BN2,ST2,BN2,BN2,ST2 9.34233901528769,5.781362521291,8.72487760599991,10.5430412003833 3 10.306543064271242 34.391620342961936 10.54304120038333 EZB FALSE 15 +01-16433_tumorA EZB 0.8459254254835458 0.23920025472044393 308,16,343,1533,11,42,575,143,155 0.121,0.129,0.141,0.149,0.15,0.21,0.225,0.245,0.256 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 8.27602292408043,45.4383744711667 2 10.868870747585984 53.714397395247104 45.43837447116667 EZB TRUE 16 +01-20774T EZB 1 0.1758428832202545 1705,333,149,404,121,80,17,187,319 0.103,0.164,0.165,0.181,0.185,0.206,0.22,0.233,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5640446071349 2 8.891327390996157 50.564044607134875 50.564044607134875 EZB TRUE 17 +01-23117_tumorA MCD 0.8785022388414534 0.4887091678374886 1194,285,2030,1398,650,1063,952 0.168,0.177,0.183,0.207,0.224,0.226,0.235 MCD,MCD,MCD,MCD,MCD,MCD,BN2 4.25797476506004,30.7877308056266 3 15.046246301622402 35.045705570686664 30.78773080562663 Other FALSE 18 +01-23117_tumorB BN2 1 4.092056531047736 19,871 0.202,0.297 BN2,BN2 8.3129492264896 8 34.01705817452499 8.3129492264896 8.3129492264896 BN2 TRUE 19 +01-23942_tumorA ST2 1 5.097541961794698 90,1946 0.309,0.33 ST2,ST2 6.2648120233629 8 31.935142171848344 6.264812023362904 6.264812023362904 BN2 FALSE 20 +01-28152_tumorA MCD 1 0 947,483,587,1572,888,540,839,516,707,515,387 0.104,0.124,0.13,0.133,0.146,0.152,0.156,0.164,0.173,0.181,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.5979028431696 0 0 75.59790284316958 75.59790284316958 MCD TRUE 21 +01-28152_tumorB ST2 0.3851327447443543 5.837128788182001 1416,1441,879 0.184,0.192,0.289 ST2,EZB,MCD 5.21272629939222,3.45667146862293,5.4302273037585 6 31.696936121140673 14.099625071773652 5.4302273037585005 Other FALSE 22 +02-11616_tumorA MCD 0.9080774374790431 0 759,1449,1009,1376,764,858,1904,479,305,1406,310 0.117,0.122,0.132,0.144,0.146,0.15,0.152,0.156,0.167,0.168,0.177 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.93120231727777,68.471420577374 0 0 75.40262289465178 68.47142057737402 MCD TRUE 23 +02-13135T EZB 1 1.0395842639746993 1289,1964,199,1542,477,72 0.262,0.289,0.31,0.347,0.381,0.388 EZB,EZB,EZB,EZB,EZB,EZB 18.5876473216996 4 19.323425659950416 18.587647321699645 18.587647321699645 EZB TRUE 24 +FL1008T2 EZB 1 0.22042471035895692 1539,654,1160,72,1450,699,25,2073,477,1542 0.206,0.218,0.22,0.261,0.303,0.305,0.309,0.325,0.339,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5301618555884 1 8.052150346383893 36.53016185558842 36.53016185558842 EZB TRUE 25 +02-15630_tumorA BN2 1 1.2285651665837694 1148,1841,70,804,1980 0.127,0.164,0.172,0.175,0.215 BN2,BN2,BN2,BN2,BN2 30.1684185018812 6 37.06386810233252 30.168418501881177 30.168418501881177 Other FALSE 26 +02-15630_tumorB EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 27 +02-15745_tumorD EZB 1 0 366,1020,427,689,161,78,645,48,128,1090,326 0.186,0.191,0.199,0.237,0.275,0.279,0.292,0.32,0.324,0.329,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.5573763503526 0 0 42.55737635035261 42.55737635035261 EZB TRUE 28 +02-17305_tumorB EZB 1 0.16170079865681677 1032,1579,474,227,1079,1545,1054,1236,1809,1017 0.132,0.141,0.169,0.249,0.258,0.268,0.269,0.291,0.301,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.8661025770108 1 7.416585417978125 45.866102577010786 45.866102577010786 EZB TRUE 29 +02-18356_tumorB EZB 0.4929941044662162 2.9365986993334645 1052,1266,1608,2042 0.161,0.167,0.203,0.207 ST2,EZB,N1,EZB 10.8114813235141,4.92447992753476,6.19428340212019 5 31.748981992499573 21.930244653169062 10.811481323514109 Other FALSE 30 +02-18356_tumorA EZB 0.5473084996468225 7.442276665611066 31,1538 0.13,0.157 EZB,BN2 6.3754385268867,7.70796821238885 7 57.36483196633338 14.08340673927555 7.707968212388849 EZB TRUE 31 +02-20170T EZB 1 9.962609464985501 140 0.295 EZB 3.38870743575862 7 33.760368773555555 3.3887074357586178 3.3887074357586178 Other FALSE 32 +02-22991T ST2 1 7.852686904832889 828 0.184 ST2 5.43965339062664 6 42.71589494740364 5.43965339062664 5.43965339062664 Other FALSE 33 +02-24492_tumorA BN2 0.6340237843221346 0.5978572442932314 1112,1407,34,273,1191,274,897,1204 0.135,0.158,0.16,0.175,0.189,0.193,0.195,0.25 BN2,BN2,EZB,BN2,BN2,MCD,MCD,BN2 28.7107172236264,6.26545214876067,10.3071747758493 3 17.164910280999518 45.28334414823642 28.71071722362644 EZB FALSE 34 +02-28397_tumorA ST2 0.6584685576946493 3.5072928561053938 1793,566,35 0.257,0.26,0.274 BN2,ST2,ST2 3.89490028529359,7.50932141389422 6 26.33738934915046 11.404221699187813 7.509321413894222 ST2 TRUE 35 +02-28397_tumorB MCD 0.3904014966866076 8.264562306722606 1631,175,2211 0.254,0.31,0.342 MCD,N1,BN2 2.92293221347562,3.93962518136516,3.22865694424684 7 32.55927777652574 10.091214339087621 3.9396251813651633 Other FALSE 36 +03-10440_tumorA BN2 1 1.2659983892194624 987,754,1113,1381,1204 0.188,0.199,0.224,0.292,0.309 BN2,BN2,BN2,BN2,BN2 21.4801136923469 6 27.193789334762048 21.480113692346862 21.480113692346862 Other FALSE 37 +03-10440_tumorB BN2 1 1.7322193168310942 1381,987,52,754 0.176,0.215,0.249,0.292 BN2,BN2,BN2,BN2 17.7630938022294 7 30.76957421090452 17.763093802229438 17.763093802229438 Other FALSE 38 +03-11110T EZB 1 0 39,174,1889,93,2016,240,1081,1485,558,1560,1138 0.135,0.176,0.2,0.202,0.204,0.205,0.213,0.215,0.219,0.238,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0741921961292 0 0 55.07419219612916 55.07419219612916 EZB TRUE 39 +03-19103_tumorB EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 40 +03-19969_tumorB BN2 1 0.3629502098026451 687,1957,1973,99,41,1765,519,2069 0.147,0.148,0.171,0.187,0.202,0.226,0.244,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.2908844073868 3 15.349485368400453 42.290884407386805 42.290884407386805 BN2 TRUE 41 +03-23488_tumorA EZB 0.9050189809221849 0.3071656426190534 42,155,1933,639,16,1533,308,107,233 0.135,0.152,0.172,0.178,0.195,0.204,0.214,0.216,0.217 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.68191422828926,44.6112421701856 2 13.703040869239285 49.2931563984749 44.61124217018563 EZB TRUE 42 +03-23488_tumorB EZB 1 0.08724175462583297 1796,43,326,48,83,164,503,47,602,10 0.185,0.213,0.231,0.238,0.251,0.256,0.259,0.275,0.275,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.2279751960237 1 3.596800895771427 41.22797519602369 41.22797519602369 EZB TRUE 43 +FL1001T2 EZB 1 0 1809,168,227,86,1041,78,645,1094,9,329,138 0.158,0.207,0.21,0.231,0.251,0.258,0.276,0.286,0.287,0.303,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3129081965523 0 0 45.31290819655225 45.31290819655225 EZB TRUE 44 +03-33266_tumorA EZB 1 0.22543415944697467 662,1060,75,259,319,320,205,1551,178 0.133,0.178,0.185,0.192,0.197,0.205,0.206,0.213,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.4719364629401 2 10.701796093843093 47.471936462940114 47.471936462940114 EZB TRUE 45 +03-33266_tumorB EZB 1 0.3419754149434457 1933,107,155,233,639,1606,1533,42 0.135,0.136,0.15,0.167,0.196,0.205,0.232,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.9631557298334 3 15.718269252819997 45.96315572983342 45.96315572983342 Other FALSE 46 +04-11038_tumorB EZB 1 0 47,1220,2061,43,1691,1796,448,83,10,503,48 0.21,0.224,0.229,0.239,0.253,0.261,0.267,0.285,0.286,0.299,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.964945692687 0 0 42.964945692686996 42.964945692686996 EZB TRUE 47 +04-11038_tumorA EZB 1 0 503,602,48,10,128,1796,116,277,326,691,366 0.108,0.133,0.176,0.19,0.234,0.264,0.269,0.28,0.297,0.325,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5511385951136 0 0 52.5511385951136 52.5511385951136 EZB TRUE 48 +04-13783_tumorB MCD 1 0.09401827849300655 214,883,1732,1127,1728,154,749,997,790,485 0.149,0.206,0.208,0.213,0.249,0.254,0.267,0.294,0.303,0.311 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.6668420897313 1 4.011463042009493 42.66684208973132 42.66684208973132 MCD TRUE 49 +04-14066_tumorA N1 0.8291542008177949 2.024480111062496 697,2077,3,1697 0.12,0.18,0.239,0.269 N1,N1,N1,BN2 3.71858892067717,18.0471725933729 7 36.536141976195644 21.76576151405009 18.04717259337292 Other FALSE 50 +04-14066_tumorB N1 0.81915070360382 1.542698283801635 2077,697,1697,3 0.123,0.146,0.236,0.238 N1,N1,BN2,N1 4.23316632038931,19.173982088504 5 29.579669261578356 23.407148408893278 19.173982088503966 Other FALSE 51 +04-14093_tumorB BN2 0.6725097636153107 4.013031873103741 52,1131,1381 0.167,0.216,0.285 BN2,ST2,BN2 9.49098412853888,4.62179852835042 8 38.08762181494826 14.112782656889301 9.49098412853888 BN2 TRUE 52 +04-14093_tumorA BN2 0.7471364901635413 2.206925621937667 52,1381,1131 0.133,0.255,0.259 BN2,BN2,ST2 11.4289950742863,3.86806941761744 5 25.22294206244177 15.297064491903711 11.428995074286275 Other FALSE 53 +04-16803_tumorB EZB 1 0.15663203453059468 1437,1241,1066,408,1372,958,2003,1242,54,1814 0.145,0.165,0.182,0.214,0.219,0.229,0.243,0.258,0.259,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.7075399442298 1 7.47252904391432 47.70753994422975 47.70753994422975 EZB TRUE 54 +04-21856_tumorA EZB 1 0.10588307612027845 55,1180,1305,1457,1517,1219,1853,1570,278,1590 0.125,0.13,0.143,0.151,0.185,0.206,0.215,0.215,0.216,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1128841422838 1 6.1531709352063535 58.11288414228376 58.11288414228376 EZB TRUE 55 +04-21856_tumorB EZB 1 0.11045050880148016 75,259,662,178,1551,2013,2072,320,56,205 0.104,0.162,0.17,0.193,0.195,0.201,0.207,0.207,0.221,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5289788176351 1 6.133203963634407 55.528978817635064 55.528978817635064 EZB TRUE 56 +04-24061_tumorB BN2 1 0.5520646080396314 57,2064,106,1014,1632,817,889 0.147,0.151,0.157,0.231,0.256,0.261,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.3663771249907 4 19.524525125289784 35.36637712499071 35.36637712499071 BN2 TRUE 57 +04-24061_tumorA BN2 1 0.5346643717343452 57,2064,106,817,1014,1632 0.106,0.108,0.207,0.256,0.282,0.296 BN2,BN2,BN2,BN2,BN2,BN2 34.4042835067503 4 18.394744626106927 34.40428350675027 34.40428350675027 Other FALSE 58 +04-24937T MCD 1 0 798,1331,23,2008,21,2034,1699,59,709,305,905 0.114,0.114,0.117,0.122,0.133,0.139,0.139,0.153,0.173,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.9172271950256 0 0 79.91722719502563 79.91722719502563 MCD TRUE 59 +04-28140T ST2 0.5671892619509608 5.64290503980861 1436,827 0.222,0.291 ST2,BN2 3.43471241489171,4.5011175286402 5 25.399378787134662 7.935829943531913 4.501117528640201 Other FALSE 60 +04-29264T ST2 0.746582025024119 3.450346524168387 35,1971,566 0.15,0.292,0.294 ST2,BN2,ST2 3.42273619742412,10.0835519723471 8 34.791748499059096 13.50628816977122 10.083551972347099 Other FALSE 61 +04-32952_tumorC EZB 0.8581292466805929 0.810714706744607 179,62,1053,993,2126,1453,1318 0.179,0.188,0.194,0.214,0.227,0.236,0.279 EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.68059136346981,28.3113485110719 4 22.952426605697983 32.991939874541664 28.311348511071852 EZB TRUE 62 +04-35039T N1 1 0.8938438126013311 63,2189,745,2175,891,814 0.146,0.177,0.193,0.197,0.241,0.247 N1,N1,N1,N1,N1,N1 31.0139232169994 5 27.721603372007667 31.013923216999384 31.013923216999384 N1 TRUE 63 +05-12939T BN2 0.4255834657449445 8.359533804108626 286,1425,147 0.197,0.267,0.322 BN2,N1,MCD 5.06840656582215,3.10082072091348,3.74008539520884 8 42.36951601995638 11.909312681944478 5.06840656582215 Other FALSE 64 +05-12963_tumorB EZB 0.39117033376552524 2.9433041741325545 65,2127,1108 0.162,0.205,0.213 EZB,ST2,BN2 4.69785244895444,6.15829472506386,4.88710887963544 3 18.12573456981895 15.743256053653742 6.158294725063859 EZB TRUE 65 +FL1003T2 EZB 1 0.14812375319544935 1750,1752,1671,109,818,398,1972,397 0.197,0.21,0.211,0.247,0.273,0.29,0.306,0.318 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.1797858569527 1 4.766590658157672 32.17978585695269 32.17978585695269 Other FALSE 66 +05-17793T EZB 1 0 1020,427,366,78,645,161,1090,689,1589,48,326 0.15,0.151,0.173,0.232,0.242,0.278,0.287,0.288,0.329,0.331,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0379274211425 0 0 47.03792742114254 47.03792742114254 Other FALSE 67 +05-18426T ST2 0.47021024415328855 1.2626817128016632 816,2138,781,911,955,1486,508 0.13,0.147,0.201,0.225,0.23,0.232,0.238 EZB,EZB,MCD,ST2,ST2,ST2,ST2 14.5195668372276,4.97789685934056,17.304802639079 4 21.850457836007067 36.802266335647246 17.304802639079043 ST2 TRUE 68 +05-21634T MCD 1 0.14438679306533414 69,900,799,2036,765,824,1526,1412,1225,296 0.135,0.188,0.212,0.215,0.224,0.233,0.266,0.296,0.301,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.5660466128713 1 6.43474855003268 44.56604661287127 44.56604661287127 MCD TRUE 69 +05-22052T BN2 1 1.1359935539138326 70,1841,1813,1148,804 0.124,0.141,0.164,0.182,0.207 BN2,BN2,BN2,BN2,BN2 31.5574451819552 6 35.849054304690256 31.55744518195521 31.55744518195521 BN2 TRUE 70 +05-23110T EZB 1 0 2097,644,1109,71,1236,240,1079,2016,1589,522,1217 0.179,0.213,0.228,0.246,0.283,0.295,0.316,0.33,0.334,0.335,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.7287448535846 0 0 40.72874485358456 40.72874485358456 EZB TRUE 71 +05-24006T EZB 1 0.07975469799940936 699,477,1450,1542,520,1160,72,1539,1289,1964 0.116,0.125,0.132,0.147,0.196,0.206,0.21,0.234,0.242,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7971687685592 1 4.609595740357336 57.79716876855923 57.79716876855923 EZB TRUE 72 +05-24065T BN2 0.6173370861909108 1.776335395786444 674,1664 0.147,0.237 BN2,ST2 6.79958654592668,4.21479554454749 2 12.078346258242846 11.014382090474172 6.799586545926678 Other FALSE 73 +05-24395T ST2 1 5.55637543793822 90,1946 0.194,0.228 ST2,ST2 9.55778131425701 9 53.10662133572251 9.5577813142570065 9.5577813142570065 Other FALSE 74 +05-24401T EZB 0.908875887109498 0.3188592221484468 319,320,2059,1551,259,178,149,1088,1541 0.143,0.15,0.176,0.176,0.192,0.212,0.216,0.219,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 44.1355189999257,4.4250376458118 2 14.073017257434305 48.560556645737506 44.1355189999257 EZB TRUE 75 +05-24666T EZB 1 0.07043073286633095 117,1048,76,2047,108,2063,1573,1281,172,1138 0.155,0.156,0.177,0.19,0.196,0.205,0.212,0.274,0.283,0.362 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4655026534399 1 3.4134608706168823 48.46550265343994 48.46550265343994 EZB TRUE 76 +05-24904T EZB 0.6747908854554445 0.7114379253797524 77,282,994,491,1975,1425 0.145,0.178,0.23,0.245,0.263,0.278 EZB,BN2,EZB,EZB,EZB,N1 5.6148782776113,19.1130246785607,3.59646402999518 3 13.597730625047257 28.324366986167213 19.113024678560734 EZB TRUE 77 +05-25439T EZB 1 0 366,1020,427,689,78,48,128,645,326,161,164 0.148,0.177,0.217,0.252,0.261,0.272,0.299,0.303,0.312,0.323,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.4734538749754 0 0 44.473453874975434 44.473453874975434 EZB TRUE 78 +05-25674T EZB 1 0 277,731,275,116,28,712,438,128,691,602,10 0.157,0.187,0.209,0.252,0.256,0.266,0.268,0.268,0.297,0.305,0.306 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5297895668937 0 0 45.529789566893655 45.529789566893655 EZB TRUE 79 +05-27675_tumorB EZB 0.919904497502712 0 187,404,2059,80,17,121,1537,1541,149,1088,319 0.132,0.15,0.152,0.154,0.16,0.163,0.189,0.193,0.195,0.202,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 59.5437180942268,5.1844338561894 0 0 64.72815195041618 59.543718094226776 EZB TRUE 80 +05-32150_tumorB MCD 0.5814716004085125 3.216178963677871 81,82,491,401 0.189,0.217,0.277,0.284 MCD,MCD,EZB,EZB 7.12333087158749,9.89661539380419 7 31.829286241163633 17.019946265391688 9.896615393804192 MCD TRUE 81 +05-32150T MCD 0.5922591795789616 3.3206861945662207 81,82,401,491 0.2,0.205,0.291,0.298 MCD,MCD,EZB,EZB 6.79497541310527,9.86996239245634 7 32.77504785751757 16.664937805561618 9.869962392456344 MCD TRUE 82 +05-32762T EZB 1 0.08324965194485405 137,435,2104,83,1511,43,2006,47,1539,138 0.202,0.206,0.211,0.223,0.226,0.229,0.274,0.281,0.282,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.0148121819066 1 3.4977184906721357 42.01481218190657 42.01481218190657 EZB TRUE 83 +05-32947T BN2 0.40009214616448496 2.580561275247409 636,2121,476,1190,15 0.252,0.276,0.324,0.344,0.364 ST2,BN2,MCD,BN2,EZB 6.53635605131023,2.74511974097415,3.08266112766982,3.97298969942966 4 16.86746730724024 16.337126619383863 6.536356051310227 Other FALSE 84 +06-10398T MCD 1 4.690241810280954 641,743 0.233,0.249 MCD,MCD 8.31426493142133 7 38.99591300310502 8.314264931421327 8.314264931421327 Other FALSE 85 +FL1018T2 EZB 1 0.08278331633934188 86,1041,168,138,1094,2006,329,2104,1511,164 0.115,0.122,0.147,0.168,0.178,0.179,0.185,0.238,0.241,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6981209581391 1 4.859225115802583 58.6981209581391 58.6981209581391 EZB TRUE 86 +06-11535T N1 1 1.635794572535611 697,3,2077 0.201,0.21,0.251 N1,N1,N1 13.710826157257 4 22.42809501302023 13.710826157256966 13.710826157256966 Other FALSE 87 +06-11677_tumorA BN2 0.910800349479615 0.09760687161765116 88,1477,454,872,836,1212,294,1213,669,560 0.165,0.167,0.188,0.206,0.215,0.241,0.249,0.256,0.26,0.268 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 42.3635726487913,4.14889594325051 1 4.134975796795609 46.5124685920418 42.36357264879128 BN2 TRUE 88 +06-11677_tumorB ST2 0.6413180050290375 2.048647949415614 838,718,572,419,1317 0.202,0.223,0.254,0.315,0.321 ST2,ST2,BN2,ST2,BN2 7.0502164945597,12.6056809115269 6 25.82460235038707 19.65589740608657 12.605680911526871 ST2 TRUE 89 +06-14634T ST2 1 5.809576157145324 90,1946 0.216,0.254 ST2,ST2 8.55621567470737 9 49.707986579173 8.556215674707365 8.556215674707365 ST2 TRUE 90 +FL1012T2 BN2 1 7.057572307807639 1807 0.237 BN2 4.2168574920497 5 29.760776661861108 4.216857492049696 4.216857492049696 Other FALSE 91 +06-15256T ST2 0.7000843591547049 4.173396450982474 1653,451,1535 0.254,0.287,0.314 ST2,ST2,EZB 3.1828620002552,7.42966221250172 8 31.006925909653262 10.61252421275692 7.429662212501718 Other FALSE 92 +06-15922T EZB 1 0.10532161075759579 93,1081,39,1138,1560,2063,174,558,382,1889 0.18,0.184,0.196,0.207,0.213,0.216,0.23,0.233,0.237,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.088438657728 1 4.959430207492153 47.08843865772798 47.08843865772798 EZB TRUE 93 +06-16041_tumorB BN2 0.6481711894724355 6.558717323559022 101,446 0.174,0.321 BN2,MCD 5.7356875871993,3.11334439749557 9 37.61875354068647 8.849031984694863 5.735687587199296 Other FALSE 94 +06-16716T MCD 1 5.674780718395486 1631 0.222 MCD 4.51153615091356 4 25.601978359548475 4.511536150913563 4.511536150913563 Other FALSE 95 +06-18449_tumorB EZB 0.887426213574401 0.12445654388864115 166,341,365,96,105,1245,1244,340,1581,1243 0.117,0.147,0.158,0.169,0.173,0.194,0.204,0.226,0.235,0.239 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 6.33785085793574,49.9616755164789 1 6.218057461666706 56.29952637441465 49.96167551647891 EZB TRUE 96 +06-19919T BN2 1 1.4391892776139859 987,1381,754,1113 0.226,0.254,0.257,0.275 BN2,BN2,BN2,BN2 15.8981616775277 5 22.880463820071434 15.898161677527693 15.898161677527693 Other FALSE 97 +06-22057T BN2 1 0.0844124250876273 99,423,1973,100,1496,1957,687,41,2085 0.151,0.153,0.161,0.174,0.179,0.251,0.257,0.257,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2104407986311 1 3.900735372180684 46.210440798631105 46.210440798631105 BN2 TRUE 98 +06-22314_tumorB BN2 1 0 1496,423,100,943,111,266,2085,1929,1973,99 0.15,0.175,0.176,0.219,0.238,0.239,0.24,0.245,0.266,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.6962396449445 0 0 46.696239644944455 46.696239644944455 BN2 TRUE 99 +06-22314_tumorA BN2 1 0 423,100,1496,1973,99,41,2085,687,266,943,1929 0.114,0.114,0.119,0.2,0.212,0.255,0.273,0.281,0.283,0.287,0.291 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 57.3254582903814 0 0 57.32545829038141 57.32545829038141 BN2 TRUE 100 +06-23907T BN2 0.36359346102250656 6.603219455516188 101,140,446 0.266,0.279,0.335 BN2,EZB,MCD 3.75864664673855,3.5904769731344,2.98837429928212 7 24.81916866415468 10.33749791915507 3.7586466467385513 BN2 TRUE 101 +06-24255_tumorD EZB 1 0 1691,1220,2061,448,385,47,102,118,5,1468,43 0.124,0.135,0.147,0.164,0.169,0.182,0.227,0.306,0.313,0.332,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5882589997249 0 0 56.588258999724935 56.588258999724935 EZB TRUE 102 +06-24255_tumorC EZB 1 0 54,1242,1372,1253,408,958,468,1872,79,1287,1080 0.151,0.155,0.19,0.203,0.213,0.238,0.244,0.254,0.258,0.282,0.283 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1636785569501 0 0 51.16367855695005 51.16367855695005 EZB TRUE 103 +06-24915T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 104 +06-24925T EZB 1 0.11270925882557234 105,1243,1245,505,1581,341,1782,166,1244,1668 0.154,0.157,0.162,0.175,0.185,0.196,0.213,0.214,0.253,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3100129479647 1 5.895822788521191 52.31001294796468 52.31001294796468 EZB TRUE 105 +06-25674T BN2 1 0.5318528977377217 106,2064,57,1014,1632,889,817 0.146,0.161,0.162,0.22,0.232,0.282,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.0959101215921 4 18.665861496911376 35.09591012159206 35.09591012159206 BN2 TRUE 106 +06-29769_tumorB EZB 1 0.18069557627290878 443,1034,354,660,548,192,289,1060,333 0.146,0.167,0.182,0.192,0.25,0.28,0.287,0.322,0.324 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.765628480072 2 7.366168730333905 40.76562848007196 40.76562848007196 EZB TRUE 107 +06-30025T EZB 1 0.08618209102885231 108,172,1573,117,2063,1048,2089,76,676,2047 0.129,0.16,0.162,0.19,0.246,0.257,0.286,0.3,0.307,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.704906081603 1 4.0251264674187075 46.704906081603 46.704906081603 EZB TRUE 108 +06-30145T EZB 1 0.24645294216537156 109,818,1750,1527,1959,165,110,1752,1292 0.161,0.183,0.206,0.234,0.266,0.28,0.321,0.358,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.7926533362361 2 9.067657664785955 36.79265333623608 36.79265333623608 EZB TRUE 109 +06-33777T EZB 1 0 1573,108,117,172,1752,1750,1048,110,76,818,2063 0.149,0.193,0.2,0.217,0.303,0.304,0.307,0.313,0.317,0.322,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.9259189029181 0 0 43.92591890291808 43.92591890291808 EZB TRUE 110 +06-34043T BN2 1 0.24945926400743962 1014,106,889,1935,1982,111,1632,579,57 0.176,0.18,0.188,0.197,0.232,0.241,0.251,0.255,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.8944715511609 2 10.450964039133218 41.89447155116091 41.89447155116091 BN2 TRUE 111 +07-10483T EZB 1 0 112,1468,1483,396,211,1663,390,336,1384,337,210 0.121,0.212,0.218,0.237,0.245,0.254,0.26,0.261,0.268,0.273,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.5231882515835 0 0 48.523188251583505 48.523188251583505 EZB TRUE 112 +07-17613T MCD 1 7.836142198344437 311 0.215 MCD 4.66065235650872 6 36.521534602651414 4.660652356508719 4.660652356508719 Other FALSE 113 +FL1013T2 EZB 1 2.1245485815759455 532,1182,1967 0.17,0.185,0.318 EZB,EZB,EZB 14.4371280932338 8 30.672380012510033 14.437128093233765 14.437128093233765 EZB TRUE 114 +07-25012T BN2 0.6230622896039634 1.0740348762569252 1816,1666,591,610,115 0.159,0.205,0.236,0.237,0.246 BN2,EZB,BN2,BN2,EZB 14.7689744248348,8.93487456628677 3 15.862393618819151 23.703848991121585 14.768974424834813 EZB FALSE 115 +07-25994_tumorC EZB 1 0 10,116,602,503,277,691,275,128,48,118,28 0.149,0.164,0.175,0.2,0.215,0.227,0.235,0.253,0.283,0.288,0.315 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9896496879254 0 0 50.989649687925414 50.989649687925414 EZB TRUE 116 +07-30628T EZB 1 0.07537405108026556 1573,172,108,117,1752,1048,2063,1972,2089,1750 0.152,0.172,0.177,0.211,0.293,0.308,0.312,0.315,0.316,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.0345735077044 1 3.1683160907068886 42.0345735077044 42.0345735077044 EZB TRUE 117 +07-31833T EZB 1 0 10,116,602,691,503,275,277,118,128,368,48 0.135,0.156,0.193,0.204,0.216,0.241,0.249,0.25,0.29,0.292,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9626897535504 0 0 50.962689753550386 50.962689753550386 EZB TRUE 118 +07-32561_tumorB EZB 1 0.13406668113291045 119,1668,520,477,1295,505,1542,1450,699,1782 0.149,0.169,0.18,0.247,0.253,0.257,0.266,0.272,0.273,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7113757373131 1 5.994305753988111 44.71137573731315 44.71137573731315 EZB TRUE 119 +07-35482T Other 1 10 0 0 1 2 Other TRUE 120 +07-40648_tumorA EZB 1 0.2235018194623753 42,333,1083,589,231,332,122,1705,639 0.19,0.194,0.202,0.207,0.22,0.22,0.223,0.232,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.3967268272381 2 9.475745585137009 42.39672682723808 42.39672682723808 EZB TRUE 121 +07-40648_tumorB EZB 1 0.1154014223440891 42,231,575,1083,589,122,343,16,332 0.169,0.182,0.189,0.191,0.192,0.194,0.207,0.214,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3452251838716 1 5.34830490507588 46.34522518387159 46.34522518387159 EZB TRUE 122 +FL1005T2 EZB 0.5670988109592271 1.693437498486674 561,141,383,844,2042 0.117,0.117,0.211,0.211,0.243 EZB,BN2,EZB,BN2,EZB 13.2966162279313,17.4185136089585 5 29.497164113310745 30.7151298368898 17.418513608958484 Other FALSE 123 +07-41887_tumorB EZB 0.9075457687985699 0.21655136545597126 575,231,343,42,122,16,589,1083,308 0.151,0.166,0.172,0.184,0.186,0.191,0.198,0.2,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.53694399118939,44.5353800358716 2 9.644197357868595 49.07232402706098 44.53538003587159 Other FALSE 124 +07-41887_tumorA EZB 0.9004151136971338 0.32788571746491624 42,155,16,1933,639,1533,308,343,107 0.114,0.175,0.182,0.195,0.196,0.2,0.204,0.207,0.239 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.89826814801916,44.2885947371994 2 14.52159766091955 49.186862885218595 44.28859473719943 Other FALSE 125 +08-10249_tumorA EZB 1 13.301099499985503 1535 0.298 EZB 3.35275370318615 9 44.59531060502389 3.3527537031861536 3.3527537031861536 Other FALSE 126 +08-10249_tumorB EZB 1 7.147380398620256 1535 0.245 EZB 4.08254836008243 6 29.179526125272403 4.082548360082426 4.082548360082426 Other FALSE 127 +08-13706T EZB 1 0 602,128,503,277,10,48,116,275,712,691,689 0.16,0.168,0.17,0.209,0.212,0.232,0.251,0.299,0.304,0.316,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7461643438647 0 0 48.746164343864734 48.746164343864734 EZB TRUE 128 +08-15460_tumorB BN2 1 0.8381695611571016 284,130,2213,973,412,1210 0.133,0.204,0.238,0.246,0.257,0.263 BN2,BN2,BN2,BN2,BN2,BN2 28.412334838745 5 23.814354223239484 28.41233483874495 28.41233483874495 Other FALSE 129 +08-15460T BN2 0.7136510315418181 0 13,1086,932,412,157,1145,1076,590,130,838 0.165,0.199,0.201,0.204,0.22,0.221,0.223,0.25,0.257,0.265 BN2,BN2,EZB,BN2,BN2,BN2,EZB,BN2,BN2,ST2 32.9441928593449,9.45130549758838,3.76738995021331 0 0 46.16288830714659 32.944192859344895 BN2 TRUE 130 +08-15555T MCD 0.5228875362771097 1.1592878545333272 151,736,2135,661,150,1199 0.147,0.151,0.203,0.241,0.257,0.289 MCD,BN2,MCD,BN2,MCD,BN2 14.2310298769682,15.5963818110939 4 18.0806960082656 29.827411688062092 15.596381811093853 Other FALSE 131 +08-17645_tumorA BN2 0.4477160069886154 0.3628730903243692 2140,972,132,133,1093,24,1070,1183,384 0.107,0.11,0.115,0.197,0.209,0.255,0.267,0.307,0.309 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2,EZB 22.9167996006093,15.6720549651122,12.5971634521512 2 8.31588989141736 51.186018017872684 22.91679960060928 BN2 TRUE 132 +08-17645_tumorB BN2 0.4573734761895185 0.4094926647343321 2140,972,132,133,1093,24,1070,1183 0.113,0.128,0.13,0.186,0.223,0.24,0.262,0.288 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2 20.867929083142,12.4794465333507,12.2782020995511 2 8.545263887742902 45.62557771604392 20.86792908314204 BN2 TRUE 133 +08-19764T EZB 1 0.09155726236412462 1066,958,1437,1914,1372,1172,468,1242,2132,54 0.126,0.149,0.167,0.171,0.212,0.213,0.225,0.226,0.23,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.418054618328 1 4.890810841671399 53.41805461832804 53.41805461832804 Other FALSE 134 +08-24058_tumorB EZB 1 0 679,135,617,103,1080,513,162,1040,468,1867,1253 0.119,0.137,0.145,0.151,0.159,0.165,0.204,0.226,0.229,0.238,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.4771752087652 0 0 63.47717520876518 63.47717520876518 EZB TRUE 135 +08-24058_tumorA EZB 1 1.2321225078017801 136,1397,1268,1879,2114 0.203,0.285,0.298,0.306,0.323 EZB,EZB,EZB,EZB,EZB 18.1659636727475 6 22.382692717101644 18.165963672747466 18.165963672747466 EZB TRUE 136 +08-29440_tumorA EZB 1 0 1511,2006,138,2104,1041,168,86,137,435,9,329 0.169,0.181,0.19,0.195,0.196,0.201,0.203,0.207,0.224,0.226,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8621248701047 0 0 54.862124870104715 54.862124870104715 EZB TRUE 137 +08-29440_tumorB EZB 1 0 1511,2006,138,2104,1041,168,86,137,435,9,329 0.169,0.181,0.19,0.195,0.196,0.201,0.203,0.207,0.224,0.226,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8621248701047 0 0 54.862124870104715 54.862124870104715 EZB TRUE 138 +08-33625T EZB 0.8917329222499222 0.12102316942806936 11,343,308,16,143,1533,575,139,218,1058 0.139,0.163,0.178,0.185,0.186,0.205,0.221,0.225,0.233,0.25 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.620236410505,46.2906170760801 1 5.6022371933283175 51.91085348658506 46.29061707608006 EZB TRUE 139 +09-11467T MCD 0.5439302074064405 5.727052082592686 140,446,768 0.173,0.281,0.299 EZB,MCD,MCD 5.78871251761582,6.90388982444415 8 39.53893659707333 12.69260234205997 6.903889824444152 EZB FALSE 140 +09-12737T EZB 0.5873650490644317 1.4995027535634688 561,141,844,383,2042 0.107,0.129,0.188,0.193,0.245 EZB,BN2,BN2,EZB,EZB 13.0933706471529,18.6377529948611 4 27.947361936029964 31.731123642013948 18.63775299486107 BN2 FALSE 141 +09-12864T EZB 1 0 1937,316,1962,463,1033,1264,372,369,1886,428,142 0.139,0.146,0.155,0.157,0.158,0.172,0.174,0.175,0.18,0.183,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.7628219002591 0 0 66.76282190025907 66.76282190025907 EZB TRUE 142 +09-15842_tumorB EZB 0.8961958995041572 0.133546370341843 11,143,308,343,16,218,139,1533,1058,575 0.13,0.176,0.187,0.192,0.204,0.204,0.207,0.209,0.239,0.255 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.34601100127851,46.1549506730881 1 6.163826135697725 51.50096167436665 46.154950673088145 EZB TRUE 143 +09-15842_tumorA EZB 1 0.07244338629902349 139,1058,143,218,220,1587,219,144,11,1291 0.116,0.127,0.149,0.173,0.174,0.178,0.181,0.216,0.248,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.5394373070382 1 4.240795070561232 58.539437307038156 58.539437307038156 EZB TRUE 144 +09-16981T BN2 1 0.1251748083071783 1375,417,564,592,1027,560,1193,931,145 0.168,0.185,0.196,0.209,0.229,0.229,0.233,0.242,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.4480987688826 1 5.313432626399047 42.448098768882566 42.448098768882566 BN2 TRUE 145 +FL1019T2 EZB 1 0.2804740733422566 42,639,155,1933,1537,233,17,16,107 0.149,0.16,0.169,0.186,0.215,0.216,0.227,0.227,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7464114206909 2 13.111156425294153 46.74641142069087 46.74641142069087 Other FALSE 146 +09-21480T BN2 0.473864744101632 4.712000828648826 147,834,1763,286 0.256,0.282,0.303,0.341 MCD,BN2,EZB,BN2 6.48178680529214,3.29571900937525,3.90105306943047 7 30.542184797661584 13.678558884097864 6.481786805292139 MCD FALSE 147 +09-27193T EZB 1 0 668,148,1047,700,1237,1078,299,12,424,1238,439 0.109,0.142,0.148,0.159,0.16,0.172,0.194,0.197,0.232,0.243,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.1192002779169 0 0 64.11920027791692 64.11920027791692 EZB TRUE 148 +09-27866_tumorB EZB 1 0.0798552724996746 1705,121,404,149,80,17,333,187,1537,2059 0.134,0.149,0.151,0.16,0.17,0.184,0.19,0.197,0.22,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6889692266406 1 4.606768357818724 57.68896922664055 57.68896922664055 EZB TRUE 149 +09-31008_tumorB MCD 0.6361499490218393 1.4340530141164496 151,150,661,736,2135 0.146,0.174,0.2,0.239,0.29 MCD,MCD,BN2,BN2,MCD 9.17653391836192,16.0441136909817 5 23.008109597379367 25.220647609343665 16.044113690981746 MCD TRUE 150 +09-31008_tumorA MCD 0.6617488788839708 0.34186052404446454 151,150,736,661,2135 0.14,0.182,0.231,0.244,0.261 MCD,MCD,BN2,BN2,MCD 8.43829717091096,16.5085445219401 1 5.643619681481809 24.94684169285104 16.508544521940077 MCD TRUE 151 +09-31233T MCD 1 2.6953559889956287 768,801,1994 0.139,0.23,0.236 MCD,MCD,MCD 15.7578572295961 8 42.473034857529896 15.757857229596093 15.757857229596093 Other FALSE 152 +FL1020T2 EZB 1 0 2016,240,174,558,39,93,382,1138,153,1903,287 0.11,0.139,0.157,0.175,0.207,0.214,0.22,0.224,0.224,0.238,0.243 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.6796016452163 0 0 59.67960164521633 59.67960164521633 EZB TRUE 153 +09-31601_tumorA MCD 0.914384243223882 0.2444552132263783 1728,214,154,997,1531,789,1127,1501,1732 0.17,0.177,0.198,0.207,0.231,0.235,0.254,0.286,0.292 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.49824896534811,37.3616242294413 2 9.133243817491895 40.85987319478941 37.3616242294413 MCD TRUE 154 +09-31895T EZB 0.8998510686529435 0.11390678674468062 343,16,42,575,308,231,1533,11,122,589 0.143,0.156,0.17,0.172,0.185,0.199,0.203,0.218,0.221,0.234 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.39090420712618,48.4379697969759 1 5.517413496009416 53.82887400410209 48.437969796975914 EZB TRUE 155 +09-32452_tumorA MCD 1 0.09933158895356725 837,156,1491,1968,767,788,471,1422,950,1224 0.112,0.141,0.148,0.152,0.154,0.16,0.161,0.161,0.163,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.58192180386 1 6.613688088359578 66.58192180386 66.58192180386 MCD TRUE 156 +09-33003T BN2 0.7472353592774243 0.1154338436066498 932,157,412,13,1086,1076,590,130,1806,973 0.103,0.119,0.159,0.176,0.189,0.243,0.247,0.257,0.276,0.288 EZB,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 40.7476367906905,13.7835578118955 1 4.703656332637139 54.53119460258604 40.74763679069052 BN2 TRUE 157 +09-33003_tumorB BN2 0.8627104306185912 1.217841602701515 158,594,922,1735,2041,903 0.178,0.185,0.213,0.233,0.266,0.275 BN2,BN2,BN2,BN2,ST2,BN2 23.6525037857372,3.76399999847877 5 28.805003118325796 27.41650378421593 23.65250378573716 BN2 TRUE 158 +09-37629T ST2 0.6528411662479223 1.9493280267073023 1123,234,1176,649,235,293 0.211,0.213,0.238,0.238,0.252,0.255 EZB,ST2,ST2,EZB,ST2,ST2 8.92804066408947,16.7894113955214 5 32.72807018520879 25.717452059610856 16.789411395521384 Other FALSE 159 +09-41082T EZB 0.7457307165496786 1.4229049411043808 160,1944,1439,388,242 0.157,0.172,0.183,0.258,0.283 ST2,EZB,EZB,EZB,EZB 18.6677965318511,6.36509552632053 6 26.562499924702113 25.032892058171605 18.66779653185107 ST2 FALSE 160 +09-41114T EZB 1 0.20645204620682625 333,1705,1083,589,332,42,121,122,231 0.158,0.2,0.212,0.218,0.221,0.222,0.231,0.24,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2792549090423 2 8.728638688071792 42.27925490904232 42.27925490904232 EZB TRUE 161 +10-10826T EZB 1 0 2132,1914,1172,468,162,1570,1779,278,958,1080,103 0.112,0.15,0.153,0.181,0.188,0.205,0.207,0.209,0.209,0.218,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2650050392373 0 0 61.265005039237295 61.265005039237295 EZB TRUE 162 +10-11584_tumorA EZB 0.6399018128462339 1.6050818727511709 1703,1392,1493,1122,1528,696 0.205,0.232,0.238,0.259,0.269,0.303 ST2,EZB,EZB,BN2,EZB,EZB 3.85506017029669,15.5256001666062,4.88181220954844 5 24.919859391002117 24.2624725464513 15.525600166606168 Other FALSE 163 +10-11584_tumorB EZB 1 0.1349977670255197 164,329,1094,326,83,138,2006,1796,43,435 0.147,0.148,0.161,0.17,0.177,0.178,0.185,0.192,0.199,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.5001777763551 1 7.762395603378356 57.50017777635514 57.50017777635514 EZB TRUE 164 +10-11741_tumorB EZB 0.769046627294242 0.3363632574885211 1292,1446,1177,165,1959,1527,1738,2080 0.147,0.158,0.183,0.232,0.246,0.318,0.319,0.348 EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 27.7930871604197,8.34657742429462 3 9.348573332941164 36.139664584714325 27.793087160419713 EZB TRUE 165 +10-15025T EZB 0.905136465656894 0.08544080104358007 1244,340,1042,341,365,1245,166,1581,105,1243 0.129,0.132,0.166,0.192,0.197,0.216,0.22,0.224,0.236,0.25 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.07968616943378,48.4676142195796 1 4.141111783592089 53.54730038901334 48.46761421957956 EZB TRUE 166 +10-18191T EZB 0.7750152118397449 0.3897589255489414 11,1533,308,218,16,221,343,143,139 0.2,0.212,0.218,0.235,0.248,0.26,0.267,0.269,0.279 EZB,EZB,BN2,EZB,EZB,BN2,EZB,EZB,EZB 8.4314669226507,29.0442530653381 2 11.320256868117726 37.475719987988796 29.044253065338097 Other FALSE 167 +FL1016T2 EZB 1 0.07859940918827148 1511,2104,137,435,2006,138,83,1041,329,1094 0.12,0.13,0.157,0.162,0.171,0.189,0.202,0.227,0.228,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.5541594040899 1 4.523722925489069 57.554159404089944 57.554159404089944 EZB TRUE 168 +10-27154T MCD 0.539770557705657 2.018656284698435 446,140 0.151,0.177 MCD,EZB 5.65044235090661,6.62700413912657 2 13.377643554170387 12.277446490033174 6.6270041391265675 Other FALSE 169 +10-28165T EZB 0.8884255687822131 0 433,565,170,1181,780,250,368,44,5,367,196 0.143,0.19,0.218,0.234,0.236,0.25,0.258,0.268,0.269,0.307,0.319 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.25081757008414,41.8103012971485 0 0 47.061118867232636 41.8103012971485 EZB TRUE 170 +10-31625T ST2 0.5724958537757704 0.875691381551024 648,171,1414,531,268,1174,1038 0.153,0.23,0.234,0.254,0.28,0.293,0.303 ST2,BN2,BN2,BN2,ST2,ST2,ST2 12.5550420271132,16.8131924987994 3 14.723167767556937 29.36823452591257 16.813192498799374 BN2 FALSE 171 +10-32847T EZB 1 0 1972,1752,172,398,397,1573,1671,108,695,2089,1750 0.199,0.205,0.212,0.229,0.242,0.268,0.278,0.282,0.283,0.293,0.321 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.0535146676212 0 0 44.053514667621165 44.053514667621165 EZB TRUE 172 +10-36955_tumorB EZB 1 0 287,558,382,676,2016,1903,174,1138,240,93,153 0.168,0.17,0.18,0.184,0.189,0.192,0.201,0.203,0.22,0.222,0.229 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.623320111347 0 0 56.623320111346985 56.623320111346985 EZB TRUE 173 +10-36955_tumorA EZB 1 0 558,174,2016,382,1138,93,240,676,287,39,1903 0.134,0.16,0.16,0.167,0.179,0.187,0.194,0.199,0.21,0.214,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.9142225264264 0 0 60.91422252642642 60.91422252642642 EZB TRUE 174 +10-39294_tumorA N1 0.4284139207894127 6.64887733125974 175,2211,2127 0.196,0.253,0.349 N1,BN2,ST2 3.94985558941864,5.1108728723598,2.86902597510424 8 33.98156678398345 11.929754436882686 5.110872872359804 N1 TRUE 175 +10-39294_tumorB N1 0.4506674720811514 1.5034729148073458 1609,464,176,1008,1064,659 0.155,0.164,0.206,0.222,0.232,0.244 MCD,BN2,N1,N1,N1,MCD 6.1157058941014,10.5386071341942,13.6630488279022 4 20.542023846441143 30.317361856197735 13.663048827902156 N1 TRUE 176 +10-40676T MCD 0.7210890979834268 4.5043048096952 751,81,401 0.183,0.237,0.267 MCD,MCD,EZB 3.74196408912898,9.67437805480997 8 43.57634760309023 13.416342143938943 9.674378054809967 Other FALSE 177 +10-41170T EZB 0.8778708402401229 0.26132529789948544 1088,320,1541,1551,2059,178,259,319,206 0.16,0.169,0.173,0.179,0.182,0.2,0.212,0.217,0.251 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 41.5480960961948,5.78016017078658 2 10.857568589494559 47.3282562669814 41.54809609619482 EZB TRUE 178 +1001-2027_FFPE EZB 0.7709123073668266 0.8227350465721601 1053,62,179,1453,2126,993,1133 0.141,0.166,0.185,0.219,0.224,0.226,0.266 EZB,EZB,EZB,EZB,EZB,BN2,BN2 8.18953452874418,27.5589355641371 4 22.67370213483951 35.7484700928813 27.558935564137116 EZB TRUE 179 +1001-2028_plasma BN2 0.5105068601590714 6.725876978357978 793,1257 0.152,0.158 BN2,EZB 6.59646268187725,6.32493602336605 6 44.36699649063573 12.921398705243305 6.5964626818772505 Other FALSE 180 +1001-2030_FFPE MCD 0.8334894456011774 1.0492987913791716 467,1420,2028,755,647,713 0.184,0.24,0.241,0.254,0.27,0.274 MCD,BN2,MCD,MCD,MCD,MCD 4.16967424548557,20.8718269406702 5 21.900782782720466 25.041501186155763 20.871826940670193 Other FALSE 181 +1001-2030_plasma N1 0.5560050805980076 3.435472486576977 901,1387,870,1488 0.154,0.2,0.208,0.248 N1,EZB,N1,EZB 9.01524780321454,11.2895967102249 7 38.78509888252765 20.304844513439456 11.289596710224917 Other FALSE 182 +1001-2035_FFPE BN2 1 0.6689673445840012 1320,183,582,1362,489,666 0.165,0.183,0.2,0.218,0.311,0.323 BN2,BN2,BN2,BN2,BN2,BN2 27.4270524756267 5 18.347802464386056 27.42705247562671 27.42705247562671 BN2 TRUE 183 +1001-2035_plasma BN2 0.7138188106278891 2.1453168359463692 811,1232,150 0.169,0.221,0.239 BN2,BN2,MCD 10.4434300626945,4.18693538467758 4 22.404466338526966 14.63036544737208 10.443430062694503 Other FALSE 184 +1001-2036_FFPE MCD 1 0.21334644331821423 296,643,185,481,739,246,1968,465,1422 0.115,0.137,0.137,0.159,0.167,0.212,0.216,0.218,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.6589923119436 2 11.44795516179256 53.658992311943564 53.658992311943564 MCD TRUE 185 +1001-2037_plasma Other 1 10 0 0 1 2 Other TRUE 186 +1001-2038_FFPE EZB 1 0.08657248015022247 121,1705,404,80,17,149,187,333,1537,639 0.134,0.15,0.153,0.156,0.168,0.176,0.186,0.196,0.202,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6265146248437 1 5.075442773636005 58.62651462484366 58.62651462484366 EZB TRUE 187 +1001-2039_FFPE MCD 1 0 705,2094,1169,1567,515,516,540,621,1273,547 0.184,0.206,0.222,0.231,0.235,0.238,0.243,0.243,0.246,0.251 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.8913741301038 0 0 43.891374130103785 43.891374130103785 MCD TRUE 188 +1001-2040_FFPE BN2 0.746474438542931 1.661301851266305 190,189,2218 0.111,0.181,0.203 BN2,BN2,ST2 14.5221795117097,4.93217654106557 4 24.125723707224875 19.45435605277524 14.522179511709668 BN2 TRUE 189 +1001-2040_plasma BN2 0.7911250990385392 1.836482575085918 189,190,2218 0.112,0.159,0.248 BN2,BN2,ST2 15.2575528446613,4.02833868273928 6 28.020229937673136 19.285891527400626 15.257552844661344 BN2 TRUE 190 +1001-2046_FFPE EZB 1 0 31 0.151 EZB 6.63219425243297 0 0 6.632194252432974 6.632194252432974 Other FALSE 191 +1003-2043_FFPE EZB 1 0.11522367530226502 333,1083,589,332,42,122,231,1705 0.182,0.192,0.197,0.206,0.206,0.216,0.217,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.1356788544056 1 4.5093567530537495 39.1356788544056 39.1356788544056 EZB TRUE 192 +1003-2043_plasma EZB 1 0.25898302770630166 332,589,1083,122,192,231,193,575,333 0.174,0.196,0.198,0.205,0.224,0.228,0.23,0.271,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.019685597031 2 10.623402371479663 41.019685597031 41.019685597031 EZB TRUE 193 +1003-2046_FFPE MCD 0.7720014960227319 2.3003207801571643 311,194,1513,1480 0.187,0.201,0.232,0.234 MCD,MCD,EZB,MCD 4.30917214571404,14.5908296987879 6 33.56358875585604 18.900001844501915 14.590829698787871 MCD TRUE 194 +1004-2031_plasma MCD 1 1.5773304641543051 768,1994,801,990 0.143,0.186,0.208,0.234 MCD,MCD,MCD,MCD 21.4567342966488 6 33.844360667368655 21.456734296648804 21.456734296648804 Other FALSE 195 +1004-2033_FFPE EZB 1 0.12540725553558665 1788,1118,196,6,1142,438,367,44,1872,28 0.124,0.155,0.157,0.162,0.164,0.18,0.211,0.215,0.216,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2413071654907 1 7.178475234893702 57.241307165490724 57.241307165490724 EZB TRUE 196 +1005-2018_FFPE BN2 0.4564039015585308 1.3459870733569892 1763,597,834,1377 0.201,0.285,0.302,0.318 EZB,BN2,BN2,MCD 6.82008523630077,4.98053437688687,3.14247183062234 2 9.179746567253687 14.94309144380998 6.820085236300773 Other FALSE 197 +1006-2010_FFPE N1 1 1.0149383858456889 198 0.18 N1 5.55070196714242 1 5.633620494842018 5.55070196714242 5.55070196714242 N1 TRUE 198 +1007-2013_FFPE EZB 1 0 199 0.149 EZB 6.715679064374 0 0 6.715679064374003 6.715679064374003 EZB TRUE 199 +1010-2010_FFPE BN2 0.5222512830963066 4.4348885360203605 101,1631 0.306,0.335 BN2,MCD 3.26273500118426,2.98470776590721 3 14.469866052824441 6.247442767091464 3.2627350011842573 Other FALSE 200 +1010-2010_plasma MCD 0.38605177907518157 7.722841452687494 446,140,101 0.212,0.262,0.272 MCD,EZB,BN2 3.67973547980611,3.81056407035323,4.70991423802683 8 36.37392091603665 12.200213788186172 4.709914238026832 Other FALSE 201 +1011-2017_FFPE EZB 0.8761428122985854 0 202,727,1221,1132,2114,1879,1896,351,527,1268 0.156,0.17,0.191,0.199,0.218,0.235,0.236,0.243,0.252,0.26 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6815569337989,5.89237319346672 0 0 47.5739301272656 41.68155693379888 EZB TRUE 202 +1011-2018_FFPE BN2 0.7874729863421264 2.6738887375217577 554,1199,736,2135 0.223,0.283,0.323,0.334 BN2,BN2,BN2,MCD 11.1012518619364,2.9960594801359 7 29.68351232602424 14.097311342072322 11.10125186193642 Other FALSE 203 +1011-2018_plasma BN2 0.3386011025892545 1.9321765115856446 1609,176,464,554,1366,659 0.209,0.211,0.221,0.251,0.273,0.292 MCD,N1,BN2,BN2,EZB,MCD 8.50666254505482,3.6632608809523,8.20635281906585,4.74668549369598 3 16.43637356154029 25.122961738768954 8.506662545054823 Other FALSE 204 +1011-2019_FFPE EZB 1 0.2602081393428047 729,45,205,548,1060,1040,733,2100,443 0.178,0.226,0.273,0.275,0.283,0.285,0.311,0.332,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5316265027871 2 8.72520214142812 33.53162650278715 33.53162650278715 EZB TRUE 205 +1011-2020_FFPE EZB 0.8515088118563579 0.2655157725862657 206,1088,1541,2059,187,320,1551,1218,178 0.157,0.162,0.164,0.237,0.266,0.272,0.276,0.28,0.285 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 34.897089010503,6.08556263631264 2 9.265727549635384 40.98265164681564 34.897089010502995 EZB TRUE 206 +1012-2006_FFPE EZB 0.8401938399664398 0.13935283997732714 1272,1318,2083,207,1600,595,1293 0.122,0.144,0.155,0.181,0.188,0.247,0.255 EZB,EZB,BN2,EZB,EZB,EZB,EZB 6.46029087954542,33.9655029583687 1 4.7331892985069866 40.42579383791413 33.96550295836871 EZB TRUE 207 +1012-2008_FFPE EZB 1 5.7107280146291455 1601,2166 0.245,0.279 EZB,EZB 7.65904821407169 9 43.73874120149455 7.659048214071694 7.659048214071694 Other FALSE 208 +1012-2008_plasma EZB 1 3.284601446199224 1601,2166,1395 0.191,0.296,0.318 EZB,EZB,EZB 11.7618406275516 8 38.63295873522065 11.76184062755156 11.76184062755156 Other FALSE 209 +1013-2006_FFPE EZB 0.9295296580252009 0 1423,210,877,390,396,693,211,25,212,216,2073 0.153,0.172,0.175,0.184,0.217,0.261,0.263,0.269,0.284,0.286,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.51966698928167,46.4256985453476 0 0 49.945365534629225 46.42569854534756 EZB TRUE 210 +1013-2006_plasma EZB 0.9295296580252009 0 1423,210,877,390,396,693,211,25,212,216,2073 0.153,0.172,0.175,0.184,0.217,0.261,0.263,0.269,0.284,0.286,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.51966698928167,46.4256985453476 0 0 49.945365534629225 46.42569854534756 EZB TRUE 211 +1013-2010_FFPE EZB 0.6767397093669042 0 216,213,212,877,1049,693,211,390,1423,210,944 0.17,0.171,0.191,0.214,0.233,0.247,0.273,0.273,0.281,0.282,0.316 EZB,BN2,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 11.0926393479139,32.207196714251,4.29186909192028 0 0 47.59170515408516 32.20719671425099 BN2 FALSE 212 +1013-2010_plasma EZB 0.5791148294864172 0.11693904596774456 213,212,216,1049,877,211,693,390,210,1423 0.134,0.168,0.183,0.186,0.249,0.282,0.29,0.309,0.322,0.334 BN2,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 13.3889686991842,25.826235836469,5.38084882283661 1 3.020095379654665 44.5960533584899 25.826235836469042 BN2 FALSE 213 +11-13204_tumorA MCD 0.8081014671440307 0.10006187845183931 749,1732,883,2084,790,1127,758,1503,1271,154 0.132,0.165,0.168,0.174,0.181,0.194,0.234,0.245,0.286,0.291 MCD,MCD,MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD 9.84181881633377,41.4447578440548 1 4.147040321857728 51.28657666038859 41.44475784405483 MCD TRUE 214 +11-14210T ST2 0.7443762221325684 0.2809016432651016 1341,1943,486,1789,1130,1555,293,1644,1713 0.165,0.169,0.198,0.214,0.221,0.235,0.249,0.263,0.273 BN2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 6.04950192395678,4.67102831199315,31.2181748617804 2 8.76923661841139 41.9387050977303 31.21817486178037 ST2 TRUE 215 +11-21727T EZB 0.8354711880066648 0.10017482681703417 693,944,216,1423,2073,213,940,877,384,210 0.13,0.2,0.237,0.286,0.295,0.302,0.324,0.333,0.341,0.361 EZB,EZB,EZB,EZB,EZB,BN2,ST2,EZB,EZB,EZB 3.31581449591407,32.4989049952922,3.08417428492829 1 3.255572179646643 38.898893776134564 32.4989049952922 EZB TRUE 216 +11-22591_tumorB EZB 0.8850392003985204 0.8266866643102037 1964,1289,2214,72,1542,199,940 0.244,0.272,0.303,0.352,0.37,0.396,0.402 EZB,EZB,EZB,EZB,EZB,EZB,ST2 19.1556863590045,2.48819828517733 4 15.835750458697925 21.643884644181863 19.15568635900453 Other FALSE 217 +11-31342_tumorA EZB 0.9144815229560894 0.11726758438551155 218,143,139,11,1058,219,308,220,1533,343 0.145,0.176,0.178,0.184,0.221,0.225,0.24,0.244,0.255,0.256 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.16387130196249,44.5258557125224 1 5.221439542105327 48.68972701448485 44.52585571252236 EZB TRUE 218 +11-31342_tumorB EZB 0.9211538791146086 0.10472518201561885 218,139,143,11,1058,219,220,1587,308,343 0.134,0.163,0.166,0.193,0.206,0.212,0.229,0.249,0.251,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.98706878886695,46.5806540628691 1 4.87816747514054 50.56772285173602 46.58065406286907 EZB TRUE 219 +11-31342_tumorC EZB 0.9211538791146086 0.10472518201561885 218,139,143,11,1058,219,220,1587,308,343 0.134,0.163,0.166,0.193,0.206,0.212,0.229,0.249,0.251,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.98706878886695,46.5806540628691 1 4.87816747514054 50.56772285173602 46.58065406286907 EZB TRUE 220 +11-35935T EZB 0.7379887262704616 0.3205633766358073 221,2148,219,220,218,1587,4,139,1058 0.237,0.25,0.254,0.278,0.297,0.312,0.327,0.328,0.344 BN2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.21227179625953,23.1196208826296,3.99598545749937 2 7.411303736675465 31.3278781363885 23.11962088262959 BN2 FALSE 221 +POG885T BN2 0.6562425993610381 3.5071237336051633 2218,189,190 0.233,0.239,0.249 ST2,BN2,BN2 8.2092628780296,4.30023115058526 5 28.790900674941433 12.509494028614856 8.209262878029598 Other FALSE 222 +12-17272_tumorA MCD 0.7779605351417452 0.12000165042790409 749,790,2084,1503,758,1732,883,1271,1127,236 0.183,0.185,0.189,0.22,0.235,0.239,0.241,0.26,0.267,0.289 MCD,MCD,BN2,BN2,MCD,MCD,MCD,MCD,MCD,MCD 9.85042510116656,34.5129726734356 1 4.1416136819854295 44.3633977746022 34.512972673435634 MCD TRUE 223 +12-17272_tumorB MCD 0.8834972480703143 0.2853198408881835 1251,738,1501,786,789,300,301,1531,1826 0.145,0.222,0.225,0.256,0.263,0.264,0.27,0.271,0.288 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.44995771822128,33.7462019819978 2 9.628460980084117 38.19615970021909 33.74620198199781 MCD TRUE 224 +12-29259T ST2 1 2.52256506599319 670 0.171 ST2 5.83794200543987 2 14.72658856021684 5.837942005439869 5.837942005439869 Other FALSE 225 +13-22818T EZB 0.6479531236229452 4.701295286762656 793,342,402 0.229,0.247,0.251 BN2,EZB,EZB 4.36538510785561,8.0346258019987 8 37.7731484138381 12.400010909854307 8.034625801998697 Other FALSE 226 +13-26601T EZB 1 0.08294928892247773 86,1041,168,1094,138,329,2006,164,2104,1511 0.107,0.128,0.149,0.175,0.179,0.187,0.193,0.24,0.254,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6042486652174 1 4.778231465693373 57.604248665217426 57.604248665217426 EZB TRUE 227 +13-26835_tumorB BN2 1 2.0637159534371436 284,1210,2213,973 0.192,0.21,0.257,0.272 BN2,BN2,BN2,BN2 17.5477447270701 7 36.213560740097115 17.547744727070118 17.547744727070118 Other FALSE 228 +13-26835_tumorA BN2 1 1.2065128049007572 1210,284,2213,973,903 0.172,0.196,0.233,0.249,0.261 BN2,BN2,BN2,BN2,BN2 23.0485349408981 5 27.808352540396122 23.04853494089814 23.04853494089814 Other FALSE 229 +13-26835_tumorD BN2 1 1.2886343269310288 284,1210,2213,130,973 0.159,0.229,0.243,0.248,0.256 BN2,BN2,BN2,BN2,BN2 22.700923474231 6 29.253189241928457 22.700923474231 22.700923474231 Other FALSE 230 +FL1017T2 EZB 1 0.1408543609933946 122,231,589,1083,575,332,193,343,42,16 0.117,0.129,0.144,0.151,0.169,0.172,0.225,0.251,0.273,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1778310975869 1 8.053746862238853 57.17783109758693 57.17783109758693 EZB TRUE 231 +13-30451T BN2 0.8803198684566201 0.10721285603344374 232,933,981,1075,1525,692,1115,892,261,742 0.166,0.18,0.185,0.212,0.215,0.225,0.231,0.262,0.265,0.268 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.9261837146758,5.56394468198044 1 4.387813042599806 46.49012839665623 40.926183714675794 BN2 TRUE 232 +13-31210T EZB 1 0.09434198528388306 121,80,17,187,404,1537,639,1705,149,1606 0.118,0.123,0.129,0.156,0.161,0.161,0.188,0.191,0.204,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.3989126022998 1 5.9811792797403545 63.39891260229978 63.39891260229978 EZB TRUE 233 +13-38657_tumorA ST2 0.6973860890207622 0.14438557014873923 1484,619,1139,235,1176,2004,1147,234,1363,1394 0.121,0.148,0.153,0.157,0.167,0.19,0.199,0.199,0.205,0.225 ST2,BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 13.2739695153284,4.43841870800586,40.8189204201388 1 5.893663097717749 58.53130864347298 40.81892042013875 ST2 TRUE 234 +13-38657_tumorB ST2 0.7000999002908329 0.1541543825914595 1484,1139,235,1176,1147,234,619,1394,1363,2004 0.121,0.125,0.125,0.164,0.167,0.168,0.178,0.206,0.225,0.228 ST2,BN2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 13.6354131914339,4.85217175149927,43.1582263150885 1 6.653029731344952 61.645811258021745 43.15822631508853 ST2 TRUE 235 +13-42815T MCD 0.8759638667766102 0.3613956074161588 2014,291,790,761,236,749,267,2130 0.16,0.28,0.282,0.294,0.316,0.328,0.342,0.345 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 3.57175237224476,25.2243111571786 2 9.115955252302761 28.796063529423385 25.22431115717863 MCD TRUE 236 +14-10498_tumorA MCD 0.44420106862532593 2.406063469920699 879,1441,1416 0.135,0.208,0.223 MCD,EZB,ST2 4.80871731669865,7.41936714177927,4.47463794770644 3 17.85146824976504 16.702722406184357 7.419367141779267 Other FALSE 237 +14-10498_tumorB MCD 1 0.08021041695505225 883,1732,749,214,790,1127,1911,1543,485,1184 0.199,0.207,0.236,0.239,0.248,0.251,0.257,0.271,0.275,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.7492967509288 1 3.2685180830171534 40.74929675092878 40.74929675092878 Other FALSE 238 +14-11247T Other 1 10 0 0 1 2 Other TRUE 239 +14-13959T EZB 1 0.0685444864600743 174,39,558,93,2016,240,1138,382,1081,1889 0.118,0.139,0.161,0.161,0.162,0.182,0.192,0.206,0.247,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7981088446431 1 3.9617416891195427 57.798108844643146 57.798108844643146 EZB TRUE 240 +14-14094T ST2 0.5784031146300327 8.761208779843884 1664,951 0.217,0.297 ST2,MCD 3.36569810660689,4.61751577234138 8 40.45501972570494 7.98321387894827 4.617515772341384 Other FALSE 241 +14-16281T EZB 0.8963907663776974 0.5395583284538098 242,1439,1269,388,578,399,160 0.167,0.175,0.187,0.223,0.226,0.236,0.287 EZB,EZB,EZB,EZB,EZB,EZB,ST2 30.1968253125443,3.49029691716055 4 16.29294859024808 33.68712222970482 30.19682531254427 EZB TRUE 242 +14-16707T EZB 1 0.08478451226521427 676,2089,382,287,695,1138,558,1903,93,172 0.141,0.142,0.197,0.199,0.2,0.225,0.238,0.257,0.266,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.2182775635117 1 4.172947657756282 49.21827756351175 49.21827756351175 Other FALSE 243 +14-19181_tumorA EZB 1 1.6909079752484582 115 0.197 EZB 5.08606419191175 1 8.600066504729181 5.086064191911749 5.086064191911749 Other FALSE 244 +14-19181_tumorB BN2 0.7552974102861254 1.5473760267917007 610,591,1666 0.132,0.133,0.204 BN2,BN2,EZB 15.1396125601883,4.90495843132755 4 23.42667353054997 20.04457099151589 15.139612560188345 Other FALSE 245 +14-20552_tumorA MCD 1 0.2717417444522819 185,643,739,481,1422,296,246,247,1968 0.141,0.155,0.169,0.171,0.189,0.189,0.192,0.2,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 50.9182660722313 2 13.836618446953578 50.91826607223132 50.91826607223132 MCD TRUE 246 +14-20552_tumorB MCD 1 0.35638747001707066 788,1310,1224,156,471,767,1802,1491 0.117,0.122,0.122,0.135,0.141,0.143,0.158,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.1194671420666 3 21.069437323518457 59.11946714206661 59.11946714206661 MCD TRUE 247 +14-20962T EZB 1 0 1510,248,930,1217,355,522,1069,1109,498,1238,161 0.153,0.161,0.178,0.185,0.234,0.263,0.309,0.313,0.315,0.318,0.335 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.610625733802 0 0 47.61062573380202 47.61062573380202 EZB TRUE 248 +14-23891T ST2 0.570860927464325 1.832152142967034 911,2138,1178,816,955 0.151,0.159,0.175,0.178,0.285 ST2,EZB,ST2,EZB,ST2 11.9305371486978,15.8705602396226 6 29.077280953111966 27.8010973883204 15.870560239622609 Other FALSE 249 +14-24534_tumorB EZB 0.9280638489033473 0 5,250,780,368,433,170,118,102,565,691,1181 0.161,0.17,0.172,0.175,0.19,0.192,0.232,0.253,0.262,0.284,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.8210027857192,49.2955836241561 0 0 53.11658640987527 49.295583624156066 EZB TRUE 250 +14-24648_tumorA MCD 0.494988438940862 3.9108560018575695 879,1416,1441 0.131,0.246,0.268 MCD,ST2,EZB 3.73326950723968,7.64987586663263,4.07151026010824 6 29.917562946485603 15.454655633980552 7.649875866632631 Other FALSE 251 +14-24648_tumorB MCD 0.494988438940862 3.9108560018575695 879,1416,1441 0.131,0.246,0.268 MCD,ST2,EZB 3.73326950723968,7.64987586663263,4.07151026010824 6 29.917562946485603 15.454655633980552 7.649875866632631 Other FALSE 252 +14-25466T MCD 1 0 305,479,1689,764,1699,709,310,1331,1449,21,1009 0.127,0.132,0.144,0.149,0.15,0.154,0.154,0.156,0.163,0.168,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.133022738429 0 0 73.13302273842903 73.13302273842903 MCD TRUE 253 +14-27873T MCD 0.8900937562011619 0.29054593584133137 1194,285,2030,1398,952,1063,1890,650 0.149,0.157,0.17,0.189,0.216,0.216,0.229,0.237 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.62488856242336,37.4554191850447 2 10.882519819448166 42.08030774746805 37.45541918504469 MCD TRUE 254 +14-27873_tumorB MCD 0.5499388727750518 1.9911248986261258 659,869,1008,464,1275,1609 0.194,0.232,0.272,0.289,0.315,0.317 MCD,MCD,N1,BN2,BN2,MCD 6.64119992098864,12.6010284806644,3.67127908887629 5 25.090221556147867 22.913507490529355 12.60102848066442 Other FALSE 255 +14-27873_tumorC BN2 0.5836406281455355 1.40358226272204 281,659,1199,1135,897,1203,869 0.276,0.297,0.298,0.315,0.328,0.336,0.339 BN2,MCD,BN2,BN2,MCD,BN2,MCD 13.1266995719114,9.36436588667442 4 18.42440268721579 22.491065458585787 13.126699571911367 Other FALSE 256 +14-29443_tumorA EZB 0.756017327804847 1.0251879334695353 1745,1492,625,207,330,1356,993 0.196,0.202,0.236,0.237,0.248,0.262,0.315 EZB,EZB,EZB,EZB,BN2,EZB,BN2 7.20784706543177,22.3346077350734 4 22.897170348772587 29.542454800505165 22.33460773507339 Other FALSE 257 +14-29443_tumorB EZB 0.8659610288273341 0.8449705151225316 1745,207,1492,330,625,1356,1272 0.183,0.219,0.22,0.252,0.253,0.268,0.302 EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.96118061688963,25.591274032937 4 21.623872002252636 29.55245464982662 25.59127403293699 Other FALSE 258 +14-32442T EZB 0.8984556326386964 0.31531976519591676 320,1551,2059,319,1088,259,178,1541,149 0.144,0.166,0.17,0.175,0.191,0.192,0.198,0.2,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 44.2746472281766,5.00396556002007 2 13.960671368120678 49.278612788196625 44.274647228176555 EZB TRUE 259 +14-33262T BN2 0.90310544489078 0 892,260,981,933,961,667,907,692,232,145,592 0.161,0.205,0.219,0.234,0.258,0.277,0.278,0.293,0.303,0.309,0.321 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.7976909304802,4.26991064985714 0 0 44.06760158033735 39.79769093048021 BN2 TRUE 260 +14-33436T BN2 0.9207553649908755 0.08931663883840861 1115,261,830,1525,981,232,1203,742,1075,933 0.153,0.173,0.224,0.236,0.266,0.278,0.278,0.283,0.284,0.298 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 39.0536269231741,3.36114295824941 1 3.48813869122709 42.41476988142349 39.05362692317408 BN2 TRUE 261 +14-33798_tumorB N1 1 5.401695161501329 262 0.179 N1 5.57458351095885 5 30.1122007785315 5.574583510958848 5.574583510958848 N1 TRUE 262 +14-35026T MCD 0.5335266260392112 10.126808562491954 751,401 0.189,0.216 MCD,EZB 4.61929872877442,5.28330019032855 8 53.502969605634505 9.90259891910297 5.283300190328547 Other FALSE 263 +14-35472_tumorB BN2 0.39196149648629086 7.429257586840452 286,1425,147 0.199,0.226,0.297 BN2,N1,MCD 5.02723897108582,3.37022815806094,4.42838187937096 7 37.34865326679928 12.825849008517721 5.027238971085816 Other FALSE 264 +14-35472_tumorA MCD 0.6545429390095517 4.085030470725605 743,1064,2108 0.189,0.223,0.31 MCD,N1,MCD 8.50158283154723,4.48699641187376 8 34.72922491626811 12.988579243420986 8.501582831547232 Other FALSE 265 +14-35632T BN2 1 0 266,1929,796,976,1724,943,111,1496,1982,100,857 0.146,0.153,0.177,0.195,0.24,0.256,0.268,0.279,0.282,0.283,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.0210004242436 0 0 50.02100042424363 50.02100042424363 BN2 TRUE 266 +14-36022T MCD 0.8167556961736179 0.6366422627217643 1994,850,801,569,2130,768,990 0.157,0.162,0.195,0.221,0.241,0.268,0.277 MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.17830879712458,27.5379304971583 4 17.531810382385565 33.71623929428292 27.53793049715834 MCD TRUE 267 +14-37722T ST2 0.6384492705580469 0.8793485420297159 2066,1152,629,1198,1174,268,1101 0.145,0.153,0.164,0.183,0.234,0.24,0.266 BN2,BN2,ST2,ST2,ST2,ST2,ST2 13.4437660758434,23.739857081531 3 20.8756087126381 37.183623157374406 23.73985708153099 ST2 TRUE 268 +14-41461T EZB 1 0.23280017744428186 1483,1663,170,1468,430,337,336,1181,112 0.168,0.183,0.191,0.203,0.21,0.25,0.252,0.266,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.798191495441 2 9.730626396988729 41.79819149544096 41.79819149544096 Other FALSE 269 +15-10535T BN2 0.527991152299494 6.110364190997689 1108,65 0.187,0.209 BN2,EZB 5.35928817270482,4.79104891033944 6 32.747202539733 10.150337083044263 5.359288172704825 Other FALSE 270 +15-11617T ST2 1 0.5482139573279067 271,1077,2198,1427,1955 0.123,0.188,0.223,0.237,0.244 ST2,ST2,ST2,ST2,ST2 26.2692499334572 3 14.401169462056398 26.26924993345716 26.26924993345716 ST2 TRUE 271 +15-13365T EZB 1 8.216100488598208 199,1182 0.34,0.35 EZB,EZB 5.79847653455456 9 47.64086588867898 5.7984765345545615 5.7984765345545615 Other FALSE 272 +15-13383T BN2 0.6082862762447929 1.0575661759173138 273,1275,897,34,1191,1112,274 0.137,0.178,0.188,0.196,0.204,0.213,0.246 BN2,BN2,MCD,EZB,BN2,BN2,MCD 22.5064517538563,5.09749016912048,9.39582715417235 4 23.802062114793287 36.9997690771491 22.50645175385626 BN2 TRUE 273 +15-13383_tumorB BN2 0.6829304781864383 0.2136885391199132 274,742,1112,34,1191,1407,817,897,1525,1203 0.176,0.198,0.214,0.224,0.224,0.233,0.245,0.245,0.245,0.25 MCD,BN2,BN2,EZB,BN2,BN2,BN2,MCD,BN2,BN2 30.653615528959,4.47232354265595,9.75947241325753 1 6.550326321126742 44.88541148487252 30.653615528959037 MCD FALSE 274 +15-15757T EZB 1 0.13292597389506602 196,1788,438,44,6,1118,367,28,1142,1872 0.118,0.153,0.171,0.176,0.178,0.195,0.197,0.199,0.202,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.2256774085693 1 7.4738529274438905 56.22567740856933 56.22567740856933 EZB TRUE 275 +15-21654T EZB 1 0 1022,1563,1103,2091,1110,29,2217,1055,1,4,1111 0.101,0.116,0.121,0.153,0.154,0.177,0.182,0.185,0.201,0.204,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 71.0368202489423 0 0 71.03682024894232 71.03682024894232 EZB TRUE 276 +15-21665_tumorA EZB 1 0 79,1872,1142,1118,1253,54,1242,6,408,1788,1372 0.159,0.17,0.212,0.22,0.231,0.249,0.255,0.266,0.271,0.271,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4924012667795 0 0 48.49240126677945 48.49240126677945 EZB TRUE 277 +15-21665_tumorB EZB 1 0.2033096254456403 689,712,128,366,2100,161,498,1020,427 0.115,0.261,0.272,0.312,0.314,0.321,0.333,0.334,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.650621976313 2 7.044804975462659 34.650621976312955 34.650621976312955 EZB TRUE 278 +POG638T ST2 0.5240502600528553 9.870376257239299 1710,1538 0.149,0.164 ST2,BN2 6.10702687666308,6.72421634944159 9 66.3705454040686 12.831243226104672 6.724216349441591 Other FALSE 279 +15-24058T BN2 1 0.6085745239110614 106,2064,57,1632,1014,2085 0.193,0.199,0.206,0.215,0.245,0.294 BN2,BN2,BN2,BN2,BN2,BN2 27.2141499097629 4 16.56183832497821 27.2141499097629 27.2141499097629 Other FALSE 280 +15-24306T BN2 0.677577154784005 1.736287198266176 661,151,1135,150,736 0.102,0.221,0.243,0.257,0.264 BN2,MCD,BN2,MCD,BN2 17.6576776508263,8.40234743436879 6 30.658799656240408 26.060025085195058 17.657677650826265 BN2 TRUE 281 +15-26538T ST2 0.31712511311458136 4.316149880425803 1074,147,282,1425 0.2,0.262,0.277,0.3 ST2,MCD,BN2,N1 3.61471742161734,3.8169747601693,3.33812967137764,5.00147397275965 5 21.587111289479328 15.771295825923927 5.0014739727596504 BN2 FALSE 282 +15-29858T N1 1 13.693269816313473 913 0.294 N1 3.40209439760669 10 46.5857965269968 3.402094397606686 3.402094397606686 Other FALSE 283 +15-31924T BN2 1 0.6316724460613692 284,2213,973,130,1210,412,157 0.112,0.194,0.202,0.224,0.246,0.247,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.327230372724 4 22.315238022112066 35.327230372724 35.327230372724 BN2 TRUE 284 +15-34472T MCD 1 0.8518566725615804 1194,650,285,2030,864,1063 0.196,0.201,0.211,0.213,0.222,0.239 MCD,MCD,MCD,MCD,MCD,MCD 28.1947744382073 5 24.017906736555577 28.194774438207308 28.194774438207308 MCD TRUE 285 +15-38154T MCD 0.5110662268254184 4.130106720238858 147,286,1074 0.123,0.228,0.294 MCD,BN2,ST2 4.37889224176699,8.12991737758996,3.39894744001759 7 33.57742639617097 15.907757059374543 8.12991737758996 BN2 FALSE 286 +POG721T EZB 1 0 1163,2215,142,1046,1033,1962,1264,1304,463,517,1516 0.126,0.128,0.157,0.163,0.169,0.172,0.179,0.194,0.208,0.215,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.8305924197706 0 0 64.83059241977057 64.83059241977057 EZB TRUE 287 +15-43657T EZB 1 0 463,348,1264,1886,1516,2215,1962,1033,349,316,1222 0.131,0.136,0.141,0.16,0.186,0.189,0.19,0.19,0.194,0.204,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.3992172642994 0 0 64.3992172642994 64.3992172642994 EZB TRUE 288 +15-43891T EZB 1 0.08380005817089328 289,498,660,192,548,1510,930,443,161,2100 0.174,0.195,0.257,0.29,0.292,0.303,0.315,0.315,0.316,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5054818504136 1 3.142961560792039 37.50548185041357 37.50548185041357 EZB TRUE 289 +16-11636T MCD 1 0.42044416599068807 2053,765,1063,704,753,1526,799,650 0.119,0.234,0.239,0.239,0.24,0.244,0.248,0.253 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.2863136021294 3 15.676813025314537 37.28631360212938 37.28631360212938 Other FALSE 290 +16-12281_tumorB MCD 0.768633955125446 0.4791931205955297 267,291,2014,990,236 0.163,0.194,0.244,0.282,0.301 MCD,BN2,MCD,MCD,MCD 5.15167192755584,17.1146547080101 2 8.201224797446358 22.26632663556599 17.11465470801015 BN2 FALSE 291 +16-13732T ST2 0.6830272585365325 3.693337664086179 2080,292,1177 0.198,0.264,0.322 ST2,EZB,ST2 3.784600186245,8.15522835791135 7 30.120012053497657 11.939828544156345 8.155228357911346 EZB FALSE 292 +16-16192T ST2 0.8575226108661033 0.9556576895440493 234,1176,235,1484,293,649 0.165,0.167,0.192,0.23,0.232,0.232 ST2,ST2,ST2,ST2,ST2,EZB 4.30321555524917,25.8995807018125 5 24.751133453653736 30.20279625706162 25.899580701812454 ST2 TRUE 293 +16-16723T BN2 0.8935578253588875 0 669,1477,1212,560,1193,88,836,294,564,872,1213 0.138,0.158,0.16,0.18,0.182,0.187,0.201,0.217,0.218,0.235,0.246 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.4527141922828,6.24825926874168 0 0 58.70097346102453 52.45271419228285 BN2 TRUE 294 +16-17861T BN2 0.7865958057463226 1.6483813835479986 459,1003,1714,2210 0.202,0.217,0.285,0.294 BN2,BN2,ST2,BN2 12.9479849977023,3.51280071093397 5 21.343217424671252 16.460785708636276 12.947984997702303 BN2 TRUE 295 +16-18029T MCD 1 0.3552180139620431 185,296,643,481,739,246,465,1968 0.121,0.136,0.151,0.176,0.182,0.185,0.207,0.232 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.9453735558435 3 17.031060373174984 47.94537355584349 47.94537355584349 MCD TRUE 296 +16-18623T EZB 1 3.4967141785641034 2162 0.325 EZB 3.08139941512181 3 10.774773024675575 3.081399415121812 3.081399415121812 Other FALSE 297 +16-22216_tumorA BN2 1 1.708525776235103 1381,987,52,754 0.157,0.24,0.297,0.307 BN2,BN2,BN2,BN2 17.1576911379371 7 29.314357569846184 17.15769113793713 17.15769113793713 Other FALSE 298 +16-23208T EZB 1 0 1237,299,12,668,1047,700,1078,148,1110,439,1111 0.138,0.145,0.149,0.153,0.157,0.163,0.179,0.192,0.226,0.228,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.7189523162902 0 0 63.71895231629018 63.71895231629018 EZB TRUE 299 +16-27074_tumorA MCD 0.8853228532452329 0.3616481993657077 300,301,1501,224,1251,2108,1531,223,789 0.177,0.192,0.229,0.23,0.235,0.26,0.284,0.292,0.296 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.35860685967961,33.6490257246951 2 12.16910956374635 38.007632584374676 33.64902572469507 MCD TRUE 300 +16-27074_tumorB MCD 0.876401713285542 0.3815877185893459 300,224,1501,301,1251,1531,223,2108,789 0.19,0.197,0.21,0.21,0.259,0.262,0.265,0.274,0.281 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.76148605281326,33.7623978891498 2 12.88331638462643 38.523883941963085 33.762397889149824 MCD TRUE 301 +16-27413T ST2 0.5156794450278864 4.230914081570616 1071,999 0.167,0.177 ST2,N1 5.63798906797407,6.00303878040767 4 25.398341308241296 11.64102784838174 6.003038780407667 Other FALSE 302 +16-29329T EZB 1 0.59007474949914 1745,1991,1293,207,367,625,6 0.141,0.243,0.259,0.264,0.28,0.284,0.315 EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1147346216414 4 17.179869738599002 29.114734621641425 29.114734621641425 Other FALSE 303 +16-31791T ST2 0.543341575977337 9.632196652971608 1052,31 0.193,0.23 ST2,EZB 4.35169550022573,5.17773672154746 9 49.872978319257655 9.529432221773192 5.177736721547462 Other FALSE 304 +16-32248_tumorB MCD 0.8648704646822406 0.09846743509193762 1376,1694,767,1224,759,1310,1449,156,858,788 0.115,0.125,0.151,0.158,0.163,0.165,0.168,0.179,0.182,0.183 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.68643962395308,55.5958773656433 1 5.474383445880805 64.28231698959637 55.5958773656433 MCD TRUE 305 +16-32248_tumorA MCD 0.8630806459253626 1.1406614502364487 467,755,2028,1420,1897,713 0.105,0.179,0.218,0.222,0.224,0.24 MCD,MCD,MCD,BN2,MCD,MCD 4.49956399369028,28.3633137499301 5 32.352938595506615 32.862877743620324 28.36331374993005 Other FALSE 306 +16-43083_tumorB EZB 0.5122742123435852 6.395038833594785 401,81 0.17,0.179 EZB,MCD 5.88199598405106,5.60012792990066 5 37.615592737055124 11.482123913951725 5.881995984051064 Other FALSE 307 +16-43741_tumorA EZB 0.873947558708268 0.2166746765446053 343,16,308,1533,11,575,42,231,143 0.112,0.129,0.148,0.173,0.174,0.195,0.195,0.233,0.252 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 6.74596399673938,46.7711589372522 2 10.134125734345446 53.51712293399158 46.7711589372522 BN2 FALSE 308 +17-12136T EZB 1 7.3774411439054495 1863 0.259 EZB 3.86497140213895 6 28.513599042157843 3.8649714021389525 3.8649714021389525 Other FALSE 309 +17-16667_tumorA MCD 0.9176856978958592 0 1449,759,1009,764,479,310,1689,305,1376,1904,858 0.109,0.145,0.145,0.145,0.145,0.147,0.148,0.155,0.163,0.167,0.178 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.13031095248353,68.3441217497181 0 0 74.47443270220163 68.3441217497181 MCD TRUE 310 +17-23504T MCD 1 3.512014170863793 311,194 0.153,0.269 MCD,MCD 10.2380956804187 7 35.95633711228985 10.238095680418697 10.238095680418697 MCD TRUE 311 +17-23711_tumorB EZB 1 9.175297577984692 401 0.189 EZB 5.28834538253453 7 48.52214257991562 5.288345382534532 5.288345382534532 Other FALSE 312 +17-23711_tumorA MCD 1 5.038484109013871 751 0.182 MCD 5.49056317288703 4 27.664115296128074 5.490563172887028 5.490563172887028 Other FALSE 313 +17-33596_tumorA Other 1 10 0 0 1 2 Other TRUE 314 +17-33596_tumorB Other 1 10 0 0 1 2 Other TRUE 315 +17-33848_tumorB EZB 1 0 316,463,1937,1886,1222,1264,1962,1033,369,372,348 0.131,0.15,0.154,0.155,0.172,0.174,0.175,0.178,0.181,0.198,0.202 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.668203023038 0 0 65.66820302303802 65.66820302303802 EZB TRUE 316 +17-36275T ST2 1 1.1805481453566158 318 0.124 ST2 8.08455851425751 1 9.544210560033742 8.08455851425751 8.08455851425751 Other FALSE 317 +17-36275_tumorB ST2 0.6843459256770398 5.890333839694892 318,1381 0.16,0.346 ST2,BN2 2.88736389870215,6.25987712739643 9 36.87276607583522 9.147241026098577 6.259877127396426 ST2 TRUE 318 +17-40409_tumorB EZB 0.9011734205710711 0.32033728913201365 319,320,2059,1551,1088,259,1541,178,149 0.154,0.155,0.161,0.18,0.201,0.201,0.208,0.214,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 43.9304296842196,4.81760114028143 2 14.072554755447465 48.74803082450108 43.930429684219646 EZB TRUE 319 +17-40409_tumorA EZB 0.9011734205710711 0.32033728913201365 319,320,2059,1551,1088,259,1541,178,149 0.154,0.155,0.161,0.18,0.201,0.201,0.208,0.214,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 43.9304296842196,4.81760114028143 2 14.072554755447465 48.74803082450108 43.930429684219646 EZB TRUE 320 +17-45529_tumorA BN2 0.5243168227680199 0.9372298993716832 897,1191,34,273,1112,274,1275 0.122,0.142,0.156,0.182,0.187,0.19,0.245 MCD,BN2,EZB,BN2,BN2,MCD,BN2 21.9469066498313,6.4293396515834,13.4818549396769 4 20.569297110941072 41.85810124109159 21.946906649831256 Other FALSE 321 +17-45529_tumorB BN2 0.7856790061971388 0.4954213257443236 281,1203,1135,830,1199,897,1191,274 0.223,0.237,0.252,0.283,0.302,0.316,0.318,0.331 BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD 22.6684672727412,6.18360474896346 3 11.23044210885324 28.852072021704625 22.668467272741164 Other FALSE 322 +18-14625_tumorA BN2 1 0.6159545947388791 931,1173,1375,1027,1126,1158,564 0.145,0.191,0.236,0.256,0.275,0.284,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.8877634725126 4 19.025459832101852 30.8877634725126 30.8877634725126 Other FALSE 323 +18-19313_tumorB EZB 1 0.5694342189724579 2100,712,689,1867,548,729,128,513 0.19,0.21,0.262,0.279,0.311,0.332,0.336,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.3656371738346 3 16.72179866871106 29.36563717383456 29.36563717383456 Other FALSE 324 +18-19313_tumorA EZB 1 0.5694342189724579 2100,712,689,1867,548,729,128,513 0.19,0.21,0.262,0.279,0.311,0.332,0.336,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.3656371738346 3 16.72179866871106 29.36563717383456 29.36563717383456 Other FALSE 325 +18-36878_tumorB EZB 1 0 366,1020,78,427,48,326,164,1796,645,1094,329 0.127,0.164,0.214,0.23,0.244,0.245,0.258,0.271,0.29,0.299,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7835105966512 0 0 48.78351059665125 48.78351059665125 EZB TRUE 326 +19-13976_tumorA ST2 0.6206723146708246 7.224957243852811 2107,1508 0.189,0.31 ST2,EZB 3.22574318328816,5.27810008480577 8 38.13404744149758 8.50384326809393 5.27810008480577 Other FALSE 327 +19-13976_tumorB ST2 0.6206723146708246 7.224957243852811 2107,1508 0.189,0.31 ST2,EZB 3.22574318328816,5.27810008480577 8 38.13404744149758 8.50384326809393 5.27810008480577 Other FALSE 328 +19-16466_tumorA EZB 1 0 1020,78,366,427,164,326,645,1094,1796,329,48 0.161,0.161,0.167,0.22,0.24,0.24,0.253,0.265,0.278,0.279,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7419212053776 0 0 49.74192120537756 49.74192120537756 EZB TRUE 329 +19-25121_tumorA EZB 0.5059366069941593 1.390883638173598 993,330,1053,1356,62,179,1133 0.13,0.231,0.236,0.239,0.261,0.262,0.271 BN2,BN2,EZB,EZB,EZB,EZB,BN2 15.7063465542195,16.0837977401461 4 22.370691116462666 31.790144294365618 16.083797740146075 BN2 FALSE 330 +2001-2024_FFPE EZB 0.9097223093025028 0.32587394553788784 331,595,651,408,1293,1241,1600,1372,2083 0.183,0.226,0.228,0.256,0.283,0.29,0.292,0.302,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.16716699309742,31.9153320011444 2 10.400375162364545 35.08249899424184 31.915332001144417 EZB TRUE 331 +2001-2026_FFPE EZB 1 0.09618325518700722 333,332,1083,589,1705,122,1034,354,231 0.15,0.192,0.206,0.212,0.213,0.241,0.242,0.244,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4555349336224 1 4.0835115506215 42.45553493362237 42.45553493362237 EZB TRUE 332 +2001-2026_plasma EZB 1 0.24104378560870418 332,1083,589,192,333,1034,122,354,231 0.173,0.206,0.21,0.216,0.23,0.233,0.235,0.256,0.257 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6998482628056 2 9.810445498966498 40.699848262805574 40.699848262805574 EZB TRUE 333 +2004-2026_plasma Other 1 10 0 0 1 2 Other TRUE 334 +2004-2027_FFPE EZB 1 13.088135406943005 1535 0.24 EZB 4.17400996931766 10 54.63000766835958 4.174009969317662 4.174009969317662 Other FALSE 335 +2004-2030_FFPE EZB 1 0.22709749627180037 336,337,1663,430,1384,1483,112,646,1468 0.126,0.133,0.148,0.162,0.188,0.201,0.225,0.239,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2522246781019 2 11.639251902756731 51.252224678101946 51.252224678101946 EZB TRUE 336 +2004-2030_plasma EZB 1 0.22152974466059083 336,337,1663,430,1384,1483,112,646,2176 0.116,0.126,0.16,0.168,0.177,0.213,0.229,0.234,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6024295047477 2 11.431473032052889 51.60242950474766 51.60242950474766 EZB TRUE 337 +2004-2033_FFPE ST2 1 0.38642884297632174 339,959,2052,1404,1101,1198,1970 0.14,0.154,0.177,0.181,0.215,0.24,0.253 ST2,ST2,ST2,ST2,ST2,ST2,ST2 37.5568597624804 3 14.513053863839295 37.55685976248045 37.55685976248045 Other FALSE 338 +2004-2033_plasma ST2 0.8756387663666526 0.5658420948748875 339,2052,444,959,1970,1128,1404 0.172,0.182,0.201,0.206,0.209,0.233,0.235 ST2,ST2,ST2,ST2,ST2,EZB,ST2 4.28986613929776,30.2053379847263 3 17.091451721681516 34.49520412402401 30.205337984726256 ST2 TRUE 339 +4002-2003_FFPE EZB 0.9238282080520466 0.09588797808615798 1245,1244,341,1581,340,105,1243,1042,166,365 0.145,0.146,0.149,0.155,0.164,0.176,0.177,0.192,0.195,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.50624618532015,54.6527425962379 1 5.24054098441649 59.15898878155802 54.65274259623787 EZB TRUE 340 +4002-2003_plasma EZB 0.9000915337748483 0.10694585056607105 341,1244,166,1245,365,340,105,1581,1042,96 0.132,0.153,0.16,0.172,0.174,0.175,0.178,0.201,0.212,0.216 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.75781573470526,51.8730933589201 1 5.5476120907629225 57.63090909362537 51.87309335892011 EZB TRUE 341 +7004-2002_FFPE EZB 1 1.1067635731375545 342,1260,1002,1102,1322,473 0.237,0.255,0.266,0.29,0.324,0.333 EZB,EZB,EZB,EZB,EZB,EZB 21.433678421308 5 23.722014515048087 21.433678421307953 21.433678421307953 EZB TRUE 342 +75-10141_tumorA EZB 0.9010403815019664 0.1146217452450829 343,16,42,575,308,231,1533,122,11,589 0.147,0.159,0.169,0.171,0.189,0.197,0.206,0.218,0.222,0.231 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.30006764052801,48.2578150693102 1 5.5313949849587924 53.55788270983818 48.25781506931017 EZB TRUE 343 +76-10146_tumorB Other 1 10 0 0 1 2 Other TRUE 344 +8002-2019_FFPE EZB 1 1.2356026834650136 1967,532,1782,1182,96 0.201,0.273,0.338,0.354,0.36 EZB,EZB,EZB,EZB,EZB 17.1890616765035 5 21.238850733733365 17.189061676503513 17.189061676503513 Other FALSE 345 +8003-2001_FFPE BN2 0.49636408027225043 2.0662028345293053 402,737,793,581 0.122,0.142,0.148,0.171 EZB,BN2,BN2,ST2 13.8198049716722,8.17928411682212,5.84298394002178 4 28.554520205111388 27.842073028516147 13.819804971672248 Other FALSE 346 +8005-2002_FFPE MCD 0.8904096057064737 0.3950374647748704 465,347,1526,246,2185,296,185,704 0.142,0.193,0.215,0.225,0.246,0.249,0.257,0.261 MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD 33.0704941892831,4.07027111394398 3 13.064084183386495 37.14076530322712 33.07049418928314 MCD TRUE 347 +8005-2003_FFPE EZB 1 0 1516,1281,1046,1163,351,1896,2215,348,1264,2047,463 0.155,0.18,0.211,0.217,0.222,0.225,0.232,0.237,0.245,0.261,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5350503868177 0 0 50.53505038681765 50.53505038681765 EZB TRUE 348 +8005-2003_plasma EZB 1 0 1281,1516,1046,1163,2047,1896,351,2215,76,1264,348 0.158,0.186,0.207,0.226,0.233,0.24,0.24,0.246,0.259,0.268,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.9791597992631 0 0 48.97915979926305 48.97915979926305 EZB TRUE 349 +8006-2002_FFPE EZB 1 0.2562030185580145 413,288,2015,2151,1435,1222,349,1120 0.148,0.162,0.167,0.198,0.203,0.213,0.215,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8248431361406 2 10.971854080752681 42.82484313614057 42.82484313614057 Other FALSE 350 +8006-2002_plasma EZB 1 0 351,1516,1896,348,1281,527,349,1264,463,2001,1163 0.164,0.172,0.175,0.208,0.229,0.229,0.249,0.257,0.262,0.263,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4128698536398 0 0 50.4128698536398 50.4128698536398 EZB TRUE 351 +81-52884T Other 1 10 0 0 1 2 Other TRUE 352 +89-62169T EZB 0.42175395955328854 2.794891451707747 353,1725,1738,1909 0.181,0.248,0.264,0.314 BN2,ST2,EZB,EZB 5.52972840672704,6.96837953915554,4.0242728908884 5 19.47586440624098 16.522380836770978 6.968379539155537 BN2 FALSE 353 +92-38267_tumorA EZB 1 0.1899990064513702 548,443,660,1034,354,729,289,2100,192 0.177,0.203,0.203,0.246,0.257,0.259,0.286,0.296,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.3000796764512 2 7.086978079082667 37.30007967645116 37.30007967645116 EZB TRUE 354 +92-38267_tumorB EZB 0.9257517570096536 0.1040146202710749 575,231,122,343,589,1083,16,332,42,308 0.107,0.148,0.17,0.189,0.196,0.2,0.224,0.231,0.241,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.03917077160394,50.3617228916859 1 5.2383554827758045 54.40089366328979 50.36172289168586 EZB TRUE 355 +92-38626_tumorA EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 356 +92-38626_tumorB BN2 0.5363283416780009 7.226529351027729 793,581 0.145,0.167 BN2,ST2 6.92025947629693,5.98276827466182 7 50.00945822218754 12.903027750958746 6.92025947629693 Other FALSE 357 +94-15772_tumorB EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 358 +94-15772_tumorA BN2 1 0.7204666472253565 1980,1148,804 0.152,0.153,0.164 BN2,BN2,BN2 19.2325614815759 2 13.85641908818656 19.23256148157595 19.23256148157595 Other FALSE 359 +94-25764_tumorB EZB 1 0 360,1294 0.144,0.171 EZB,EZB 12.7956264273476 0 0 12.795626427347566 12.795626427347566 EZB TRUE 360 +94-26795T ST2 1 2.1791339451099363 1052 0.168 ST2 5.95135791973542 2 12.968806062394304 5.951357919735417 5.951357919735417 Other FALSE 361 +95-32141_tumorB ST2 0.7716308756660343 0.2582486273214168 2082,1394,1045,1417,1147,1687,1917,1139,1939 0.135,0.154,0.161,0.198,0.202,0.225,0.232,0.235,0.249 ST2,EZB,ST2,ST2,ST2,ST2,ST2,BN2,ST2 4.26058520800005,6.50424634319393,36.3730273103724 2 9.393284374428076 47.13785886156637 36.37302731037239 Other FALSE 362 +95-32141_tumorA ST2 1 1.1484765857045414 874 0.181 ST2 5.51389405026123 1 6.332578212780607 5.513894050261235 5.513894050261235 Other FALSE 363 +95-32814T EZB 1 0.08324724746599496 137,1539,1511,2104,1450,699,435,520,1160,477 0.178,0.214,0.216,0.253,0.256,0.272,0.277,0.296,0.3,0.302 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0813077732149 1 3.3366585469575254 40.08130777321487 40.08130777321487 EZB TRUE 364 +FL1007T2 EZB 0.3981088443562669 5.5783073461638395 1725,353,2183,1527 0.191,0.215,0.273,0.348 ST2,BN2,EZB,EZB 4.6468630475492,6.5328672894117,5.23002137296456 7 36.442341592038744 16.409751709925462 6.532867289411701 BN2 FALSE 365 +96-31596T EZB 1 0 366,1020,427,78,48,689,645,326,128,164,1796 0.133,0.166,0.213,0.248,0.267,0.267,0.295,0.299,0.307,0.313,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0853084683072 0 0 46.085308468307154 46.085308468307154 EZB TRUE 366 +97-18502_tumorA EZB 1 0.09621931619662515 44,196,28,438,1788,275,367,433,6,691 0.125,0.17,0.17,0.197,0.229,0.234,0.236,0.249,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6216194512166 1 4.774558292165213 49.62161945121658 49.62161945121658 EZB TRUE 367 +97-18502_tumorB EZB 0.9181933249192705 0 433,780,44,250,368,565,170,5,691,196,28 0.125,0.201,0.22,0.22,0.228,0.25,0.259,0.265,0.272,0.276,0.286 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.00077016667432,44.9044097923722 0 0 48.90517995904655 44.90440979237223 EZB TRUE 368 +FL1004T2 EZB 1 0 2016,558,174,240,382,1138,93,287,1903,676,39 0.144,0.155,0.165,0.177,0.191,0.202,0.206,0.209,0.216,0.218,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6629614869566 0 0 58.662961486956604 58.662961486956604 EZB TRUE 369 +98-22532T EZB 1 2.0552858338868862 2050 0.116 EZB 8.6386081763156 2 17.754809009280883 8.638608176315602 8.638608176315602 Other FALSE 370 +98-28290T EZB 0.8292527246362427 0.3356238514049938 473,1864,1102,809,371,1049,1011,1865 0.113,0.163,0.174,0.246,0.28,0.299,0.304,0.316 EZB,EZB,EZB,EZB,EZB,ST2,EZB,BN2 3.16116314205511,31.6204659743226,3.34964864118785 3 10.612582573522708 38.131277757565556 31.620465974322595 EZB TRUE 371 +99-13280T EZB 1 0 142,1033,1962,1304,517,372,2215,1264,1937,428,276 0.125,0.129,0.129,0.151,0.158,0.163,0.177,0.181,0.182,0.184,0.191 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 69.9251841421404 0 0 69.92518414214044 69.92518414214044 EZB TRUE 372 +99-25549T MCD 0.5238374126071436 0.9446953273052487 150,151,661,811,736 0.135,0.182,0.223,0.272,0.278 MCD,MCD,BN2,BN2,BN2 11.7505091480618,12.9270053379032 2 12.212081538767137 24.677514485964934 12.927005337903175 Other FALSE 373 +99-27137T BN2 0.5624828745451326 7.641675144068769 1108,318 0.237,0.305 BN2,ST2 4.21802356906512,3.28091330519286 7 32.2327658648212 7.498936874257987 4.218023569065124 Other FALSE 374 +FL1010T2 EZB 1 0 375,1046,1163,1560,2215,1081,142,1304,1516,1033,1962 0.147,0.16,0.203,0.218,0.231,0.245,0.257,0.271,0.272,0.277,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3797544955643 0 0 49.37975449556432 49.37975449556432 EZB TRUE 375 +BLGSP-71-30-00639-01A-01E EZB 0.5374394629718922 3.60581781242441 816,2138,1178,781 0.227,0.228,0.257,0.272 EZB,EZB,ST2,MCD 8.7922555808858,3.67946037470823,3.88781137101322 7 31.703271784945944 16.359527326607257 8.792255580885799 Other FALSE 376 +BLGSP-71-30-00659-01A-01E EZB 0.7082982230851945 0.6964270563821087 994,1975,77,1425,282,141,491,383 0.122,0.196,0.215,0.277,0.309,0.353,0.359,0.361 EZB,EZB,EZB,N1,BN2,BN2,EZB,EZB 6.07216178844299,23.5206639432759,3.61446348160845 3 16.380426754168443 33.20728921332735 23.52066394327591 Other FALSE 377 +BLGSP-71-30-00668-01A-01E EZB 1 0.2342560564873601 818,110,1750,109,1573,117,1527,1752,108 0.16,0.192,0.219,0.297,0.329,0.349,0.353,0.356,0.375 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.6195369324175 2 7.875580142719292 33.61953693241754 33.61953693241754 Other FALSE 378 +BLGSP-71-30-00674-01A-01E ST2 0.6031449788561672 6.2452820852022874 1436,1901 0.174,0.265 ST2,BN2 3.77662758142923,5.7397634939415 7 35.846442122010956 9.516391075370738 5.739763493941503 Other FALSE 379 +CABN-0003P EZB 0.7507397975392421 0.2756098132499715 384,1330,944,757,1093,1107,400,216,132,972 0.183,0.206,0.236,0.263,0.268,0.282,0.32,0.338,0.344,0.346 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,BN2,BN2 5.80353098909094,28.1448851086008,3.54111782718271 1 7.757006528723358 37.489533924874394 28.14488510860075 EZB TRUE 380 +CABN0001_2015-08-11 EZB 0.8915345615917477 0.10922451591833257 1102,473,371,1864,1865,381,380,809 0.139,0.194,0.235,0.248,0.265,0.272,0.285,0.32 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.76796467258553,30.9708860422691 1 3.3827800355286803 34.738850714854586 30.970886042269054 EZB TRUE 381 +CABN0002_2015-08-19 EZB 1 0.1001116004854138 1138,382,93,558,676,174,39,2016,2089,287 0.108,0.124,0.15,0.151,0.177,0.189,0.215,0.225,0.249,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.7373427199119 1 5.880289387950644 58.73734271991185 58.73734271991185 EZB TRUE 382 +CABN0003_2016-07-05 EZB 0.605897034975147 1.3959104738877244 383,844,561,141,2042 0.108,0.158,0.195,0.199,0.321 EZB,BN2,EZB,BN2,EZB 11.3726260054018,17.4843657319849 5 24.406609254561303 28.85699173738664 17.48436573198488 EZB TRUE 383 +CABN0003_2013-11-28 EZB 0.6088602169897053 0.3403502305181175 384,1093,940,944,132,972,1330,2140,757 0.148,0.197,0.248,0.262,0.292,0.297,0.302,0.312,0.348 EZB,EZB,ST2,EZB,BN2,BN2,EZB,ST2,EZB 6.79285605525162,21.8256690412116,7.22823978024515 2 7.428371489388507 35.846764876708356 21.825669041211594 EZB TRUE 384 +CAGJ0001_Biopsy EZB 1 0 385,1691,1220,448,2061,47,102,1468,25,396,112 0.178,0.178,0.208,0.217,0.225,0.241,0.249,0.289,0.294,0.323,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.1906657489761 0 0 46.19066574897609 46.19066574897609 EZB TRUE 385 +CAHN0001_2011-05-25 EZB 0.37537867174430495 7.27123436662168 1835,921,70 0.18,0.191,0.249 EZB,MCD,BN2 4.01647840841043,5.55632763200194,5.22912078772325 8 40.40136043002218 14.80192682813562 5.556327632001942 Other FALSE 386 +CAHN0001_2016-07-13 MCD 1 0 888,1572,587,483,516,947,387,515,540,1567,59 0.109,0.11,0.119,0.128,0.133,0.139,0.143,0.143,0.144,0.153,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 82.3396767047713 0 0 82.33967670477135 82.33967670477135 MCD TRUE 387 +CALM0002_Biopsy EZB 0.905736523009142 0.48995435768759993 242,1269,1439,578,388,399,160 0.149,0.165,0.191,0.206,0.217,0.217,0.3 EZB,EZB,EZB,EZB,EZB,EZB,ST2 32.062057376797,3.33682139456661 4 15.708944728191574 35.39887877136366 32.06205737679705 EZB TRUE 388 +CAMP-0004P ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 389 +CAMP-0009P EZB 0.8679866089542307 0 877,390,210,396,1423,211,212,25,216,693,213 0.136,0.139,0.143,0.186,0.198,0.214,0.258,0.285,0.3,0.302,0.308 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,BN2 7.12355468288586,46.8372944889691 0 0 53.96084917185494 46.83729448896908 EZB TRUE 390 +CAMP0003_2016-05-06 ST2 0.6422041587780154 1.8699566131668057 618,701,744,1077,1427,1211 0.23,0.252,0.266,0.308,0.309,0.327 ST2,EZB,BN2,ST2,ST2,ST2 3.76008103311438,3.97573828195485,13.884944326702 5 25.964243467169286 21.620763641771205 13.884944326701978 Other FALSE 391 +CAMP0005_2016-06-20 BN2 0.5105068601590714 6.725876978357978 793,1257 0.152,0.158 BN2,EZB 6.59646268187725,6.32493602336605 6 44.36699649063573 12.921398705243305 6.5964626818772505 Other FALSE 392 +CAMP0005_2015-12-15 BN2 0.6377997402617273 4.204142031723825 2218,190,189 0.203,0.228,0.232 ST2,BN2,BN2 8.6908648485841,4.93545749049011 6 36.53763020196355 13.62632233907421 8.690864848584104 Other FALSE 393 +CAMP0006_2015-08-18 MCD 0.5339596058401412 6.5960236326861565 81,401 0.162,0.186 MCD,EZB 5.37225132499877,6.15518576483428 6 40.59975076842032 11.527437089833047 6.155185764834279 Other FALSE 394 +CAMP0007_2016-05-20 BN2 1 3.750755325719512 1901 0.21 BN2 4.76083385642487 3 17.856722941851356 4.760833856424873 4.760833856424873 Other FALSE 395 +CAMP0009_2016-07-04 EZB 1 0 396,210,390,112,25,1468,877,211,1423,385,1483 0.171,0.216,0.221,0.234,0.245,0.267,0.281,0.281,0.29,0.305,0.306 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2432634561524 0 0 44.24326345615242 44.24326345615242 EZB TRUE 396 +CAMP0010_2017-07-18 EZB 0.9279161387644281 0.18951998975603238 398,397,1972,1671,1752,1493,695,1703 0.119,0.13,0.156,0.192,0.208,0.295,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 39.0098255874462,3.0304234800934 2 7.393141745717408 42.04024906753958 39.00982558744617 EZB TRUE 397 +CAMP0010-Biopsy EZB 0.9279161387644281 0.18951998975603238 398,397,1972,1671,1752,1493,695,1703 0.119,0.13,0.156,0.192,0.208,0.295,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 39.0098255874462,3.0304234800934 2 7.393141745717408 42.04024906753958 39.00982558744617 EZB TRUE 398 +CARM0001_2016-03-24 EZB 0.9033111883562182 0.49276667842506555 399,578,242,1269,1439,2093,388,365 0.196,0.197,0.235,0.238,0.264,0.284,0.309,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.12374674954804,29.1834736665685 3 14.38064338358035 32.307220416116586 29.183473666568545 EZB TRUE 399 +CASA0002_2016-08-24 EZB 1 4.616202296107246 401,380 0.145,0.217 EZB,EZB 11.5027086171145 9 53.0988299297767 11.50270861711453 11.50270861711453 EZB TRUE 400 +CASA0002_2015-03-10 MCD 1 10.730276640130027 751 0.216 MCD 4.62120521458593 9 49.58681036331843 4.621205214585926 4.621205214585926 EZB FALSE 401 +CATC0001_2016-01-11 BN2 0.41989618827576136 1.7660642377801627 737,402,581,1415,793 0.167,0.189,0.233,0.241,0.253 BN2,EZB,ST2,EZB,BN2 9.94035828730155,9.44721538962459,4.28579742015405 4 17.55531128192493 23.673371097080178 9.940358287301546 EZB FALSE 402 +CATC0002_2016-09-21 EZB 0.8863380302117354 0.610131369059192 388,1037,242,1269,1439,160,1944 0.148,0.163,0.219,0.225,0.248,0.264,0.278 EZB,EZB,EZB,EZB,EZB,ST2,EZB 29.5249607080034,3.78621369906819 4 18.01410469819299 33.31117440707163 29.524960708003444 Other FALSE 403 +CATC0003_Biopsy EZB 1 0.37450378908717397 1060,662,319,205,149,354,75,320 0.168,0.193,0.204,0.222,0.239,0.256,0.259,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.3405320160399 3 13.609666937450685 36.34053201603986 36.34053201603986 EZB TRUE 404 +CAVA0006_2016-08-23 Other 1 10 0 0 1 2 Other TRUE 405 +CAVA0006-Diagnostic Other 1 10 0 0 1 2 Other TRUE 406 +DFCIDL008-Tumor N1 0.762677990283213 3.343940309116601 901,870,1387 0.113,0.216,0.238 N1,N1,EZB 4.20264125238294,13.5059617440195 8 45.16312988921355 17.70860299640244 13.505961744019498 Other FALSE 407 +DFCIDL009-Tumor EZB 1 0.08753616143092323 54,408,1242,1372,79,1253,958,1872,468,1066 0.169,0.173,0.176,0.178,0.23,0.258,0.259,0.265,0.291,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5938772111464 1 3.991112995816604 45.59387721114641 45.59387721114641 EZB TRUE 408 +DLBCL-BWH_DLBCL64-Tumor EZB 0.4022471420065838 5.325385894713633 1835,921,1532 0.121,0.126,0.228 EZB,MCD,BN2 4.39259105227054,8.28743617287011,7.92281940175835 8 44.13379569834201 20.602846626899 8.287436172870109 Other FALSE 409 +DLBCL-DFCI_DLBCL_Goe05-Tumor MCD 0.8431399711452777 1.2593654662113636 569,713,2028,2130,850,801 0.227,0.246,0.25,0.254,0.277,0.332 MCD,MCD,MCD,MCD,BN2,MCD 3.612244464164,19.4162127568246 5 24.452107830557384 23.02845722098856 19.416212756824557 Other FALSE 410 +DLBCL-DFCI_DLBCL_Goe07-Tumor N1 0.5068342810404087 5.136473423073945 2077,1697 0.295,0.303 N1,BN2 3.29739204323191,3.38878243415931 3 17.406390909639132 6.686174477391223 3.3887824341593107 Other FALSE 411 +DLBCL-DFCI_DLBCL_Goe08-Tumor BN2 0.7668319393222718 0.11635947376479021 932,157,13,1086,412,590,1076,1806,130,973 0.131,0.135,0.181,0.182,0.187,0.233,0.236,0.252,0.285,0.289 EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 38.9652835575807,11.8480453603805 1 4.53397988985592 50.81332891796122 38.965283557580676 BN2 TRUE 412 +DLBCL-DFCI_DLBCL_Goe16-Tumor EZB 0.9105171808988074 0.09273685001309398 527,2001,1268,1132,351,727,1896,1879,349,2114 0.127,0.162,0.186,0.197,0.211,0.218,0.225,0.229,0.235,0.235 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 46.5963693122914,4.57934740103221 1 4.321200512068698 51.17571671332356 46.59636931229135 EZB TRUE 413 +DLBCL-MC_F064AB-Tumor EZB 0.8901838470958318 0.1579003186383353 2059,319,1088,1541,320,404,149,1551,187 0.129,0.168,0.181,0.184,0.184,0.198,0.206,0.208,0.213 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 44.0629983812847,5.43576361606835 1 6.957561484565307 49.49876199735306 44.0629983812847 Other FALSE 414 +DLBCL-MC_F089_HCZ-Tumor EZB 0.882482988817308 1.27632947006241 1260,1322,1002,1011,809,1521 0.131,0.187,0.207,0.243,0.284,0.295 EZB,EZB,EZB,EZB,EZB,BN2 3.38887704722345,25.4484547834432 5 32.48061280765928 28.837331830666667 25.448454783443214 Other FALSE 415 +DLBCL-MC_F089_MLB-Tumor MCD 1 0 1699,798,1331,905,910,23,2008,305,21,479,2034 0.109,0.125,0.138,0.144,0.147,0.149,0.152,0.154,0.161,0.165,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 76.0233153557741 0 0 76.02331535577407 76.02331535577407 MCD TRUE 416 +DLBCL-MC_F132_MLM-Tumor BN2 1 0 592,145,417,1193,981,1375,669,560,564,1027,892 0.168,0.176,0.196,0.26,0.265,0.268,0.288,0.291,0.295,0.301,0.304 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.9981352280878 0 0 44.998135228087804 44.998135228087804 BN2 TRUE 417 +DLBCL-MC_F139_AD-Tumor BN2 0.8342121789790495 0 1758,863,1806,590,1444,684,960,795,1722,1086,1076 0.218,0.218,0.272,0.277,0.282,0.289,0.29,0.295,0.302,0.308,0.313 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,EZB 33.4186517400801,3.19845380506935,3.44302853366431 0 0 40.06013407881378 33.41865174008012 BN2 TRUE 418 +DLBCL-MC_F139_LJF-Tumor ST2 0.7537837740335606 0.9033279476145906 572,1302,419,1461,718,1482 0.138,0.202,0.207,0.219,0.238,0.279 BN2,ST2,ST2,ST2,ST2,ST2 7.22445651714181,22.1174623136933 4 19.979321838271588 29.341918830835077 22.117462313693263 ST2 TRUE 419 +DLBCL-MC_F210_GWC-Tumor BN2 1 11.87402028268948 2058 0.303 BN2 3.30061046784138 8 39.19151564040578 3.300610467841382 3.300610467841382 Other FALSE 420 +DLBCL-MC_F218_RAP-Tumor EZB 0.6946562786895626 1.5615876083575568 1983,292,2080 0.201,0.23,0.244 EZB,EZB,ST2 9.30906465942968,4.09190060211061 3 14.536920017564643 13.400965261540287 9.309064659429676 Other FALSE 421 +DLBCL-MC_F231_CAO-Tumor Other 1 10 0 0 1 2 Other TRUE 422 +DLBCL-MC_F262_WLC-Tumor BN2 1 0.08705993184488671 1973,99,423,100,1496,687,41,1957,519 0.131,0.139,0.165,0.177,0.192,0.227,0.229,0.229,0.287 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.3853428466669 1 4.212424650522297 48.3853428466669 48.3853428466669 BN2 TRUE 423 +DLBCL-MC_F300_TBF-Tumor EZB 0.9095900635352931 0.09791979475139946 143,343,11,575,139,1058,16,308,218,231 0.17,0.2,0.202,0.208,0.212,0.217,0.236,0.238,0.248,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.19333082923733,42.1879740716269 1 4.131037762071071 46.381304900864265 42.18797407162693 EZB TRUE 424 +DLBCL-MC_F307_JPS-Tumor Other 1 10 0 0 1 2 Other TRUE 425 +DLBCL-MC_F339_JPS2-Tumor MCD 0.8687950019906079 0.16487072482907925 285,1890,1398,1194,952,2030,1063,753 0.137,0.144,0.151,0.152,0.158,0.176,0.218,0.237 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 6.32738999851094,41.8978307972548 1 6.907725732309525 48.225220795765786 41.89783079725484 Other FALSE 426 +DLBCL-MC_F344_CJS-Tumor EZB 1 0 427,161,1020,645,1090,366,1589,78,689,498,48 0.168,0.202,0.227,0.233,0.235,0.261,0.271,0.284,0.316,0.318,0.417 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6438125608739 0 0 43.64381256087387 43.64381256087387 EZB TRUE 427 +DLBCL-MC_F349_BJJ-Tumor EZB 1 0 1,1279,369,1055,1563,1022,1937,1120,1111,429,316 0.128,0.143,0.148,0.161,0.169,0.184,0.187,0.189,0.191,0.193,0.194 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.3577739500436 0 0 65.35777395004361 65.35777395004361 EZB TRUE 428 +DLBCL-MC_F358_WDS-Tumor EZB 0.9279340142726902 0.08469213932747817 2091,1103,1022,29,1563,4,2217,1110,2148,1055 0.112,0.128,0.149,0.161,0.163,0.168,0.174,0.188,0.226,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 57.041436317617,4.42999962529946 1 4.830961272051096 61.4714359429165 57.04143631761704 EZB TRUE 429 +DLBCL-MC_F362_MJB1-Tumor EZB 1 0.22791417451804666 1663,1483,430,1468,336,337,112,170,1384 0.14,0.142,0.18,0.196,0.207,0.207,0.23,0.238,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7287568177402 2 10.650146036369797 46.72875681774018 46.72875681774018 EZB TRUE 430 +DLBCL-MC_F496DMP-Tumor EZB 1 6.513938878063861 2050 0.179 EZB 5.58545535601871 6 36.383314795260276 5.585455356018707 5.585455356018707 Other FALSE 431 +DLBCL-MC_F500_ECU-Tumor Other 1 10 0 0 1 2 Other TRUE 432 +DLBCL-MC_F502_RFK-Tumor EZB 1 0 275,28,691,116,438,44,196,731,780,1788,277 0.15,0.157,0.188,0.21,0.211,0.213,0.242,0.248,0.261,0.273,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.811765565305 0 0 51.811765565305 51.811765565305 EZB TRUE 433 +DLBCL-MC_F519_ALT-Tumor Other 1 10 0 0 1 2 Other TRUE 434 +DLBCL-MC_F606_DMJ-Tumor EZB 1 0.11465500449732624 2006,138,329,435,2104,1094,83,1041,1511,86 0.124,0.128,0.149,0.162,0.165,0.166,0.171,0.179,0.2,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.2741082404743 1 7.14003816037856 62.274108240474284 62.274108240474284 EZB TRUE 435 +DLBCL-MC_F648_JKW-Tumor EZB 0.5613678709448959 1.4615728679687008 141,561,383,844,2042 0.122,0.127,0.18,0.192,0.269 BN2,EZB,EZB,BN2,EZB 13.4055271840332,17.1565914936643 4 25.075608633962297 30.56211867769747 17.156591493664266 Other FALSE 436 +DLBCL-MC_F729_MAN-Tumor ST2 0.35709171600885536 8.053118699175224 581,793,1257 0.161,0.171,0.188 ST2,BN2,EZB 5.85316357110215,5.33054740678578,6.21178890346369 7 50.02427337381261 17.395499881351626 6.211788903463692 Other FALSE 437 +DLBCL-MC_F739_AMA3-Tumor EZB 1 0.10235581641696058 438,1788,196,28,44,1118,1142,6,731,367 0.125,0.147,0.159,0.169,0.197,0.202,0.212,0.215,0.223,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0005331204247 1 5.629624470909148 55.00053312042467 55.00053312042467 EZB TRUE 438 +DLBCL-MC_F815_BAT-Tumor EZB 1 0.09955551051171004 144,2217,4,439,1291,1587,2091,1058,1110,220 0.132,0.161,0.176,0.177,0.181,0.185,0.216,0.221,0.222,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.1928048611474 1 5.395192354013014 54.19280486114744 54.19280486114744 EZB TRUE 439 +DLBCL-MC_F927_JHS-Tumor BN2 0.7178460551354712 4.731338652367879 1875,674,951 0.286,0.39,0.419 BN2,BN2,MCD 6.06613675006079,2.38433352370284 8 28.70094727611188 8.450470273763633 6.066136750060789 Other FALSE 440 +DLBCL-MC_F959_ERM-Tumor BN2 0.5937132057538095 8.940598905675085 101,1631 0.214,0.313 BN2,MCD 4.66845360742395,3.19469237312846 9 41.7387712137295 7.86314598055241 4.668453607423953 Other FALSE 441 +DLBCL-MC_F990_MNA-Tumor MCD 0.915211713742439 0.10157309707868124 767,156,1224,788,1694,837,1310,471,1491,1376 0.109,0.124,0.13,0.139,0.144,0.146,0.152,0.162,0.165,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.99196225965483,64.6777319178296 1 6.569517542918624 70.66969417748442 64.67773191782958 Other FALSE 442 +DLBCL-RICOVER_1013-Tumor EZB 1 0.10353557191833908 333,1705,354,1034,149,332,404,121,1083,589 0.144,0.18,0.204,0.216,0.233,0.244,0.259,0.259,0.26,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.7573531468839 1 4.737513727532034 45.757353146883865 45.757353146883865 EZB TRUE 443 +DLBCL-RICOVER_1018-Tumor ST2 0.6564525696686803 0.9754843641246125 444,1970,1128,545,2052,339 0.17,0.21,0.213,0.244,0.315,0.332 ST2,ST2,EZB,BN2,ST2,ST2 4.09058150261745,4.70102361994547,16.7990538268701 4 16.387214340199556 25.590658949433056 16.79905382687014 ST2 TRUE 444 +DLBCL-RICOVER_102-Tumor ST2 0.8015274827424206 0.13554090971824614 1497,1776,1176,722,2004,293,235,1484,234,619 0.167,0.174,0.18,0.181,0.199,0.208,0.228,0.229,0.242,0.243 ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 9.85082457748925,39.782367531949 1 5.392138286025993 49.6331921094383 39.78236753194905 Other FALSE 445 +DLBCL-RICOVER_1032-Tumor MCD 0.6670995350841592 4.04811546915588 446,140,990 0.174,0.206,0.253 MCD,EZB,MCD 4.84445680087217,9.70781125347518 8 39.298340906838405 14.552268054347351 9.70781125347518 MCD TRUE 446 +DLBCL-RICOVER_1045-Tumor BN2 0.7649960535429724 2.916061726668925 572,1317,915,874 0.281,0.306,0.319,0.327 BN2,BN2,BN2,ST2 9.95888634554382,3.05933289809885 7 29.04072731248608 13.018219243642662 9.958886345543815 Other FALSE 447 +DLBCL-RICOVER_1046-Tumor EZB 1 0 2061,448,118,1220,47,10,1691,102,385,368,5 0.194,0.216,0.224,0.231,0.248,0.249,0.253,0.26,0.265,0.286,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.1128260038872 0 0 45.112826003887236 45.112826003887236 EZB TRUE 448 +DLBCL-RICOVER_105-Tumor ST2 1 6.010811091935245 530,702 0.288,0.309 ST2,ST2 6.71064719262994 8 40.33643257952414 6.7106471926299385 6.7106471926299385 Other FALSE 449 +DLBCL-RICOVER_1060-Tumor EZB 0.5040138106660828 4.423244489655276 1535,1653 0.283,0.288 EZB,ST2 3.53148183551199,3.47523456943123 4 15.620607569246097 7.006716404943219 3.5314818355119875 Other FALSE 450 +DLBCL-RICOVER_1061-Tumor ST2 0.6515969861001726 1.326649559885773 1847,1822,1121 0.141,0.156,0.204 ST2,EZB,ST2 6.42974984215548,12.0251704244188 3 15.953187051106594 18.454920266574263 12.025170424418784 ST2 TRUE 451 +DLBCL-RICOVER_107-Tumor EZB 1 0.36101959455768895 809,1011,1864,1031,1701,473,466,1322 0.107,0.171,0.191,0.194,0.211,0.228,0.264,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2911794953006 3 15.267944474759881 42.291179495300625 42.291179495300625 Other FALSE 452 +DLBCL-RICOVER_1072-Tumor BN2 0.7218127325727216 3.387158285832756 1793,2058,566 0.191,0.212,0.26 BN2,BN2,ST2 9.96271242887066,3.83963820764562 7 33.74528395281824 13.802350636516284 9.962712428870661 Other FALSE 453 +DLBCL-RICOVER_1081-Tumor BN2 0.9300001527696008 0 454,872,936,1213,836,903,294,1735,1549,1477,1212 0.139,0.17,0.184,0.208,0.211,0.212,0.229,0.244,0.251,0.264,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 49.021387600999,3.68977320366337 0 0 52.71116080466237 49.021387600999 BN2 TRUE 454 +DLBCL-RICOVER_1106-Tumor MCD 1 0.18467329116074485 214,1728,896,1171,154,1127,997,883,1732 0.159,0.164,0.26,0.265,0.268,0.281,0.289,0.295,0.296 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.5250254072322 2 6.929869942844138 37.525025407232185 37.525025407232185 MCD TRUE 455 +DLBCL-RICOVER_111-Tumor MCD 0.40368192326826613 3.9324623683503592 921,1835,1532 0.111,0.121,0.199 MCD,EZB,BN2 5.0252879400664,8.24164531177735,8.98114838364247 6 35.31802804324468 22.24808163548623 8.981148383642473 Other FALSE 456 +DLBCL-RICOVER_1138-Tumor ST2 0.6416772102850898 1.008368734332538 444,1128,1970,545,2052,339 0.164,0.185,0.187,0.217,0.298,0.32 ST2,EZB,ST2,BN2,ST2,ST2 4.60647562168546,5.403524598779,17.9257060973744 4 18.07572156942648 27.93570631783886 17.9257060973744 ST2 TRUE 457 +DLBCL-RICOVER_1144-Tumor MCD 0.6155105712146044 4.819649985521322 1631,101 0.232,0.372 MCD,BN2 2.69104269444272,4.30796038073607 5 20.762861186641008 6.999003075178783 4.307960380736065 Other FALSE 458 +DLBCL-RICOVER_1150-Tumor ST2 1 4.313806614404737 1714,35 0.193,0.328 ST2,ST2 8.2288982496927 9 35.49787569878795 8.228898249692703 8.228898249692703 BN2 FALSE 459 +DLBCL-RICOVER_1165-Tumor BN2 0.4062601128013723 5.890172673539721 147,286,1074,834 0.156,0.281,0.308,0.329 MCD,BN2,ST2,BN2 6.5960107318134,6.39764919043091,3.24227002502645 7 38.851642166901996 16.235929947270755 6.596010731813395 Other FALSE 460 +DLBCL-RICOVER_1190-Tumor BN2 0.8615452256955253 0.11488004936521369 692,933,232,892,981,1935,1075,260,961,579 0.141,0.142,0.167,0.196,0.217,0.22,0.235,0.241,0.244,0.245 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9049487488508,7.05575237209887 1 5.043802679645154 50.96070112094964 43.90494874885077 Other FALSE 461 +DLBCL-RICOVER_1192-Tumor MCD 0.8633121954757629 0.3824279769955255 1640,1736,188,792,708,556,1273,49 0.136,0.156,0.164,0.165,0.175,0.184,0.201,0.202 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.42400427974798,40.5736360884452 3 15.516493568656735 46.99764036819316 40.57363608844518 MCD TRUE 462 +DLBCL-RICOVER_1199-Tumor EZB 1 0 348,349,1886,463,2015,1222,1264,413,1516,316 0.13,0.14,0.15,0.176,0.186,0.186,0.196,0.205,0.213,0.216 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2879034372034 0 0 57.28790343720344 57.28790343720344 EZB TRUE 463 +DLBCL-RICOVER_120-Tumor MCD 0.5158682545857977 0.694845259772892 1008,659,869,464,1609 0.114,0.194,0.202,0.213,0.234 N1,MCD,MCD,BN2,MCD 4.69529425792169,14.3635445779472,8.78459686745817 2 9.980440863523253 27.843435703327085 14.363544577947224 BN2 FALSE 464 +DLBCL-RICOVER_1204-Tumor MCD 1 0.07394671614942 765,1526,799,900,69,2053,465,296,824 0.134,0.15,0.152,0.156,0.217,0.229,0.24,0.274,0.297 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.262695127822 1 3.4949211010736323 47.26269512782204 47.26269512782204 MCD TRUE 465 +DLBCL-RICOVER_1210-Tumor EZB 0.8623423488898558 0.2196963740969635 2176,1550,1384,336,337,466,1031,211,646 0.114,0.168,0.171,0.241,0.249,0.265,0.268,0.277,0.291 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.96931100844026,37.3941414426264 2 8.215357287414015 43.36345245106666 37.3941414426264 EZB TRUE 466 +DLBCL-RICOVER_1219-Tumor MCD 1 0.9014054431271853 755,761,467,1897,1911,986 0.142,0.177,0.205,0.209,0.214,0.23 MCD,MCD,MCD,MCD,MCD,MCD 31.4011656964215 5 28.305181679293018 31.40116569642153 31.40116569642153 MCD TRUE 467 +DLBCL-RICOVER_1235-Tumor EZB 1 0 1242,54,1372,958,408,468,1066,1914,1437,1253 0.117,0.123,0.138,0.171,0.182,0.21,0.228,0.232,0.247,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.583610129278 0 0 56.583610129278 56.583610129278 EZB TRUE 468 +DLBCL-RICOVER_1237-Tumor MCD 1 2.19864900225346 311 0.184 MCD 5.42054512392782 2 11.917876128393761 5.420545123927821 5.420545123927821 Other FALSE 469 +DLBCL-RICOVER_1247-Tumor BN2 1 3.6092107382229144 2058,1793 0.223,0.262 BN2,BN2 8.30046064194144 6 29.958111681091722 8.300460641941443 8.300460641941443 Other FALSE 470 +DLBCL-RICOVER_1248-Tumor MCD 0.9033980909390785 0.0989383953576424 1694,767,156,837,1224,1376,788,1310,1968,1491 0.135,0.145,0.162,0.164,0.168,0.174,0.179,0.188,0.193,0.196 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.74620313827279,53.7371258573165 1 5.316665003454571 59.48332899558932 53.73712585731653 MCD TRUE 471 +DLBCL-RICOVER_1257-Tumor ST2 1 1.8609818807379295 833,1664 0.132,0.244 ST2,ST2 11.6490628013303 4 21.678694800853965 11.649062801330327 11.649062801330327 Other FALSE 472 +DLBCL-RICOVER_126-Tumor EZB 0.8276713139708571 0.2590799567311587 1864,473,809,1102,1011,1031,1049,1550 0.115,0.139,0.206,0.216,0.268,0.269,0.292,0.294 EZB,EZB,EZB,EZB,EZB,EZB,ST2,BN2 3.39661302158466,32.7826708186575,3.42903683645077 2 8.493332937229608 39.60832067669294 32.782670818657515 EZB TRUE 473 +DLBCL-RICOVER_1263-Tumor EZB 1 0 728,1017,1295,1243,505,1581,1245,364,1668,474,105 0.173,0.195,0.205,0.221,0.225,0.239,0.267,0.28,0.286,0.288,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.697533389301 0 0 46.69753338930096 46.69753338930096 EZB TRUE 474 +DLBCL-RICOVER_1269-Tumor EZB 1 0.11259499305387771 1034,333,332,354,1083,589,443,192,1705 0.197,0.197,0.205,0.212,0.232,0.237,0.242,0.243,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.4971141232536 1 4.559772283409833 40.49711412325361 40.49711412325361 Other FALSE 475 +DLBCL-RICOVER_1278-Tumor BN2 0.6489736262589824 3.126267069833224 674,1664 0.165,0.305 BN2,ST2 6.05524786611398,3.27525128069965 4 18.930322003510042 9.330499146813631 6.055247866113983 MCD FALSE 476 +DLBCL-RICOVER_1283-Tumor EZB 1 0.08569672351678628 1160,72,1539,699,1450,477,1542,1964,1289,520 0.118,0.167,0.176,0.195,0.198,0.226,0.23,0.276,0.285,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6763808914332 1 4.257103078567719 49.67638089143323 49.67638089143323 EZB TRUE 477 +DLBCL-RICOVER_1287-Tumor ST2 1 12.200463606602614 670 0.377 ST2 2.65580305705212 10 32.402028543868376 2.6558030570521214 2.6558030570521214 Other FALSE 478 +DLBCL-RICOVER_1289-Tumor MCD 1 0 21,2034,1331,23,2008,59,798,709,480,1699,1233 0.101,0.116,0.123,0.126,0.133,0.135,0.147,0.15,0.162,0.167,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 81.0146768307213 0 0 81.01467683072134 81.01467683072134 MCD TRUE 479 +DLBCL-RICOVER_1293-Tumor MCD 1 0 798,1331,23,2008,1699,21,2034,59,905,910,846 0.106,0.121,0.124,0.126,0.134,0.141,0.147,0.159,0.17,0.173,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 78.8069140026974 0 0 78.8069140026974 78.8069140026974 MCD TRUE 480 +DLBCL-RICOVER_1299-Tumor MCD 1 0.2037280018000001 643,296,481,739,185,1968,1422,246,465 0.123,0.132,0.138,0.147,0.148,0.194,0.223,0.23,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.638125010714 2 11.13131603053137 54.638125010713985 54.638125010713985 MCD TRUE 481 +DLBCL-RICOVER_1329-Tumor ST2 1 2.1373376986210473 1946,90 0.19,0.228 ST2,ST2 9.63900068734979 4 20.601799546106896 9.639000687349792 9.639000687349792 Other FALSE 482 +DLBCL-RICOVER_134-Tumor MCD 1 0 387,515,1567,888,516,1572,59,587,483,540,2034 0.109,0.118,0.121,0.131,0.132,0.145,0.151,0.152,0.162,0.163,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4249231355605 0 0 79.42492313556049 79.42492313556049 MCD TRUE 483 +DLBCL-RICOVER_147-Tumor Other 1 10 0 0 1 2 Other TRUE 484 +DLBCL-RICOVER_150-Tumor MCD 0.8668850878475052 0.47570064573743276 1184,986,647,1420,1897,1543,1977,485 0.155,0.167,0.17,0.175,0.181,0.192,0.237,0.253 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.72137210927094,37.2593279246713 3 17.724286353508877 42.9807000339422 37.259327924671254 MCD TRUE 485 +DLBCL-RICOVER_151-Tumor ST2 0.41845134022361785 0.6249868793534881 486,1789,1123,854,1130,1341,1943 0.209,0.215,0.221,0.239,0.248,0.275,0.293 ST2,EZB,EZB,BN2,ST2,BN2,ST2 7.81796821436569,9.16940691559908,12.2232074144035 2 7.639344257618481 29.2105825443683 12.223207414403532 ST2 TRUE 486 +DLBCL-RICOVER_173-Tumor EZB 0.5892146715619988 5.693435507214928 140,446 0.192,0.276 EZB,MCD 5.20149298059983,3.62634724749693 6 29.614364826276283 8.827840228096756 5.201492980599831 Other FALSE 487 +DLBCL-RICOVER_174-Tumor EZB 0.5011891967451105 3.349357816653937 1266,1052,1608,2042 0.165,0.168,0.201,0.204 EZB,ST2,N1,EZB 10.9882906333564,4.97012628132938,5.96601941538283 6 36.80371712449752 21.924436330068612 10.988290633356407 Other FALSE 488 +DLBCL-RICOVER_181-Tumor BN2 0.8966669287022204 0.4127746016880262 582,1362,1320,183,489,2041,1173,922 0.14,0.163,0.221,0.233,0.269,0.27,0.318,0.321 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2 32.0866970037819,3.69771298914845 3 13.24457357522046 35.78440999293036 32.08669700378191 BN2 TRUE 489 +DLBCL-RICOVER_197-Tumor ST2 1 1.1372915927988911 670 0.149 ST2 6.73389335003346 1 7.658400293797412 6.7338933500334575 6.7338933500334575 Other FALSE 490 +DLBCL-RICOVER_208-Tumor EZB 0.6159024496904834 0.6171318848441328 77,282,491,994,1975,1425,1074 0.167,0.172,0.183,0.284,0.337,0.363,0.381 EZB,BN2,EZB,EZB,EZB,N1,ST2 5.82727235031142,17.9655637888601,2.75467637056595,2.62198307822216 3 11.087122243306709 29.16949558795959 17.96556378886006 EZB TRUE 491 +DLBCL-RICOVER_215-Tumor BN2 0.8951366402987163 0 981,933,261,892,232,1115,592,692,417,1525,1075 0.143,0.215,0.237,0.249,0.25,0.279,0.28,0.291,0.296,0.304,0.307 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.7577420292015,4.65753519141807 0 0 44.4152772206196 39.75774202920154 Other FALSE 492 +DLBCL-RICOVER_216-Tumor EZB 1 1.2300887160413843 1967,199,1782,532 0.311,0.325,0.346,0.358 EZB,EZB,EZB,EZB 11.9783853751539 4 14.734476686371963 11.978385375153906 11.978385375153906 Other FALSE 493 +DLBCL-RICOVER_221-Tumor MCD 1 2.570876229205813 864,650,2030 0.233,0.246,0.261 MCD,MCD,MCD 12.1910523052056 6 31.341686580457836 12.191052305205611 12.191052305205611 Other FALSE 494 +DLBCL-RICOVER_224-Tumor ST2 1 11.130974388650513 1028 0.252 ST2 3.96923112077151 8 44.181409947942214 3.9692311207715067 3.9692311207715067 Other FALSE 495 +DLBCL-RICOVER_253-Tumor ST2 0.4309545266766602 6.333714128133038 2218,1257,190 0.142,0.204,0.228 ST2,EZB,BN2 4.39071871129008,4.90206830592203,7.03769526031199 8 44.57474989973295 16.3304822775241 7.0376952603119864 Other FALSE 496 +DLBCL-RICOVER_258-Tumor BN2 1 1.0432032936655753 2069,1765,1957,523,596 0.11,0.194,0.206,0.217,0.218 BN2,BN2,BN2,BN2,BN2 28.2683029090176 5 29.489586701023278 28.268302909017553 28.268302909017553 Other FALSE 497 +DLBCL-RICOVER_267-Tumor EZB 1 0.1022930048543147 144,2217,4,439,1587,1291,2091,1110,1103,220 0.141,0.151,0.167,0.187,0.187,0.191,0.205,0.214,0.219,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2520426668025 1 5.549604463871713 54.25204266680247 54.25204266680247 EZB TRUE 498 +DLBCL-RICOVER_269-Tumor EZB 1 0 2013,2072,499,75,1540,1726,1590,662,205,1853,56 0.107,0.147,0.183,0.199,0.21,0.218,0.226,0.231,0.232,0.234,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.3830878237049 0 0 57.38308782370489 57.38308782370489 EZB TRUE 499 +DLBCL-RICOVER_274-Tumor MCD 1 0.9198016881993809 755,761,467,1897,1911,986 0.138,0.183,0.199,0.205,0.216,0.227 MCD,MCD,MCD,MCD,MCD,MCD 31.6376106903234 5 29.10032772355425 31.63761069032341 31.63761069032341 Other FALSE 500 +DLBCL-RICOVER_288-Tumor EZB 0.6108334623303947 1.7722390209521872 2042,561,844,141,383 0.176,0.191,0.21,0.216,0.258 EZB,EZB,BN2,BN2,EZB 9.41002240174224,14.7699146968866 5 26.17581916195769 24.179937098628876 14.769914696886635 Other FALSE 501 +DLBCL-RICOVER_290-Tumor BN2 1 0.0870025962309671 571,995,907,1252,502,957,1056,418,585,812 0.131,0.133,0.177,0.192,0.204,0.211,0.213,0.217,0.231,0.232 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.5845834822555 1 4.661997880911222 53.584583482255475 53.584583482255475 BN2 TRUE 502 +DLBCL-RICOVER_299-Tumor EZB 1 0 10,602,116,503,277,128,691,275,48,118,28 0.154,0.163,0.178,0.188,0.21,0.238,0.242,0.245,0.271,0.302,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8186320636966 0 0 50.8186320636966 50.8186320636966 EZB TRUE 503 +DLBCL-RICOVER_314-Tumor EZB 0.8312731508877905 0.7628741591303965 628,1260,1322,1521,1002,1036 0.207,0.237,0.241,0.243,0.245,0.331 EZB,EZB,EZB,BN2,EZB,EZB 4.12262758893989,20.3111101986851 4 15.49482111382672 24.433737787625002 20.311110198685107 Other FALSE 504 +DLBCL-RICOVER_320-Tumor EZB 1 0.11155347465956056 1782,505,105,166,1245,1668,341,1243,119,1581 0.135,0.191,0.2,0.23,0.234,0.237,0.243,0.243,0.257,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3496141273409 1 5.170460505034731 46.34961412734088 46.34961412734088 EZB TRUE 505 +DLBCL-RICOVER_325-Tumor MCD 1 2.424574326671671 951 0.18 MCD 5.56090441013083 2 13.482826065878475 5.560904410130827 5.560904410130827 Other FALSE 506 +DLBCL-RICOVER_335-Tumor MCD 0.5483194221224362 4.85661891504173 904,1733 0.195,0.236 MCD,EZB 4.23284919094953,5.13848399950906 5 24.95565858665498 9.37133319045859 5.13848399950906 Other FALSE 507 +DLBCL-RICOVER_336-Tumor MCD 0.569126521594143 3.0155450287183547 2134,68,781,508 0.125,0.177,0.215,0.255 MCD,ST2,MCD,ST2 12.6597748111455,9.58444388329356 7 38.176120996443736 22.244218694439084 12.659774811145525 ST2 FALSE 508 +DLBCL-RICOVER_338-Tumor BN2 0.535046038576323 4.086279664286107 572,838,1317,718 0.235,0.293,0.295,0.309 BN2,ST2,BN2,ST2 7.65176595323619,6.64936965295648 6 31.26725561058585 14.301135606192672 7.6517659532361915 Other FALSE 509 +DLBCL-RICOVER_342-Tumor ST2 0.5150901280077722 2.947294602511029 2017,1082,1983,2216 0.175,0.202,0.229,0.239 ST2,BN2,EZB,ST2 4.95123653573091,4.37616899289008,9.90793296902562 6 29.201597361650297 19.235338497646616 9.907932969025625 Other FALSE 510 +DLBCL-RICOVER_361-Tumor ST2 0.4414085335917521 1.9109843114823903 511,1848,1719 0.135,0.186,0.249 ST2,BN2,EZB 5.38547384788445,4.01892175279726,7.43151430168777 3 14.201507241082346 16.835909902369487 7.431514301687773 ST2 TRUE 511 +DLBCL-RICOVER_384-Tumor EZB 1 0 366,1020,427,78,48,326,164,689,645,1796,128 0.112,0.16,0.219,0.232,0.254,0.275,0.289,0.289,0.292,0.297,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.5078261756113 0 0 48.507826175611285 48.507826175611285 Other FALSE 512 +DLBCL-RICOVER_401-Tumor EZB 1 0 1287,1253,1872,1118,1142,731,1788,513,438,79,135 0.153,0.172,0.2,0.242,0.247,0.25,0.254,0.257,0.276,0.291,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.7158584252054 0 0 47.715858425205425 47.715858425205425 EZB TRUE 513 +DLBCL-RICOVER_408-Tumor EZB 0.8830965250756875 0.5159609432194588 1227,1671,1703,1226,398,397,1493,1752 0.192,0.267,0.312,0.315,0.316,0.322,0.345,0.348 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 24.1772737863547,3.2005644225348 3 12.474528987282692 27.37783820888955 24.177273786354746 Other FALSE 514 +DLBCL-RICOVER_417-Tumor MCD 1 0 846,2008,587,888,1572,59,483,23,387,707,2034 0.142,0.143,0.147,0.147,0.149,0.149,0.15,0.152,0.156,0.156,0.16 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.4214393769503 0 0 73.42143937695033 73.42143937695033 MCD TRUE 515 +DLBCL-RICOVER_431-Tumor MCD 1 0 59,2034,387,21,2008,1567,23,515,1331,888,516 0.115,0.131,0.134,0.146,0.15,0.15,0.15,0.155,0.164,0.164,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.0754006809142 0 0 75.07540068091417 75.07540068091417 MCD TRUE 516 +DLBCL-RICOVER_440-Tumor EZB 1 0 372,1937,428,1962,1033,142,517,429,369,1304,316 0.128,0.142,0.145,0.156,0.159,0.169,0.17,0.176,0.176,0.178,0.184 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 68.6892742217395 0 0 68.68927422173948 68.68927422173948 EZB TRUE 517 +DLBCL-RICOVER_449-Tumor MCD 0.8953958952672124 0.3878128404028103 1184,986,1897,1543,1420,647,1911,485 0.128,0.128,0.15,0.16,0.204,0.21,0.222,0.243 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.90257160867421,41.965298645537 3 16.274681666077903 46.86787025421119 41.96529864553698 MCD TRUE 518 +DLBCL-RICOVER_451-Tumor BN2 1 0.08091877539400466 519,41,1001,1409,687,98,1927,1973,991,1765 0.121,0.151,0.161,0.196,0.205,0.21,0.223,0.257,0.264,0.28 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.5849129083409 1 4.174187981349329 51.5849129083409 51.5849129083409 BN2 TRUE 519 +DLBCL-RICOVER_463-Tumor EZB 1 0.1057163696698689 1668,119,1782,505,520,1295,477,1542,728,105 0.137,0.139,0.21,0.22,0.252,0.288,0.308,0.317,0.324,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7940408837036 1 4.629747015398958 43.79404088370356 43.79404088370356 EZB TRUE 520 +DLBCL-RICOVER_467-Tumor BN2 0.47294246785795074 4.592076949657778 581,793,402,737 0.138,0.162,0.172,0.18 ST2,BN2,EZB,BN2 11.7189877414591,5.8233688616324,7.23652991673553 7 53.8144934808764 24.778886519827026 11.718987741459102 Other FALSE 521 +DLBCL-RICOVER_469-Tumor EZB 1 0 644,2097,240,1109,2016,71,1485,39,174,1889,1085 0.188,0.237,0.243,0.246,0.277,0.278,0.298,0.304,0.304,0.31,0.312 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.289530771369 0 0 41.28953077136899 41.28953077136899 EZB TRUE 522 +DLBCL-RICOVER_473-Tumor BN2 1 1.1935722450704889 523,1096,20,596,2069 0.219,0.236,0.242,0.294,0.317 BN2,BN2,BN2,BN2,BN2 19.4927560877044 6 23.266012646212758 19.492756087704382 19.492756087704382 BN2 TRUE 523 +DLBCL-RICOVER_478-Tumor Other 1 10 0 0 1 2 Other TRUE 524 +DLBCL-RICOVER_496-Tumor Other 1 10 0 0 1 2 Other TRUE 525 +DLBCL-RICOVER_506-Tumor MCD 0.8139039858176129 1.2900253777805009 920,254,2145,1358,815 0.202,0.202,0.22,0.244,0.285 MCD,MCD,MCD,EZB,MCD 4.1009378166449,17.9357395117889 5 23.137559139468102 22.03667732843378 17.935739511788878 Other FALSE 526 +DLBCL-RICOVER_522-Tumor EZB 1 0.08204420475930826 2089,695,676,172,287,382,1972,1138,108,397 0.138,0.153,0.216,0.229,0.265,0.268,0.285,0.29,0.309,0.312 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6759545931143 1 3.5833589616957213 43.67595459311432 43.67595459311432 EZB TRUE 527 +DLBCL-RICOVER_533-Tumor EZB 1 2.4728179931750893 1099 0.279 EZB 3.58324929518157 2 8.860723331156942 3.5832492951815698 3.5832492951815698 Other FALSE 528 +DLBCL-RICOVER_543-Tumor BN2 1 10.585190210204185 1096 0.292 BN2 3.420512288084 8 36.2067731857099 3.4205122880840024 3.4205122880840024 Other FALSE 529 +DLBCL-RICOVER_554-Tumor ST2 1 10.624841424274901 530 0.268 ST2 3.73686968749509 9 39.703647852815074 3.7368696874950933 3.7368696874950933 ST2 TRUE 530 +DLBCL-RICOVER_575-Tumor ST2 0.7309410160707627 0.5525044657280527 1038,666,648,955,1486,508,171 0.183,0.226,0.243,0.248,0.254,0.263,0.291 ST2,BN2,ST2,ST2,ST2,ST2,BN2 7.85611544782246,21.3423723078907 3 11.79175600934034 29.198487755713163 21.342372307890702 BN2 FALSE 531 +DLBCL-RICOVER_577-Tumor EZB 1 1.0447130934437654 532,1182,1967,1037 0.132,0.172,0.254,0.303 EZB,EZB,EZB,EZB 20.6583844560449 5 21.58208473062523 20.658384456044864 20.658384456044864 EZB TRUE 532 +DLBCL-RICOVER_583-Tumor Other 1 10 0 0 1 2 Other TRUE 533 +DLBCL-RICOVER_585-Tumor MCD 1 0 880,771,1239,2180,1240,929,1250,777,518,783,1598 0.117,0.12,0.131,0.137,0.143,0.148,0.148,0.157,0.164,0.164,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 76.8684796188538 0 0 76.86847961885377 76.86847961885377 MCD TRUE 534 +DLBCL-RICOVER_597-Tumor MCD 1 0 535,783,534,1565,1459,518,1811,777,1240,462,586 0.114,0.141,0.143,0.159,0.162,0.166,0.168,0.175,0.182,0.183,0.184 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.3652761625808 0 0 69.36527616258078 69.36527616258078 MCD TRUE 535 +DLBCL-RICOVER_617-Tumor BN2 0.6932689184642865 3.8080460791664628 915,1317,874 0.26,0.306,0.318 BN2,BN2,ST2 7.1096147101113,3.1455900463061 7 27.073740421223537 10.2552047564174 7.109614710111298 Other FALSE 536 +DLBCL-RICOVER_623-Tumor ST2 0.5753261167485851 0 537,1433 0.162,0.22 ST2,BN2 4.55222041505245,6.16711174683953 0 0 10.719332161891987 6.167111746839534 ST2 TRUE 537 +DLBCL-RICOVER_632-Tumor BN2 0.5791697997412985 9.66439393083916 1697,697 0.196,0.269 BN2,N1 5.10829007274041,3.71173140459173 9 49.36852757595832 8.820021477332139 5.108290072740406 Other FALSE 538 +DLBCL-RICOVER_678-Tumor BN2 0.5782067293300225 5.860916139163426 1089,1710 0.153,0.21 BN2,ST2 6.53846936645188,4.76971684927776 7 38.321420635263486 11.308186215729643 6.5384693664518805 Other FALSE 539 +DLBCL-RICOVER_685-Tumor MCD 1 0 888,387,59,1572,587,2008,483,2034,23,515,1567 0.136,0.137,0.137,0.143,0.145,0.146,0.151,0.153,0.153,0.158,0.161 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.9236441656326 0 0 74.92364416563261 74.92364416563261 MCD TRUE 540 +DLBCL-RICOVER_692-Tumor BN2 0.5482418318525007 1.2797201972434735 831,130,600,1145,89,284,412 0.216,0.221,0.247,0.27,0.28,0.285,0.318 ST2,BN2,ST2,BN2,ST2,BN2,BN2 14.8855889776802,12.2659126277008 4 19.049388862602168 27.151501605380993 14.88558897768019 Other FALSE 541 +DLBCL-RICOVER_694-Tumor ST2 1 13.826186214288185 511 0.299 ST2 3.34640639705252 10 46.268037994333405 3.346406397052524 3.346406397052524 Other FALSE 542 +DLBCL-RICOVER_701-Tumor ST2 0.8473533346597938 1.300699732284823 271,2198,1077,1427,1822 0.156,0.183,0.201,0.244,0.265 ST2,ST2,ST2,ST2,EZB 3.77755842347169,20.9695162345471 6 27.275044152417628 24.74707465801876 20.969516234547072 Other FALSE 543 +DLBCL-RICOVER_704-Tumor Other 1 10 0 0 1 2 Other TRUE 544 +DLBCL-RICOVER_711-Tumor ST2 0.4370077283285695 1.5098397428283696 545,1417,1687,649,1128,1394,2082 0.243,0.26,0.297,0.333,0.342,0.356,0.361 BN2,ST2,ST2,EZB,EZB,EZB,ST2 4.11740836175712,8.73297371986033,9.97476619877866 4 15.060298432337081 22.825148280396107 9.974766198778656 BN2 FALSE 545 +DLBCL-RICOVER_712-Tumor BN2 1 3.865978264032031 1791 0.249 BN2 4.01700484605902 3 15.529653421375517 4.017004846059023 4.017004846059023 Other FALSE 546 +DLBCL-RICOVER_720-Tumor MCD 0.9062743224925509 0.11567293724039235 1169,621,556,2094,705,1736,1273,540,188,708 0.124,0.13,0.171,0.172,0.177,0.186,0.198,0.21,0.216,0.231 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.36287012147509,51.8559226799839 1 5.99832688970442 57.21879280145903 51.855922679983934 MCD TRUE 547 +DLBCL-RICOVER_744-Tumor EZB 1 0.16989946820770147 354,1034,149,1705,1060,443,333,319,404,662 0.161,0.2,0.238,0.241,0.246,0.253,0.256,0.259,0.288,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2690950789879 1 7.181496775540811 42.269095078987874 42.269095078987874 EZB TRUE 548 +DLBCL-RICOVER_763-Tumor MCD 0.5595437185947215 2.38428280716079 632,1907,1480,1513,717 0.163,0.197,0.235,0.247,0.278 EZB,MCD,MCD,EZB,MCD 10.1716894151089,12.9218384661511 6 30.809317291753047 23.09352788126002 12.921838466151112 Other FALSE 549 +DLBCL-RICOVER_773-Tumor N1 0.6093039471654457 8.410887598740237 901,1387 0.193,0.301 N1,EZB 3.3212328857163,5.17957704471502 9 43.56484033211315 8.500809930431316 5.1795770447150185 Other FALSE 550 +DLBCL-RICOVER_780-Tumor EZB 1 0.20686417988781078 55,1180,1457,1305,1517,1853,1219,1590,278 0.126,0.135,0.139,0.143,0.18,0.192,0.196,0.2,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.294229492135 2 11.438395436418913 55.29422949213503 55.29422949213503 Other FALSE 551 +DLBCL-RICOVER_787-Tumor BN2 0.5390598526455814 10.4642113222737 1971,1667 0.251,0.294 BN2,ST2 3.977505986143,3.40109207977179 9 41.62146317460899 7.378598065914792 3.9775059861429996 Other FALSE 552 +DLBCL-RICOVER_822-Tumor ST2 0.6159991578695471 7.409426583808948 833,286 0.195,0.312 ST2,BN2 3.20182192340092,5.13623771635684 9 38.056576276336564 8.338059639757768 5.1362377163568445 Other FALSE 553 +DLBCL-RICOVER_826-Tumor MCD 0.6026401845385866 2.905626532119975 254,920,1358,554 0.161,0.228,0.28,0.293 MCD,MCD,EZB,BN2 3.41875607645475,3.56794996582744,10.5961137860622 7 30.788349354144447 17.58281982834435 10.596113786062158 BN2 FALSE 554 +DLBCL-RICOVER_829-Tumor BN2 0.7452547575481607 2.0848937323537315 1003,459,1714,1854 0.189,0.195,0.209,0.277 BN2,BN2,ST2,BN2 14.0197708948844,4.79228062560307 7 29.229732467779748 18.81205152048747 14.019770894884397 BN2 TRUE 555 +DLBCL-RICOVER_850-Tumor MCD 1 0.11232351164830678 708,49,1870,188,800,586,1660,547,1273,1811 0.117,0.118,0.134,0.135,0.138,0.142,0.147,0.148,0.154,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.4803432258508 1 8.141246676602124 72.48034322585079 72.48034322585079 MCD TRUE 556 +DLBCL-RICOVER_865-Tumor BN2 1 0.07798017622003081 841,555,557,935,2210,1758,1029,991,857,795 0.177,0.203,0.215,0.237,0.269,0.309,0.312,0.323,0.325,0.335 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.7413619167765 1 3.021058229274225 38.74136191677653 38.74136191677653 BN2 TRUE 557 +DLBCL-RICOVER_866-Tumor EZB 1 0.08004326970222009 382,558,1138,676,93,174,2016,287,39,240 0.134,0.137,0.156,0.164,0.181,0.181,0.195,0.205,0.228,0.23 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.069338352888 1 4.56801644150747 57.06933835288804 57.06933835288804 EZB TRUE 558 +DLBCL-RICOVER_910-Tumor BN2 1 12.646912079222597 1803 0.297 BN2 3.36193138339813 9 42.5180506222154 3.361931383398134 3.361931383398134 Other FALSE 559 +DLBCL-RICOVER_945-Tumor BN2 0.8680231597555237 0.3201838134275178 88,2041,1477,922,1362,594,564,560,582 0.219,0.224,0.254,0.268,0.288,0.291,0.293,0.296,0.297 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3018133555129,4.45513543807191 2 9.381966340509486 33.75694879358479 29.301813355512884 BN2 TRUE 560 +DLBCL-RICOVER_948-Tumor EZB 0.5818948933952555 1.740841112455501 383,141,561,844,2042 0.158,0.169,0.182,0.208,0.324 EZB,BN2,EZB,BN2,EZB 10.7103631084474,14.9060738574192 6 25.94910619629356 25.616436965866612 14.906073857419234 EZB TRUE 561 +DLBCL-RICOVER_950-Tumor ST2 0.5719573579278916 1.544454930403322 1082,2017,1121,1863,1847 0.129,0.132,0.185,0.194,0.236 BN2,ST2,ST2,EZB,ST2 7.72363832799241,5.14884360129751,17.2004142358576 5 26.56526457154974 30.072896165147505 17.20041423585759 Other FALSE 562 +DLBCL-RICOVER_956-Tumor Other 1 10 0 0 1 2 Other TRUE 563 +DLBCL-RICOVER_977-Tumor BN2 1 0.0837865655664606 417,592,1375,145,564,1193,1027,560,669,1158 0.119,0.141,0.173,0.201,0.224,0.226,0.227,0.241,0.272,0.288 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.8804930704022 1 4.263101768697101 50.880493070402245 50.880493070402245 BN2 TRUE 564 +DLBCL-RICOVER_978-Tumor EZB 0.8635851487114643 0.08821763479013033 433,565,170,1181,780,250,368,5,44,367 0.163,0.169,0.207,0.212,0.252,0.264,0.271,0.275,0.287,0.318 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.9047935465631,37.3807687715284 1 3.2976430076610033 43.28556231809152 37.38076877152842 BN2 FALSE 565 +DLBCL-RICOVER_985-Tumor ST2 0.716440916161354 3.966506225748658 566,1667,1971 0.143,0.143,0.181 ST2,ST2,BN2 5.53593327927179,13.9870994669544 8 55.47991711584045 19.523032746226214 13.987099466954422 ST2 TRUE 566 +DLBCL-RICOVER_99-Tumor BN2 1 2.478039150091049 1803 0.219 BN2 4.56534167416804 2 11.31309540213061 4.565341674168038 4.565341674168038 Other FALSE 567 +DLBCL-c_D_1103-Tumor ST2 0.85163256623885 0.9614046591011175 1077,271,701,1427,1442,618,1284 0.245,0.252,0.255,0.27,0.27,0.277,0.289 ST2,ST2,EZB,ST2,ST2,ST2,ST2 3.92752245686165,22.5440714589864 4 21.673975335778064 26.47159391584806 22.544071458986412 Other FALSE 568 +DLBCL-c_D_1104-Tumor MCD 0.7808590959919052 0.5318713247705068 569,850,801,1994,2130 0.188,0.189,0.207,0.225,0.236 MCD,BN2,MCD,MCD,MCD 5.28872726848395,18.8451846199538 2 10.023213309359594 24.133911888437723 18.845184619953773 MCD TRUE 569 +DLBCL-c_D_1105-Tumor BN2 1 1.6531809889201894 1813,70,1841,1148 0.152,0.21,0.234,0.296 BN2,BN2,BN2,BN2 19.008356731496 6 31.42425397912237 19.008356731496043 19.008356731496043 Other FALSE 570 +DLBCL-c_D_1106-Tumor BN2 1 0 961,667,260,579,892,502,907,1982,1935,692,571 0.137,0.144,0.15,0.212,0.226,0.228,0.229,0.25,0.283,0.288,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2466684073496 0 0 53.24666840734955 53.24666840734955 BN2 TRUE 571 +DLBCL-c_D_1107-Tumor ST2 1 0.5998754024682023 271,1077,1427,1955,2198,1442 0.151,0.186,0.232,0.242,0.27,0.274 ST2,ST2,ST2,ST2,ST2,ST2 27.7928937944654 3 16.67227335071094 27.792893794465417 27.792893794465417 BN2 FALSE 572 +DLBCL-c_D_1108-Tumor EZB 0.5205491445903789 10.238907391875909 31,1052 0.209,0.227 EZB,ST2 4.79144516781217,4.41315197273748 9 49.059163346480084 9.204597140549645 4.7914451678121655 Other FALSE 573 +DLBCL-c_D_1109-Tumor Other 1 10 0 0 1 2 Other TRUE 574 +DLBCL-c_D_1110-Tumor EZB 1 0.1897681257357857 231,122,589,1083,575,332,42,343,16 0.13,0.139,0.144,0.145,0.162,0.179,0.222,0.224,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.4780899625714 2 10.1484369001269 53.47808996257135 53.47808996257135 EZB TRUE 575 +DLBCL-c_D_1111-Tumor Other 1 10 0 0 1 2 Other TRUE 576 +DLBCL-c_D_1112-Tumor ST2 0.687877849763702 3.877995653676498 724,1944 0.119,0.263 ST2,EZB 3.79931703249416,8.37321551483648 6 32.47129337383251 12.172532547330649 8.373215514836485 Other FALSE 577 +DLBCL-c_D_1113-Tumor EZB 0.9046503002872133 0 2093,340,1244,1042,365,399,578,1162,341,1554,166 0.205,0.218,0.226,0.226,0.235,0.244,0.258,0.27,0.281,0.289,0.294 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 4.25837655252652,40.4022418380258 0 0 44.66061839055227 40.40224183802575 EZB TRUE 578 +DLBCL-c_D_1114-Tumor BN2 0.8927546593315688 0.13963072792236517 692,232,933,1935,1075,579,892,981,961,1525 0.107,0.162,0.183,0.186,0.207,0.237,0.241,0.257,0.261,0.265 BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.5411831319263,5.47079720993414 1 6.358948551156609 51.01198034186046 45.54118313192632 BN2 TRUE 579 +DLBCL-c_D_1115-Tumor Other 1 10 0 0 1 2 Other TRUE 580 +DLBCL-c_D_1117-Tumor ST2 1 3.1496217712509096 2218,581 0.153,0.195 ST2,ST2 11.6553185053344 6 36.70984491526491 11.655318505334423 11.655318505334423 ST2 TRUE 581 +DLBCL-c_D_1118-Tumor BN2 0.9140109865669415 0.25029812690352354 582,1362,183,1320,489,1173,2041,931 0.109,0.181,0.194,0.214,0.296,0.298,0.308,0.314 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2 34.4645516419965,3.24238202566828 2 8.62641272056149 37.70693366766481 34.464551641996536 BN2 TRUE 582 +DLBCL-c_D_1120-Tumor EZB 0.5478425248611235 5.702687054527026 901,1387,1488 0.166,0.269,0.28 N1,EZB,EZB 7.28898378039671,6.01590485022656 8 41.566793445125754 13.304888630623264 7.2889837803967055 Other FALSE 583 +DLBCL-c_D_1121-Tumor ST2 0.5999314985736212 0.7534369207272651 545,2082,1045,1128,1417,1394,1970,1687 0.177,0.203,0.222,0.225,0.227,0.233,0.249,0.273 BN2,ST2,ST2,EZB,ST2,EZB,ST2,ST2 5.64019746337557,8.73032481779172,21.5496319672465 3 16.236288352208053 35.92015424841381 21.54963196724652 Other FALSE 584 +DLBCL-c_D_1123-Tumor BN2 1 0.09658815628101129 1252,995,1056,957,585,571,812,1029,418,795 0.126,0.135,0.15,0.153,0.164,0.176,0.187,0.215,0.225,0.228 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.1404806221615 1 5.712269984867451 59.140480622161455 59.140480622161455 BN2 TRUE 585 +DLBCL-c_D_1124-Tumor MCD 0.8894783902687728 0.25481461352802254 188,708,1273,1736,49,800,547,556,1640 0.117,0.134,0.137,0.154,0.161,0.172,0.173,0.18,0.182 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 6.48221240217734,52.1688732808955 2 13.293391283263762 58.651085683072814 52.168873280895475 MCD TRUE 586 +DLBCL-c_D_1125-Tumor MCD 1 0 1689,310,479,305,1449,764,709,1009,759,1699,1904 0.122,0.129,0.137,0.141,0.142,0.149,0.161,0.163,0.175,0.178,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.2861305411564 0 0 73.28613054115642 73.28613054115642 MCD TRUE 587 +DLBCL-c_D_1126-Tumor BN2 0.37778920726701404 8.343483560662408 286,1425,1975 0.218,0.231,0.309 BN2,N1,EZB 4.5930865045963,3.24133290992708,4.32338291610275 7 38.32234174379962 12.157802330626136 4.593086504596304 Other FALSE 588 +DLBCL-c_D_1127-Tumor EZB 1 0.10056855259833969 333,332,1083,589,1705,122,231,1034,354 0.158,0.178,0.19,0.197,0.222,0.226,0.239,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1555722921303 1 4.340093441972554 43.15557229213027 43.15557229213027 EZB TRUE 589 +DLBCL-c_D_1128-Tumor BN2 0.5504898334275351 1.2682561901966236 838,1317,1722,960,1076,915,590 0.176,0.199,0.211,0.224,0.253,0.265,0.27 ST2,BN2,BN2,ST2,EZB,BN2,BN2 17.2422261866763,3.94538469225128,10.133996060333 4 21.867560094022558 31.321606939260633 17.242226186676312 BN2 TRUE 590 +DLBCL-c_D_1129-Tumor BN2 0.5136299255437169 2.805990101614703 610,1666,591,2216 0.159,0.164,0.21,0.229 BN2,EZB,BN2,ST2 11.0514450106791,6.09553916348099,4.36937315058168 6 31.01024530850487 21.51635732474181 11.051445010679144 BN2 TRUE 591 +DLBCL-c_D_1130-Tumor BN2 1 0.35253972025974295 1375,1027,417,592,1158,931,564,145 0.123,0.174,0.19,0.218,0.231,0.245,0.253,0.283 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.5996603694481 3 13.960453189026053 39.599660369448074 39.599660369448074 BN2 TRUE 592 +DLBCL-c_D_1131-Tumor EZB 0.5377826678386013 1.997634170791435 1415,581,737,402 0.131,0.178,0.223,0.242 EZB,ST2,BN2,EZB 4.4771377951192,11.749309612031,5.62124370430304 5 23.470822364201464 21.84769111145328 11.749309612031041 Other FALSE 593 +DLBCL-c_D_1132-Tumor BN2 1 0.255220786113911 1735,903,594,158,454,1210,922 0.136,0.162,0.178,0.185,0.222,0.241,0.244 BN2,BN2,BN2,BN2,BN2,BN2,BN2 37.276993722279 2 9.513863641763365 37.27699372227897 37.27699372227897 BN2 TRUE 594 +DLBCL-c_D_1133-Tumor EZB 1 0.1692854037204382 196,6,367,1788,44,1118,438,1142,28 0.13,0.159,0.166,0.174,0.18,0.2,0.203,0.204,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5694402390219 1 8.560668106779401 50.56944023902193 50.56944023902193 EZB TRUE 595 +DLBCL-c_D_1134-Tumor BN2 1 1.0239621216135375 523,20,1096,596 0.203,0.225,0.242,0.266 BN2,BN2,BN2,BN2 17.2569466318278 4 17.670459685698006 17.256946631827823 17.256946631827823 BN2 TRUE 596 +DLBCL-c_D_1135-Tumor BN2 0.4136850644911677 0.7711721980571692 597,1377,1185,1763,15,827,476 0.118,0.254,0.298,0.317,0.335,0.355,0.393 BN2,MCD,ST2,EZB,EZB,BN2,MCD 11.2755668746574,6.13417430573087,6.48611824400706,3.36054367666019 3 8.695403691070172 27.25640310105554 11.275566874657425 BN2 TRUE 597 +DLBCL-c_D_1136-Tumor MCD 0.7934728010441972 1.2514719779101549 801,850,1994,569,2130 0.185,0.209,0.215,0.219,0.266 MCD,BN2,MCD,MCD,MCD 4.78584170205797,18.3870949680518 5 23.010934107689607 23.17293667010975 18.387094968051773 Other FALSE 598 +DLBCL-c_D_1138-Tumor ST2 0.5888888185233533 7.872279597116689 2216,292 0.148,0.211 ST2,EZB 4.73063861194972,6.77631820434998 9 53.34507154367471 11.506956816299697 6.776318204349976 Other FALSE 599 +DLBCL-c_D_1139-Tumor ST2 0.7838698476190759 0.12651448161538978 1145,1411,718,89,457,600,675,419,130,838 0.204,0.213,0.248,0.267,0.288,0.296,0.31,0.318,0.335,0.341 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 7.89492324083683,28.633636767404 1 3.622569712391486 36.528560008240866 28.633636767404035 ST2 TRUE 600 +DLBCL-c_D_1140-Tumor Other 1 10 0 0 1 2 Other TRUE 601 +DLBCL-c_D_1141-Tumor EZB 1 0 10,602,503,116,277,128,691,48,275,118,1796 0.145,0.147,0.172,0.192,0.222,0.237,0.253,0.255,0.262,0.304,0.338 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3912283354475 0 0 51.39122833544745 51.39122833544745 EZB TRUE 602 +DLBCL-c_D_1142-Tumor Other 1 10 0 0 1 2 Other TRUE 603 +DLBCL-c_D_1144-Tumor ST2 0.5720397888818423 9.188866440192683 1664,951 0.221,0.295 ST2,MCD 3.38420370834401,4.5235494435247 8 41.56629167215638 7.907753151868707 4.523549443524698 Other FALSE 604 +DLBCL-c_D_1145-Tumor EZB 0.5538381977483537 3.359279555828533 292,1983,2080,2216 0.176,0.201,0.217,0.251 EZB,EZB,ST2,ST2 10.6728450202116,8.59784642650044 7 35.85307007892327 19.270691446712057 10.672845020211621 Other FALSE 605 +DLBCL-c_D_1146-Tumor ST2 1 0.9178571770333572 1249,1955,606,1625,271,2198 0.197,0.274,0.28,0.283,0.284,0.291 ST2,ST2,ST2,ST2,ST2,ST2 22.7836571665727 5 20.912143249406245 22.78365716657271 22.78365716657271 ST2 TRUE 606 +DLBCL-c_D_1147-Tumor N1 1 2.3479561645376745 63,2175,849,2189 0.278,0.308,0.335,0.342 N1,N1,N1,N1 12.7454622714714 7 29.92578671018366 12.745462271471416 12.745462271471416 Other FALSE 607 +DLBCL-c_D_1148-Tumor BN2 0.5083423886383214 0.8373667477930336 1987,1497,2004,608,619,722,1776,1363 0.17,0.207,0.216,0.259,0.292,0.299,0.3,0.314 BN2,ST2,ST2,BN2,BN2,ST2,BN2,ST2 16.516763068709,15.974651060559 3 13.830588174912926 32.491414129267966 16.516763068708983 BN2 TRUE 608 +DLBCL-c_D_1149-Tumor Other 1 10 0 0 1 2 Other TRUE 609 +DLBCL-c_D_1150-Tumor BN2 0.6043274649249739 3.127124246677717 1875,951 0.206,0.314 BN2,MCD 4.86431432625502,3.18482229019665 4 15.211315273093842 8.049136616451662 4.864314326255015 BN2 TRUE 610 +DLBCL-c_D_1151-Tumor BN2 0.794590305598639 1.4514156677341001 591,610,1666,1816 0.142,0.19,0.236,0.246 BN2,BN2,EZB,BN2 16.3704402909316,4.23192570269605 4 23.760313525963706 20.60236599362765 16.3704402909316 Other FALSE 611 +DLBCL-c_D_1152-Tumor EZB 1 0.12850259475663803 105,1245,1243,341,166,505,1581,1782,1244,96 0.135,0.159,0.172,0.18,0.191,0.192,0.194,0.207,0.246,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3796605182931 1 6.8594248838291225 53.37966051829305 53.37966051829305 Other FALSE 612 +DLBCL-c_D_1153-Tumor MCD 1 7.294627623878429 311 0.201 MCD 4.98413219518038 6 36.35738839202465 4.984132195180383 4.984132195180383 Other FALSE 613 +DLBCL-c_D_1154-Tumor BN2 0.39658199813027906 5.167262488845525 1532,921,1835 0.145,0.186,0.195 BN2,MCD,EZB 6.90107478118727,5.1260935872919,5.37421347366949 5 35.659664849546814 17.401381842148652 6.901074781187269 Other FALSE 614 +DLBCL-c_D_1155-Tumor BN2 1 1.0677449537034216 915,1317 0.214,0.239 BN2,BN2 8.86878961723221 2 9.469605359256988 8.868789617232206 8.868789617232206 Other FALSE 615 +DLBCL-c_D_1156-Tumor BN2 0.5307825223912699 8.110758795141527 1433,537 0.216,0.245 BN2,ST2 4.61939476462369,4.08359482104031 7 37.466796715402346 8.702989585664007 4.619394764623694 Other FALSE 616 +DLBCL-c_D_1157-Tumor EZB 1 0 2132,1172,1914,1570,468,278,162,1779,1517,958,1080 0.11,0.14,0.154,0.191,0.195,0.196,0.197,0.199,0.215,0.216,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.9285401468248 0 0 61.9285401468248 61.9285401468248 EZB TRUE 617 +DLBCL-c_D_1158-Tumor ST2 0.6366222677061613 1.8508937011578956 618,701,744,1211,1018,1427 0.215,0.257,0.267,0.331,0.349,0.352 ST2,EZB,BN2,ST2,ST2,ST2 3.75143283567068,3.8848700082218,13.3784764484102 5 24.762137789451767 21.014779292302727 13.37847644841024 ST2 TRUE 618 +DLBCL-c_D_1159-Tumor ST2 0.6894332968200055 0.16399452313906193 1139,1484,235,1147,1176,234,619,1394,1363,1687 0.118,0.12,0.131,0.161,0.171,0.173,0.175,0.199,0.22,0.229 BN2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 14.2059581428402,5.03048951502486,42.7033786689651 1 7.003120221243718 61.93982632683014 42.70337866896508 BN2 FALSE 619 +DLBCL-c_D_1162-Tumor BN2 0.5200081640885238 1.7582835996829136 2196,938,628,1521 0.215,0.279,0.314,0.373 BN2,ST2,EZB,BN2 7.33090993564073,3.18765404800849,3.57911840272337 4 12.889818710589616 14.097682386372586 7.330909935640728 Other FALSE 620 +DLBCL-c_D_1163-Tumor MCD 1 0 540,947,1572,587,483,888,516,515,2094,839,387 0.128,0.129,0.14,0.142,0.143,0.148,0.149,0.17,0.177,0.178,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.8514725591579 0 0 72.85147255915787 72.85147255915787 MCD TRUE 621 +DLBCL-c_D_1166-Tumor ST2 0.5031384894369876 3.851256464686211 190,2218,581,1415 0.197,0.205,0.233,0.251 BN2,ST2,ST2,EZB 5.08326885513454,3.98125058110134,9.17903383470381 7 35.3508133954765 18.243553270939685 9.179033834703807 Other FALSE 622 +DLBCL-c_D_pair13-Tumor N1 1 0.8938438126013311 63,2189,745,2175,891,814 0.146,0.177,0.193,0.197,0.241,0.247 N1,N1,N1,N1,N1,N1 31.0139232169994 5 27.721603372007667 31.013923216999384 31.013923216999384 Other FALSE 623 +DLBCL-c_D_pair2-Tumor BN2 1 0.9550231536624303 1096,20,523 0.187,0.219,0.276 BN2,BN2,BN2 13.5354826328818 3 12.92669931039779 13.535482632881756 13.535482632881756 Other FALSE 624 +DLBCL-c_D_pair20-Tumor EZB 0.75938670060942 1.0854748044408802 1492,625,1745,330,1356,207,993 0.166,0.206,0.23,0.241,0.251,0.275,0.324 EZB,EZB,EZB,BN2,EZB,EZB,BN2 7.23690184824563,22.8399969207546 4 24.792241190986456 30.076898769000277 22.839996920754647 EZB TRUE 625 +DLBCL-c_D_pair22-Tumor EZB 1 0.23497182239058853 1663,430,1483,337,336,1468,112,1384,646 0.106,0.153,0.165,0.173,0.173,0.222,0.23,0.24,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4611998645756 2 11.856960092195058 50.46119986457564 50.46119986457564 Other FALSE 626 +DLBCL-c_D_pair23-Tumor BN2 0.6915735845649462 2.7116428006228364 811,150,1232 0.147,0.213,0.266 BN2,MCD,BN2 10.5419497687631,4.70147479811766 6 28.58600219499396 15.243424566880734 10.541949768763072 Other FALSE 627 +DLBCL-c_D_pair5-Tumor EZB 0.8352057437118978 0.947807707575971 1260,1002,628,1322,1521,1011 0.185,0.193,0.214,0.219,0.22,0.315 EZB,EZB,EZB,EZB,BN2,EZB 4.54031509023533,23.0111007934451 5 21.810098691834853 27.551415883680477 23.01110079344515 EZB TRUE 628 +DLBCL-c_D_pair9-Tumor ST2 0.6638409731722162 0.9813224104695397 629,2066,1152,1198,1174,268,1101 0.154,0.17,0.192,0.217,0.26,0.278,0.297 ST2,BN2,BN2,ST2,ST2,ST2,ST2 11.0912387560086,21.9027845212265 4 21.4936933023649 32.99402327723507 21.902784521226486 ST2 TRUE 629 +DLBCL10450T MCD 1 5.25620890109995 1480,864 0.26,0.285 MCD,MCD 7.36636906765172 9 38.7191746621783 7.366369067651718 7.366369067651718 Other FALSE 630 +DLBCL10451T EZB 1 0.0991969104201324 44,196,433,367,28,438,780,6,1788,250 0.144,0.196,0.205,0.216,0.23,0.251,0.261,0.262,0.267,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8210993634875 1 4.446114578491726 44.821099363487534 44.821099363487534 Other FALSE 631 +DLBCL10452T EZB 0.34777132395559285 1.8484239677123533 554,1366,1358,176,920,934 0.169,0.202,0.247,0.267,0.268,0.286 BN2,EZB,EZB,N1,MCD,MCD 5.90013376200425,8.99628483533895,7.22986972502947,3.74210339266043 4 16.628948510007696 25.8683917150331 8.99628483533895 EZB TRUE 632 +DLBCL10453T EZB 0.5978246508248192 5.786299509078485 140,446 0.19,0.283 EZB,MCD 5.25769508145505,3.53701600013217 6 30.422598468707704 8.79471108158722 5.257695081455047 Other FALSE 633 +DLBCL10454T BN2 1 0 1108 0.197 BN2 5.0778386066419 0 0 5.077838606641898 5.077838606641898 Other FALSE 634 +DLBCL10455T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 635 +DLBCL10456T BN2 0.45985899052629775 1.1980940323749418 636,1190,476,2121,826,15 0.179,0.271,0.307,0.321,0.322,0.358 ST2,BN2,MCD,BN2,BN2,EZB 9.90704772016862,2.79382656916738,3.25634159318704,5.5864484238554 3 11.869574751987793 21.543664306378428 9.907047720168617 ST2 FALSE 636 +DLBCL10457T Other 1 10 0 0 1 2 Other TRUE 637 +DLBCL10458T BN2 1 0.0936961554809364 418,907,571,995,638,1996,502,1252,260,812 0.207,0.22,0.238,0.239,0.261,0.269,0.275,0.285,0.287,0.288 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.4110179780211 1 3.6926608681306483 39.411017978021135 39.411017978021135 BN2 TRUE 638 +DLBCL10459T EZB 1 0.2583623432164357 42,639,333,155,1083,589,121,1705,231 0.17,0.206,0.21,0.226,0.227,0.232,0.235,0.235,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.3700513866035 2 10.688463415227243 41.37005138660353 41.37005138660353 EZB TRUE 639 +DLBCL10460T ST2 0.42926163131726647 3.1172309763568524 1183,1070,133,2140,24 0.196,0.249,0.264,0.348,0.356 ST2,EZB,BN2,ST2,EZB 3.79244203175053,6.82243164962181,7.983619540437 6 24.886786134898063 18.59849322180933 7.9836195404369965 Other FALSE 640 +DLBCL10461T MCD 1 1.1210738683253358 743,641 0.186,0.218 MCD,MCD 9.95823082351835 2 11.163912350998316 9.958230823518354 9.958230823518354 MCD TRUE 641 +DLBCL10462T MCD 1 1.059359718878089 467,2028,755,713 0.16,0.164,0.185,0.192 MCD,MCD,MCD,MCD 22.9209896433282 4 24.281573144963723 22.920989643328173 22.920989643328173 Other FALSE 642 +DLBCL10463T MCD 1 0.2664694838298949 2036,69,824,1225,1399,900,799,1412,765 0.18,0.217,0.222,0.239,0.267,0.295,0.301,0.308,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.6955266455967 2 9.51176856028841 35.69552664559669 35.69552664559669 MCD TRUE 643 +DLBCL10464T EZB 1 0 375,1560,1046,1304,276,1163,1085,1889,1081,142,1485 0.153,0.216,0.22,0.239,0.239,0.24,0.243,0.248,0.249,0.25,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4120557718347 0 0 48.41205577183473 48.41205577183473 EZB TRUE 644 +DLBCL10465T EZB 1 0 427,1020,161,366,645,1090,78,689,1589,498,48 0.166,0.205,0.221,0.23,0.253,0.271,0.278,0.279,0.31,0.324,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5198903615842 0 0 43.51989036158417 43.51989036158417 EZB TRUE 645 +DLBCL10466T EZB 1 0.21887752136900937 336,337,1384,430,1663,646,2176,1483,112 0.128,0.128,0.156,0.182,0.195,0.203,0.236,0.255,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4706301219046 2 10.828008901645523 49.470630121904556 49.470630121904556 EZB TRUE 646 +DLBCL10467T MCD 0.85693169902323 0.2569593213536922 1897,986,1420,1184,647,1543,467 0.134,0.141,0.172,0.179,0.194,0.209,0.21 MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.81607920437343,34.836386538669 1 8.951534243391286 40.652465743042434 34.836386538669004 MCD TRUE 647 +DLBCL10469T BN2 0.5706418431222059 0.9661140091527787 648,489,666,1038,922,2041,594 0.212,0.212,0.22,0.24,0.256,0.283,0.3 ST2,BN2,BN2,ST2,BN2,ST2,BN2 16.5003701209983,12.4150876567179 4 15.94123873010243 28.915457777716284 16.500370120998344 ST2 FALSE 648 +DLBCL10470T ST2 0.7078107488221468 0.14391635692901597 235,234,1139,1176,1484,1147,649,1687,619 0.119,0.127,0.154,0.16,0.162,0.172,0.196,0.212,0.219 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2 11.0666056842435,5.10032083641576,39.1633994769203 1 5.636253777674098 55.33032599757958 39.1633994769203 EZB FALSE 649 +DLBCL10471T MCD 0.8063794610160391 1.6790462044820775 1890,753,952,1412,1194 0.208,0.254,0.256,0.262,0.273 MCD,MCD,BN2,MCD,MCD 3.90106516277922,16.246927313899 6 27.279341640898245 20.14799247667818 16.24692731389896 MCD TRUE 650 +DLBCL10472T EZB 1 0.19941832420455258 1814,1241,651,331,1437,2003,1066,408,1372 0.152,0.162,0.188,0.21,0.255,0.291,0.298,0.305,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.8658693619692 2 7.949984861121508 39.865869361969175 39.865869361969175 EZB TRUE 651 +DLBCL10473T BN2 1 0.8446523366203109 2064,57,1113,106,817,1407 0.191,0.197,0.224,0.289,0.314,0.317 BN2,BN2,BN2,BN2,BN2,BN2 24.589738149636 5 20.769779784971607 24.589738149635952 24.589738149635952 Other FALSE 652 +DLBCL10474T EZB 1 10.371427976123565 199 0.267 EZB 3.75005877439714 7 38.893464484890174 3.750058774397143 3.750058774397143 Other FALSE 653 +DLBCL10475T EZB 1 0.20213674813683324 654,1539,25,1160,72,2073,1450,699,47,1423 0.177,0.253,0.261,0.267,0.304,0.305,0.353,0.355,0.362,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8756082401281 1 7.04964203895363 34.87560824012805 34.87560824012805 EZB TRUE 654 +DLBCL10476T EZB 0.44803323197721084 2.444690237990901 1123,854,649,234,1341 0.134,0.144,0.315,0.324,0.325 EZB,BN2,EZB,ST2,BN2 10.0166338539885,10.6342715981725,3.08454727584883 6 25.997499964196322 23.735452728009825 10.634271598172548 Other FALSE 655 +DLBCL10477T Other 1 10 0 0 1 2 Other TRUE 656 +DLBCL10478T EZB 0.5689283954234057 1.5519994162833228 2214,940,1964,826,1190,1289 0.158,0.277,0.368,0.387,0.4,0.413 EZB,ST2,EZB,BN2,BN2,EZB 5.08442960876949,11.4836347878973,3.61661065255621 4 17.822594487627455 20.184675049222996 11.48363478789729 Other FALSE 657 +DLBCL10479T MCD 0.8662291048837701 1.2003024477560196 713,2028,2130,569,850,755 0.165,0.188,0.189,0.195,0.254,0.27 MCD,MCD,MCD,MCD,BN2,MCD 3.93760202227201,25.4978145446183 5 30.60508921033435 29.435416566890286 25.497814544618272 Other FALSE 658 +DLBCL10480T MCD 0.5681377143853601 1.2274583468857083 659,869,1008,464,1609,1275 0.134,0.179,0.187,0.23,0.26,0.316 MCD,MCD,N1,BN2,MCD,BN2 7.5028986093508,16.8901900788607,5.3359534985642 5 20.732004792783734 29.729042186775693 16.89019007886069 MCD TRUE 659 +DLBCL10481T EZB 1 0.19966609652800618 1034,354,443,333,660,192,1705,332,149 0.129,0.146,0.19,0.231,0.246,0.257,0.261,0.275,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.9968872005664 2 8.585020630192076 42.99688720056636 42.99688720056636 EZB TRUE 660 +DLBCL10482T BN2 0.8295378804948987 1.5937878480812109 661,1135,151,281,1199 0.125,0.223,0.242,0.257,0.268 BN2,BN2,MCD,BN2,BN2 20.1101831095028,4.1324507500928 6 32.051365462613575 24.242633859595593 20.110183109502795 BN2 TRUE 661 +DLBCL10483T EZB 1 0.0796778662117477 205,2013,662,1060,499,75,1540,45,2072,259 0.147,0.185,0.193,0.198,0.217,0.218,0.228,0.23,0.234,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.3161831204414 1 3.8497303745328337 48.316183120441416 48.316183120441416 EZB TRUE 662 +DLBCL10484T EZB 1 13.206066320578888 1535 0.293 EZB 3.40924920461968 9 45.02277109958827 3.4092492046196763 3.4092492046196763 Other FALSE 663 +DLBCL10485T BN2 1 3.0745616885656486 1381,987,754 0.247,0.289,0.325 BN2,BN2,BN2 10.5863911560057 8 32.54851266842543 10.586391156005732 10.586391156005732 Other FALSE 664 +DLBCL10486T BN2 1 5.660725821361762 2032,523 0.339,0.353 BN2,BN2 5.78899620518279 9 32.76992029844346 5.788996205182788 5.788996205182788 Other FALSE 665 +DLBCL10487T ST2 0.7051907348315548 0.6140289547328851 911,2138,955,816,1486,508,1178 0.171,0.19,0.193,0.2,0.2,0.215,0.257 ST2,EZB,ST2,EZB,ST2,ST2,ST2 10.2669280289158,24.5587346687935 3 15.07977417824156 34.82566269770936 24.558734668793534 BN2 FALSE 666 +DLBCL10488T BN2 1 0.07667039845133464 1982,889,579,1935,111,1014,943,1929,961,266 0.138,0.139,0.179,0.187,0.198,0.205,0.214,0.251,0.256,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.7653470064053 1 3.9688697809526956 51.765347006405285 51.765347006405285 BN2 TRUE 667 +DLBCL10489T EZB 1 0 299,12,1110,2217,1237,1111,700,1055,1022,1563,1047 0.135,0.139,0.159,0.183,0.186,0.199,0.21,0.211,0.212,0.215,0.216 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.2639141530857 0 0 60.26391415308574 60.26391415308574 EZB TRUE 668 +DLBCL10490T BN2 0.9145902476931468 0 145,892,981,592,417,260,933,1193,907,669,418 0.225,0.241,0.248,0.249,0.277,0.284,0.295,0.31,0.316,0.324,0.332 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 36.2403989913838,3.38433906239739 0 0 39.62473805378122 36.24039899138383 BN2 TRUE 669 +DLBCL10491T EZB 1 9.83157154819241 2162 0.189 EZB 5.29489005853032 8 52.05709045025351 5.294890058530317 5.294890058530317 ST2 FALSE 670 +DLBCL10492T BN2 0.6795918699524844 2.7511341734370545 811,150,1232 0.15,0.206,0.278 BN2,MCD,BN2 10.2741661994739,4.84397551724827 6 28.26560973494442 15.118141716722125 10.274166199473852 Other FALSE 671 +DLBCL10493T Other 1 10 0 0 1 2 Other TRUE 672 +DLBCL10494T EZB 1 0.5534914557168562 1601,1395 0.192,0.206 EZB,EZB 10.0563312709461 1 5.566093434326888 10.056331270946078 10.056331270946078 Other FALSE 673 +DLBCL10495T BN2 0.7212407645165446 2.208724186079343 286,147,834 0.136,0.236,0.277 BN2,MCD,BN2 10.9520436855176,4.23296002521813 6 24.19004377520033 15.18500371073576 10.952043685517626 BN2 TRUE 674 +DLBCL10497T ST2 0.8077094139883653 0.3910721227380466 89,1411,675,600,457,1145,718,608 0.176,0.184,0.202,0.219,0.241,0.276,0.319,0.33 ST2,ST2,ST2,ST2,ST2,BN2,ST2,BN2 6.64887465307102,27.9283492816916 3 10.921998838160722 34.57722393476259 27.92834928169157 ST2 TRUE 675 +DLBCL10498T EZB 1 0.10172569227242338 382,1138,676,558,93,174,2089,287,2016,39 0.116,0.137,0.149,0.166,0.178,0.21,0.22,0.233,0.238,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1910177359534 1 5.71607017868188 56.1910177359534 56.1910177359534 EZB TRUE 676 +DLBCL10499T EZB 0.4073994433095862 4.064315455890879 65,2127,1108 0.154,0.206,0.218 EZB,ST2,BN2 4.58332316365941,6.49014530266461,4.85719960219539 5 26.377997864597365 15.930668068519413 6.49014530266461 Other FALSE 677 +DLBCL10500T EZB 0.4156574478292495 2.951075105419559 1441,879,1416 0.153,0.205,0.231 EZB,MCD,ST2 6.5554148563567,4.88587169213183,4.32990891689295 4 19.345521588291806 15.771195465381492 6.5554148563567045 Other FALSE 678 +DLBCL10501T EZB 1 0.4626887812622263 2100,1867,729,548,712,135,513,679 0.181,0.213,0.23,0.268,0.285,0.294,0.298,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.7153738116759 3 14.674347656200252 31.715373811675903 31.715373811675903 EZB TRUE 679 +DLBCL10502T ST2 0.5086304248416844 11.082150078082442 724,1471 0.325,0.337 ST2,EZB 2.9699053071109,3.07423225707186 9 34.0691032477525 6.044137564182763 3.0742322570718623 Other FALSE 680 +DLBCL10503T Other 1 10 0 0 1 2 Other TRUE 681 +DLBCL10504T MCD 1 0 880,518,771,783,1239,1240,1598,2180,1459,777,929 0.129,0.139,0.142,0.153,0.154,0.154,0.16,0.161,0.162,0.166,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.1809263997427 0 0 72.18092639974275 72.18092639974275 Other FALSE 682 +DLBCL10505T ST2 0.5647124752436234 3.8258180269698885 1500,1226,2080,1099 0.229,0.344,0.369,0.393 ST2,EZB,ST2,EZB 5.45557928567033,7.07769808937903 7 27.077984939796632 12.533277375049364 7.07769808937903 Other FALSE 683 +DLBCL10506T ST2 0.7085226183438205 2.2787679432600894 1479,1710,951 0.298,0.412,0.42 ST2,ST2,MCD 2.37926345213515,5.78350869373817 5 13.179274210856565 8.162772145873312 5.783508693738165 BN2 FALSE 684 +DLBCL10507T MCD 1 0 751 0.141 MCD 7.0817535719006 0 0 7.081753571900604 7.081753571900604 Other FALSE 685 +DLBCL10508T MCD 0.7816031222898273 0.6237716250638063 715,236,686,1325,291,1271,1503 0.128,0.154,0.174,0.221,0.225,0.273,0.289 MCD,MCD,MCD,MCD,BN2,MCD,BN2 7.90059509774956,28.2748080517139 4 17.637022966784752 36.17540314946342 28.274808051713862 MCD TRUE 686 +DLBCL10509T BN2 1 1.3773615890738373 1957,99,1973,2069 0.202,0.212,0.244,0.254 BN2,BN2,BN2,BN2 17.7150641333361 5 24.400048885236743 17.715064133336092 17.715064133336092 BN2 TRUE 687 +DLBCL10510T ST2 1 0.9929827663588061 90,1946 0.114,0.155 ST2,ST2 15.2231786864012 2 15.116354084797042 15.223178686401162 15.223178686401162 Other FALSE 688 +DLBCL10512T EZB 1 0.09804396336271859 689,128,712,366,48,503,1020,277,602 0.158,0.207,0.256,0.275,0.3,0.309,0.31,0.315,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8131510779915 1 3.413219308831384 34.81315107799148 34.81315107799148 EZB TRUE 689 +DLBCL10513T EZB 0.8748495412399255 0.8183543621117276 62,179,1053,2126,1453,993 0.151,0.155,0.163,0.2,0.202,0.24 EZB,EZB,EZB,EZB,EZB,BN2 4.16932019466826,29.1451417415941 4 23.851053878598087 33.31446193626232 29.145141741594053 Other FALSE 690 +DLBCL10514T EZB 1 0 731,438,28,275,277,1788,196,116,1287,44,1118 0.121,0.191,0.199,0.201,0.235,0.243,0.271,0.28,0.282,0.286,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6241893717564 0 0 49.62418937175642 49.62418937175642 EZB TRUE 691 +DLBCL10515T BN2 0.8565565948320637 0.10103896286621017 933,892,692,232,981,260,961,1935,579,1075 0.139,0.164,0.172,0.193,0.203,0.217,0.229,0.24,0.248,0.264 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.9766791462408,7.19709735088231 1 4.342319088370054 50.17377649712312 42.97667914624081 BN2 TRUE 692 +DLBCL10516T EZB 0.5013907400769715 1.7717922691225159 1797,1388,1415,737,402,938 0.306,0.313,0.393,0.4,0.422,0.437 ST2,EZB,EZB,BN2,EZB,ST2 2.5010514150899,8.10687123639743,5.56084671021428 3 14.363691783420661 16.16876936170161 8.106871236397431 EZB TRUE 693 +DLBCL10517T EZB 1 0.2166479328044984 178,259,1551,320,75,56,662,1218,319 0.121,0.123,0.124,0.149,0.174,0.207,0.223,0.232,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8374097232914 2 11.880411456904373 54.83740972329135 54.83740972329135 Other FALSE 694 +DLBCL10518T EZB 1 0 695,2089,676,287,382,1903,2201,172,1138,558,173 0.137,0.159,0.215,0.219,0.271,0.271,0.289,0.293,0.299,0.308,0.309 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.8856871249275 0 0 46.88568712492748 46.88568712492748 EZB TRUE 695 +DLBCL10519T EZB 0.6531622547726014 0.23549103554838574 696,1392,1122,1227,1703 0.158,0.188,0.205,0.229,0.275 EZB,EZB,BN2,EZB,ST2 4.87145459312715,16.0259725147615,3.63854752576643 1 3.7739728631711587 24.53597463365511 16.025972514761524 EZB TRUE 696 +DLBCL10520T N1 1 1.7071822383991986 3,697,2077 0.187,0.267,0.295 N1,N1,N1 12.4940198924381 5 21.329568846576652 12.494019892438137 12.494019892438137 N1 TRUE 697 +DLBCL10521T EZB 0.5217165541727116 7.34836725568776 140,768 0.219,0.239 EZB,MCD 4.56733489780582,4.18710247106062 7 33.56245420879628 8.75443736886644 4.567334897805819 Other FALSE 698 +DLBCL10522T EZB 1 0.10733965769934511 520,477,1450,699,1542,1539,1160,119,72,1668 0.137,0.155,0.157,0.162,0.191,0.261,0.264,0.266,0.275,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0258645875386 1 5.369759180940183 50.02586458753859 50.02586458753859 EZB TRUE 699 +DLBCL10523T EZB 1 0 2097,1109,1217,1589,248,1090,522,644,161,1510,71 0.196,0.229,0.232,0.236,0.256,0.269,0.298,0.308,0.329,0.334,0.348 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.137930472908 0 0 41.13793047290798 41.13793047290798 EZB TRUE 700 +DLBCL10524T ST2 0.7957386761694797 1.3225384155278141 618,701,1211,1018,1130 0.235,0.286,0.3,0.324,0.337 ST2,EZB,ST2,ST2,ST2 3.50216560853512,13.6433494740996 5 18.04385379596789 17.14551508263469 13.643349474099576 EZB FALSE 701 +DLBCL10527T ST2 1 3.356090777425447 530,702 0.212,0.238 ST2,ST2 8.91352468637643 6 29.91459799430199 8.91352468637643 8.91352468637643 ST2 TRUE 702 +DLBCL10528T BN2 0.6141911922793254 2.987561750857352 2218,190,189 0.148,0.166,0.211 ST2,BN2,BN2 10.7678254470173,6.7638903808454 5 32.169543445417425 17.53171582786272 10.767825447017323 Other FALSE 703 +DLBCL10529T MCD 0.8867288696991388 0.5626796721217948 465,347,1526,704,2053,246,2185 0.177,0.211,0.216,0.218,0.232,0.269,0.283 MCD,MCD,MCD,MCD,MCD,MCD,ST2 27.6435159735184,3.53119471668125 4 15.554444504272917 31.17471069019963 27.643515973518376 MCD TRUE 704 +DLBCL10530T MCD 1 0 1567,515,516,387,540,888,2094,705,1572,59,587 0.124,0.129,0.141,0.149,0.166,0.169,0.178,0.179,0.18,0.185,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.0570861337582 0 0 69.05708613375823 69.05708613375823 MCD TRUE 705 +DLBCL10531T EZB 1 3.0093872112614894 1601,1649 0.209,0.305 EZB,EZB 8.07583915535868 6 24.303327074341208 8.075839155358683 8.075839155358683 Other FALSE 706 +DLBCL10532T MCD 1 0 709,21,480,1331,2034,1233,23,1689,2008,59,310 0.115,0.136,0.138,0.145,0.149,0.15,0.159,0.161,0.167,0.167,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.9450590046452 0 0 73.94505900464523 73.94505900464523 MCD TRUE 707 +DLBCL10533T MCD 0.8663709664744967 0.2455953248838852 1736,188,1640,708,556,1273,792,49,800 0.138,0.144,0.154,0.159,0.17,0.178,0.187,0.188,0.205 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.22215298200944,46.8241331540875 2 11.499788194384422 54.04628613609695 46.82413315408751 MCD TRUE 708 +DLBCL10534T MCD 1 0 305,1699,479,1331,764,798,905,910,21,709,1689 0.128,0.132,0.137,0.144,0.152,0.153,0.159,0.159,0.161,0.162,0.162 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.8304848138622 0 0 73.8304848138622 73.8304848138622 MCD TRUE 709 +DLBCL10535T ST2 0.4853869533071614 2.542993210210247 1388,1797,938,1841 0.174,0.19,0.372,0.372 EZB,ST2,ST2,BN2 2.68631464659672,5.75838750263162,7.96510751941148 5 20.255214340457982 16.40980966863982 7.965107519411482 Other FALSE 710 +DLBCL10536T N1 1 1.2267208728454966 814,745,891,2189,63 0.304,0.338,0.355,0.356,0.385 N1,N1,N1,N1,N1 14.4653682294374 5 17.744969140446997 14.465368229437429 14.465368229437429 Other FALSE 711 +DLBCL10537T EZB 1 0.23746204499157522 689,712,2100,128,498,161,366,427,1020 0.153,0.259,0.278,0.3,0.313,0.321,0.348,0.365,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9594019573011 2 7.589144945488478 31.95940195730113 31.95940195730113 EZB TRUE 712 +DLBCL10538T MCD 0.8635413469203368 1.1616013713478066 713,2130,569,2028,850,755 0.168,0.171,0.187,0.197,0.243,0.265 MCD,MCD,MCD,MCD,BN2,MCD 4.11323170934655,26.029464385897 5 30.235861526106813 30.142696095243522 26.02946438589697 MCD TRUE 713 +DLBCL10539T ST2 1 3.3916175127990877 1667,566 0.185,0.213 ST2,ST2 10.0843742217619 6 34.202340216147434 10.08437422176193 10.08437422176193 Other FALSE 714 +DLBCL10540T MCD 0.6730835004856938 0.4334309965742236 2014,236,790,1503,749,2084,291,1271,758 0.225,0.237,0.237,0.253,0.258,0.26,0.267,0.282,0.288 MCD,MCD,MCD,BN2,MCD,BN2,BN2,MCD,MCD 11.5437015791744,23.7671548515187 2 10.301421613027655 35.310856430693164 23.767154851518725 MCD TRUE 715 +DLBCL10541T ST2 0.5265024179274038 5.652353567205613 833,286 0.231,0.257 ST2,BN2 3.88587602709348,4.32087343524633 5 24.42310437515858 8.206749462339813 4.320873435246333 Other FALSE 716 +DLBCL10542T MCD 0.5163645662482238 2.5331152900741443 1278,952,1398,2030,1890 0.187,0.302,0.314,0.319,0.343 EZB,BN2,MCD,MCD,MCD 3.30928902234086,5.33474040335377,9.22899811209149 6 23.378116229804363 17.87302753778612 9.228998112091489 MCD TRUE 717 +DLBCL10543T ST2 0.9000880686348081 0.19714911984250558 457,1411,419,1302,718,608,1482,675,1461 0.15,0.172,0.202,0.24,0.243,0.249,0.255,0.304,0.309 ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.01158733896524,36.1396466943589 2 7.124899537211965 40.151234033324094 36.13964669435886 ST2 TRUE 718 +DLBCL10544T ST2 0.8685739828371077 0.6368735552370425 444,339,2052,1970,959,1128,1404 0.186,0.198,0.208,0.209,0.23,0.234,0.257 ST2,ST2,ST2,ST2,ST2,EZB,ST2 4.27465914605044,28.2505534285065 3 17.992030399426955 32.52521257455694 28.2505534285065 Other FALSE 719 +DLBCL10545T BN2 1 1.1229929823468272 2032 0.211 BN2 4.74210912692761 1 5.325355271062547 4.742109126927611 4.742109126927611 Other FALSE 720 +DLBCL10546T BN2 1 1.019155187692765 1135,661,1126,281,830,1158 0.216,0.226,0.239,0.254,0.278,0.284 BN2,BN2,BN2,BN2,BN2,BN2 24.2820065738701 5 24.747132967349533 24.2820065738701 24.2820065738701 Other FALSE 721 +DLBCL10547T ST2 0.6381022052041397 0.33366234589366167 1341,1943,486,1555,293,1789,1130,722,1776 0.142,0.193,0.215,0.221,0.225,0.232,0.243,0.254,0.258 BN2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2 10.9311331810924,4.30600838153511,26.8662969820105 2 8.964271676493425 42.10343854463802 26.866296982010496 ST2 TRUE 722 +DLBCL10548T BN2 1 1.7176785406176867 915,1119,684 0.226,0.247,0.258 BN2,BN2,BN2 12.3516233934538 5 21.216118444727044 12.351623393453824 12.351623393453824 Other FALSE 723 +DLBCL10549T EZB 0.6562544870670588 3.8640951098294916 1944,160,1471 0.241,0.253,0.294 EZB,ST2,EZB 7.55227920580018,3.95587708819017 7 29.182725147199434 11.50815629399035 7.552279205800181 ST2 FALSE 724 +DLBCL10550T BN2 1 2.6113224285545202 1381,52,1108 0.175,0.249,0.323 BN2,BN2,BN2 12.8299701679959 8 33.50318885737301 12.829970167995867 12.829970167995867 Other FALSE 725 +DLBCL10552T ST2 0.690989962055712 0.49740400890213854 1776,722,293,1497,1176,1341,1555 0.119,0.129,0.14,0.202,0.22,0.227,0.24 BN2,ST2,ST2,ST2,ST2,BN2,ST2 12.7850945650746,28.5892719445974 3 14.220418476836162 41.37436650967197 28.589271944597353 ST2 TRUE 726 +DLBCL10553T EZB 1 0 172,1573,108,1972,1752,2089,117,398,695,397 0.162,0.223,0.232,0.247,0.252,0.271,0.28,0.28,0.281,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.8485328685839 0 0 40.84853286858388 40.84853286858388 ST2 FALSE 727 +DLBCL10554T EZB 1 0.14561293433805073 1032,474,1579,1545,1017,227,1581,728,1243,1042 0.139,0.185,0.21,0.236,0.278,0.282,0.294,0.298,0.306,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.984413850185 1 6.113473697188533 41.98441385018498 41.98441385018498 EZB TRUE 728 +DLBCL10555T EZB 1 0.3385557460207684 548,2100,660,289,729,443,498,689 0.176,0.179,0.266,0.271,0.287,0.312,0.314,0.352 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.4469550215704 3 10.646547317409302 31.44695502157036 31.44695502157036 EZB TRUE 729 +DLBCL10564T EZB 0.4960124196820567 2.060878584437799 2183,1725,353,1909 0.233,0.244,0.304,0.337 EZB,ST2,BN2,EZB 3.28943114889535,7.26574854188819,4.093140096341 4 14.97382556988753 14.648319787124532 7.265748541888189 Other FALSE 730 +DLBCL10565T EZB 1 0.18146824444713897 277,128,712,602,503,731,275,116,689 0.163,0.178,0.205,0.266,0.279,0.28,0.287,0.288,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.8358657923167 2 6.866008142469266 37.835865792316675 37.835865792316675 EZB TRUE 731 +DLBCL10566T EZB 1 8.73065473051215 2162 0.267 EZB 3.74110957854424 6 32.662336039281556 3.7411095785442368 3.7411095785442368 Other FALSE 732 +DLBCL10780T EZB 1 0.17941917561783294 2132,1172,278,1570,1779,1914,1517,162,733 0.132,0.135,0.167,0.17,0.176,0.178,0.188,0.208,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.8275049419587 2 9.478267386633227 52.827504941958715 52.827504941958715 EZB TRUE 733 +DLBCL10781T N1 1 9.20939888680807 913 0.316 N1 3.16489182700849 7 29.146751268519917 3.1648918270084865 3.1648918270084865 Other FALSE 734 +DLBCL10782T BN2 1 1.1038831054802167 1210,903,1735,158,2213 0.114,0.194,0.207,0.223,0.242 BN2,BN2,BN2,BN2,BN2 27.4104524758564 6 30.257935401666295 27.41045247585643 27.41045247585643 Other FALSE 735 +DLBCL10833T MCD 0.6400054338811402 1.1833257797800962 2135,151,736,150,1278 0.212,0.228,0.251,0.276,0.315 MCD,MCD,BN2,MCD,EZB 3.99128264085302,3.17027868951982,12.7319648624872 4 15.066062249035499 19.893526192860076 12.731964862487239 BN2 FALSE 736 +DLBCL10834T EZB 0.3910017378429145 3.0122457451765805 581,737,1415,402,793 0.145,0.167,0.175,0.185,0.224 ST2,BN2,EZB,EZB,BN2 10.4543785250712,11.129160692189,6.87966085337522 6 33.523766942432914 28.46320007063551 11.12916069218905 BN2 FALSE 737 +DLBCL10835T MCD 1 0 485,1171,455,1977,214,2207,1543,2010,1728,896 0.193,0.209,0.245,0.25,0.265,0.27,0.273,0.282,0.291,0.293 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.6083187156748 0 0 39.60831871567482 39.60831871567482 MCD TRUE 738 +DLBCL10836T MCD 1 0.10065441232024418 837,156,767,1491,788,471,1224,1968,950,1422 0.116,0.131,0.142,0.15,0.152,0.158,0.158,0.164,0.165,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.2531437469398 1 6.769325660537128 67.25314374693978 67.25314374693978 MCD TRUE 739 +DLBCL10837T BN2 0.6814315172267115 2.492645925506224 811,150,1232 0.16,0.21,0.255 BN2,MCD,BN2 10.1673179572363,4.75320993178067 5 25.343523679431307 14.920527889016963 10.16731795723629 Other FALSE 740 +DLBCL10838T Other 1 10 0 0 1 2 Other TRUE 741 +DLBCL10839T BN2 0.9123622700034913 0.3686339394004162 742,1525,1075,817,1115,232,274,1203 0.11,0.149,0.188,0.212,0.227,0.254,0.275,0.276 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 37.8546735391682,3.63616270400891 3 13.954517431460266 41.4908362431771 37.85467353916819 BN2 TRUE 742 +DLBCL10842T MCD 0.6864857602281158 5.2718472453626175 743,1064 0.128,0.28 MCD,N1 7.82931221501377,3.57560347094617 9 41.274938033804226 11.404915685959933 7.8293122150137675 MCD TRUE 743 +DLBCL10843T BN2 0.8259613349694882 1.4037181516243407 1614,744,591,1427 0.208,0.306,0.461,0.464 BN2,BN2,BN2,ST2 10.2372497413669,2.15709525753681 6 14.370213284668344 12.394344998903733 10.237249741366927 BN2 TRUE 744 +DLBCL10844T N1 1 0.6549029885091987 891,814,745,2189,63,2175 0.12,0.146,0.165,0.188,0.262,0.276 N1,N1,N1,N1,N1,N1 34.0204500855218 4 22.280094431436275 34.02045008552184 34.02045008552184 N1 TRUE 745 +DLBCL10845T MCD 1 2.1160619044916644 801,1994,768 0.137,0.176,0.212 MCD,MCD,MCD 17.686342675697 6 37.42539596582755 17.686342675696977 17.686342675696977 Other FALSE 746 +DLBCL10846T BN2 1 1.5638463525173991 987,754,1113,1381 0.193,0.222,0.252,0.265 BN2,BN2,BN2,BN2 17.3989385885237 6 27.20926664933694 17.398938588523652 17.398938588523652 Other FALSE 747 +DLBCL10847T EZB 0.5915227197901995 3.529754936546928 140,446 0.144,0.209 EZB,MCD 6.940492719655,4.79277210255951 4 24.498238439270267 11.733264822214519 6.940492719655005 Other FALSE 748 +DLBCL10848T MCD 1 0 1911,761,790,1897,986,1543,749,755,1184,883 0.16,0.196,0.227,0.26,0.262,0.264,0.267,0.282,0.283,0.286 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.4953536326717 0 0 41.49535363267165 41.49535363267165 MCD TRUE 749 +DLBCL10849T BN2 0.6144597533267385 1.6089763385980342 147,286,834 0.179,0.191,0.272 MCD,BN2,BN2 8.90516544658269,5.58750945746001 3 14.328200494852345 14.492674904042698 8.90516544658269 Other FALSE 750 +DLBCL10850T MCD 0.7188625556418341 4.308669770341578 751,81,401 0.205,0.235,0.28 MCD,MCD,EZB 3.57178096763126,9.13296910856582 8 39.350947911541034 12.704750076197087 9.132969108565824 MCD TRUE 751 +DLBCL10851T MCD 0.709194605658743 4.025433594920878 81,401,82 0.159,0.233,0.238 MCD,EZB,MCD 4.29258871473429,10.4684466658439 8 42.14003689532552 14.761035380578194 10.468446665843906 Other FALSE 752 +DLBCL10852T MCD 1 1.3795461935104951 650,2053,1063,864,753 0.167,0.2,0.207,0.212,0.238 MCD,MCD,MCD,MCD,MCD 24.7558438683167 6 34.15183017567643 24.755843868316695 24.755843868316695 MCD TRUE 753 +DLBCL10853T BN2 1 0.7801681580991618 754,987,1113,1204 0.15,0.164,0.205,0.267 BN2,BN2,BN2,BN2 21.413368104816 3 16.706027953033672 21.413368104816044 21.413368104816044 BN2 TRUE 754 +DLBCL10854T MCD 0.875598146836821 1.242655430556482 467,755,2028,713,1897,1420 0.13,0.155,0.195,0.213,0.232,0.249 MCD,MCD,MCD,MCD,MCD,BN2 4.01666204339965,28.2711370630249 5 35.13128199937452 32.287799106424544 28.271137063024895 MCD TRUE 755 +DLBCL10855T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 756 +DLBCL10856T EZB 0.3645903404039752 0.5883098848700333 757,1330,972,2140,132,82,133,1070,1107 0.201,0.239,0.258,0.264,0.265,0.272,0.283,0.284,0.289 EZB,EZB,BN2,ST2,BN2,MCD,BN2,EZB,ST2 11.1821652928043,12.676923137779,3.67201346074683,7.2392190220892 2 7.457959191693032 34.77032091341933 12.676923137779012 EZB TRUE 757 +DLBCL10857T MCD 0.7671760607103316 0.12351670064061385 2084,1127,749,1732,883,758,1503,790,1271,154 0.138,0.179,0.181,0.184,0.191,0.192,0.216,0.229,0.254,0.259 BN2,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 11.8724008895611,39.1206410020205 1 4.832052503515495 50.99304189158167 39.12064100202054 MCD TRUE 758 +DLBCL10858T MCD 0.8492175205179433 0 1376,1694,767,1224,759,1310,1449,858,156 0.112,0.129,0.151,0.155,0.16,0.161,0.168,0.177,0.177 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.96621213653231,50.4983368437708 0 0 59.46454898030309 50.49833684377079 MCD TRUE 759 +DLBCL10859T MCD 0.6703308952894761 5.651588538894485 751,401,81 0.197,0.211,0.22 MCD,EZB,MCD 4.73517043832162,9.62823326151698 8 54.41481275059204 14.3634036998386 9.628233261516982 Other FALSE 760 +DLBCL10860T MCD 1 0.196507462413082 761,790,1911,749,755,2014,1897,883,986 0.175,0.223,0.224,0.276,0.284,0.306,0.31,0.317,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.5972028601534 2 6.798608540639365 34.59720286015339 34.59720286015339 MCD TRUE 761 +DLBCL10861T Other 1 10 0 0 1 2 Other TRUE 762 +DLBCL10862T MCD 0.7158934522972052 4.035585269850525 743,1064 0.11,0.277 MCD,N1 9.08996186764926,3.60740509175064 8 36.68331621658834 12.6973669593999 9.089961867649263 Other FALSE 763 +DLBCL10863T MCD 1 0.08775492942519349 1905,918,2036,1399,1826,896,1728,1233,480,738 0.215,0.226,0.312,0.333,0.346,0.354,0.388,0.402,0.412,0.414 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 30.9220227672957 1 2.713559925628259 30.922022767295687 30.922022767295687 MCD TRUE 764 +DLBCL10864T MCD 1 0.1658397353119751 1526,765,799,2053,900,465,69,704,296 0.151,0.16,0.18,0.187,0.199,0.229,0.258,0.29,0.291 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.9492325382877 2 7.28852909131407 43.94923253828766 43.94923253828766 MCD TRUE 765 +DLBCL10865T EZB 0.421804334234771 2.982271264949275 1983,1082,2017,1863,1121 0.194,0.21,0.211,0.216,0.255 EZB,BN2,ST2,EZB,ST2 4.76437489637644,9.79654310861215,8.66440784754169 6 29.21594900865085 23.225325852530272 9.796543108612147 Other FALSE 766 +DLBCL10866T MCD 1 0.0992486981651572 837,156,767,788,1491,1224,471,1694,1968,950 0.123,0.132,0.139,0.153,0.155,0.157,0.162,0.165,0.167,0.171 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.3656808542789 1 6.586707427631481 66.36568085427892 66.36568085427892 MCD TRUE 767 +DLBCL10867T MCD 0.5067789911196383 4.570923209005617 768,140 0.226,0.232 MCD,EZB 4.30679378716375,4.42518175649852 4 20.22716599484732 8.731975543662266 4.425181756498518 MCD TRUE 768 +DLBCL10868T MCD 0.7471438585356942 1.5588634190641166 1907,717,632 0.123,0.216,0.231 MCD,MCD,EZB 4.32697092078248,12.7854112255428 3 19.93070985719035 17.112382146325256 12.785411225542777 Other FALSE 769 +DLBCL10869T Other 1 10 0 0 1 2 Other TRUE 770 +DLBCL10870T MCD 1 0.20924621415955405 2180,1250,1239,771,880,929,1240,1977,777 0.139,0.145,0.164,0.169,0.172,0.175,0.19,0.197,0.202 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.9574346116053 2 11.081142704080552 52.95743461160534 52.95743461160534 MCD TRUE 771 +DLBCL10871T BN2 0.47246557545934953 6.4941895633331015 286,147,833 0.176,0.315,0.316 BN2,MCD,ST2 5.68369823022535,3.17785996347451,3.16830921981368 8 36.91101372786427 12.029867413513532 5.683698230225348 Other FALSE 772 +DLBCL10872T EZB 1 2.2451099995562105 31 0.171 EZB 5.86188434639605 2 13.160575162335805 5.861884346396055 5.861884346396055 Other FALSE 773 +DLBCL10873T ST2 0.680795028976003 0.6158442810965242 1789,486,1130,1943,1341,1713,1211 0.121,0.132,0.15,0.215,0.269,0.293,0.311 EZB,ST2,ST2,ST2,BN2,ST2,ST2 3.72362647038447,8.23800107503416,25.5115593759699 4 15.711147943545452 37.4731869213885 25.511559375969863 Other FALSE 774 +DLBCL10874T EZB 0.4891592100824958 1.2130371032991352 1797,1415,1388,737,581,402 0.338,0.415,0.428,0.481,0.505,0.507 ST2,EZB,EZB,BN2,ST2,EZB 2.07946233320672,6.71600065745866,4.93421953197591 3 8.146757983278741 13.729682522641287 6.71600065745866 Other FALSE 775 +DLBCL10875T ST2 0.5939874222392578 2.1272181148441374 744,1427,1077,618,701 0.236,0.261,0.266,0.282,0.295 BN2,ST2,ST2,ST2,EZB 4.23923899932323,3.38488230337794,11.1539208573473 5 23.72682249928698 18.77804216004844 11.153920857347277 Other FALSE 776 +DLBCL10876T MCD 0.8768187047595247 0.32593720936961845 986,1897,1184,1543,1420,647,1911 0.119,0.129,0.154,0.183,0.19,0.204,0.221 MCD,MCD,MCD,MCD,BN2,MCD,MCD 5.26616727649252,37.4851876772923 2 12.21781746423305 42.751354953784784 37.48518767729227 MCD TRUE 777 +DLBCL10877T BN2 0.6237577419418868 3.6934811948362647 1603,2096,596 0.197,0.21,0.272 MCD,BN2,BN2 8.43310869316318,5.0867374363761 7 31.14752837220843 13.519846129539278 8.43310869316318 Other FALSE 778 +DLBCL10878T BN2 1 0.7482809600095612 1204,871,1275,273,754 0.194,0.194,0.25,0.253,0.266 BN2,BN2,BN2,BN2,BN2 22.0084237466905 3 16.468484449470786 22.008423746690493 22.008423746690493 Other FALSE 779 +DLBCL10879T EZB 0.9242299442345111 0 433,780,44,250,368,691,196,28,5,565,170 0.143,0.194,0.206,0.214,0.222,0.254,0.264,0.266,0.267,0.271,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.69093849332107,45.0214249335273 0 0 48.71236342684838 45.021424933527314 EZB TRUE 780 +DLBCL10880T N1 1 2.474559423924395 913 0.196 N1 5.09007382337646 2 12.595690148107096 5.090073823376461 5.090073823376461 MCD FALSE 781 +DLBCL10881T MCD 0.7721201494031461 2.468752273186658 254,1890,952,1398 0.198,0.238,0.268,0.294 MCD,MCD,BN2,MCD 3.73487575816001,12.6547951512151 6 31.241554296273833 16.38967090937513 12.654795151215124 Other FALSE 782 +DLBCL10883T MCD 0.8264446698600141 0.4636865944814753 647,1420,986,1184,1897,1543,467,1977 0.122,0.137,0.203,0.203,0.208,0.24,0.252,0.265 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 7.30468928559341,34.7838439775545 3 16.1288021569272 42.08853326314786 34.783843977554454 MCD TRUE 783 +DLBCL10884T EZB 0.7457632442101885 2.97799603553086 904,1400,816,2138 0.308,0.313,0.315,0.318 MCD,EZB,EZB,EZB 9.5217248773612,3.24603345785325 6 28.355658936197223 12.767758335214447 9.521724877361201 Other FALSE 784 +DLBCL10885T BN2 1 0.0757059217422146 1193,564,560,145,669,417,592,1375,1477,88 0.149,0.149,0.158,0.188,0.196,0.2,0.204,0.238,0.263,0.264 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.8438977036282 1 3.9248900623622522 51.84389770362815 51.84389770362815 Other FALSE 785 +DLBCL10887T MCD 0.9159945736719849 0.23551518018201822 1251,786,1197,738,934,1826,2145,1501,988 0.171,0.179,0.218,0.264,0.274,0.288,0.329,0.331,0.331 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.02534503834269,32.9883408696326 2 7.769255043817356 36.01368590797529 32.988340869632594 MCD TRUE 786 +DLBCL10888T BN2 1 3.5364292542169897 894,2211 0.197,0.279 BN2,BN2 8.65914268515598 7 30.622445508224654 8.659142685155977 8.659142685155977 Other FALSE 787 +DLBCL10889T MCD 1 0.1577035103379182 2036,69,900,481,296,739,1968,799,643,765 0.174,0.227,0.241,0.286,0.288,0.292,0.293,0.295,0.299,0.301 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.1663706000567 1 6.018970620486864 38.16637060005673 38.16637060005673 MCD TRUE 788 +DLBCL10890T MCD 0.90941800394791406 0.2832223925247621 1127,154,1732,997,214,883,1531,2084,789 0.146,0.156,0.202,0.205,0.208,0.209,0.222,0.244,0.255 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 4.09842590708321,41.1470542733994 2 11.653767156658429 45.24548018048266 41.14705427339945 MCD TRUE 789 +DLBCL10891T MCD 0.8078303048154267 0.09777815550904957 749,790,1732,883,2084,1127,758,1503,1271 0.119,0.152,0.18,0.18,0.195,0.22,0.255,0.258,0.3 MCD,MCD,MCD,MCD,BN2,MCD,MCD,BN2,MCD 9.01072026405775,37.8786722356453 1 3.703706704333243 46.88939249970303 37.87867223564528 MCD TRUE 790 +DLBCL10892T BN2 0.4794139194483298 4.344999164716219 1835,921,70,804 0.186,0.195,0.205,0.208 EZB,MCD,BN2,BN2 9.67600963033196,5.3886234953749,5.11836373317934 7 42.04225376157848 20.182996858886206 9.676009630331965 Other FALSE 791 +DLBCL10893T MCD 0.8861070291526828 0.6016842726211622 704,347,465,2053,1526,246,2185 0.188,0.207,0.211,0.237,0.25,0.289,0.293 MCD,MCD,MCD,MCD,MCD,MCD,ST2 26.5623379453462,3.41410628932572 4 15.982140985763134 29.976444234671934 26.562337945346215 MCD TRUE 792 +DLBCL10894T BN2 0.6628680822387302 2.624571624461786 793,402,737 0.135,0.151,0.178 BN2,EZB,BN2 13.0129357910296,6.61832439437636 5 34.15338202807937 19.63126018540592 13.012935791029562 BN2 TRUE 793 +DLBCL10895T BN2 0.6453256844375596 3.8241450890136868 114,1657,2170 0.253,0.273,0.282 EZB,BN2,BN2 7.19904854880922,3.95663411254902 7 27.53020615349989 11.155682661358238 7.199048548809221 Other FALSE 794 +DLBCL10896T BN2 1 0 795,585,1029,1444,557,1056,1252,957,935,812 0.116,0.188,0.188,0.188,0.214,0.215,0.226,0.226,0.243,0.251 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.8569379546921 0 0 50.85693795469206 50.85693795469206 BN2 TRUE 795 +DLBCL10897T BN2 0.8949776287696195 0.10267362137550759 692,1935,579,933,961,892,260,232,1982,981 0.156,0.186,0.191,0.192,0.198,0.202,0.213,0.214,0.235,0.262 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 44.4316245253404,5.21388961608376 1 4.561955793613519 49.64551414142417 44.43162452534042 BN2 TRUE 796 +DLBCL10898T ST2 0.5006521848369643 8.764845744026001 1664,951 0.286,0.287 ST2,MCD 3.48797938405862,3.49709049738225 5 30.651458762454556 6.985069881440871 3.497090497382247 Other FALSE 797 +DLBCL10899T MCD 1 0 1699,305,910,905,479,764,798,1331,1009,1904,1449 0.124,0.124,0.133,0.134,0.135,0.143,0.157,0.161,0.162,0.164,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.9988386695093 0 0 75.99883866950934 75.99883866950934 MCD TRUE 798 +DLBCL10900T MCD 1 0.25281266546894476 824,69,1412,799,1225,765,900 0.144,0.164,0.207,0.209,0.227,0.23,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.4832010843717 2 8.97060264551055 35.48320108437166 35.48320108437166 MCD TRUE 799 +DLBCL10901T MCD 1 0.10394603071277377 1273,547,705,1169,800,2094,49,708,188,621 0.178,0.184,0.187,0.189,0.198,0.208,0.212,0.218,0.219,0.221 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.8871966412803 1 5.1855760742487105 49.88719664128034 49.88719664128034 MCD TRUE 800 +DLBCL10902T MCD 0.5663907241999885 4.282295287228473 140,768,446 0.168,0.254,0.261 EZB,MCD,MCD 5.94656095141171,7.76753900745165 6 33.262895684973536 13.714099958863361 7.767539007451651 MCD TRUE 801 +DLBCL10903T BN2 0.6119994848489703 3.160934530498542 2218,190,189 0.156,0.184,0.215 ST2,BN2,BN2 10.0876202619329,6.39543325635941 5 31.886307216500445 16.483053518292305 10.087620261932898 Other FALSE 802 +DLBCL10904T MCD 1 0.10008644095151151 803,1598,1647,1459,518,880,792,2180,783,771 0.18,0.196,0.21,0.227,0.229,0.242,0.247,0.267,0.271,0.274 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.4431110855154 1 4.348066372410393 43.44311108551541 43.44311108551541 MCD TRUE 803 +DLBCL10905T BN2 1 0.8163972746507866 70,1841,1813,1148,804 0.117,0.149,0.166,0.179,0.2 BN2,BN2,BN2,BN2,BN2 31.8511742171516 4 26.003211825309958 31.851174217151588 31.851174217151588 BN2 TRUE 804 +DLBCL10906T MCD 0.7914386491994155 1.2389950253743298 1890,952,1398,285,1194 0.165,0.204,0.227,0.239,0.254 MCD,BN2,MCD,MCD,MCD 4.89700965739807,18.5829382744713 5 23.02416807890819 23.479947931869383 18.582938274471314 Other FALSE 805 +DLBCL10907T MCD 1 0.25256391418830126 765,799,1526,900,2053,69,465,753 0.131,0.151,0.166,0.185,0.202,0.232,0.252,0.289 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.3405069067762 3 10.693684153092201 42.3405069067762 42.3405069067762 Other FALSE 806 +DLBCL10908T EZB 1 4.835531836672584 1300,2050 0.153,0.169 EZB,EZB 12.4708600986625 9 60.30324103777219 12.470860098662472 12.470860098662472 Other FALSE 807 +DLBCL10909T MCD 1 3.0199400602230524 864,717,650 0.167,0.204,0.21 MCD,MCD,MCD 15.6587266512561 8 47.288415906210766 15.658726651256135 15.658726651256135 Other FALSE 808 +DLBCL10910T EZB 1 0.37005449377335875 809,1011,1031,1701,1864,1322,473,466 0.129,0.137,0.202,0.203,0.224,0.229,0.255,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.4680584185439 3 15.34544136583834 41.468058418543926 41.468058418543926 EZB TRUE 809 +DLBCL10911T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 810 +DLBCL10912T BN2 0.7335580793280144 1.429822826080897 811,150,661,1126 0.187,0.204,0.238,0.252 BN2,MCD,BN2,BN2 13.5288089826073,4.9139147278323 4 19.3437998930202 18.4427237104396 13.528808982607305 BN2 TRUE 811 +DLBCL10913T BN2 1 0.10530649101974109 1252,995,957,1056,585,812,571,418,1444,1029 0.124,0.138,0.142,0.144,0.165,0.172,0.185,0.207,0.225,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.1973197467393 1 6.339168511322487 60.197319746739325 60.197319746739325 BN2 TRUE 812 +DLBCL10914T BN2 1 0 1444,795,1758,585,945,1056,957,812,1806,557,1029 0.19,0.205,0.24,0.245,0.264,0.264,0.27,0.274,0.277,0.28,0.281 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.0714850563735 0 0 44.07148505637347 44.07148505637347 Other FALSE 813 +DLBCL10915T N1 1 0.7038883705026121 814,745,891,2189,63,2175 0.144,0.175,0.188,0.2,0.256,0.292 N1,N1,N1,N1,N1,N1 30.3209117180265 5 21.342537141355226 30.320911718026494 30.320911718026494 N1 TRUE 814 +DLBCL10916T MCD 1 0.9516918715817807 753,1412,1063,824,1890,1194 0.174,0.184,0.221,0.268,0.269,0.27 MCD,MCD,MCD,MCD,MCD,MCD 26.8435755170435 4 25.546812623761973 26.843575517043476 26.843575517043476 MCD TRUE 815 +DLBCL10917T EZB 0.5887108620323257 1.1173432552660445 781,1400,816,2138,2134 0.152,0.176,0.204,0.222,0.253 MCD,EZB,EZB,EZB,MCD 15.0901520723134,10.542383431142 3 16.860879638938332 25.632535503455408 15.090152072313428 EZB TRUE 816 +DLBCL10918T BN2 1 0.7303696926140945 817,742,57,1075,2064,1525 0.125,0.226,0.23,0.231,0.239,0.263 BN2,BN2,BN2,BN2,BN2,BN2 29.0618575764061 4 21.225899984874307 29.061857576406087 29.061857576406087 BN2 TRUE 817 +DLBCL10919T EZB 1 0.09204130219247082 1750,818,1752,1573,110,172,108,117,1671,109 0.193,0.23,0.243,0.26,0.292,0.299,0.303,0.305,0.312,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.150348179079 1 3.419366423306117 37.15034817907898 37.15034817907898 EZB TRUE 818 +DLBCL10920T Other 1 10 0 0 1 2 Other TRUE 819 +DLBCL10921T N1 0.5026495329699907 1.8136486052917897 1609,1064,464,176,1008,743 0.2,0.202,0.21,0.225,0.234,0.267 MCD,N1,BN2,N1,N1,MCD 4.76352895833576,8.73934584639643,13.6467424166335 4 24.750395350703606 27.14961722136567 13.646742416633472 Other FALSE 820 +DLBCL10922T MCD 0.4139724404738713 8.567049498059548 147,1074,286 0.209,0.257,0.347 MCD,ST2,BN2 2.8787968845145,4.78015535471701,3.8880854071519 7 40.951827532275 11.547037646383412 4.7801553547170075 Other FALSE 821 +DLBCL10923T BN2 1 1.0582794144824703 1108 0.157 BN2 6.38426244337464 1 6.756333520476942 6.384262443374642 6.384262443374642 Other FALSE 822 +DLBCL10924T MCD 1 0 823,2203,910,905,1904,253,764,1009,1699,858,305 0.107,0.13,0.146,0.149,0.162,0.166,0.182,0.182,0.187,0.188,0.189 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.7918396615104 0 0 69.79183966151035 69.79183966151035 MCD TRUE 823 +DLBCL10925T MCD 1 0.22531862701797692 2036,918,1399,1905,69,1225,824,900,1826 0.194,0.268,0.297,0.329,0.33,0.342,0.349,0.387,0.398 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.2157112498422 2 6.58284394616811 29.21571124984221 29.21571124984221 MCD TRUE 824 +DLBCL10926T ST2 0.6587963052998417 3.805068846115495 702,1697,530 0.216,0.22,0.243 ST2,BN2,ST2 4.53648107980578,8.7590404818594 6 33.32875205938765 13.295521561665174 8.759040481859397 Other FALSE 825 +DLBCL10927T BN2 0.5059656497133177 0.8788256571263758 24,826,1190,132,1093,2140,972,2214 0.181,0.199,0.25,0.293,0.295,0.298,0.3,0.32 EZB,BN2,BN2,BN2,EZB,ST2,BN2,EZB 15.7732778087319,12.0503784239244,3.35094587258365 2 13.861961235295702 31.17460210523998 15.77327780873191 BN2 TRUE 826 +DLBCL10928T BN2 1 6.756910762585228 2032 0.296 BN2 3.38041309872143 5 22.841149648734884 3.380413098721426 3.380413098721426 BN2 TRUE 827 +DLBCL10929T ST2 0.562552901696659 1.3605054839071467 1789,486,1130,854,1123,1943,1211 0.282,0.294,0.295,0.341,0.358,0.374,0.405 EZB,ST2,ST2,BN2,EZB,ST2,ST2 2.93569291690538,6.34217993779078,11.9312582395112 4 16.232542264767265 21.20913109420732 11.931258239511163 ST2 TRUE 828 +DLBCL10930T BN2 0.5662077616063375 0.8742968274864823 897,1191,273,34,1275,1112,274 0.183,0.203,0.208,0.217,0.218,0.247,0.251 MCD,BN2,BN2,EZB,BN2,BN2,MCD 18.3517782253912,4.60914700763635,9.450813831317 3 16.04490148119507 32.4117390643446 18.351778225391243 Other FALSE 829 +DLBCL10931T BN2 0.9292242683525783 0.10109244864924764 830,1115,1203,261,1525,742,1075,1158,232,274 0.162,0.163,0.204,0.22,0.256,0.283,0.318,0.331,0.336,0.342 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 38.3542224039928,2.92130570074665 1 3.8773222588574643 41.275528104739436 38.354222403992786 BN2 TRUE 830 +DLBCL10932T BN2 0.597774535282264 1.1661325825989624 130,831,284,1145,600,412,89 0.192,0.233,0.249,0.27,0.282,0.29,0.313 BN2,ST2,BN2,BN2,ST2,BN2,ST2 16.3805546982447,11.0220088627975 4 19.10189855466761 27.40256356104213 16.38055469824466 ST2 FALSE 831 +DLBCL10933T BN2 1 0 19 0.181 BN2 5.51070958457825 0 0 5.5107095845782466 5.5107095845782466 Other FALSE 832 +DLBCL10934T ST2 1 2.8149833178203663 833,1664 0.124,0.26 ST2,ST2 11.930139460707 7 33.583143561160725 11.930139460707021 11.930139460707021 ST2 TRUE 833 +DLBCL10935T BN2 0.5724845087181609 8.305882388097928 834,1763 0.261,0.349 BN2,EZB 3.83246614639914,2.86197900982062 9 31.832013068358183 6.694445156219765 3.832466146399144 BN2 TRUE 834 +DLBCL10936T Other 1 10 0 0 1 2 Other TRUE 835 +DLBCL10937T BN2 0.9157942421417414 0.1298392362678342 88,1477,560,564,669,1193,1212,836,872,294 0.14,0.16,0.173,0.188,0.197,0.205,0.239,0.258,0.276,0.285 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2 45.4961337456813,4.1832938507194 1 5.907183258678496 49.67942759640068 45.49613374568128 BN2 TRUE 836 +DLBCL10938T MCD 1 0.09138390065935136 739,481,1968,643,1422,837,185,296,1491,247 0.122,0.127,0.131,0.152,0.179,0.186,0.193,0.195,0.205,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.1640093524854 1 5.589405754595166 61.16400935248544 61.16400935248544 MCD TRUE 837 +DLBCL10939T BN2 1 1.3102213692774614 1317,915 0.228,0.24 BN2,BN2 8.55597332519675 2 11.210219085640723 8.555973325196753 8.555973325196753 ST2 FALSE 838 +DLBCL10940T MCD 1 0 2008,23,798,1331,2034,21,59,1699,846,387,888 0.106,0.113,0.127,0.132,0.134,0.138,0.138,0.16,0.163,0.172,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4279947578727 0 0 79.42799475787272 79.42799475787272 MCD TRUE 839 +DLBCL10941T N1 1 11.008255573541392 697 0.258 N1 3.87145453945042 10 42.617961011617176 3.871454539450417 3.871454539450417 Other FALSE 840 +DLBCL10942T BN2 0.9088760746946924 0.09198790244618194 863,1758,684,1119,960,590,1722,2210,795,1444 0.172,0.182,0.244,0.271,0.277,0.281,0.285,0.302,0.307,0.312 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 35.9618204472075,3.60553251594286 1 3.308052431084836 39.56735296315037 35.961820447207515 BN2 TRUE 841 +DLBCL10943T EZB 0.6105388342012874 0.9065661666948344 207,1272,1318,993,2083,330,1356 0.157,0.208,0.211,0.228,0.245,0.245,0.264 EZB,EZB,EZB,BN2,BN2,BN2,EZB 12.549433430372,19.6731205298808 4 17.834985465699503 32.222553960252796 19.67312052988082 Other FALSE 842 +DLBCL10945T BN2 1 1.085644191114206 1173,1126,931,811,1158,1027 0.198,0.211,0.214,0.241,0.27,0.271 BN2,BN2,BN2,BN2,BN2,BN2 26.0046093044734 5 28.23175303359599 26.00460930447341 26.00460930447341 Other FALSE 843 +DLBCL10947T EZB 0.6084506398049997 1.796716804905044 561,2042,141,844,383 0.166,0.18,0.188,0.212,0.248 EZB,EZB,BN2,BN2,EZB 10.0451523764069,15.6097289683211 5 28.046262357395584 25.654881344727983 15.609728968321093 BN2 FALSE 844 +DLBCL10948T MCD 0.9004843366827219 0.22540936235884373 1251,786,738,1826,1197,1501,789,1531 0.158,0.166,0.195,0.225,0.262,0.278,0.281,0.306 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 3.59884835780234,32.5647889816626 2 7.3404083197068735 36.16363733946498 32.56478898166263 Other FALSE 845 +DLBCL10949T MCD 1 0 798,1331,23,2008,21,1699,2034,59,905,846,305 0.11,0.117,0.119,0.122,0.136,0.138,0.142,0.154,0.174,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4321606599702 0 0 79.43216065997021 79.43216065997021 MCD TRUE 846 +DLBCL10950T BN2 0.8908149916103685 0.5675670192992588 742,1525,817,1075,1115,274,232 0.123,0.178,0.189,0.202,0.257,0.259,0.274 BN2,BN2,BN2,BN2,BN2,MCD,BN2 31.5445915653874,3.86634321060098 4 17.903669809779466 35.41093477598838 31.544591565387396 MCD FALSE 847 +DLBCL10951T MCD 0.7852080501887585 1.5169408724829243 1890,952,1398,285,254 0.17,0.199,0.225,0.243,0.258 MCD,BN2,MCD,MCD,MCD 5.01374134522927,18.3285736234431 6 27.80336246371329 23.34231496867237 18.328573623443102 Other FALSE 848 +DLBCL10952T N1 1 0.8007664288126405 63,2189,2175,745,849,891 0.122,0.166,0.172,0.188,0.225,0.234 N1,N1,N1,N1,N1,N1 34.0461243268659 4 27.262993392135606 34.04612432686594 34.04612432686594 N1 TRUE 849 +DLBCL10953T MCD 0.7344611769177855 0 850,569,1994,801,2130 0.131,0.168,0.191,0.199,0.206 BN2,MCD,MCD,MCD,MCD 7.62601236540372,21.092998575767 0 0 28.719010941170723 21.092998575767005 BN2 FALSE 850 +DLBCL10954T ST2 1 4.6802789608432125 1052 0.212 ST2 4.71086260360404 4 22.048151131071084 4.710862603604044 4.710862603604044 Other FALSE 851 +DLBCL10955T BN2 0.6093241263284839 3.0292507472262438 2218,190,189 0.145,0.164,0.214 ST2,BN2,BN2 10.7832040236598,6.91378770492082 5 32.6650288461644 17.696991728580585 10.78320402365977 Other FALSE 852 +DLBCL10956T Other 1 10 0 0 1 2 Other TRUE 853 +DLBCL10957T BN2 0.7521065116129825 1.4337344195436927 1813,1841,1797,70 0.276,0.295,0.301,0.325 BN2,BN2,ST2,BN2 10.0838925607732,3.32364002280608 4 14.457623847361175 13.407532583579313 10.083892560773235 BN2 TRUE 854 +DLBCL10958T ST2 0.5758679649231996 4.518846975543084 1667,1971 0.134,0.181 ST2,BN2 5.5149253203212,7.48792488721517 5 33.83678672968605 13.002850207536362 7.487924887215168 Other FALSE 855 +DLBCL10959T BN2 1 1.125782624234604 871,1204,754,1275,987 0.146,0.248,0.274,0.286,0.309 BN2,BN2,BN2,BN2,BN2 21.2684213435454 6 23.943619193463753 21.268421343545356 21.268421343545356 Other FALSE 856 +DLBCL10960T BN2 1 0 857,935,1724,976,555,796,557,1029,841,1409,98 0.173,0.202,0.209,0.239,0.243,0.253,0.266,0.27,0.295,0.308,0.31 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.1083391883439 0 0 45.10833918834394 45.10833918834394 BN2 TRUE 857 +DLBCL10961T MCD 0.9113859379568929 0.09524672345066647 858,759,1009,1904,1406,764,1449,1376,479,2203 0.116,0.118,0.13,0.132,0.138,0.151,0.153,0.16,0.167,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.26363302787349,64.4207807486499 1 6.135868288442675 70.6844137765234 64.4207807486499 MCD TRUE 858 +DLBCL10962T Other 1 10 0 0 1 2 Other TRUE 859 +DLBCL10963T ST2 1 1.0663811083547394 874 0.212 ST2 4.7095571900874 1 5.02218281622543 4.709557190087397 4.709557190087397 Other FALSE 860 +DLBCL10964T MCD 0.5763119988823973 1.9543341553397684 267,291,990,1416 0.156,0.247,0.269,0.295 MCD,BN2,MCD,ST2 4.05373304057672,10.1178875066284,3.38464676329567 5 19.773733134089387 17.556267310500772 10.117887506628389 Other FALSE 861 +DLBCL10965T BN2 0.6650416075835202 0.734408768658513 130,1145,412,600,284,831,89,157 0.173,0.209,0.263,0.272,0.274,0.283,0.291,0.317 BN2,BN2,BN2,ST2,BN2,ST2,ST2,BN2 21.1638565909941,10.6595005488616 3 15.542921859057358 31.82335713985571 21.163856590994136 Other FALSE 862 +DLBCL10966T BN2 0.5230624875164384 0.40171396842017687 838,1076,13,1086,590,960,1722,1145,932 0.157,0.204,0.222,0.232,0.239,0.252,0.256,0.28,0.291 ST2,EZB,BN2,BN2,BN2,ST2,BN2,BN2,EZB 20.4672024951529,8.32631636743071,10.3360363695906 2 8.221961136787211 39.12955523217417 20.467202495152886 BN2 TRUE 863 +DLBCL10967T MCD 1 1.3301936243247985 864,717,650,704 0.149,0.154,0.188,0.229 MCD,MCD,MCD,MCD 22.8787083666518 5 30.433112002106604 22.878708366651768 22.878708366651768 MCD TRUE 864 +DLBCL10968T BN2 1 0 865,964,1549,954,1806,973,936,2213,945 0.154,0.162,0.167,0.176,0.183,0.21,0.224,0.224,0.243 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.6413489166743 0 0 47.6413489166743 47.6413489166743 BN2 TRUE 865 +DLBCL10969T BN2 1 2.782793002915591 19,2211,894 0.23,0.261,0.279 BN2,BN2,BN2 11.7599620815165 6 32.7255401949967 11.759962081516468 11.759962081516468 Other FALSE 866 +DLBCL10970T ST2 0.5057219511233866 2.9834757552042426 1131,2211,2127,175 0.224,0.229,0.248,0.254 ST2,BN2,ST2,N1 4.37050869026872,3.94415398831807,8.50717008838356 5 25.380935704091087 16.821832766970346 8.50717008838356 Other FALSE 867 +DLBCL10971T ST2 1 3.365250832876903 1946,90 0.19,0.211 ST2,ST2 10.0095033915209 6 33.6844896249999 10.009503391520902 10.009503391520902 Other FALSE 868 +DLBCL10972T MCD 0.5425187023582727 2.115718828455687 659,869,897,1008,1191,1275 0.246,0.279,0.313,0.326,0.329,0.329 MCD,MCD,MCD,N1,BN2,BN2 6.0790068642551,10.8453965505574,3.066421792528 4 22.945809684082725 19.990825207340535 10.845396550557435 MCD TRUE 869 +DLBCL10973T N1 1 11.549292378404655 262 0.193 N1 5.19101721071467 9 59.95257550787437 5.191017210714674 5.191017210714674 N1 TRUE 870 +DLBCL10974T BN2 1 1.2451754428758584 987,754,1204,871,1113 0.174,0.19,0.277,0.28,0.291 BN2,BN2,BN2,BN2,BN2 21.612766450075 6 26.91168603624457 21.61276645007495 21.61276645007495 BN2 TRUE 871 +DLBCL10975T BN2 1 0.08743649817740932 936,903,2213,973,1549,1210,454,872,1735,964 0.157,0.206,0.209,0.217,0.217,0.218,0.224,0.246,0.248,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.3683231742576 1 4.054283804715499 46.36832317425758 46.36832317425758 BN2 TRUE 872 +DLBCL10977T EZB 1 10.923068260934544 65 0.242 EZB 4.12555166393883 8 45.0636824392159 4.125551663938827 4.125551663938827 Other FALSE 873 +DLBCL10978T BN2 0.6985132601197312 3.0679756931185853 1793,2058,874 0.207,0.255,0.265 BN2,BN2,ST2 8.74266871439116,3.77344116288245 5 26.822295108740402 12.516109877273616 8.742668714391163 ST2 FALSE 874 +DLBCL10979T Other 1 10 0 0 1 2 Other TRUE 875 +DLBCL10980T ST2 1 3.1175638101240915 1188,828,1638 0.188,0.244,0.251 ST2,ST2,ST2 13.4048794105405 8 41.7905669293787 13.404879410540524 13.404879410540524 Other FALSE 876 +DLBCL10981T EZB 0.7928190471565788 0 877,390,211,210,396,212,1423,112,1049,213,216 0.136,0.147,0.163,0.17,0.193,0.232,0.248,0.287,0.295,0.304,0.312 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,ST2,BN2,EZB 7.60724185285356,42.0671457435708,3.38582317227919 0 0 53.060210768703534 42.06714574357078 EZB TRUE 877 +DLBCL10982T MCD 1 2.7268488513218387 801,1994,768 0.164,0.202,0.213 MCD,MCD,MCD 15.7125493469187 7 42.84574713798302 15.71254934691872 15.71254934691872 Other FALSE 878 +DLBCL10983T MCD 0.6031490668507639 2.369294062481878 267,1416,879,291,990 0.216,0.242,0.257,0.279,0.312 MCD,ST2,MCD,BN2,MCD 3.57918539555501,11.7230925372758,4.13419835732719 6 27.775453542493132 19.436476290157987 11.723092537275786 MCD TRUE 879 +DLBCL10984T MCD 1 0 2180,1250,1239,771,880,929,1240,777,1598,1565 0.115,0.128,0.138,0.14,0.144,0.153,0.164,0.177,0.186,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.587896328477 0 0 66.58789632847703 66.58789632847703 MCD TRUE 880 +DLBCL10985T EZB 0.5504037489015415 0.7399646482340434 881,1494,1036,1322,466,1701,1011 0.148,0.17,0.22,0.348,0.362,0.368,0.392 N1,BN2,EZB,EZB,EZB,EZB,EZB 5.88029620221954,15.4539321793256,6.74321878368131 3 11.435363488907422 28.07744716522643 15.453932179325587 N1 FALSE 881 +DLBCL10986T BN2 1 2.2798155101591777 1807,1854,1006 0.141,0.252,0.293 BN2,BN2,BN2 14.4601516272037 7 32.96647795895248 14.460151627203706 14.460151627203706 Other FALSE 882 +DLBCL10987T MCD 0.8470553630569861 0.08813601051233852 749,883,1732,790,1127,2084,214,758,1911,1503 0.131,0.149,0.154,0.159,0.211,0.232,0.284,0.292,0.294,0.302 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,BN2 7.62722678919204,42.2419738682582 1 3.723039052914736 49.869200657450264 42.24197386825823 MCD TRUE 883 +DLBCL10988T ST2 1 1.9283568666984585 833,1664 0.191,0.236 ST2,ST2 9.48174559340998 3 18.284189223339986 9.48174559340998 9.48174559340998 Other FALSE 884 +DLBCL10989T BN2 0.3468194404222955 6.972535090125205 793,581,1257 0.164,0.172,0.176 BN2,ST2,EZB 6.10930134024627,5.69507057856458,5.81084980234596 6 42.597317971016054 17.615221721156807 6.109301340246269 Other FALSE 885 +DLBCL10990T N1 1 1.7357726998532101 814,3,891,745,2189 0.309,0.313,0.326,0.368,0.394 N1,N1,N1,N1,N1 14.7573863356503 6 25.61546832260866 14.757386335650338 14.757386335650338 Other FALSE 886 +DLBCL10991T BN2 1 1.3139795309109679 1126,1158,811,661,1027 0.128,0.24,0.251,0.275,0.283 BN2,BN2,BN2,BN2,BN2 23.1042930231624 6 30.358568108604437 23.10429302316237 23.10429302316237 Other FALSE 887 +DLBCL10992T MCD 1 0 587,888,1572,483,846,387,59,2008,707,947,23 0.134,0.135,0.136,0.138,0.148,0.15,0.154,0.155,0.156,0.159,0.163 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.6762256539954 0 0 74.67622565399543 74.67622565399543 MCD TRUE 888 +DLBCL10993T BN2 1 0.0737379337024628 111,943,889,1014,1929,1982,1632,266,106,579 0.103,0.119,0.165,0.187,0.215,0.215,0.217,0.22,0.261,0.265 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.6334832502212 1 4.102298099541885 55.6334832502212 55.6334832502212 BN2 TRUE 889 +DLBCL10994T BN2 1 10.98494828657852 674 0.265 BN2 3.77942159142539 7 41.51675073498622 3.7794215914253915 3.7794215914253915 Other FALSE 890 +DLBCL10995T N1 1 0.44870832634355445 2189,745,891,814,63,2175 0.127,0.131,0.148,0.188,0.197,0.208 N1,N1,N1,N1,N1,N1 37.4374763611023 3 16.79850736051659 37.43747636110229 37.43747636110229 N1 TRUE 891 +DLBCL10996T BN2 0.830409757388718 0.10287048061815254 933,981,232,892,692,1075,260,1525,961,1935 0.12,0.156,0.179,0.188,0.198,0.252,0.259,0.279,0.281,0.281 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.8786458710281,8.34843209536511 1 4.205205947771915 49.22707796639318 40.87864587102807 BN2 TRUE 892 +DLBCL10997T BN2 0.41559960221613024 3.3907733725137703 150,1232,811,1283,151 0.249,0.261,0.296,0.315,0.341 MCD,BN2,BN2,ST2,MCD 7.2017025460673,6.95189205550674,3.17486891875939 6 24.419341229969632 17.32846352033344 7.201702546067302 Other FALSE 893 +DLBCL10998T BN2 0.6589613414236586 4.347174960738846 1131,19,52 0.233,0.237,0.245 ST2,BN2,BN2 8.29119392303105,4.29102206116958 7 36.043270616830654 12.582215984200634 8.291193923031049 BN2 TRUE 894 +DLBCL10999T MCD 1 0.09108916986326007 1968,837,739,1422,156,1491,481,767,950,471 0.125,0.147,0.174,0.176,0.178,0.18,0.181,0.186,0.193,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.6142727176111 1 5.339125443985929 58.614272717611115 58.614272717611115 MCD TRUE 895 +DLBCL11000T MCD 1 0 1171,214,485,1728,896,455,2207,1977,2010,1543,883 0.209,0.225,0.236,0.247,0.267,0.271,0.281,0.295,0.304,0.309,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.5457153639829 0 0 41.5457153639829 41.5457153639829 MCD TRUE 896 +DLBCL11001T BN2 0.6209376253593256 0.6499345771161187 273,34,1112,897,1191,274,1407,1275 0.137,0.15,0.152,0.17,0.174,0.199,0.201,0.238 BN2,EZB,BN2,MCD,BN2,MCD,BN2,BN2 28.8145507069665,6.68804160358397,10.9023113869699 3 18.727572828523265 46.40490369752047 28.81455070696655 MCD FALSE 897 +DLBCL11002T ST2 0.5877072339190105 0.9408842689274162 1082,2017,1121,1863,1847 0.114,0.114,0.169,0.206,0.21 BN2,ST2,ST2,EZB,ST2 8.75939096533658,4.86529890278359,19.4214632274784 3 18.273349230286673 33.046153095598534 19.42146322747836 Other FALSE 898 +DLBCL11003T N1 1 4.835134973880531 198 0.265 N1 3.77881434339092 3 18.271077391530817 3.778814343390917 3.778814343390917 Other FALSE 899 +DLBCL11004T MCD 1 0.17641533872962994 2036,69,900,799,765,824,296,481,1526,739 0.168,0.208,0.232,0.28,0.288,0.292,0.296,0.301,0.307,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.7499826856686 1 6.836091321259519 38.749982685668584 38.749982685668584 MCD TRUE 900 +DLBCL11005T Other 1 10 0 0 1 2 N1 FALSE 901 +DLBCL11180T EZB 1 8.924658045509826 2050 0.147 EZB 6.79219405515095 8 60.61800932096693 6.792194055150949 6.792194055150949 Other FALSE 902 +DLBCL11181T BN2 0.8649469624176396 0.11632947617961574 594,2041,922,1735,454,903,88,158,1477 0.186,0.191,0.197,0.242,0.244,0.262,0.262,0.269,0.283 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 33.5793800972694,5.24309290548056 1 3.906271697151561 38.82247300274994 33.57938009726938 BN2 TRUE 903 +DLBCL11182T MCD 1 3.3121888582605696 904 0.222 MCD 4.49944338929356 3 14.903006262392289 4.499443389293555 4.499443389293555 MCD TRUE 904 +DLBCL11187T MCD 1 0 479,764,305,1449,1009,1689,310,759,1904,1699,910 0.116,0.124,0.125,0.13,0.138,0.147,0.153,0.154,0.156,0.171,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 77.5543984705094 0 0 77.55439847050941 77.55439847050941 MCD TRUE 905 +DLBCL11188T ST2 1 5.685075990297036 1710 0.149 ST2 6.69562015635925 5 38.06510939106684 6.695620156359247 6.695620156359247 Other FALSE 906 +DLBCL11191T BN2 0.9064950532286004 0 892,260,961,933,692,981,667,579,232,1935,907 0.128,0.152,0.19,0.204,0.234,0.235,0.236,0.246,0.265,0.276,0.278 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.5073111855289,4.9003782070667 0 0 52.40768939259564 47.50731118552894 BN2 TRUE 907 +DLBCL11193T EZB 0.8680541104268369 0.2509625709105192 16,1533,308,42,343,155,11,1933,575 0.133,0.142,0.146,0.164,0.172,0.2,0.204,0.217,0.242 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 6.85779135361128,45.1164791279915 2 11.322547592391537 51.974270481602815 45.11647912799154 Other FALSE 908 +DLBCL11194T MCD 1 0.360673198465011 824,1225,69,1412,2036,1399,799,765 0.153,0.172,0.217,0.238,0.255,0.256,0.281,0.302 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.8332375421752 3 12.924088395692838 35.8332375421752 35.8332375421752 Other FALSE 909 +DLBCL11195T MCD 0.8951147919561917 0.3955760840342102 1406,858,759,1904,1009,2203,1376,1310 0.109,0.117,0.15,0.152,0.161,0.167,0.175,0.18 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 5.69963026603252,48.6419720660323 3 19.241600829582495 54.34160233206481 48.641972066032295 MCD TRUE 910 +DLBCL11196T EZB 0.5254656318224508 2.3344272389635576 1178,1733,911,2138,816 0.152,0.208,0.238,0.272,0.289 ST2,EZB,ST2,EZB,EZB 11.9479070480469,10.7898446229967 5 27.891519661565376 22.737751671043597 11.94790704804691 ST2 FALSE 911 +DLBCL11202T EZB 0.8402532014003598 0.17452189370568236 1102,371,380,381,1865,473 0.198,0.221,0.225,0.234,0.238,0.263 EZB,EZB,EZB,EZB,BN2,EZB 4.20364059920375,22.1107559086034 1 3.858810992433575 26.31439650780717 22.110755908603423 Other FALSE 912 +DLBCL11203T EZB 0.4258950414259634 1.7154913903281084 1763,1377,834,597 0.13,0.256,0.289,0.334 EZB,MCD,BN2,BN2 6.45283072695032,7.6833278551825,3.9042671280551 3 13.180682784633706 18.040425710187918 7.683327855182498 N1 FALSE 913 +DLBCL11204T BN2 0.45390225261971406 5.105161857278512 147,286,834,1074 0.185,0.285,0.296,0.346 MCD,BN2,BN2,ST2 6.88407097900543,5.39426844625423,2.88807787995034 7 35.14429658481649 15.166417305210004 6.884070979005434 Other FALSE 914 +DLBCL11205T BN2 1 1.2630340923092547 915,1317 0.201,0.254 BN2,BN2 8.9242670486658 2 11.271653531337 8.9242670486658 8.9242670486658 BN2 TRUE 915 +DLBCL11206T EZB 0.37765897824355954 1.405226528526885 1183,1185,15,476,133,24,1070 0.196,0.282,0.31,0.31,0.333,0.341,0.348 ST2,ST2,EZB,MCD,BN2,EZB,EZB 3.00343920769889,9.02551893461213,3.22196983449328,8.64766693172558 3 12.682898640638667 23.898594908529873 9.025518934612126 Other FALSE 916 +DLBCL11208T BN2 0.8069262618708926 2.4420962190060744 1799,1433,537,1791 0.135,0.187,0.253,0.269 BN2,BN2,ST2,BN2 16.5099890976539,3.95035514665759 7 40.31898195121215 20.460344244311514 16.50998909765392 Other FALSE 917 +DLBCL11242T MCD 0.9075921188134087 0 1694,767,1968,837,1376,156,1224,310,788,1449,1689 0.191,0.216,0.22,0.224,0.226,0.232,0.24,0.243,0.251,0.251,0.253 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 4.41585335447198,43.3706914485133 0 0 47.78654480298531 43.37069144851333 MCD TRUE 918 +DLBCL11255T EZB 0.7241639281266607 1.8224764884934779 1387,1488,901 0.16,0.17,0.216 EZB,EZB,N1 12.1324495898965,4.62128408651283 4 22.111104125418727 16.75373367640934 12.13244958989651 Other FALSE 919 +DLBCL11256T MCD 0.6404189430289536 1.5665071115164728 920,254,1358,2145,554 0.165,0.203,0.218,0.262,0.268 MCD,MCD,EZB,MCD,BN2 3.72731780361063,4.57953761512692,14.7946268693197 5 23.175888203022065 23.101482288057287 14.794626869319742 MCD TRUE 920 +DLBCL11257T EZB 0.5215871104147224 2.0162476963952454 1835,921 0.125,0.137 EZB,MCD 7.96995441176203,7.31024376151144 2 16.069402223090318 15.280198173273472 7.96995441176203 MCD FALSE 921 +DLBCL11258T BN2 0.6342352922797466 0.3020156675085839 666,1038,489,648,922,1320,1362,2041 0.155,0.186,0.199,0.26,0.291,0.292,0.295,0.301 BN2,ST2,BN2,ST2,BN2,BN2,BN2,ST2 21.7304457555895,12.5319896845447 1 6.562935080133428 34.26243544013422 21.730445755589475 BN2 TRUE 922 +DLBCL11259T ST2 1 7.5654366848038785 833 0.276 ST2 3.62967475882719 6 27.46007457433793 3.629674758827195 3.629674758827195 Other FALSE 923 +DLBCL11260T EZB 1 10.243408965855814 401 0.187 EZB 5.35653905697952 9 54.86922020222082 5.356539056979517 5.356539056979517 Other FALSE 924 +DLBCL11261T ST2 0.3817910383903363 4.904494419585468 2127,65,175 0.201,0.244,0.253 ST2,EZB,N1 4.09732710814606,3.9449654016794,4.96672710853354 5 24.359285387406622 13.009019618359007 4.966727108533542 Other FALSE 925 +DLBCL11262T EZB 1 1.0395276372566387 2050 0.119 EZB 8.41797067294369 1 8.750713164140835 8.417970672943694 8.417970672943694 Other FALSE 926 +DLBCL11271T ST2 0.43162726936808715 4.685973412556138 1388,1797,1841,938 0.187,0.278,0.294,0.328 EZB,ST2,BN2,ST2 3.4038420667319,5.34498718376808,6.64393816952225 6 31.13331761704817 15.392767420022233 6.643938169522253 Other FALSE 927 +DLBCL11346T MCD 0.7631665055325884 0.423090255498058 1420,647,1897,986,467,1184 0.121,0.152,0.186,0.191,0.208,0.213 BN2,MCD,MCD,MCD,MCD,MCD 8.28721024335711,26.7045051894337 2 11.298415923546727 34.991715432790826 26.704505189433718 Other FALSE 928 +DLBCL11353T MCD 1 0.23085183913873217 2180,1250,880,647,1239,771,1598,929,1240 0.169,0.191,0.199,0.207,0.208,0.209,0.213,0.221,0.234 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.05261445382 2 10.169627065533852 44.05261445382004 44.05261445382004 MCD TRUE 929 +DLBCL11428T EZB 1 0.08752008522270717 193,424,1078,1291,700,439,1238,148,122,231 0.151,0.178,0.237,0.252,0.252,0.255,0.271,0.274,0.283,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6938796302205 1 3.7365719837249003 42.69387963022051 42.69387963022051 EZB TRUE 930 +DLBCL11429T BN2 1 0.37929565517768316 1027,1158,1375,1126,417,261,592 0.131,0.137,0.189,0.225,0.252,0.263,0.274 BN2,BN2,BN2,BN2,BN2,BN2,BN2 36.1046564437613 3 13.694339320801589 36.104656443761264 36.104656443761264 BN2 TRUE 931 +DLBCL11430T BN2 0.698442225594901 0 1086,590,1076,13,932,157,960,1722,412,1806 0.148,0.158,0.178,0.18,0.202,0.216,0.223,0.242,0.259,0.276 BN2,BN2,EZB,BN2,EZB,BN2,ST2,BN2,BN2,BN2 34.8666101925431,10.5729267530189,4.48099894821978 0 0 49.92053589378177 34.86661019254308 EZB FALSE 932 +DLBCL11432T BN2 0.8747225374187714 0.13370838366733478 232,692,933,1075,981,1935,892,1525,742,579 0.123,0.146,0.156,0.191,0.222,0.23,0.236,0.237,0.276,0.277 BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8020080301637,6.41652826406561 1 5.990404078764139 51.21853629422929 44.80200803016368 MCD FALSE 933 +DLBCL11433T MCD 0.36795899081604605 1.3870732643085215 1366,554,1358,920,934,176,2145 0.178,0.182,0.196,0.223,0.262,0.298,0.324 EZB,BN2,EZB,MCD,MCD,N1,MCD 5.50733649084399,10.7145364740832,11.3964416180317,3.35372823289156 4 15.807699476624766 30.97204281585054 11.396441618031734 MCD TRUE 934 +DLBCL11434T BN2 1 0 555,1724,991,98,1409,857,976,1001,935,796,557 0.181,0.212,0.228,0.228,0.231,0.255,0.258,0.26,0.27,0.277,0.314 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.4480707298726 0 0 45.44807072987256 45.44807072987256 BN2 TRUE 935 +DLBCL11435T BN2 1 0.09262068773609192 903,936,454,1210,1735,2213,872,973,1549,158 0.165,0.187,0.2,0.203,0.207,0.241,0.242,0.252,0.256,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.0286618831149 1 4.263206319186144 46.02866188311492 46.02866188311492 BN2 TRUE 936 +DLBCL11436T ST2 1 11.871470755733496 1028 0.261 ST2 3.83643156510779 9 45.544085131550055 3.8364315651077936 3.8364315651077936 Other FALSE 937 +DLBCL11437T ST2 0.4622683963676368 3.6487788481492127 1388,938,1797,2196 0.174,0.224,0.334,0.341 EZB,ST2,ST2,BN2 2.93251253349604,5.73173764165187,7.44834227175519 7 27.177353734956004 16.1125924469031 7.448342271755193 ST2 TRUE 938 +DLBCL11438T EZB 1 10.1724471675787 401 0.192 EZB 5.22013155724759 8 53.1015124739114 5.220131557247587 5.220131557247587 Other FALSE 939 +DLBCL11439T EZB 0.7054158559450692 0.673256848350345 2214,940,384,944,826,1964,1093,2073 0.184,0.201,0.386,0.393,0.395,0.397,0.404,0.412 EZB,ST2,EZB,EZB,BN2,EZB,EZB,EZB 2.52959598038486,17.9847513318942,4.98089947062666 3 12.108357000075774 25.495246782905735 17.98475133189422 ST2 FALSE 940 +DLBCL11440T MCD 0.8799397133626384 0.21578617363268549 1325,1271,223,1503,758,715,686,236,224 0.125,0.135,0.166,0.166,0.203,0.217,0.222,0.226,0.243 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.00905862489909,44.041285191531 2 9.50350041334632 50.05034381643005 44.04128519153096 Other FALSE 941 +DLBCL11441T Other 1 10 0 0 1 2 Other TRUE 942 +DLBCL11443T BN2 1 0.2686130455794272 106,1014,889,1935,57,1632,2064,111,1982 0.163,0.172,0.198,0.214,0.24,0.242,0.245,0.245,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.1790034554696 2 11.329830577678862 42.179003455469555 42.179003455469555 BN2 TRUE 943 +DLBCL11444T EZB 0.8166229755411253 0.12445557770696954 944,693,940,216,384,2073,213,1423,1093,877 0.166,0.189,0.272,0.273,0.289,0.326,0.338,0.342,0.384,0.391 EZB,EZB,ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB 2.96100400943181,29.5877805070516,3.68308917950906 1 3.6823643160721162 36.23187369599245 29.58778050705158 EZB TRUE 944 +DLBCL11445T BN2 1 0 1549,936,964,954,865,1213,973,872,2213,294,454 0.135,0.137,0.167,0.182,0.185,0.218,0.232,0.235,0.236,0.25,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.9046198662042 0 0 56.90461986620421 56.90461986620421 BN2 TRUE 945 +DLBCL11446T EZB 0.4456341669389855 2.995830137417641 353,1725,1738,1527,1909 0.148,0.207,0.264,0.362,0.363 BN2,ST2,EZB,EZB,EZB 6.75662064121667,9.30660435688856,4.82073105224578 6 27.88100580938908 20.88395605035102 9.306604356888563 Other FALSE 946 +DLBCL11447T MCD 1 0 798,1699,2008,23,1331,905,846,910,21,2034,707 0.123,0.137,0.146,0.148,0.149,0.157,0.16,0.162,0.169,0.172,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 71.7821576737497 0 0 71.78215767374965 71.78215767374965 MCD TRUE 947 +DLBCL11448T ST2 0.5752227086754167 0.822235855802289 1071,999 0.106,0.144 ST2,N1 6.93756987900025,9.39468238751528 1 7.724644712889316 16.332252266515532 9.394682387515278 Other FALSE 948 +DLBCL11449T MCD 0.538933581066942 1.6561668714290965 659,464,1609,1008,869,176 0.176,0.217,0.241,0.269,0.28,0.317 MCD,BN2,MCD,N1,MCD,N1 4.60097842833752,13.4099762681199,6.8714737496821 5 22.209158441910617 24.88242844613955 13.409976268119932 Other FALSE 949 +DLBCL11450T MCD 0.8695328141652399 0.2292460528990749 759,1376,858,1449,1406,1009,1904,1694,1310 0.125,0.126,0.142,0.15,0.151,0.152,0.163,0.166,0.169 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.95143740913261,52.9944422636558 2 12.148766714530998 60.945879672788365 52.994442263655756 MCD TRUE 950 +DLBCL11451T MCD 0.5775847451235009 2.682173288160081 951,1479 0.149,0.204 MCD,ST2 6.70433957687537,4.90320309712969 3 17.982200527849585 11.60754267400506 6.704339576875373 MCD TRUE 951 +DLBCL11452T MCD 0.8467764065182629 0.5585282686043226 2030,1398,285,952,1194,1890 0.131,0.167,0.17,0.193,0.205,0.232 MCD,MCD,MCD,BN2,MCD,MCD 5.18766589451831,28.66916892209 3 16.01254128037976 33.85683481660828 28.669168922089966 BN2 FALSE 952 +DLBCL11453T BN2 0.6608134198929266 1.2821735361114746 844,383,189,561,141,190 0.211,0.25,0.285,0.305,0.323,0.353 BN2,EZB,BN2,EZB,BN2,BN2 14.1743233656618,7.27548824373093 5 18.173942311738134 21.449811609392768 14.174323365661836 Other FALSE 953 +DLBCL11454T BN2 1 0 1549,964,865,954,936,973,1806,945,1213,2213,872 0.109,0.132,0.147,0.151,0.175,0.231,0.234,0.238,0.239,0.24,0.266 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.7854496507468 0 0 60.785449650746784 60.785449650746784 BN2 TRUE 954 +DLBCL11455T ST2 0.6151110777528513 0.5705575795650993 816,2138,955,1486,508,911 0.158,0.16,0.192,0.195,0.204,0.207 EZB,EZB,ST2,ST2,ST2,ST2 12.5811546668013,20.1066000062795 2 11.471973032866453 32.687754673080775 20.106600006279518 ST2 TRUE 955 +DLBCL11456T EZB 1 9.483416417159733 2162 0.211 EZB 4.72989052596728 8 44.85552146532635 4.729890525967276 4.729890525967276 Other FALSE 956 +DLBCL11457T BN2 1 0.0868928122830552 1444,795,585,1056,957,1252,812,1029,995,945 0.141,0.147,0.148,0.17,0.179,0.188,0.197,0.209,0.246,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.4522789688145 1 4.81840446710481 55.45227896881452 55.45227896881452 BN2 TRUE 957 +DLBCL11458T EZB 1 0.09058769930364553 1066,958,1914,1437,1172,1372,468,2132,1242,54 0.127,0.15,0.167,0.169,0.209,0.215,0.224,0.226,0.229,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3592837349476 1 4.833694750039338 53.3592837349476 53.3592837349476 EZB TRUE 958 +DLBCL11459T ST2 0.8667043704257463 0.7336088710842473 2052,339,1884,1970,959,1128 0.117,0.16,0.189,0.204,0.206,0.218 ST2,ST2,ST2,ST2,ST2,EZB 4.58940740563165,29.8408842722742 4 21.891537423138743 34.43029167790584 29.840884272274195 ST2 TRUE 959 +DLBCL11460T BN2 0.5320649412010293 0.3531368591764273 1076,590,960,838,1722,1086,13,932,1317 0.143,0.173,0.181,0.182,0.188,0.193,0.203,0.275,0.292 EZB,BN2,ST2,ST2,BN2,BN2,BN2,EZB,BN2 24.643131573143,10.6321455870654,11.0407449357337 2 8.702398084011179 46.316022095942145 24.64313157314303 ST2 FALSE 960 +DLBCL11461T BN2 0.9120568418138598 0 260,892,961,933,667,579,692,981,907,1935,232 0.137,0.143,0.175,0.216,0.221,0.236,0.239,0.251,0.268,0.273,0.275 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.9754161755449,4.62592836361061 0 0 52.60134453915548 47.975416175544865 BN2 TRUE 961 +DLBCL11462T BN2 1 1.1452266776259652 2064,57,106,1632,1113 0.197,0.207,0.244,0.276,0.305 BN2,BN2,BN2,BN2,BN2 20.9106099144574 6 23.947388319466665 20.910609914457442 20.910609914457442 Other FALSE 962 +DLBCL11463T ST2 0.563723610046993 4.86599413056673 1971,1667,566 0.157,0.239,0.247 BN2,ST2,ST2 6.37192866983625,8.23332803571822 8 40.06332589683535 14.60525670555447 8.233328035718218 Other FALSE 963 +DLBCL11464T BN2 1 0.12855332129812816 903,1210,1735,936,454,2213,973,158 0.153,0.167,0.193,0.216,0.23,0.231,0.245,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.1265493688241 1 5.029847872297513 39.12654936882406 39.12654936882406 BN2 TRUE 964 +DLBCL11466T N1 1 0.5504961761455962 745,814,2189,891,63,2175 0.138,0.146,0.162,0.164,0.223,0.256 N1,N1,N1,N1,N1,N1 34.7526362026362 4 19.131193340530267 34.752636202636246 34.752636202636246 Other FALSE 965 +DLBCL11467T ST2 1 7.935528726795499 318 0.215 ST2 4.6584511630505 7 36.96727302676115 4.6584511630505006 4.6584511630505006 Other FALSE 966 +DLBCL11468T EZB 1 0.1484268029496227 1241,1437,1814,1066,2003,651,331,408,1372,958 0.112,0.181,0.207,0.225,0.236,0.247,0.25,0.261,0.273,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0437137471057 1 6.982548030360122 47.04371374710575 47.04371374710575 Other FALSE 967 +DLBCL11469T N1 1 1.62458605925895 63,2175,849,2189 0.153,0.165,0.198,0.227 N1,N1,N1,N1 22.050436260851 6 35.822831349956594 22.050436260851008 22.050436260851008 Other FALSE 968 +DLBCL11470T BN2 0.5807498551673445 0.7831152869546278 648,489,922,666,2041,594,1038 0.214,0.215,0.228,0.253,0.262,0.266,0.277 ST2,BN2,BN2,BN2,ST2,BN2,ST2 16.7493974715924,12.0915868567911 3 13.116709207283183 28.840984328383513 16.749397471592378 Other FALSE 969 +DLBCL11471T BN2 0.47816674243565693 3.7615588911621423 581,793,402,737 0.14,0.158,0.159,0.167 ST2,BN2,EZB,BN2 12.3258714476745,6.30723117308753,7.14424731555874 6 46.36449133532156 25.777349936320764 12.325871447674489 Other FALSE 970 +DLBCL11472T Other 1 10 0 0 1 2 Other TRUE 971 +DLBCL11473T BN2 0.45568242125553043 0.29704822852449414 2140,972,132,133,1070,1093,24,1183 0.135,0.15,0.156,0.156,0.23,0.25,0.257,0.274 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2 19.5045178882143,12.2329595219801,11.0653979662004 1 5.7937824869183725 42.80287537639484 19.504517888214323 BN2 TRUE 972 +DLBCL11474T BN2 0.880110557517446 0.42166413314291806 412,130,284,157,932,973,2213,13 0.163,0.17,0.186,0.201,0.214,0.241,0.244,0.274 BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 34.2464923644398,4.66508763187327 3 14.440517516037056 38.91157999631304 34.24649236443977 BN2 TRUE 973 +DLBCL11475T Other 1 10 0 0 1 2 Other TRUE 974 +DLBCL11476T Other 1 10 0 0 1 2 Other TRUE 975 +DLBCL11477T BN2 1 0 1982,579,889,1929,961,266,111,1935,943,667,1014 0.133,0.161,0.192,0.21,0.216,0.22,0.221,0.226,0.229,0.256,0.263 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.8949860409904 0 0 53.894986040990425 53.894986040990425 BN2 TRUE 976 +DLBCL11478T EZB 1 0.20940392439429784 595,408,331,1293,79,1372,651,54,1242,1241 0.219,0.224,0.24,0.26,0.265,0.268,0.286,0.289,0.295,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0861317606122 1 7.975385455670512 38.08613176061225 38.08613176061225 Other FALSE 977 +DLBCL11479T EZB 1 4.690274512135857 1508 0.29 EZB 3.44579708996619 4 16.161734265060325 3.44579708996619 3.44579708996619 Other FALSE 978 +DLBCL11480T N1 1 3.6174674136150617 901,870 0.162,0.233 N1,N1 10.4778647357435 7 37.903334245818584 10.477864735743523 10.477864735743523 Other FALSE 979 +DLBCL11481T Other 1 10 0 0 1 2 Other TRUE 980 +DLBCL11482T BN2 0.9011210977339955 0.09310969784016183 1115,261,981,1525,232,933,1075,742,692,830 0.186,0.21,0.21,0.218,0.221,0.235,0.244,0.272,0.283,0.286 BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 38.8163039842497,4.25926497297384 1 3.6141743352453557 43.075568957223496 38.81630398424966 BN2 TRUE 981 +DLBCL11483T Other 1 10 0 0 1 2 Other TRUE 982 +DLBCL11484T ST2 1 7.2522430289106605 2107 0.261 ST2 3.83839876369643 6 27.837000676196748 3.838398763696432 3.838398763696432 Other FALSE 983 +DLBCL11486T N1 1 10.000777844883713 913 0.274 N1 3.64790781394613 7 36.48191564589059 3.647907813946125 3.647907813946125 Other FALSE 984 +DLBCL11488T BN2 0.788795572688273 1.3574474891432349 661,151,736,1199,1135 0.156,0.193,0.208,0.24,0.249 BN2,MCD,BN2,BN2,BN2 19.366089693662,5.18537885434334 5 26.288449829184138 24.551468548005325 19.366089693661984 Other FALSE 985 +DLBCL11489T MCD 1 0.5184535659682165 1911,761,1897,986,755,1543,1184 0.152,0.178,0.186,0.199,0.202,0.238,0.239 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.9419334822003 4 18.63422358163916 35.941933482200255 35.941933482200255 MCD TRUE 986 +DLBCL11490T BN2 1 1.093812332220477 987,754,1381,52,1113 0.128,0.206,0.265,0.275,0.293 BN2,BN2,BN2,BN2,BN2 23.4905606817682 5 25.694264964491552 23.490560681768237 23.490560681768237 BN2 TRUE 987 +DLBCL11491T MCD 0.6088768192756671 0.39681506256153437 934,988,1366,1251,176,301,1064,1197,1358 0.148,0.193,0.23,0.286,0.299,0.309,0.313,0.313,0.32 MCD,MCD,EZB,MCD,N1,MCD,N1,MCD,EZB 7.48373535282983,21.8403540823014,6.5458162516761 2 8.666581471534505 35.869905686807364 21.84035408230143 MCD TRUE 988 +DLBCL11492T Other 1 10 0 0 1 2 Other TRUE 989 +DLBCL11493T MCD 0.8699369311996448 0.640471948369461 990,267,1994,850,2014,768,801 0.182,0.22,0.265,0.282,0.294,0.302,0.313 MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.54503288187375,23.7112275967662 4 15.186376137132566 27.256260478639927 23.71122759676618 MCD TRUE 990 +DLBCL11494T BN2 1 0.09898363588714591 991,98,555,1409,295,1001,1927,519,1006,1724 0.129,0.181,0.193,0.199,0.224,0.236,0.278,0.289,0.3,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.90835746181 1 4.544176139176739 45.90835746181 45.90835746181 BN2 TRUE 991 +DLBCL11495T MCD 1 1.8691810344660635 864,650,717 0.123,0.144,0.214 MCD,MCD,MCD 19.7616182885345 6 36.93804211528644 19.761618288534525 19.761618288534525 Other FALSE 992 +DLBCL11496T EZB 0.5140222537204949 1.8184872332482656 993,1053,62,179,1133,330 0.171,0.188,0.218,0.227,0.253,0.268 BN2,EZB,EZB,EZB,BN2,BN2 13.5281662246011,14.3088413918351 5 26.02044539362656 27.83700761643626 14.308841391835148 BN2 FALSE 993 +DLBCL11497T EZB 0.5156070060163884 3.229954968980665 141,561,2042,1052 0.179,0.18,0.214,0.249 BN2,EZB,EZB,ST2 5.58256452711108,10.2215009993953,4.02014337702901 6 33.01498794343776 19.824208903535414 10.221500999395325 EZB TRUE 994 +DLBCL11498T BN2 0.9226861349025091 0.10783573421120729 418,638,1996,812,995,957,1252,1056,571,1212 0.135,0.174,0.18,0.229,0.239,0.249,0.257,0.261,0.266,0.279 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 42.7781322785598,3.58447214388526 1 4.6130113024426445 46.36260442244508 42.77813227855982 BN2 TRUE 995 +DLBCL11499T EZB 0.8554540074967736 1.0683729585781963 1527,818,109,1959,165,353 0.213,0.306,0.307,0.308,0.322,0.337 EZB,EZB,EZB,EZB,EZB,BN2 2.96565505123935,17.5513790074749 5 18.751418717343164 20.517034058714206 17.55137900747486 Other FALSE 996 +DLBCL11500T MCD 1 0.17954923773830594 1399,918,1225,1826,2036,786,1905,824,815 0.163,0.185,0.271,0.277,0.298,0.316,0.323,0.328,0.329 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.5405227705442 2 6.201724534533816 34.54052277054423 34.54052277054423 MCD TRUE 997 +DLBCL11501T BN2 0.7581419463436394 1.8330401137026702 190,189,2218 0.144,0.173,0.247 BN2,BN2,ST2 12.694973392937,4.04987690081366 4 23.27039547164152 16.744850293750613 12.694973392936951 Other FALSE 998 +DLBCL11503T MCD 1 4.007583061133663 311,194 0.13,0.255 MCD,MCD 11.6209038490401 9 46.57173742047589 11.620903849040051 11.620903849040051 N1 FALSE 999 +DLBCL11504T EZB 0.5326926031766777 10.190107013508953 65,2127 0.214,0.244 EZB,ST2 4.66387684828609,4.09141057352785 9 47.52540418186216 8.755287421813946 4.663876848286094 Other FALSE 1000 +DLBCL11506T BN2 1 0 98,1409,991,1001,555,1724,519,976,1927,41,796 0.146,0.147,0.175,0.18,0.226,0.229,0.249,0.272,0.283,0.287,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.8015281200607 0 0 51.80152812006067 51.80152812006067 BN2 TRUE 1001 +DLBCL11507T EZB 0.7736203262988727 0.7314113959179602 1002,342,1521,1260 0.186,0.215,0.242,0.244 EZB,EZB,BN2,EZB 4.1350862505187,14.1310689343233 2 10.335624855066364 18.266155184842045 14.131068934323348 EZB TRUE 1002 +DLBCL11508T BN2 0.7338404746656302 0.8609387690233193 1003,1714,459,1854 0.179,0.186,0.202,0.232 BN2,ST2,BN2,BN2 14.8605239376633,5.38982263041666 2 12.79400118593345 20.250346568080005 14.860523937663345 BN2 TRUE 1003 +DLBCL11509T ST2 1 10.547634157520125 451 0.319 ST2 3.13954997807344 7 33.11482458796902 3.1395499780734437 3.1395499780734437 Other FALSE 1004 +DLBCL11510T Other 1 10 0 0 1 2 Other TRUE 1005 +DLBCL11511T BN2 1 2.015905235004477 1807,1006,1927,1854 0.197,0.23,0.297,0.315 BN2,BN2,BN2,BN2 15.9611258626882 7 32.17611718315858 15.961125862688245 15.961125862688245 BN2 TRUE 1006 +DLBCL11512T BN2 0.36232880963556297 1.2030297436437756 476,15,2121,1185,636,1190,826 0.176,0.213,0.229,0.267,0.29,0.338,0.375 MCD,EZB,BN2,ST2,ST2,BN2,BN2 9.9858644904606,4.69278274962532,5.68575002350284,7.19583042160296 3 12.013291998020303 27.560227685191727 9.985864490460605 Other FALSE 1007 +DLBCL11513T MCD 0.6233017747160112 0.6324855464203482 659,464,1609,1008,869 0.136,0.191,0.219,0.226,0.246 MCD,BN2,MCD,N1,MCD 5.24694750313055,16.0127792342746,4.43052457235624 2 10.127851423698594 25.69025130976143 16.012779234274632 N1 FALSE 1008 +DLBCL11514T MCD 0.9124584954465184 0.09240209207934026 759,858,1009,1904,1406,764,1449,1376,479,305 0.116,0.119,0.127,0.13,0.142,0.148,0.15,0.16,0.164,0.173 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.23347190416167,64.9724313523155 1 6.003588584435274 71.20590325647721 64.97243135231554 MCD TRUE 1009 +DLBCL11515T EZB 0.5742549067456566 1.4382729496523101 1480,1513,632 0.123,0.171,0.196 MCD,EZB,EZB 10.9223951642662,8.09772122649171 2 15.709385510177345 19.020116390757963 10.922395164266248 Other FALSE 1010 +DLBCL11516T EZB 0.8867131115733871 0.3750035914276733 1036,1322,1701,1011,466,881,1031,809 0.179,0.205,0.211,0.218,0.238,0.248,0.255,0.275 EZB,EZB,EZB,EZB,EZB,N1,EZB,EZB 31.5340929636505,4.02881069940269 3 11.825398113783056 35.56290366305318 31.534092963650487 EZB TRUE 1011 +DLBCL11518T EZB 0.48274507514003706 2.165259682662179 1509,1494,881,2196,1036,628 0.315,0.338,0.339,0.385,0.411,0.431 EZB,BN2,N1,BN2,EZB,EZB 5.55248621366425,7.93629548717953,2.95114880466646 4 17.18414064808363 16.43993050551024 7.936295487179528 BN2 FALSE 1012 +DLBCL11519T ST2 0.7434926086071293 0.537905518310335 1555,1341,1644,1943,293,722,1776,486 0.166,0.178,0.198,0.215,0.225,0.239,0.246,0.267 ST2,BN2,ST2,ST2,ST2,ST2,BN2,ST2 9.69630852130123,28.1049745865614 3 15.117820922083101 37.80128310786263 28.104974586561397 ST2 TRUE 1013 +DLBCL11520T BN2 0.8986054240006325 0.2877758129200703 692,1935,232,1075,933,579,817,1982,1525 0.155,0.164,0.193,0.202,0.231,0.239,0.258,0.259,0.268 BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 38.348764687784,4.32710134143886 2 11.035846932507521 42.67586602922284 38.34876468778398 BN2 TRUE 1014 +DLBCL11523T MCD 1 2.6518534949375927 864,650,717 0.176,0.203,0.237 MCD,MCD,MCD 14.8279714276686 7 39.32160785329785 14.82797142766864 14.82797142766864 Other FALSE 1015 +DLBCL11525T EZB 0.6210298863758215 1.0718030635846245 1703,1392,1122,1493,696,1227 0.172,0.182,0.221,0.239,0.246,0.31 ST2,EZB,BN2,EZB,EZB,EZB 4.51831435329154,16.9547235262445,5.82794054066228 4 18.17212461765921 27.30097842019837 16.954723526244546 Other FALSE 1016 +DLBCL11526T EZB 1 0 728,1017,1295,505,1243,364,1581,9,1668,119,474 0.143,0.17,0.177,0.235,0.249,0.252,0.269,0.274,0.281,0.286,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.6454217567903 0 0 48.64542175679035 48.64542175679035 EZB TRUE 1017 +DLBCL11527T ST2 0.6276566288787618 1.3449163213119997 618,701,1211,1018,744 0.217,0.285,0.332,0.356,0.373 ST2,EZB,ST2,ST2,BN2 2.6800733401575,3.50455760583509,10.4253893354718 4 14.021276273308079 16.61002028146438 10.425389335471795 ST2 TRUE 1018 +DLBCL11528T EZB 1 14.314903571621906 1535 0.31 EZB 3.22091438137584 9 46.107078781845374 3.22091438137584 3.22091438137584 Other FALSE 1019 +DLBCL11530T EZB 1 0 366,1020,427,689,161,78,645,48,128,1090,326 0.185,0.188,0.197,0.239,0.276,0.276,0.291,0.319,0.325,0.328,0.356 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.7296132755986 0 0 42.729613275598595 42.729613275598595 EZB TRUE 1020 +DLBCL11531T EZB 1 0 1972,695,397,398,2089,172,1752,1671,676,1493,108 0.185,0.197,0.208,0.22,0.242,0.262,0.272,0.311,0.321,0.335,0.35 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6687073979041 0 0 43.668707397904086 43.668707397904086 Other FALSE 1021 +DLBCL11532T EZB 1 0 1937,369,316,1,428,372,1279,1222,1055,429,1120 0.129,0.129,0.13,0.173,0.176,0.18,0.184,0.187,0.19,0.194,0.196 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.4548365774755 0 0 66.45483657747552 66.45483657747552 EZB TRUE 1022 +DLBCL11533T MCD 0.6777735402782542 3.9948670850952173 81,401,82 0.136,0.172,0.205 MCD,EZB,MCD 5.80836306697421,12.2173542251127 8 48.80670626085163 18.025717292086892 12.217354225112679 Other FALSE 1023 +DLBCL11534T Other 1 10 0 0 1 2 Other TRUE 1024 +DLBCL11535T Other 1 10 0 0 1 2 Other TRUE 1025 +DLBCL11536T MCD 0.6537351537561876 2.5361023607960305 904,1733 0.122,0.23 MCD,EZB 4.33948773556226,8.1927914797143 4 20.777757813213032 12.53227921527656 8.192791479714296 Other FALSE 1026 +DLBCL11537T BN2 0.9329767812664944 0.07294028873365689 145,592,417,1193,560,564,669,1375,1027,1212 0.137,0.147,0.169,0.207,0.237,0.24,0.242,0.243,0.29,0.31 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 44.9008133240256,3.22558620230964 1 3.2750782882304526 48.1263995263352 44.900813324025556 BN2 TRUE 1027 +DLBCL11538T ST2 0.6302997618782207 1.6593018113989328 1847,2017,1082,1121,610 0.18,0.183,0.19,0.197,0.239 ST2,ST2,BN2,ST2,BN2 9.42897402975705,16.0754023743803 5 26.673944278775874 25.504376404137314 16.07540237438026 ST2 TRUE 1028 +DLBCL11539T BN2 1 0 1029,935,557,795,857,585,1252,841,1056,995,957 0.14,0.146,0.193,0.208,0.222,0.24,0.252,0.254,0.26,0.271,0.272 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.7497510658789 0 0 51.74975106587889 51.74975106587889 BN2 TRUE 1029 +DLBCL11540T Other 1 10 0 0 1 2 Other TRUE 1030 +DLBCL11541T EZB 0.8987785101000821 0.20879710506426388 466,1701,1031,646,2176,1550,1036,1384,337 0.156,0.216,0.223,0.245,0.246,0.256,0.261,0.263,0.296 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.90457672726938,34.6700059145195 2 7.238996867512581 38.57458264178889 34.670005914519514 EZB TRUE 1031 +DLBCL11542T EZB 1 0.10034280248779753 1581,1243,1032,474,1545,1017,1245,728,1042,340 0.211,0.218,0.228,0.252,0.252,0.255,0.255,0.256,0.269,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6722608530796 1 4.081168637512748 40.67226085307962 40.67226085307962 EZB TRUE 1032 +DLBCL11558T EZB 1 0.13176962256037258 93,2063,1138,1081,382,39,558,174,1560,1048 0.185,0.189,0.195,0.222,0.223,0.224,0.234,0.244,0.249,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5370580317872 1 6.000400949358385 45.53705803178722 45.53705803178722 EZB TRUE 1033 +DLBCL11559T EZB 1 0.18548962357190563 1034,443,354,660,192,548,333,289,1705 0.139,0.15,0.154,0.207,0.273,0.279,0.294,0.298,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4004653680081 2 7.864846360385436 42.400465368008064 42.400465368008064 EZB TRUE 1034 +DLBCL11560T ST2 0.8082723711620188 1.5300360219897895 1884,1045,2082,2052,1128 0.198,0.225,0.23,0.232,0.232 ST2,ST2,ST2,ST2,EZB 4.30402649345971,18.1446238108572 6 27.761928036065193 22.448650304316928 18.14462381085722 Other FALSE 1035 +DLBCL11562T EZB 0.9050771766004391 0.3468717658096254 1701,466,1036,1011,1031,1322,809,881 0.162,0.188,0.195,0.202,0.206,0.233,0.246,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,N1 34.7839386308432,3.64807526823133 3 12.065566214694224 38.43201389907452 34.783938630843195 EZB TRUE 1036 +DLBCL11563T EZB 0.6847818570592594 1.4225833193918715 160,1037,1944,388,1439,724 0.184,0.186,0.192,0.196,0.233,0.266 ST2,EZB,EZB,EZB,EZB,ST2 19.9854449507161,9.19968129743243 5 28.43096061751318 29.1851262481485 19.985444950716065 EZB TRUE 1037 +DLBCL11564T ST2 0.6424497529284393 1.3718633888485299 2134,508,1486,781,955,68 0.173,0.202,0.212,0.213,0.219,0.219 MCD,ST2,ST2,MCD,ST2,ST2 10.4593968702646,18.7935457746645 4 25.782177394911184 29.25294264492906 18.79354577466448 ST2 TRUE 1038 +DLBCL11565T Other 1 10 0 0 1 2 Other TRUE 1039 +DLBCL11566T EZB 1 0.37198836676007047 2100,548,729,660,289,1867,443,712 0.147,0.176,0.254,0.291,0.309,0.327,0.331,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.0732698640974 3 11.930883273400578 32.0732698640974 32.0732698640974 EZB TRUE 1040 +DLBCL11567T EZB 1 0.15169100725566625 329,1094,138,2006,164,1041,83,435,86,326 0.118,0.137,0.144,0.154,0.167,0.177,0.185,0.192,0.196,0.198 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.5652205536942 1 9.338890317707122 61.56522055369421 61.56522055369421 EZB TRUE 1041 +DLBCL11568T EZB 0.9166659241212828 0 340,1244,1042,341,1245,1581,365,166,105,1243 0.112,0.121,0.148,0.196,0.212,0.212,0.215,0.23,0.237,0.241 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.65538383649503,51.2087244218167 0 0 55.864108258311774 51.20872442181675 EZB TRUE 1042 +DLBCL11569T EZB 1 0.08195256281348061 80,121,17,187,404,1537,149,639,1705,2059 0.104,0.118,0.12,0.135,0.149,0.159,0.198,0.2,0.2,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.8394216862115 1 5.477661904155965 66.83942168621148 66.83942168621148 Other FALSE 1043 +DLBCL11570T BN2 1 1.7240777237654195 2032,2064,57,1632,2085 0.293,0.33,0.34,0.345,0.347 BN2,BN2,BN2,BN2,BN2 15.1670994796789 6 26.149258347048544 15.16709947967894 15.16709947967894 Other FALSE 1044 +DLBCL11571T ST2 0.6846710766461268 0.294185102236411 2082,1394,1045,1417,1147,1687,1139,1917,545 0.141,0.155,0.167,0.192,0.201,0.22,0.235,0.238,0.249 ST2,EZB,ST2,ST2,ST2,ST2,BN2,ST2,BN2 8.27358200852404,6.46127503656343,31.9936729240822 2 9.412061940089423 46.7285299691697 31.993672924082222 ST2 TRUE 1045 +DLBCL11572T EZB 1 0.09215670155216699 172,108,2089,1573,695,676,117,382,2063,1138 0.126,0.206,0.219,0.225,0.256,0.262,0.265,0.289,0.296,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5250672219157 1 4.011126630008092 43.52506722191571 43.52506722191571 EZB TRUE 1046 +DLBCL11573T EZB 1 0 12,1237,299,1111,1047,429,428,1055,668,372,1110 0.148,0.152,0.154,0.173,0.175,0.179,0.2,0.202,0.207,0.209,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2150015201613 0 0 61.215001520161266 61.215001520161266 EZB TRUE 1047 +DLBCL11575T EZB 1 0.16222361436949453 1138,382,676,93,2089,172,2063,108,558,174 0.193,0.197,0.203,0.224,0.227,0.227,0.227,0.238,0.24,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8486991283946 1 7.2755180723781665 44.84869912839457 44.84869912839457 EZB TRUE 1048 +DLBCL11576T EZB 0.7041695002137258 0 216,213,877,212,693,1049,1423,390,210,211,396 0.177,0.192,0.203,0.207,0.231,0.254,0.259,0.26,0.266,0.273,0.31 EZB,BN2,EZB,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB 10.0360471606089,33.2520821705845,3.93357248987664 0 0 47.2217018210701 33.252082170584515 ST2 FALSE 1049 +DLBCL11577T MCD 1 0 904 0.211 MCD 4.72814497551971 0 0 4.728144975519705 4.728144975519705 Other FALSE 1050 +DLBCL11578T BN2 1 4.944689987238244 1803 0.239 BN2 4.18500855430925 4 20.69356989499935 4.18500855430925 4.18500855430925 Other FALSE 1051 +DLBCL11579T ST2 0.5602785613931073 5.479655423692083 1052,2042 0.178,0.227 ST2,EZB 4.40296025274363,5.61010680783205 5 30.74145219702878 10.01306706057568 5.610106807832052 ST2 TRUE 1052 +DLBCL11581T EZB 0.8921433518888187 0.8008203097109667 62,1453,179,2126,1053,1087 0.113,0.144,0.15,0.159,0.167,0.238 EZB,EZB,EZB,EZB,EZB,BN2 4.20025568227685,34.7426908660631 5 27.82265245955305 38.942946548339975 34.74269086606313 EZB TRUE 1053 +DLBCL11584T EZB 1 0 1474,1673,2201,153,1054,1903,173,287,1277,1079,1545 0.133,0.168,0.186,0.187,0.191,0.209,0.209,0.265,0.29,0.302,0.308 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7935660793881 0 0 52.79356607938809 52.79356607938809 EZB TRUE 1054 +DLBCL11585T EZB 1 0 316,369,1937,1,1222,1279,1120,1886,428,372,1055 0.125,0.139,0.146,0.166,0.173,0.174,0.18,0.189,0.192,0.197,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.701754148347 0 0 65.70175414834705 65.70175414834705 EZB TRUE 1055 +DLBCL11586T BN2 0.6955250069124519 0 590,1086,1076,13,932,960,157,1722,412,1806 0.157,0.162,0.182,0.194,0.216,0.217,0.229,0.236,0.274,0.275 BN2,BN2,EZB,BN2,EZB,ST2,BN2,BN2,BN2,BN2 33.6255307117022,10.1098279942406,4.61017938041781 0 0 48.34553808636065 33.6255307117022 BN2 TRUE 1056 +DLBCL11587T BN2 0.5322145612616535 3.2276023115208115 2170,114,1657,1719 0.197,0.249,0.273,0.273 BN2,EZB,BN2,EZB 8.74262058555936,7.68425162333763 5 28.217702410700824 16.426872208896995 8.7426205855593615 ST2 FALSE 1057 +DLBCL11589T EZB 0.8358362320759486 0.12586470047482343 218,139,143,11,1058,219,220,308,221,1533 0.141,0.177,0.178,0.188,0.22,0.222,0.241,0.244,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,BN2,EZB 8.00152769350471,40.7396031582587 1 5.127677948977401 48.74113085176341 40.739603158258696 EZB TRUE 1058 +DLBCL11590T EZB 0.4079965709363233 4.33225943459348 1388,1797,1841 0.198,0.259,0.289 EZB,ST2,BN2 3.45595257737565,5.04508744618125,3.86447431055413 4 21.856627687067853 12.365514334111026 5.045087446181252 EZB TRUE 1059 +DLBCL11591T EZB 1 0.19232897208833394 354,149,1705,333,1034,319,404,121,1060,80 0.188,0.193,0.196,0.221,0.224,0.231,0.241,0.266,0.266,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.1011668888786 1 8.48193209563409 44.1011668888786 44.1011668888786 EZB TRUE 1060 +DLBCL11595T EZB 1 17.76704165061437 2166 0.329 EZB 3.03863001802654 10 53.98746609108461 3.038630018026539 3.038630018026539 Other FALSE 1061 +DLBCL11596T EZB 1 10.156294496611709 2162 0.311 EZB 3.21667685690072 8 32.66951745911903 3.2166768569007194 3.2166768569007194 Other FALSE 1062 +DLBCL11646T MCD 1 1.2719415255595548 650,1063,864,1194,753 0.15,0.185,0.202,0.211,0.23 MCD,MCD,MCD,MCD,MCD 26.1268715786891 6 33.23185289389639 26.1268715786891 26.1268715786891 MCD TRUE 1063 +DLBCL11648T MCD 0.5622328220913498 1.7215068039763564 1064,988,176,2108,1609,743 0.133,0.212,0.24,0.283,0.293,0.301 N1,MCD,N1,MCD,MCD,MCD 14.9772196236125,11.6616015819895 5 25.78338548669705 26.638821205601914 14.977219623612458 N1 FALSE 1064 +DLBCL11649T BN2 0.8444295993069658 1.0867488283758027 1614,1816,591,610,1666 0.268,0.369,0.428,0.475,0.499 BN2,BN2,BN2,BN2,EZB 10.8819655043249,2.00479913923553 4 11.825963262251024 12.886764643560463 10.881965504324937 Other FALSE 1065 +DLBCL11651T EZB 1 0 1914,958,1066,1172,2132,468,1437,1570,1242,1372,54 0.124,0.148,0.168,0.177,0.184,0.19,0.211,0.224,0.234,0.235,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.9640048237265 0 0 58.96400482372649 58.96400482372649 EZB TRUE 1066 +DLBCL11652T BN2 1 0 1532 0.124 BN2 8.06824837427792 0 0 8.06824837427792 8.06824837427792 Other FALSE 1067 +DLBCL11655T BN2 0.6900347695184301 0.8626531534550874 648,922,489,594,666,2041,1414 0.197,0.237,0.24,0.265,0.273,0.277,0.286 ST2,BN2,BN2,BN2,BN2,ST2,BN2 19.3280063336406,8.68218559788983 4 16.67336561371495 28.010191931530414 19.32800633364058 Other FALSE 1068 +DLBCL11656T EZB 1 0.14447356573977033 4,2217,2091,1587,144,220,1103,1022,219,1110 0.116,0.17,0.17,0.181,0.19,0.198,0.199,0.221,0.227,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.4773345994133 1 7.870534781575803 54.47733459941331 54.47733459941331 EZB TRUE 1069 +DLBCL11657T BN2 0.3986600165137226 1.3034439775739983 1070,133,1183,2140,972,132,1074 0.174,0.213,0.229,0.304,0.319,0.324,0.326 EZB,BN2,ST2,ST2,BN2,BN2,ST2 10.9176168755431,5.74811831478757,10.7200482424238 3 14.230501965886928 27.38578343275444 10.917616875543116 EZB FALSE 1070 +DLBCL11658T MCD 0.8624213119332564 0.7636016202878455 2030,285,1398,1194,952,1890,650 0.191,0.225,0.228,0.25,0.253,0.292,0.301 MCD,MCD,MCD,MCD,BN2,MCD,MCD 3.95828064801148,24.8127499791392 4 18.94705608786786 28.771030627150633 24.812749979139152 ST2 FALSE 1071 +DLBCL11659T EZB 1 0 2132,1914,1172,468,1570,958,278,162,1779,1066,1517 0.127,0.135,0.137,0.19,0.192,0.199,0.208,0.21,0.217,0.22,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2801886440991 0 0 61.280188644099134 61.280188644099134 Other FALSE 1072 +DLBCL11660T ST2 0.7466598656343291 0.6670467895286004 508,1486,955,171,2134,68,1174 0.164,0.18,0.189,0.232,0.239,0.243,0.249 ST2,ST2,ST2,BN2,MCD,ST2,ST2 4.31890962781472,4.18202413071582,25.0544828745889 3 16.712512364793795 33.55541663311939 25.054482874588857 Other FALSE 1073 +DLBCL11661T BN2 0.4055518783368154 0.9328063754350081 15,1185,2121,476,597,827,1377 0.196,0.214,0.237,0.242,0.285,0.297,0.425 EZB,ST2,BN2,MCD,BN2,BN2,MCD 11.0903467114807,5.09525645039849,6.48067204647212,4.68003312823598 3 10.345146118253854 27.346308336587267 11.090346711480679 ST2 FALSE 1074 +DLBCL11662T BN2 0.8944533410773117 0.2987767964475576 232,1075,692,933,1525,1935,742,817,981 0.146,0.151,0.163,0.208,0.212,0.221,0.237,0.247,0.269 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2 40.8049690446251,4.81503947977498 2 12.191577930294844 45.62000852440009 40.8049690446251 BN2 TRUE 1075 +DLBCL11664T BN2 0.8202305225241888 0.4304218605497936 2041,489,922,1362,594,1320,582,666 0.148,0.167,0.18,0.215,0.248,0.276,0.279,0.29 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.8579739945739,6.76312537589471 3 13.28194657954165 37.62109937046863 30.857973994573914 EZB FALSE 1076 +DLBCL11665T ST2 0.6784038302324997 1.207612539398397 1822,1427,1847,1077 0.146,0.186,0.218,0.222 EZB,ST2,ST2,ST2 6.86091729193236,14.473034840932 2 17.47781835705932 21.333952132864322 14.473034840931962 ST2 TRUE 1077 +DLBCL11666T EZB 1 0.08123449350656657 1237,1047,12,299,668,1111,429,276,517,428 0.134,0.151,0.156,0.161,0.186,0.199,0.203,0.213,0.221,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7640535923635 1 4.5299646494486865 55.76405359236354 55.76405359236354 EZB TRUE 1078 +DLBCL11667T EZB 1 0 240,2016,153,1673,174,1903,558,1474,287,39,382 0.154,0.163,0.177,0.197,0.214,0.22,0.228,0.246,0.248,0.259,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6633086955053 0 0 52.66330869550529 52.66330869550529 EZB TRUE 1079 +DLBCL11669T EZB 1 0 617,513,679,1080,135,1253,103,1287,468,162,1867 0.175,0.175,0.18,0.181,0.186,0.19,0.19,0.211,0.219,0.24,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6894816559056 0 0 55.68948165590564 55.68948165590564 EZB TRUE 1080 +DLBCL11670T EZB 1 0.10134616943230179 93,1081,39,1138,1560,174,2063,558,382,1889 0.174,0.18,0.186,0.204,0.211,0.221,0.226,0.227,0.235,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.0131190061824 1 4.86594569377383 48.01311900618239 48.01311900618239 EZB TRUE 1081 +DLBCL11671T EZB 0.5527796335114438 3.0774517338066656 1983,1863,2017,1082 0.184,0.258,0.265,0.265 EZB,EZB,ST2,BN2 3.76746724446993,9.3176833062003,3.77090336544679 6 28.67472064572754 16.856053916117016 9.317683306200301 BN2 FALSE 1082 +DLBCL11672T EZB 0.8861074604119302 0.2066952323295722 343,16,575,308,11,1533,42,231,143 0.116,0.157,0.172,0.174,0.181,0.2,0.213,0.216,0.237 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.7616415125478,44.8267599173148 2 9.265477555691344 50.58840142986263 44.82675991731483 EZB TRUE 1083 +DLBCL11673T ST2 1 5.272615045333875 2107 0.306 ST2 3.26300277768004 4 17.204557538561993 3.2630027776800374 3.2630027776800374 Other FALSE 1084 +DLBCL11675T EZB 1 0 240,2016,174,558,39,93,153,1138,382,1673,1903 0.112,0.126,0.165,0.197,0.2,0.226,0.237,0.244,0.246,0.256,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.543528092941 0 0 57.543528092941 57.543528092941 EZB TRUE 1085 +DLBCL11676T EZB 0.4383470721865005 2.8832879963057643 1123,854,649,234,1687 0.17,0.172,0.28,0.302,0.338 EZB,BN2,EZB,ST2,ST2 5.82268402120512,9.44094938166094,6.27397909804319 6 27.22097602587333 21.537612500909255 9.440949381660944 BN2 FALSE 1086 +DLBCL11677T EZB 0.8696073454696892 0.6063469923902671 2126,1453,179,62,1087,1059,1053 0.123,0.132,0.171,0.178,0.186,0.225,0.237 EZB,EZB,EZB,EZB,BN2,EZB,EZB 5.37319905836831,35.8346364422064 4 21.7282240301305 41.207835500574696 35.83463644220638 BN2 FALSE 1087 +DLBCL11678T EZB 0.8881637723430585 0 1541,1088,187,206,2059,17,1537,80,1606,404,121 0.155,0.169,0.172,0.186,0.189,0.2,0.2,0.205,0.207,0.222,0.223 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0938591016991,6.43366081381012 0 0 57.52751991550918 51.09385910169906 EZB TRUE 1088 +DLBCL11679T EZB 0.5369265597122261 3.3834549723960063 2042,844,561,141 0.218,0.266,0.287,0.312 EZB,BN2,EZB,BN2 6.97299375306198,8.08507943016499 7 27.3555022002084 15.058073183226972 8.085079430164988 BN2 FALSE 1089 +DLBCL11680T EZB 1 0 2097,71,1236,1079,1589,1109,1090,644,1054,240,1579 0.181,0.182,0.215,0.247,0.267,0.295,0.307,0.308,0.336,0.357,0.36 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8957984434221 0 0 41.89579844342209 41.89579844342209 EZB TRUE 1090 +DLBCL11681T BN2 1 0.7502167486734478 1204,273,871,1275,1407,1112,754 0.181,0.215,0.228,0.233,0.27,0.277,0.282 BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.6979914460476 4 22.27993058478568 29.697991446047585 29.697991446047585 Other FALSE 1091 +DLBCL11683T Other 1 10 0 0 1 2 Other TRUE 1092 +DLBCL11684T EZB 0.6708355459038603 0.9621125512732438 491,77,282,751,383,994 0.261,0.273,0.3,0.309,0.324,0.358 EZB,EZB,BN2,MCD,EZB,EZB 3.33569844340565,13.3881412162589,3.23357210542043 4 12.880898702381344 19.95741176508501 13.388141216258926 EZB TRUE 1093 +DLBCL11685T EZB 1 0.09562168984887888 1094,86,1041,329,164,78,138,168,326,2006 0.164,0.177,0.18,0.183,0.196,0.202,0.21,0.219,0.225,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9784571982631 1 4.874646223186664 50.97845719826313 50.97845719826313 EZB TRUE 1094 +DLBCL_P01-Tumor ST2 0.64398828866145 1.0347463193117425 1847,1121,1082,1822,2017 0.138,0.184,0.207,0.209,0.211 ST2,ST2,BN2,EZB,ST2 4.8411828073779,4.77908574389845,17.4020687620261 3 18.00672659991632 27.02233731330241 17.40206876202606 Other FALSE 1095 +DLBCL_P04-Tumor BN2 1 3.2795665508139966 1096,523 0.29,0.315 BN2,BN2 6.62459477575775 6 21.725799439272265 6.62459477575775 6.62459477575775 BN2 TRUE 1096 +DLBCL_P07-Tumor ST2 1 14.016899216734158 1028 0.257 ST2 3.88925809517028 10 54.51533874786924 3.889258095170277 3.889258095170277 Other FALSE 1097 +DLBCL_P10-Tumor BN2 1 10.467923618812291 1803 0.291 BN2 3.43513513300666 8 35.95873219261233 3.435135133006661 3.435135133006661 Other FALSE 1098 +SP124957 EZB 0.5546830654108883 0 1099,1500 0.205,0.255 EZB,ST2 4.88388929217018,3.92093926078443 0 0 8.80482855295461 4.8838892921701795 EZB TRUE 1099 +SP124959 EZB 0.6663158731346609 2.3160277858904204 115,2216,1666 0.165,0.176,0.189 EZB,ST2,EZB 11.3597740366268,5.68885784300281 4 26.309552310264362 17.048631879629657 11.359774036626849 Other FALSE 1100 +SP124969 ST2 0.75407442573606 0.7658079750151477 600,89,831,1145,675,130,1411 0.161,0.192,0.247,0.266,0.267,0.285,0.328 ST2,ST2,ST2,BN2,ST2,BN2,ST2 7.25857322632517,22.2567516765452 4 17.044397931830094 29.515324902870397 22.256751676545225 ST2 TRUE 1101 +SP124971 EZB 0.9179287169103686 0.20001924152783254 1102,473,1864,809,371,1865,381,1011,380 0.12,0.158,0.211,0.272,0.278,0.311,0.32,0.32,0.334 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.2146078340342,35.9538773293265 2 7.19146727339662 39.16848516336071 35.9538773293265 EZB TRUE 1102 +SP124973 EZB 0.9265329202222824 0 29,1022,1563,1103,2091,1279,1,1120,1110,2148,1055 0.12,0.172,0.173,0.173,0.182,0.184,0.189,0.222,0.226,0.229,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 55.1306482888515,4.37144503733623 0 0 59.50209332618774 55.13064828885151 EZB TRUE 1103 +SP124975 MCD 0.4325153200949604 4.245636939965954 68,2134,781,1400 0.114,0.179,0.28,0.304 ST2,MCD,MCD,EZB 3.28745714835304,9.16235403383654,8.73407204262869 7 38.90002874310247 21.18388322481827 9.162354033836536 Other FALSE 1104 +SP124977 BN2 0.7029980965641499 2.9609955660308342 52,1381,1131 0.208,0.249,0.268 BN2,BN2,ST2 8.82215681686867,3.72717562085242 5 26.12236721757683 12.549332437721091 8.82215681686867 Other FALSE 1105 +SP124981 Other 1 10 0 0 1 2 Other TRUE 1106 +SP192765 EZB 0.48471925832721646 0.374648676546756 1093,1330,972,132,757,384,2140,133,1107 0.192,0.206,0.216,0.217,0.222,0.227,0.231,0.302,0.304 EZB,EZB,BN2,BN2,EZB,EZB,ST2,BN2,ST2 12.5467439553207,18.9692931854715,7.61856058683927 2 7.106820586964306 39.134597727631544 18.969293185471532 ST2 FALSE 1107 +SP192767 BN2 0.37872324894774345 4.927952901641618 1108,65,2127 0.182,0.204,0.243 BN2,EZB,ST2 5.49674431380925,4.89980140536964,4.11733566803876 5 27.087697090818345 14.513881387217644 5.4967443138092476 BN2 TRUE 1108 +SP193976 EZB 1 0.1828174367848319 276,1304,517,1047,142,1237,1085,375,372,668 0.178,0.214,0.215,0.241,0.257,0.258,0.269,0.274,0.274,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.5232401841678 1 7.591172337470496 41.52324018416785 41.52324018416785 EZB TRUE 1109 +SP192997 EZB 1 0 369,1,1279,1937,1055,316,428,429,1120,1111,1563 0.124,0.138,0.153,0.162,0.163,0.17,0.182,0.185,0.188,0.188,0.192 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.8052005742785 0 0 66.80520057427847 66.80520057427847 EZB TRUE 1110 +SP192993 EZB 1 0 372,428,1937,429,369,517,1111,1962,1055,1033,1304 0.108,0.121,0.144,0.152,0.167,0.171,0.173,0.179,0.181,0.182,0.185 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 70.6185114857347 0 0 70.61851148573471 70.61851148573471 EZB TRUE 1111 +SP192970 BN2 1 0.6939094301436548 817,57,2064,742,1075,106,1407 0.148,0.207,0.216,0.249,0.261,0.27,0.276 BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.3652367140104 4 21.76463353453978 31.365236714010376 31.365236714010376 BN2 TRUE 1112 +SP193005 BN2 1 1.241540839079802 987,1381,52,754 0.19,0.22,0.226,0.269 BN2,BN2,BN2,BN2 17.9608288528104 4 22.299102524486965 17.96082885281042 17.96082885281042 BN2 TRUE 1113 +SP193684 EZB 0.5836677039482706 7.531586983596391 2166,1901 0.213,0.299 EZB,BN2 3.34432037476217,4.68849477428559 7 35.311806214669026 8.032815149047753 4.688494774285587 Other FALSE 1114 +SP193025 BN2 0.911546277380667 0.20925055034580892 1115,1525,742,1075,232,261,830,981,933 0.144,0.168,0.218,0.218,0.23,0.24,0.266,0.267,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 38.1147869018618,3.69854484843051 2 7.975540135527812 41.81333175029231 38.114786901861805 BN2 TRUE 1115 +SP192940 MCD 0.43676407420808794 3.2363166348334347 2135,1278,254,952,736 0.256,0.289,0.293,0.334,0.335 MCD,EZB,MCD,BN2,BN2 5.97455033138583,3.45848565147562,7.31489423767369 5 23.673313903430603 16.747930220535146 7.314894237673691 Other FALSE 1116 +SP192870 MCD 1 3.793025214731644 904 0.287 MCD 3.48358675350341 3 13.213332393743595 3.4835867535034133 3.4835867535034133 Other FALSE 1117 +SP193794 EZB 1 0.20752038249276425 6,1142,1118,1788,1872,196,1991,367,79 0.131,0.137,0.14,0.169,0.197,0.198,0.203,0.205,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5606367159511 2 10.907403435357411 52.56063671595115 52.56063671595115 EZB TRUE 1118 +SP192856 BN2 0.8651541528314151 0.42611723830944404 684,1722,960,863,1317,1119,915,590 0.178,0.188,0.2,0.216,0.226,0.238,0.247,0.261 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 32.0498515480228,4.99539806805118 3 13.656994229871117 37.045249616073946 32.04985154802276 BN2 TRUE 1119 +SP192850 EZB 1 0 316,1222,1886,1937,369,1120,463,1,1279,2015,349 0.122,0.139,0.156,0.17,0.175,0.178,0.186,0.19,0.193,0.21,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.3429189441507 0 0 64.34291894415068 64.34291894415068 EZB TRUE 1120 +SP192833 EZB 0.40674572370669987 5.711356792704715 1863,1121,1082 0.147,0.186,0.22 EZB,ST2,BN2 4.53522041428891,6.80000889686465,5.3828540036401 7 38.8372770035604 16.718083314793653 6.800008896864648 ST2 FALSE 1121 +SP192815 EZB 0.6390297307608582 0 1122,696,1392 0.161,0.167,0.199 BN2,EZB,EZB 6.22259696406521,11.0159334478196 0 0 17.23853041188486 11.015933447819648 BN2 FALSE 1122 +SP193512 EZB 0.41869441956110554 2.1223318469553236 1123,854,1341,649,234,293 0.122,0.158,0.31,0.311,0.315,0.318 EZB,BN2,BN2,EZB,ST2,ST2 9.56506050758269,11.4409838575394,6.31933501187649 5 24.28156440135772 27.32537937699862 11.44098385753944 EZB TRUE 1123 +SP193656 EZB 0.5096904348303333 3.2311802841585093 1178,2138,816,911 0.209,0.215,0.221,0.246 ST2,EZB,EZB,ST2 9.19350662490242,8.84392550380286 6 29.705877348665346 18.037432128705287 9.193506624902422 Other FALSE 1124 +SP193528 ST2 1 13.023047065108381 1946 0.267 ST2 3.73981884528815 9 48.70383683716686 3.7398188452881502 3.7398188452881502 Other FALSE 1125 +SP193337 BN2 1 1.3768538199369253 1126,811,1158,1027,661 0.134,0.234,0.246,0.283,0.291 BN2,BN2,BN2,BN2,BN2 22.7945409557911 6 31.38475078868965 22.794540955791085 22.794540955791085 BN2 TRUE 1126 +SP192798 MCD 0.9135557558239127 0.24742998283594925 1728,214,154,997,1531,789,1127,1501,1732 0.176,0.178,0.192,0.202,0.226,0.23,0.249,0.28,0.289 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.5690160190816,37.7178973329688 2 9.332538689704563 41.28691335205039 37.71789733296879 MCD TRUE 1127 +SP193546 ST2 0.6042256355094994 0.9002191288219875 545,1128,1970,444,2052,2082,1417 0.142,0.162,0.185,0.22,0.291,0.292,0.296 BN2,EZB,ST2,ST2,ST2,ST2,ST2 7.05574844813604,6.16594067357872,20.1854496623641 3 18.17132790993347 33.40713878407883 20.185449662364075 EZB FALSE 1128 +SP192800 MCD 0.7572159720893612 0.5106504066447656 2014,291,236,267,715,1325,790,1503,686 0.154,0.178,0.229,0.286,0.291,0.334,0.335,0.338,0.342 MCD,BN2,MCD,MCD,MCD,MCD,MCD,BN2,MCD 8.5684558390767,26.7240463605758 2 13.646645141221576 35.29250219965245 26.724046360575752 Other FALSE 1129 +SP193375 ST2 0.8431112037477578 0.4869045720581796 1130,1789,1943,486,1713,1018,1211 0.133,0.166,0.173,0.177,0.212,0.23,0.231 ST2,EZB,ST2,ST2,ST2,ST2,ST2 6.02136851691572,32.3584818022537 3 15.755492734378727 38.379850319169414 32.35848180225369 ST2 TRUE 1130 +SP193420 BN2 1 1.4419119796730935 987,52,754,1381 0.188,0.213,0.26,0.264 BN2,BN2,BN2,BN2 17.648365160272 5 25.447389146241452 17.648365160272 17.648365160272 ST2 FALSE 1131 +SP194216 EZB 0.9067389784692703 0 351,1896,527,727,1132,1268,2001,2114,1879,1516,1281 0.135,0.143,0.176,0.198,0.199,0.236,0.247,0.249,0.254,0.258,0.267 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.9984107129314,5.03964420299275 0 0 54.038054915924164 48.99841071293141 EZB TRUE 1132 +SP194228 EZB 0.58892364426975 0.6954705291435269 1509,1133,1012,1850,1356,330,1494,1492 0.121,0.31,0.312,0.315,0.36,0.38,0.384,0.397 EZB,BN2,BN2,EZB,EZB,BN2,BN2,EZB 11.6706824221667,16.71986415022 3 11.628172767761406 28.390546572386683 16.719864150220022 BN2 FALSE 1133 +SP194234 MCD 0.44420106862532593 2.406063469920699 879,1441,1416 0.135,0.208,0.223 MCD,EZB,ST2 4.80871731669865,7.41936714177927,4.47463794770644 3 17.85146824976504 16.702722406184357 7.419367141779267 Other FALSE 1134 +SP193725 BN2 0.8937172731352914 0.8893718489233517 281,1135,1199,661,736,151 0.152,0.154,0.166,0.242,0.284,0.314 BN2,BN2,BN2,BN2,BN2,MCD 26.7566860366888,3.18196105113397 5 23.796643331511532 29.938647087822755 26.756686036688784 BN2 TRUE 1135 +SP193300 EZB 0.4070330700329336 2.716012451974231 65,2127,1108 0.151,0.195,0.222 EZB,ST2,BN2 4.50210919323995,6.61025490391365,5.12772888442848 3 17.953534629753204 16.24009298158208 6.610254903913653 Other FALSE 1136 +SP193914 EZB 0.5072055975475537 1.4673462642980029 491,282,77,1074,81,994 0.164,0.17,0.266,0.283,0.364,0.377 EZB,BN2,EZB,ST2,MCD,EZB 5.87318642217345,12.5115905853493,2.7442406871163,3.53867257898072 5 18.358835705838324 24.667690273619737 12.511590585349277 Other FALSE 1137 +SP193957 EZB 1 0.0919998312526624 93,1138,558,382,174,39,2016,676,240,1081 0.11,0.132,0.149,0.16,0.166,0.177,0.212,0.215,0.242,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.8037686109732 1 5.409936789230139 58.80376861097319 58.80376861097319 EZB TRUE 1138 +SP193967 ST2 0.7860378552948143 0.11602310300028787 1484,1176,619,2004,235,1139,234,1363,1497,1147 0.154,0.161,0.162,0.165,0.177,0.185,0.213,0.221,0.223,0.231 ST2,ST2,BN2,ST2,ST2,BN2,ST2,ST2,ST2,ST2 11.5480391182387,42.4243078787055 1 4.922199842726974 53.97234699694419 42.42430787870552 BN2 FALSE 1139 +SP194143 BN2 1 1.1084321449743202 754,987,1204,1113,871 0.144,0.166,0.242,0.244,0.293 BN2,BN2,BN2,BN2,BN2 24.6356107057903 6 27.306902817371476 24.635610705790306 24.635610705790306 Other FALSE 1140 +SP194195 ST2 0.6021912819715227 1.5017730562674527 1128,1970,545,444,2052,1884 0.126,0.146,0.196,0.204,0.233,0.27 EZB,ST2,BN2,ST2,ST2,ST2 5.10415904693656,7.94177306961334,19.7485530852953 5 29.65784492376391 32.79448520184516 19.74855308529527 Other FALSE 1141 +SP194035 EZB 1 0.10856961497828012 438,1788,196,28,44,1118,6,1142,367,731 0.136,0.143,0.148,0.175,0.191,0.197,0.205,0.207,0.233,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.3756439408838 1 6.012112341836087 55.37564394088382 55.37564394088382 EZB TRUE 1142 +SP194053 N1 1 3.7684840256577465 3,697 0.262,0.315 N1,N1 6.99399762237826 7 26.356768315420737 6.993997622378261 6.993997622378261 Other FALSE 1143 +SP194080 BN2 1 1.225644396525998 523,20,596,1096,2069 0.181,0.245,0.245,0.264,0.289 BN2,BN2,BN2,BN2,BN2 20.9256732678121 6 25.64743418422782 20.92567326781214 20.92567326781214 Other FALSE 1144 +SP193934 BN2 0.6297235641716348 0 13,1076,1086,838,590,932,1145,412,960,157,1722 0.174,0.189,0.194,0.209,0.223,0.239,0.253,0.257,0.26,0.26,0.27 BN2,EZB,BN2,ST2,BN2,EZB,BN2,BN2,ST2,BN2,BN2 30.7909475757072,9.46644338129717,8.63858392399109 0 0 48.89597488099541 30.790947575707158 BN2 TRUE 1145 +SP59280 ST2 1 4.681893743673735 1028 0.205 ST2 4.88186809475933 4 22.85638769029411 4.881868094759327 4.881868094759327 Other FALSE 1146 +SP59304 ST2 0.6794807384473429 0.292985227034343 234,1147,235,1139,649,1687,1484,1176,1394 0.147,0.153,0.154,0.164,0.165,0.173,0.187,0.199,0.207 ST2,ST2,ST2,BN2,EZB,ST2,ST2,ST2,EZB 6.11396996872831,10.8731806735404,36.0116942944745 2 10.550894428757967 52.998844936743225 36.01169429447451 ST2 TRUE 1147 +SP59312 BN2 1 1.4244816822361894 1148,804,1980,1841 0.124,0.163,0.18,0.207 BN2,BN2,BN2,BN2 24.5809470816127 6 35.015108849774386 24.580947081612685 24.580947081612685 BN2 TRUE 1148 +SP59324 N1 1 6.367329721651783 697 0.235 N1 4.25438785364591 6 27.089090227953953 4.2543878536459125 4.2543878536459125 Other FALSE 1149 +SP59360 Other 1 10 0 0 1 2 Other TRUE 1150 +SP59364 EZB 0.8631881290473808 0.5212782043896503 1600,2083,1318,1272,1059,651,331,595,179 0.199,0.224,0.246,0.281,0.291,0.322,0.323,0.334,0.342 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.46881201863648,28.1951080602295 2 14.697495302208585 32.663920078865964 28.195108060229487 Other FALSE 1151 +SP59368 ST2 0.6384096854269461 0.6004868186477054 1152,1174,2066,629,68,268 0.185,0.202,0.224,0.23,0.245,0.247 BN2,ST2,BN2,ST2,ST2,ST2 9.87077184843882,17.4274478510951 2 10.464952717252903 27.298219699533945 17.427447851095128 BN2 FALSE 1152 +SP59372 BN2 0.6330361705099534 2.3088261393483815 114,1657,2170 0.205,0.208,0.276 EZB,BN2,BN2 8.42999588221002,4.88677221876451 4 19.463394847445723 13.316768100974535 8.429995882210024 Other FALSE 1153 +SP59376 Other 1 10 0 0 1 2 Other TRUE 1154 +SP59400 MCD 0.3857628777564817 3.7117203602440347 921,1835,1532 0.132,0.133,0.219 MCD,EZB,BN2 4.57395590049619,7.4926953237418,7.5782884696773 5 28.128487608703832 19.644939693915283 7.578288469677297 Other FALSE 1155 +SP59412 EZB 0.7554525310850035 0.2491839165416679 944,384,1330,693,216,1093,940,757,213,1107 0.166,0.197,0.276,0.286,0.29,0.301,0.316,0.333,0.336,0.341 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,BN2,ST2 2.97388491547197,28.0112969306834,6.09364942131386 1 6.979964676599293 37.07883126746924 28.011296930683407 Other FALSE 1156 +SP59448 BN2 0.6849403303281856 0 610,1666,591 0.121,0.152,0.164 BN2,EZB,BN2 14.3105152894058,6.58256788259693 0 0 20.893083172002743 14.310515289405814 Other FALSE 1157 +SP59452 BN2 1 0 261,830,1115,1158,1027,1203,981,1525,1375,1126,417 0.142,0.192,0.219,0.239,0.271,0.295,0.314,0.314,0.325,0.328,0.344 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.4596759343514 0 0 43.459675934351445 43.459675934351445 BN2 TRUE 1158 +SP59456 BN2 0.5493605145048053 1.4549225087324922 897,1191,34,274,273,1275,1112 0.216,0.23,0.255,0.269,0.274,0.286,0.288 MCD,BN2,EZB,MCD,BN2,BN2,BN2 14.9796434402832,3.92520005034783,8.36257570819124 4 21.794220414055083 27.267419198822292 14.97964344028322 Other FALSE 1159 +SP59460 EZB 1 0 1539,1450,699,477,520,137,1160,1542,1511,72 0.182,0.191,0.207,0.237,0.243,0.243,0.248,0.27,0.28,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8028161527974 0 0 42.80281615279739 42.80281615279739 EZB TRUE 1160 +SP116610 EZB 0.8840622814616396 0.17678155200001427 206,1541,1088,1606,2059,187,1537,17,233,80 0.111,0.197,0.202,0.265,0.267,0.268,0.283,0.293,0.297,0.301 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.8045925072874,5.08891287236856 1 6.859936088166387 43.893505379655934 38.804592507287374 Other FALSE 1161 +SP116618 EZB 1 0 2093,1162,2005,1554,1277,1528,1042,399,340,578,1244 0.125,0.218,0.238,0.239,0.263,0.274,0.281,0.285,0.286,0.297,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.6974921757627 0 0 45.69749217576272 45.69749217576272 EZB TRUE 1162 +SP116620 EZB 1 0 1046,1163,1516,2215,1281,1264,375,142,348,1033,1962 0.156,0.18,0.184,0.205,0.205,0.239,0.245,0.26,0.261,0.262,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.705114941938 0 0 50.70511494193801 50.70511494193801 EZB TRUE 1163 +SP116624 BN2 0.5513025723364164 4.720821457485995 318,1381,1108 0.199,0.316,0.332 ST2,BN2,BN2 6.17498202856739,5.02573122476981 7 29.15098766005134 11.200713253337202 6.174982028567391 Other FALSE 1164 +SP116627 N1 1 1.7645224003607864 870,262,901 0.152,0.186,0.22 N1,N1,N1 16.4767306496611 5 29.073560316038183 16.47673064966112 16.47673064966112 Other FALSE 1165 +SP116630 EZB 0.7563780617762772 3.106529764857696 2042,1266,1052 0.117,0.241,0.244 EZB,EZB,ST2 12.7326095177419,4.10104835943218 8 39.554230451175506 16.833657877174048 12.732609517741867 Other FALSE 1166 +SP116635 ST2 1 11.75338241721894 670 0.294 ST2 3.39744321126602 7 39.93144930279389 3.3974432112660193 3.3974432112660193 Other FALSE 1167 +SP116642 EZB 0.765754261415552 0.32628007614995563 2148,219,220,1587,4,218,221,2091,139 0.2,0.204,0.218,0.246,0.252,0.273,0.275,0.28,0.285 ST2,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.63813686001558,28.2554699281346,5.00526710894356 2 9.21919687980455 36.89887389709377 28.255469928134634 Other FALSE 1168 +SP116726 MCD 1 0 705,1567,2094,515,516,540,387,888,1169,1572,621 0.154,0.163,0.164,0.166,0.169,0.18,0.188,0.202,0.21,0.212,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.6714348113907 0 0 60.67143481139069 60.67143481139069 MCD TRUE 1169 +SP116648 ST2 1 4.221928526707437 318 0.256 ST2 3.91373920959108 3 16.52352721506601 3.9137392095910837 3.9137392095910837 Other FALSE 1170 +SP116657 MCD 1 0.1850900335199017 214,1127,883,1732,154,1728,749,997,1531 0.139,0.205,0.205,0.207,0.24,0.242,0.268,0.281,0.304 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.6449579313547 2 7.5229766259294415 40.64495793135473 40.64495793135473 MCD TRUE 1171 +SP116659 EZB 1 0.08597330462945471 1372,408,958,1242,54,1066,1437,1914,468,1241 0.137,0.165,0.168,0.171,0.172,0.188,0.195,0.23,0.242,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.9062657851123 1 4.634499809779815 53.90626578511234 53.90626578511234 EZB TRUE 1172 +SP116663 BN2 1 1.4251178141852985 811,1173,1126,931,1232 0.157,0.221,0.244,0.276,0.287 BN2,BN2,BN2,BN2,BN2 22.1083399876529 6 31.506989158469302 22.10833998765288 22.10833998765288 BN2 TRUE 1173 +SP116668 ST2 0.5598326930043394 0.8065846042758461 1152,1174,268,2066,171,1198,629 0.156,0.16,0.201,0.221,0.23,0.243,0.244 BN2,ST2,ST2,BN2,BN2,ST2,ST2 15.2761681558644,19.42919935571 3 15.671293073721872 34.70536751157439 19.429199355709997 ST2 TRUE 1174 +SP116670 ST2 1 0.4376660411707736 1404,959,1101,339,2052,1198,1884 0.108,0.133,0.151,0.18,0.216,0.223,0.25 ST2,ST2,ST2,ST2,ST2,ST2,ST2 42.0427752250254 4 18.40069499256955 42.04277522502541 42.04277522502541 Other FALSE 1175 +SP116676 ST2 0.7581225404914695 0.300563484446366 234,235,649,1176,1139,1147,1484,1687 0.119,0.157,0.167,0.183,0.188,0.19,0.2,0.201 ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2 5.32311978616659,6.00212026847335,35.496981733416 2 10.669096517124524 46.822221788055955 35.49698173341602 ST2 TRUE 1176 +SP116688 ST2 1 8.995984657796582 1177 0.224 ST2 4.46038269091262 7 40.125534255351404 4.460382690912625 4.460382690912625 ST2 TRUE 1177 +SP116690 ST2 0.5010107557782449 1.06880213572438 1178,2138,816,911 0.176,0.18,0.193,0.197 ST2,EZB,EZB,ST2 10.7225938539667,10.7660333622173 2 11.506759450817775 21.48862721618398 10.766033362217298 ST2 TRUE 1178 +SP116697 ST2 1 0.5463689178518155 959,339,1404,2052,1101,1884 0.132,0.134,0.154,0.164,0.202,0.21 ST2,ST2,ST2,ST2,ST2,ST2 37.3800272568346 4 20.423285041588063 37.38002725683455 37.38002725683455 Other FALSE 1179 +SP116701 EZB 1 0 1305,1219,55,1180,1926,1590,1726,1457,1853 0.127,0.164,0.186,0.193,0.202,0.203,0.207,0.208,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7318440343827 0 0 48.731844034382696 48.731844034382696 EZB TRUE 1180 +SP116709 EZB 1 0.2419788007812092 1663,430,1483,337,336,1468,112,1384,170 0.111,0.152,0.165,0.178,0.18,0.222,0.236,0.247,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.5034292307386 2 11.97878043981159 49.503429230738625 49.503429230738625 EZB TRUE 1181 +DOHH-2 EZB 1 0 1182,532 0.158,0.211 EZB,EZB 11.0576260286425 0 0 11.057626028642494 11.057626028642494 EZB TRUE 1182 +Farage BN2 0.44028139663286364 1.0724850142895916 133,1183,1070,2140,972,132,24 0.155,0.187,0.201,0.231,0.247,0.251,0.268 BN2,ST2,EZB,ST2,BN2,BN2,EZB 14.4700602269138,8.70809783737775,9.68732373343228 3 15.51892274923291 32.865481797723845 14.470060226913812 ST2 FALSE 1183 +HBL-1 MCD 1 0.10218915337535538 1911,761,1897,986,1543,1184,755,790 0.113,0.206,0.214,0.215,0.22,0.236,0.261,0.271 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.3168626980128 1 4.017756912485014 39.316862698012756 39.316862698012756 MCD TRUE 1184 +HT BN2 0.3307831700761498 0.8488234898884465 1185,15,597,476,2121,827,1377,1183 0.16,0.208,0.224,0.263,0.355,0.39,0.394,0.397 ST2,EZB,BN2,MCD,BN2,BN2,MCD,ST2 9.85061553573278,4.80635122040104,6.3491955295533,8.77351342594314 3 8.361433856590049 29.779675711630265 9.850615535732782 ST2 FALSE 1185 +HTMCP-01-01-00012-01A-01D ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1186 +HTMCP-01-01-00451-01A-01D MCD 0.5397948994521631 2.9563594805719413 2135,736,151,1199 0.165,0.207,0.293,0.308 MCD,BN2,MCD,BN2 8.08093644453905,9.4784874621481 7 28.0218162702038 17.559423906687144 9.478487462148095 Other FALSE 1187 +HTMCP-01-02-00013-01A-01D ST2 1 1.5367331088447944 1188,1638,828 0.154,0.247,0.276 ST2,ST2,ST2 14.1474397905 5 21.74083913144968 14.147439790500044 14.147439790500044 ST2 TRUE 1188 +HTMCP-01-06-00036-01E BN2 0.8307399312079529 0.2939378462007821 1362,1320,489,582,666,2041,183,922,1038 0.139,0.155,0.199,0.206,0.251,0.266,0.27,0.3,0.306 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,ST2 34.5157076961308,7.03244281343071 2 10.145472780296455 41.54815050956154 34.515707696130825 Other FALSE 1189 +HTMCP-01-06-00105-01A-01D BN2 0.4561389521283627 1.0031670461073996 826,1190,24,636,1183,2214,476 0.124,0.161,0.221,0.254,0.341,0.352,0.353 BN2,BN2,EZB,ST2,ST2,EZB,MCD 14.3202023425145,7.36835668166352,2.83095175226509,6.87487524495645 3 14.365555083600569 31.3943860213996 14.320202342514532 BN2 TRUE 1190 +HTMCP-01-06-00136-01A-01D BN2 0.5410079840614617 0.956011124615244 897,1191,34,273,1112,274,1275 0.132,0.15,0.153,0.164,0.182,0.197,0.231 MCD,BN2,EZB,BN2,BN2,MCD,BN2 22.5834823300988,6.51579503028586,12.644066537352 4 21.590060340126254 41.74334389773663 22.58348233009881 BN2 TRUE 1191 +HTMCP-01-06-00146-01A-01D EZB 1 1.1150946572862872 1102,1260,809,473,1011,1322 0.242,0.243,0.262,0.263,0.265,0.276 EZB,EZB,EZB,EZB,EZB,EZB 23.273821863715 5 25.952514414861383 23.273821863715007 23.273821863715007 Other FALSE 1192 +HTMCP-01-06-00175-01A-01D BN2 1 0.3081984080914163 1027,1375,1158,417,592,261,1126,931 0.116,0.15,0.176,0.206,0.228,0.262,0.27,0.297 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.0937945845782 3 12.66504207340267 41.09379458457822 41.09379458457822 BN2 TRUE 1193 +HTMCP-01-06-00185-01A-01D MCD 1 1.4482260468902362 650,864,1194,1063 0.149,0.178,0.218,0.226 MCD,MCD,MCD,MCD 21.3516863263102 6 30.922068282792495 21.351686326310176 21.351686326310176 MCD TRUE 1194 +HTMCP-01-06-00206-01A-01D BN2 0.5111272867087627 0.7256122166796479 2121,15,827,1185,476,597 0.207,0.243,0.253,0.267,0.279,0.314 BN2,EZB,BN2,ST2,MCD,BN2 11.973053511745,4.11836600415881,3.58267406388652,3.75070455384398 2 8.68779389908133 23.424798133634315 11.973053511744997 Other FALSE 1195 +HTMCP-01-06-00232-01A-01D EZB 1 7.2056946798437345 1535 0.242 EZB 4.13231798984954 6 29.77622175488135 4.132317989849535 4.132317989849535 Other FALSE 1196 +HTMCP-01-06-00242-01A-01D MCD 1 1.244263117461521 1412,1225,824,815,753 0.187,0.229,0.237,0.251,0.3 MCD,MCD,MCD,MCD,MCD 21.2318299108416 6 26.41798287427659 21.23182991084164 21.23182991084164 MCD TRUE 1197 +HTMCP-01-06-00253-01A-01D ST2 0.6607733427170249 0.3460216167803125 1198,1101,1152,268,2066,531,1404,629,1174 0.165,0.189,0.193,0.202,0.203,0.222,0.236,0.252,0.254 ST2,ST2,BN2,ST2,BN2,BN2,ST2,ST2,ST2 14.6148442647721,28.4679853153948 2 9.850538305311101 43.08282958016689 28.46798531539479 ST2 TRUE 1198 +HTMCP-01-06-00299-01A-01D BN2 0.7184365398888908 1.096580697527942 281,897,1203,1191,1135,1199,659 0.276,0.297,0.301,0.307,0.313,0.316,0.321 BN2,MCD,BN2,BN2,BN2,BN2,MCD 16.5573750825178,6.4890238173467 4 18.15649791721909 23.04639889986446 16.557375082517762 BN2 TRUE 1199 +HTMCP-01-06-00306-01A-01D BN2 0.7786585270663833 1.8965954976413293 1414,648,531,158 0.135,0.238,0.248,0.297 BN2,ST2,BN2,BN2 14.7837558307296,4.20243043300758 7 28.038804746790476 18.986186263737167 14.783755830729582 Other FALSE 1200 +HTMCP-01-06-00307-01A-01D EZB 1 1.4043434539105635 199,1289,1782,1967,1668 0.304,0.357,0.363,0.383,0.383 EZB,EZB,EZB,EZB,EZB 14.0697505787737 6 19.758762123455273 14.069750578773746 14.069750578773746 Other FALSE 1201 +HTMCP-01-06-00422-01A-01D N1 0.5355273170736593 3.920623859633503 999,1071 0.146,0.168 N1,ST2 6.86255223299878,5.9520176576626 4 26.905486022676207 12.814569890661378 6.862552232998784 Other FALSE 1202 +HTMCP-01-06-00443-01A-01D BN2 0.5535100955916062 1.008989089543749 897,1191,1203,274,34,1112,273 0.207,0.207,0.209,0.222,0.237,0.265,0.294 MCD,BN2,BN2,MCD,EZB,BN2,BN2 16.7892507983347,4.22012422388998,9.32295483418003 4 16.940170877133397 30.332329856404716 16.789250798334706 BN2 TRUE 1203 +HTMCP-01-06-00497-01A-01D BN2 1 0.624735319514068 1204,754,1113,871,1407,273,987 0.123,0.217,0.231,0.264,0.265,0.266,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.8197792725712 4 19.8789399707169 31.819779272571218 31.819779272571218 BN2 TRUE 1204 +HTMCP-01-06-00500-01A-01D BN2 0.5800634545470482 9.493984546815053 1697,697 0.19,0.262 BN2,N1 5.27125872468305,3.81612418724198 9 50.04524887440489 9.087382911925033 5.271258724683049 Other FALSE 1205 +HTMCP-01-06-00526-01A-01D BN2 1 1.0959220475485771 523,596,2069,1096,20 0.182,0.265,0.271,0.283,0.284 BN2,BN2,BN2,BN2,BN2 20.0087710826432 5 21.928053373821136 20.008771082643236 20.008771082643236 Other FALSE 1206 +HTMCP-01-06-00594-01A-01D ST2 1 0.39507512276348705 2052,339,959,1884,1404 0.122,0.136,0.174,0.184,0.198 ST2,ST2,ST2,ST2,ST2 31.7391842554012 2 12.539362116115548 31.73918425540116 31.73918425540116 Other FALSE 1207 +HTMCP-01-06-00606-01A-01D MCD 1 3.6741519829928992 311,194 0.137,0.214 MCD,MCD 11.9501293032541 8 43.90659127657249 11.95012930325407 11.95012930325407 Other FALSE 1208 +HTMCP-01-06-00611-01A-01D BN2 1 2.6779643356298695 1210,284,158 0.211,0.251,0.259 BN2,BN2,BN2 12.588020699248 6 33.71027048875669 12.588020699247991 12.588020699247991 Other FALSE 1209 +HTMCP-01-06-00634-01A-01D BN2 1 0.12509244763919167 903,1210,1735,936,454,2213,973,158 0.158,0.174,0.199,0.208,0.225,0.228,0.242,0.255 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.789134724207 1 4.852227804457419 38.789134724207024 38.789134724207024 BN2 TRUE 1210 +HTMCP-01-10-00160-01A-01D ST2 0.5935689205642372 0.779120572828252 1789,486,1130,1943,1341,1713,1123 0.139,0.15,0.163,0.233,0.284,0.306,0.311 EZB,ST2,ST2,ST2,BN2,ST2,EZB 3.52255786471987,10.4109489283784,20.3490751699785 4 15.85438310295878 34.282581963076765 20.349075169978462 ST2 TRUE 1211 +HTMCP-01-10-00778-01A-01D BN2 0.8817144277039662 0 294,1212,836,1213,1996,872,638,669,1477,418,1193 0.145,0.153,0.172,0.172,0.204,0.208,0.227,0.229,0.243,0.258,0.274 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.6079963123332,6.52096016727341 0 0 55.12895647960662 48.60799631233321 ST2 FALSE 1212 +HTMCP-01-15-00366-01A-01E BN2 1 0.09842847769219877 638,1996,418,812,945,957,1056,1252,995,294 0.114,0.13,0.146,0.195,0.224,0.228,0.24,0.246,0.251,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.3543192381552 1 5.251584420915207 53.35431923815516 53.35431923815516 BN2 TRUE 1213 +HTMCP-01-15-00367-01A-01E BN2 1 5.694289569427237 2058,1793 0.27,0.307 BN2,BN2 6.96287287422657 8 39.64861438095619 6.9628728742265675 6.9628728742265675 Other FALSE 1214 +HTMCP-01-16-00265-01A-01E ST2 0.5452217102086364 1.0993832862725317 1152,1174,2066,629,268,1198,171 0.144,0.188,0.194,0.214,0.215,0.222,0.258 BN2,ST2,BN2,ST2,ST2,ST2,BN2 15.9814189554463,19.159702145075 4 21.06385630825546 35.14112110052132 19.15970214507503 Other FALSE 1215 +HTMCP-01-20-00272-01A-01E BN2 0.9083537686705491 0.3745019837282277 1320,1362,582,183,489,666,2041 0.122,0.16,0.183,0.224,0.25,0.281,0.311 BN2,BN2,BN2,BN2,BN2,BN2,ST2 31.8888422334914,3.2173502361709 3 11.942434675239003 35.10619246966226 31.888842233491363 Other FALSE 1216 +Karpas422 EZB 1 0 1237,299,1047,12,668,700,1078,148,1110,1111,439 0.127,0.145,0.146,0.148,0.149,0.172,0.188,0.19,0.229,0.233,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.2913264218177 0 0 64.29132642181771 64.29132642181771 EZB TRUE 1217 +LY_RELY_028_tumorB EZB 1 0.2139794496316679 178,56,259,75,1551,320,2072,1218,662 0.153,0.161,0.161,0.163,0.18,0.203,0.21,0.226,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0821131503559 2 10.50256355867241 49.08211315035592 49.08211315035592 EZB TRUE 1218 +LY_RELY_109_tumorB EZB 1 0.29501009478143597 1926,1219,1726,1590,2072,1305,1853,56,2013 0.142,0.203,0.211,0.239,0.246,0.257,0.267,0.278,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.6453270309454 2 11.695771685040214 39.64532703094535 39.64532703094535 EZB TRUE 1219 +LY_RELY_116_tumorA EZB 1 0 5,102,368,250,118,780,448,170,385,2061,1468 0.129,0.165,0.196,0.199,0.208,0.217,0.225,0.237,0.237,0.26,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8287257547481 0 0 53.82872575474815 53.82872575474815 EZB TRUE 1220 +LY_RELY_128_tumorA EZB 1 0.791398041412613 110,818,2087,1750,202,109 0.159,0.265,0.31,0.339,0.354,0.384 EZB,EZB,EZB,EZB,EZB,EZB 21.6589414685499 5 17.140843857280778 21.658941468549852 21.658941468549852 EZB TRUE 1221 +LY_RELY_128_tumorB EZB 1 0 1886,463,348,1264,1222,316,349,1962,1033,1937,2015 0.124,0.134,0.154,0.162,0.169,0.171,0.178,0.195,0.197,0.203,0.208 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.5051064401127 0 0 65.50510644011271 65.50510644011271 EZB TRUE 1222 +MD903 ST2 0.5990276542989001 1.5287271852675075 629,2066,1152,68,1198 0.209,0.241,0.257,0.267,0.289 ST2,BN2,BN2,ST2,ST2 8.03578211658769,12.0049568589073 4 18.35230390817525 20.040738975495017 12.004956858907324 Other FALSE 1223 +OCI-Ly10 MCD 0.8946471512224767 0 1694,1376,767,1224,156,1310,1449,759,788,837,310 0.115,0.148,0.155,0.173,0.181,0.187,0.188,0.192,0.193,0.195,0.207 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.75984774433082,57.404034132331 0 0 64.16388187666179 57.40403413233098 MCD TRUE 1224 +OCI-Ly3 MCD 1 0.11883334327995665 765,799,2053,1526,900,753,69 0.154,0.157,0.202,0.219,0.225,0.238,0.239 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.2022417453282 1 4.183200077546605 35.20224174532819 35.20224174532819 MCD TRUE 1225 +OZM073-003_LowGradeDiagnosis EZB 1 1.3956065483215665 1226,1227 0.189,0.276 EZB,EZB 8.91047934257402 3 12.435523319180351 8.910479342574021 8.910479342574021 EZB TRUE 1226 +OZM073-003_Transformation EZB 0.8754314408851125 0.5857505237072698 1227,1671,1703,398,397,1493,1972,1752 0.213,0.25,0.278,0.282,0.284,0.304,0.319,0.327 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 25.2511508510603,3.5930848843274 3 14.790874835219858 28.844235735387727 25.25115085106033 EZB TRUE 1227 +OZM073-005P MCD 0.5209079579943119 2.3843527468330987 82,81,401,1107,757 0.115,0.191,0.215,0.243,0.245 MCD,MCD,EZB,ST2,EZB 8.73110924034305,13.9723815082636,4.11963854675374 6 33.31508622902826 26.823129295360374 13.97238150826358 Other FALSE 1228 +OZM073-006_LowGradeDiagnosis ST2 0.5379213228644167 9.732587933862954 1664,951 0.266,0.31 ST2,MCD 3.22978392279396,3.75990004794307 9 36.5935578391415 6.989683970737033 3.759900047943074 Other FALSE 1229 +OZM073-006_Transformation MCD 0.5674655518127071 9.371596362035255 951,1479 0.226,0.297 MCD,ST2 4.41928944192555,3.36847745918168 9 41.41579685673026 7.787766901107222 4.419289441925546 Other FALSE 1230 +OZM073-006_Relapse ST2 1 3.8618191771942647 1664,833 0.25,0.31 ST2,ST2 7.23131716901249 7 27.926039319666558 7.231317169012486 7.231317169012486 Other FALSE 1231 +OZM073-007_Relapse MCD 0.5215056397013654 2.2065802626710806 150,151,811,661,1232 0.134,0.24,0.247,0.288,0.319 MCD,MCD,BN2,BN2,BN2 10.6538631660266,11.6115260422754 6 25.62176418437624 22.265389208302057 11.611526042275443 BN2 FALSE 1232 +OZM073-008_Diagnosis MCD 1 0 709,1331,21,23,2034,1699,305,1689,798,480,2008 0.132,0.134,0.138,0.153,0.154,0.155,0.157,0.157,0.157,0.16,0.161 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.3282567127493 0 0 73.32825671274932 73.32825671274932 MCD TRUE 1233 +OZM073-008_Relapse N1 0.46189574990779414 1.9057034587320265 1064,176,988,1609,464,743 0.124,0.205,0.229,0.256,0.281,0.307 N1,N1,MCD,MCD,BN2,MCD 3.56069009820212,11.54041202737,12.9624229682819 5 24.70253428420223 28.06352509385396 12.962422968281874 Other FALSE 1234 +OZM073-009_Diagnosis Other 1 10 0 0 1 2 Other TRUE 1235 +OZM073-011_LowGradeDiagnosis EZB 1 0 644,2097,1109,71,240,1236,2016,1485,1079,522,1085 0.197,0.197,0.227,0.26,0.279,0.296,0.314,0.327,0.329,0.33,0.335 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6675160057095 0 0 40.66751600570947 40.66751600570947 EZB TRUE 1236 +OZM073-012_Relapse EZB 1 0 299,12,700,1078,439,1237,2217,1110,1291,668,144 0.156,0.163,0.174,0.184,0.189,0.191,0.192,0.196,0.199,0.206,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.1621755169881 0 0 59.16217551698806 59.16217551698806 EZB TRUE 1237 +OZM073-012_LowGradeDiagnosis EZB 1 0.09991114802914804 193,575,424,231,1291,122,439,1078,589,143 0.173,0.23,0.239,0.242,0.242,0.246,0.249,0.264,0.273,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.7403800800407 1 4.170329292969844 41.74038008004065 41.74038008004065 EZB TRUE 1238 +OZM073-013_Relapse MCD 1 0.10097524244264183 1598,2180,880,1250,771,1239,647,929,1240 0.194,0.195,0.21,0.232,0.236,0.24,0.247,0.257,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.4612479635242 1 3.984609080206067 39.461247963524244 39.461247963524244 MCD TRUE 1239 +OZM073-013_Diagnosis MCD 1 0.10097524244264183 1598,2180,880,1250,771,1239,647,929,1240 0.194,0.195,0.21,0.232,0.236,0.24,0.247,0.257,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.4612479635242 1 3.984609080206067 39.461247963524244 39.461247963524244 MCD TRUE 1240 +OZM073-014_LowGradeDiagnosis2 EZB 1 0.25046718962158177 331,651,1241,408,1372,1814,1437,595,54 0.162,0.199,0.218,0.223,0.264,0.271,0.272,0.296,0.301 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0849132627476 2 9.539021191902108 38.08491326274765 38.08491326274765 EZB TRUE 1241 +OZM073-014_LowGradeDiagnosis EZB 1 0.16965192958915684 408,1372,54,1242,958,1437,1066,1241,1914 0.12,0.13,0.169,0.172,0.209,0.226,0.229,0.258,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8937634140018 2 8.29492130806113 48.893763414001825 48.893763414001825 EZB TRUE 1242 +OZM073-015_PlasmaEx-T3 EZB 1 0.08602666129706092 1581,1243,1245,105,341,1244,340,1042,166,505 0.11,0.133,0.136,0.177,0.178,0.189,0.202,0.218,0.221,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6313548418742 1 5.043859704369701 58.631354841874156 58.631354841874156 EZB TRUE 1243 +OZM073-015_Relapse EZB 0.9221231502590342 0.10760989297016137 341,1245,105,1581,1244,166,1243,340,365,1042 0.127,0.132,0.153,0.163,0.163,0.173,0.175,0.184,0.212,0.215 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.71296395830804,55.8051999631181 1 6.005191595209597 60.51816392142619 55.80519996311815 EZB TRUE 1244 +OZM073-015_Diagnosis EZB 1 0.08448052162352077 1581,1245,1243,341,1244,105,340,1042,166,505 0.113,0.143,0.144,0.18,0.181,0.185,0.192,0.207,0.225,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6666851199757 1 4.871711639234873 57.66668511997573 57.66668511997573 EZB TRUE 1245 +OZM073-016_Diagnosis EZB 1 7.508976076552813 65 0.206 EZB 4.85436944947219 6 36.451344062835496 4.854369449472187 4.854369449472187 Other FALSE 1246 +OZM073-016_Relapse EZB 1 7.508976076552813 65 0.206 EZB 4.85436944947219 6 36.451344062835496 4.854369449472187 4.854369449472187 Other FALSE 1247 +OZM073-017_Diagnosis ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1248 +OZM073-018_Diagnosis ST2 1 2.7971738610604246 2198,271,1077 0.183,0.227,0.286 ST2,ST2,ST2 13.3719197304746 8 37.403584342281796 13.37191973047463 13.37191973047463 ST2 TRUE 1249 +OZM073-021_Diagnosis MCD 1 0.178039900706257 1911,1543,986,1897,761,1184,790,755,485 0.121,0.21,0.214,0.217,0.221,0.23,0.271,0.274,0.285 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.0362854067688 2 7.484136079881004 42.03628540676884 42.03628540676884 MCD TRUE 1250 +OZM073-021_Relapse MCD 0.9264534478378721 0.10655095700023128 786,1251,1197,738,1826,934,2145,815,1399,1501 0.145,0.189,0.203,0.252,0.263,0.301,0.318,0.319,0.33,0.336 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 2.97892356139191,37.5249950291776 1 3.9983241317877973 40.503918590569526 37.524995029177624 MCD TRUE 1251 +OZM073-022_Diagnosis BN2 1 0.09150703074138683 418,907,571,995,638,1996,260,502,145,892 0.221,0.228,0.252,0.256,0.273,0.279,0.281,0.284,0.289,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.0377982112212 1 3.4807259702488915 38.037798211221244 38.037798211221244 BN2 TRUE 1252 +OZM073-023_Relapse EZB 1 0 54,1242,1372,1253,408,1872,79,958,468,1287,1142 0.168,0.173,0.202,0.203,0.218,0.236,0.237,0.258,0.264,0.28,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4011698222552 0 0 49.40116982225517 49.40116982225517 EZB TRUE 1253 +OZM073-025P MCD 0.6684110882370056 6.601879174449466 743,1008,869 0.274,0.29,0.302 MCD,N1,MCD 6.95739605710529,3.45146187410595 8 45.93188813780021 10.40885793121123 6.957396057105285 Other FALSE 1254 +OZM073-025_Diagnosis MCD 1 7.3493914864896395 743,641 0.299,0.315 MCD,MCD 6.52034880588183 9 47.920596002890825 6.520348805881833 6.520348805881833 Other FALSE 1255 +OZM073-025_Progression MCD 1 7.3493914864896395 743,641 0.299,0.315 MCD,MCD 6.52034880588183 9 47.920596002890825 6.520348805881833 6.520348805881833 Other FALSE 1256 +OZM073-026_Transformation BN2 0.5024092807582259 3.3022147836696933 793,1257 0.153,0.155 BN2,EZB 6.51743630961438,6.45492817333943 3 21.52197453323425 12.972364482953811 6.517436309614379 EZB FALSE 1257 +OZM073-027_LowGradeDiagnosis BN2 1 4.272265176752989 1096,20 0.271,0.334 BN2,BN2 6.69090629556083 7 28.585325967441896 6.6909062955608345 6.6909062955608345 Other FALSE 1258 +OZM073-028_Transformation Other 1 10 0 0 1 2 Other TRUE 1259 +OZM073-029_Transformation EZB 0.8605957088412093 0.6765300805222193 1260,1322,1002,628,1521,1011 0.167,0.179,0.211,0.254,0.258,0.274 EZB,EZB,EZB,EZB,BN2,EZB 3.87103460035884,23.8973688553818 3 16.167288876000647 27.768403455740675 23.89736885538183 EZB TRUE 1260 +OZM073-030_Transformation EZB 1 1.658614443692417 2087,1221,202,2114 0.198,0.206,0.223,0.266 EZB,EZB,EZB,EZB 18.1414335143436 6 30.089643656176026 18.14143351434363 18.14143351434363 Other FALSE 1261 +OZM073-032_Diagnosis EZB 1 9.116184594608844 31 0.149 EZB 6.73363332740811 8 61.38504440506252 6.7336333274081115 6.7336333274081115 Other FALSE 1262 +OZM073-033_Diagnosis ST2 1 12.25998564676955 2107 0.289 ST2 3.45773842814218 8 42.39182349930669 3.457738428142185 3.457738428142185 Other FALSE 1263 +OZM073-036_Transformation EZB 1 0 558,382,287,676,2016,1138,174,1903,93,240,153 0.163,0.172,0.174,0.18,0.187,0.195,0.196,0.2,0.215,0.219,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2486870136044 0 0 57.24868701360443 57.24868701360443 EZB TRUE 1264 +QC2-11T EZB 0.517103157365798 3.1043764671140335 1835,921 0.134,0.143 EZB,MCD 7.47296865130129,6.97863262951418 3 23.198908020580614 14.451601280815463 7.472968651301287 Other FALSE 1265 +QC2-12T EZB 0.60891050920471 3.6796765662621196 1052,2042,1266 0.161,0.204,0.211 ST2,EZB,EZB 9.64412483907678,6.19420393549609 6 35.48726017245725 15.838328774572869 9.644124839076776 EZB TRUE 1266 +QC2-13T MCD 1 5.884227873538137 879,1631 0.304,0.304 MCD,MCD 6.5763453567419 7 38.69671465415378 6.576345356741898 6.576345356741898 Other FALSE 1267 +QC2-15T EZB 0.8938557531362521 0.11096042742470302 1268,527,1132,1879,2001,2114,727,136 0.123,0.156,0.173,0.174,0.178,0.186,0.202,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 41.6620689667625,4.94731830918634 1 4.622840979949423 46.60938727594885 41.662068966762504 EZB TRUE 1268 +QC2-17T EZB 0.9227769479915345 0.2139498888666421 1269,242,578,399,388,1439,96,365,1037 0.126,0.152,0.186,0.199,0.211,0.255,0.282,0.295,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.38730410647055,40.4765942810112 2 8.659962848122506 43.86389838748174 40.476594281011195 EZB TRUE 1269 +QC2-18T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1270 +QC2-19T MCD 0.6794592323887763 0.4012479912069851 790,2014,749,236,2084,1503,291,758,1271 0.218,0.229,0.244,0.258,0.259,0.262,0.284,0.293,0.294 MCD,MCD,MCD,MCD,BN2,BN2,BN2,MCD,MCD 11.2001847101509,23.7413448607003 2 9.526166933908291 34.94152957085128 23.741344860700337 MCD TRUE 1271 +QC2-20T EZB 0.8908261454464651 0.2205828362626619 207,1272,1293,1745,1318,2083,595,1600 0.133,0.175,0.211,0.217,0.225,0.232,0.238,0.26 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.30178931310906,35.1013198901079 1 7.742748697922994 39.403109203216985 35.10131989010792 EZB TRUE 1272 +QC2-22T MCD 0.8658254519787383 0.14309126489672955 556,1736,1169,188,1273,621,708,1640,49,547 0.128,0.131,0.171,0.176,0.178,0.183,0.194,0.218,0.219,0.224 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.61845408829037,49.1617192057022 1 7.034612585641773 56.7801732939926 49.16171920570223 MCD TRUE 1273 +QC2-23T MCD 1 2.2603770027061345 801,1994,768 0.156,0.188,0.199 MCD,MCD,MCD 16.7753865525918 6 37.91869797498415 16.775386552591755 16.775386552591755 Other FALSE 1274 +QC2-25T BN2 0.5625295097708443 1.4040193452264238 897,1191,1203,34,274,281,273 0.256,0.267,0.28,0.295,0.298,0.304,0.323 MCD,BN2,BN2,EZB,MCD,BN2,BN2 13.7023962062118,3.39514433864156,7.26099703976811 4 19.23842934947853 24.358537584621473 13.702396206211803 BN2 TRUE 1275 +QC2-26T EZB 0.4263297840935787 1.4513879739709592 1415,581,1797,737,402 0.376,0.481,0.483,0.486,0.512 EZB,ST2,ST2,BN2,EZB 2.05891745418976,4.61433623074423,4.1501426690289 3 6.697192113160657 10.823396353962895 4.614336230744228 Other FALSE 1276 +QC2-3T EZB 0.9314085841870083 0 1162,1042,1277,2093,1554,340,1244,1545,2005,365,173 0.198,0.202,0.206,0.207,0.211,0.224,0.245,0.247,0.28,0.31,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.22551519537394,43.799541178561 0 0 47.025056373934945 43.799541178561 EZB TRUE 1277 +QC2-30T MCD 0.6981782245620822 2.182017069303387 194,1513,311 0.149,0.2,0.206 MCD,EZB,MCD 4.99435565511891,11.5530112400381 4 25.20886772761702 16.547366895157012 11.553011240038098 EZB FALSE 1278 +QC2-32T EZB 1 0 316,369,1937,1222,1,1886,1279,1120,463,428,372 0.116,0.149,0.149,0.165,0.174,0.179,0.18,0.181,0.196,0.198,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.6699416077587 0 0 65.66994160775866 65.66994160775866 EZB TRUE 1279 +QC2-33T BN2 0.6422743437372785 2.997117826414485 1657,114,1791 0.208,0.214,0.278 BN2,EZB,BN2 8.39479614114601,4.67562496940031 5 25.160193163744225 13.070421110546317 8.394796141146006 Other FALSE 1280 +QC2-34T EZB 1 0 2047,1281,76,1048,2063,117,1896,1046,1516,351,108 0.13,0.161,0.162,0.181,0.236,0.271,0.28,0.284,0.289,0.289,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.7832265798335 0 0 50.783226579833524 50.783226579833524 EZB TRUE 1281 +QC2-35T EZB 0.8385103364934512 1.2700992491306622 1260,1002,1322,1521,628 0.14,0.143,0.234,0.235,0.269 EZB,EZB,EZB,BN2,EZB 4.25932739846424,22.1159049599307 5 28.08939428345309 26.375232358394957 22.115904959930717 Other FALSE 1282 +QC2-36T MCD 0.43858067465890643 3.513305124984858 1278,2030,1398,952 0.198,0.309,0.33,0.338 EZB,MCD,MCD,BN2 2.9623504881266,5.05555056481062,6.26358284159365 6 22.005877698138207 14.281483894530869 6.263582841593655 ST2 FALSE 1283 +QC2-39T ST2 0.8733985653077084 0.28350482116028236 1713,1401,2076,1018,1211,1284,1943,726,1942 0.125,0.163,0.175,0.175,0.199,0.208,0.221,0.227,0.244 ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.1343949496103,42.3199923527147 2 11.997921863460904 48.454387302325024 42.31999235271472 ST2 TRUE 1284 +QC2-4T EZB 1 9.949151851825656 532 0.283 EZB 3.53388456376385 7 35.15915415170919 3.533884563763848 3.533884563763848 Other FALSE 1285 +QC2-40T MCD 0.8859452844524389 0.3824159227763751 1325,715,236,1271,686,1503,223,758 0.148,0.166,0.169,0.19,0.192,0.206,0.224,0.252 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 4.84327045182035,37.6211767967522 3 14.386937040663135 42.464447248572526 37.621176796752174 MCD TRUE 1286 +QC2-42T EZB 1 0 1872,1253,1142,79,1118,1287,1788,54,6,1242 0.14,0.194,0.2,0.2,0.203,0.24,0.246,0.264,0.265,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7264912196443 0 0 46.72649121964434 46.72649121964434 EZB TRUE 1287 +QC2-5T BN2 1 0 1697 0.139 BN2 7.20951505616804 0 0 7.209515056168045 7.209515056168045 Other FALSE 1288 +QC2-7T EZB 1 0.08623854404782674 1964,72,1289,1160,1542,699,477,1450,2073,1539 0.173,0.197,0.235,0.242,0.28,0.304,0.308,0.318,0.326,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.5533036542376 1 3.3247807753752054 38.55330365423756 38.55330365423756 EZB TRUE 1289 +QC2-9T MCD 0.5927584474653894 3.6529250431964675 951,1479 0.152,0.221 MCD,ST2 6.59784973211189,4.53290641370775 4 24.101450517678636 11.13075614581964 6.597849732111892 Other FALSE 1290 +Reddy_1008T EZB 1 0.09495808296552909 575,193,143,231,1291,439,122,1058,343,139 0.21,0.217,0.232,0.236,0.237,0.246,0.248,0.249,0.254,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.9445252739604 1 3.98297171091446 41.944525273960366 41.944525273960366 EZB TRUE 1291 +Reddy_1016T EZB 0.8514913181269089 0.27377983905903547 1446,1292,1177,1738,165,1959,1527 0.15,0.18,0.196,0.227,0.227,0.232,0.256 EZB,EZB,ST2,EZB,EZB,EZB,EZB 29.2922895071287,5.10887100213545 2 8.019638306932363 34.40116050926413 29.29228950712868 EZB TRUE 1292 +Reddy_2043T EZB 0.8725964617629686 0.15315401636026224 1272,207,1318,2083,1600,1293,595,1745 0.128,0.155,0.179,0.185,0.214,0.227,0.232,0.265 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 5.39977837803307,36.9834901932597 1 5.6641700621181 42.38326857129282 36.98349019325974 EZB TRUE 1293 +Reddy_2044T EZB 1 8.466862207179497 1294 0.17 EZB 5.88054761205566 7 49.78978633403368 5.880547612055656 5.880547612055656 EZB TRUE 1294 +Reddy_2045T EZB 1 0.08519140332157582 1243,1581,1245,505,728,105,1017,1295,341,1244 0.169,0.182,0.214,0.221,0.231,0.243,0.25,0.259,0.263,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3790818357656 1 3.780716259711929 44.379081835765625 44.379081835765625 EZB TRUE 1295 +Reddy_2046T BN2 0.5132594981375155 2.1860220214443697 2170,1719,1848,114,511,1657 0.225,0.265,0.287,0.296,0.297,0.317 BN2,EZB,BN2,EZB,ST2,BN2 11.0899868878832,7.15363608916981,3.36335541911088 5 24.242955554442048 21.60697839616391 11.089986887883228 Other FALSE 1296 +Reddy_2047T EZB 1 0.40618464407825605 2151,1435,288,413,1120,2015,1297 0.128,0.131,0.172,0.218,0.22,0.235,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.8518955359984 3 15.78104336005508 38.85189553599836 38.85189553599836 EZB TRUE 1297 +Reddy_2048T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1298 +Reddy_2057T BN2 0.6089849895666785 5.174926254405789 1089,1608 0.153,0.239 BN2,N1 6.52125302502259,4.18714395806688 7 33.747003490812595 10.708396983089473 6.521253025022594 Other FALSE 1299 +Reddy_2060T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 EZB TRUE 1300 +Reddy_2072T Other 1 10 0 0 1 2 Other TRUE 1301 +Reddy_2073T ST2 0.7212235215305182 1.0174226857783915 572,1302,419,1461,718,1482 0.123,0.218,0.22,0.233,0.239,0.295 BN2,ST2,ST2,ST2,ST2,ST2 8.11158436353837,20.9855059220983 4 21.351129797679636 29.097090285636718 20.985505922098344 ST2 TRUE 1302 +Reddy_2074T MCD 0.758551127245738 0.47575835254180715 2014,291,236,715,1325,1503,1271,686,790 0.186,0.189,0.198,0.262,0.295,0.296,0.311,0.312,0.313 MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD,MCD 8.67112528408728,27.2417584050093 2 12.96049409910913 35.91288368909653 27.241758405009254 Other FALSE 1303 +Reddy_2075T EZB 1 0 1560,1081,375,1889,2063,1485,39,1046,93,1048 0.156,0.159,0.192,0.233,0.24,0.258,0.259,0.26,0.27,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.227657395638 0 0 45.22765739563798 45.22765739563798 EZB TRUE 1304 +Reddy_2076T EZB 1 0.33789521713654536 55,1457,1180,1305,1517,1853,278,1590 0.13,0.135,0.139,0.15,0.176,0.185,0.195,0.196 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.250976773345 3 16.979564708152914 50.25097677334502 50.25097677334502 EZB TRUE 1305 +Reddy_2077T MCD 0.8622746917990568 0.11977268439678804 556,1736,188,621,1169,1640,1273,708,792,49 0.124,0.144,0.211,0.212,0.214,0.222,0.226,0.229,0.247,0.258 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.93782606591826,43.4365470725021 1 5.202511843801024 50.37437313842038 43.43654707250212 Other FALSE 1306 +Reddy_2078T EZB 1 0 1471 0.192 EZB 5.22068216676074 0 0 5.220682166760742 5.220682166760742 Other FALSE 1307 +Reddy_2079T ST2 1 13.252580378074267 1071 0.241 ST2 4.14156256859336 9 54.88639083110728 4.141562568593364 4.141562568593364 Other FALSE 1308 +Reddy_2080T BN2 0.5311837044224742 9.134570627339627 1538,1479 0.2,0.227 BN2,ST2 4.99752712893652,4.41075683634447 9 45.6502645213165 9.408283965280994 4.997527128936523 Other FALSE 1309 +Reddy_2081T MCD 1 0 1009,1904,759,764,858,1449,479,305,1406,910,2203 0.107,0.118,0.125,0.127,0.138,0.142,0.143,0.151,0.162,0.17,0.173 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4810258052397 0 0 79.48102580523967 79.48102580523967 MCD TRUE 1310 +Reddy_2083T EZB 0.7568686202766444 2.983392285933021 2183,1725,2087 0.133,0.292,0.318 EZB,ST2,EZB 10.6788128751873,3.43038994958308 8 31.859087954755935 14.109202824770357 10.678812875187273 Other FALSE 1311 +Reddy_2084T EZB 0.43781743472426965 2.537113601113526 1074,1070,282,491 0.181,0.268,0.329,0.339 ST2,EZB,BN2,EZB 3.03819571379806,6.67238951133917,5.52953310304523 5 16.92861018114584 15.24011832818246 6.672389511339169 Other FALSE 1312 +Reddy_2085T MCD 0.8660494946452881 1.2383297006833742 713,2028,569,2130,850,755 0.173,0.187,0.207,0.208,0.266,0.28 MCD,MCD,MCD,MCD,BN2,MCD 3.76486804599883,24.3415436172432 5 30.142856421712047 28.106411663242017 24.341543617243182 Other FALSE 1313 +Reddy_2087T MCD 0.8994646361536652 0.10562491626635864 621,556,1169,1736,2094,2039,540,705,947,839 0.157,0.171,0.182,0.201,0.206,0.215,0.219,0.221,0.236,0.249 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.98093843294148,44.5632044674084 1 4.70698474043063 49.54414290034986 44.56320446740838 Other FALSE 1314 +Reddy_2088T ST2 0.4686051726124937 0.8942669024475971 1341,1123,486,1789,293,854,1130,1943 0.2,0.204,0.221,0.237,0.25,0.256,0.268,0.274 BN2,EZB,ST2,EZB,ST2,BN2,ST2,ST2 8.90385113988751,9.13058823367247,15.9034885927746 3 14.221963481971216 33.93792796633455 15.903488592774577 Other FALSE 1315 +Reddy_2089T ST2 0.4727471045895638 3.758654438463251 1733,1178,911,904 0.176,0.22,0.303,0.327 EZB,ST2,ST2,MCD 5.67703495770737,3.06008724068612,7.83390524298669 7 29.444942712052438 16.571027441380174 7.833905242986686 Other FALSE 1316 +Reddy_2091T BN2 1 5.2025400909795865 915 0.258 BN2 3.88037095661293 4 20.187785469651587 3.8803709566129316 3.8803709566129316 BN2 TRUE 1317 +Reddy_2092T EZB 0.7203123132438068 0.5919188926352248 1318,2083,1600,1272,207,179,993 0.153,0.177,0.197,0.214,0.263,0.278,0.28 EZB,BN2,EZB,EZB,EZB,EZB,BN2 9.20619064440884,23.709776272769 3 14.034264516006338 32.91596691717782 23.709776272768973 EZB TRUE 1318 +Reddy_2093T BN2 1 12.053798413567398 1791 0.317 BN2 3.15891409080576 8 38.07691365635023 3.158914090805765 3.158914090805765 Other FALSE 1319 +Reddy_2095T BN2 0.8940233443537149 0.5279756659481588 1320,1362,582,183,666,489,1038 0.133,0.209,0.235,0.25,0.256,0.266,0.299 BN2,BN2,BN2,BN2,BN2,BN2,ST2 28.2398714286957,3.34752682778961 4 14.909964923855975 31.587398256485272 28.239871428695665 BN2 TRUE 1320 +Reddy_2097T BN2 1 9.816545003233209 2058 0.285 BN2 3.50603925236696 8 34.41719210396237 3.5060392523669592 3.5060392523669592 Other FALSE 1321 +Reddy_2100T EZB 0.8806484706666996 0.23660960923700086 466,1031,1701,1550,2176,809,1384,1011,1036 0.154,0.161,0.181,0.195,0.211,0.254,0.266,0.267,0.297 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.11568426361408,37.7466426138912 2 8.931218358881516 42.862326877505254 37.746642613891176 EZB TRUE 1322 +Reddy_2101T EZB 1 3.1588593335482975 1395,2166,1601 0.246,0.29,0.306 EZB,EZB,EZB 10.7787226985951 8 34.04846880018604 10.778722698595105 10.778722698595105 Other FALSE 1323 +Reddy_2102T EZB 0.742540602164638 0.4498248546561965 1227,696,1226,1392,1122,1703 0.138,0.278,0.3,0.309,0.331,0.331 EZB,EZB,EZB,EZB,BN2,ST2 3.02488270695354,17.4299046673175,3.01854811271272 2 7.840404333647442 23.473335486983736 17.42990466731747 Other FALSE 1324 +Reddy_2103T MCD 0.7522229837198902 0.14170969486367277 1503,2084,1271,758,1325,223,236,749,790,715 0.166,0.186,0.2,0.201,0.226,0.238,0.238,0.24,0.251,0.274 BN2,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 11.3999965484247,34.6090995314841 1 4.904444934113093 46.009096079908815 34.60909953148411 MCD TRUE 1325 +Reddy_2106T EZB 0.5131949461300642 5.785628339981264 1835,921 0.166,0.175 EZB,MCD 6.02453339164199,5.71473535422584 5 34.85571112584732 11.739268745867829 6.024533391641987 Other FALSE 1326 +Reddy_2107T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 1327 +Reddy_2109T ST2 1 4.05716434043974 1052 0.215 ST2 4.66159446331039 3 18.912854826134247 4.661594463310391 4.661594463310391 Other FALSE 1328 +Reddy_2110T BN2 1 5.463206783006061 1096,20 0.246,0.319 BN2,BN2 7.20561362781423 9 39.36575724719562 7.20561362781423 7.20561362781423 Other FALSE 1329 +Reddy_2111T EZB 0.6326523163190454 0.1690031836717498 1330,384,1093,757,132,972,2140,1107,944,400 0.184,0.197,0.201,0.219,0.252,0.252,0.268,0.287,0.323,0.336 EZB,EZB,EZB,EZB,BN2,BN2,ST2,ST2,EZB,EZB 7.93877656410994,26.0995893243082,7.21587226274074 1 4.410913688333299 41.25423815115888 26.099589324308198 EZB TRUE 1330 +Reddy_2112T MCD 0.8943145536182163 0.4284516269757691 1406,858,759,1904,2203,1009,1310,1376 0.109,0.128,0.16,0.162,0.17,0.172,0.177,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.53108075247733,46.804230700876 3 20.053348793139566 52.33531145335333 46.804230700876005 MCD TRUE 1331 +Reddy_2113T MCD 1 0.08903598410189376 1598,803,880,2180,1647,518,1459,771,1239,1250 0.214,0.258,0.265,0.276,0.283,0.283,0.29,0.3,0.31,0.316 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.1686584555351 1 3.22031209923385 36.168658455535116 36.168658455535116 Other FALSE 1332 +Reddy_2114T MCD 1 3.324397525710624 311 0.213 MCD 4.70380262118217 3 15.637309795289141 4.703802621182166 4.703802621182166 Other FALSE 1333 +Reddy_2115T MCD 1 0.22072853248691365 69,799,765,824,900,1412,1526 0.152,0.159,0.179,0.19,0.205,0.219,0.257 MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0483947111261 2 8.177637795582786 37.04839471112605 37.04839471112605 Other FALSE 1334 +Reddy_2120T EZB 0.55438840010664725 3.1551469701625967 1387,901,1488,870 0.154,0.196,0.202,0.244 EZB,N1,EZB,N1 11.4323458703136,9.18920008576048 7 36.07073143457089 20.621545956074094 11.432345870313618 Other FALSE 1335 +Reddy_2121T BN2 0.5763068560610556 1.8548076767994826 1848,1719,511,2170,1657 0.176,0.204,0.245,0.296,0.319 BN2,EZB,ST2,BN2,BN2 12.2101068921109,4.90244709260649,4.07426127471677 6 22.647399998029563 21.186815259434155 12.210106892110897 Other FALSE 1336 +Reddy_2122T BN2 1 1.4731988985216309 2069,523,1957,596,1765 0.184,0.197,0.229,0.258,0.283 BN2,BN2,BN2,BN2,BN2 22.2874718151158 6 32.83387892886048 22.287471815115794 22.287471815115794 Other FALSE 1337 +Reddy_2123T EZB 1 8.714650371552592 1300 0.126 EZB 7.95688137992726 8 69.34143927398296 7.956881379927256 7.956881379927256 Other FALSE 1338 +Reddy_2124T ST2 0.760418326491399 0.296718002994885 234,1176,235,1484,1139,649,1147 0.13,0.142,0.15,0.19,0.193,0.213,0.215 ST2,ST2,ST2,ST2,BN2,EZB,ST2 5.18076227404029,4.7057377558333,31.379177286295 2 9.31076682001191 41.2656773161686 31.37917728629501 Other FALSE 1339 +Reddy_2125T MCD 0.5807410831377178 1.5975481865154881 1278,150,151,2135,736 0.23,0.293,0.298,0.306,0.344 EZB,MCD,MCD,MCD,BN2 2.9060812426195,4.35047597200708,10.0514997467718 5 16.05775519221622 17.308056961398407 10.051499746771826 Other FALSE 1340 +Reddy_2127T ST2 0.5474811806431125 0.486087516299867 1341,486,1789,1943,293,1130,1123 0.167,0.195,0.214,0.233,0.239,0.24,0.248 BN2,ST2,EZB,ST2,ST2,ST2,EZB 5.98340314913132,8.71771758735416,17.7861927356447 2 8.645646251300288 32.48731347213021 17.78619273564474 BN2 FALSE 1341 +Reddy_2128T EZB 0.9115363808764263 0.40664547406921975 809,1011,1864,1031,1701,473,466,1550 0.116,0.179,0.186,0.19,0.21,0.226,0.261,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.67775331977718,37.8958715919481 3 15.410184668774006 41.57362491172527 37.89587159194809 Other FALSE 1342 +Reddy_2130T ST2 1 10.325876460134841 90 0.235 ST2 4.26032556360195 8 43.991595449708065 4.260325563601949 4.260325563601949 Other FALSE 1343 +Reddy_2133T MCD 1 2.9307135485301474 311,1480,194 0.22,0.254,0.271 MCD,MCD,MCD 12.1806405325125 8 35.69796823840996 12.180640532512538 12.180640532512538 Other FALSE 1344 +Reddy_2134T N1 0.5068342810404087 5.136473423073945 2077,1697 0.295,0.303 N1,BN2 3.29739204323191,3.38878243415931 3 17.406390909639132 6.686174477391223 3.3887824341593107 Other FALSE 1345 +Reddy_2135T N1 1 10.771654970141187 999 0.207 N1 4.83947742115465 9 52.129181016466546 4.83947742115465 4.83947742115465 Other FALSE 1346 +Reddy_2136T Other 1 10 0 0 1 2 Other TRUE 1347 +Reddy_2137T BN2 0.7118894919828159 3.4348111029285318 1793,2058,874 0.191,0.235,0.26 BN2,BN2,ST2 9.48555013584704,3.83891980319795 6 32.58107292399265 13.324469939044988 9.485550135847038 Other FALSE 1348 +Reddy_2138T N1 0.5593886109640178 7.484093802334156 262,2050 0.137,0.174 N1,EZB 5.74821160887027,7.29777801351973 8 54.61725520179351 13.04598962239 7.297778013519734 Other FALSE 1349 +Reddy_2140T Other 1 10 0 0 1 2 Other TRUE 1350 +Reddy_2141T EZB 0.5846920560437975 5.346859937033938 1500,1099,1226 0.218,0.258,0.386 ST2,EZB,EZB 6.46142218141578,4.58956117747524 7 34.54831939807448 11.050983358891022 6.4614221814157835 Other FALSE 1351 +Reddy_2142T EZB 1 1.0383006714722411 1649 0.159 EZB 6.28523311064902 1 6.525961759146445 6.285233110649025 6.285233110649025 Other FALSE 1352 +Reddy_2143T ST2 1 7.5497870508345155 828 0.169 ST2 5.90164720200727 6 44.55617962430827 5.9016472020072746 5.9016472020072746 Other FALSE 1353 +Reddy_2144T Other 1 10 0 0 1 2 Other TRUE 1354 +Reddy_2145T MCD 1 3.1487545300882442 743,641 0.173,0.249 MCD,MCD 9.77986974590246 6 30.794409166083337 9.77986974590246 9.77986974590246 Other FALSE 1355 +Reddy_2146T BN2 0.5247414650271768 0.6470694307023062 1133,1012,1509,1850,1356,330,993,1053 0.183,0.198,0.213,0.235,0.259,0.277,0.301,0.335 BN2,BN2,EZB,EZB,EZB,BN2,BN2,EZB 17.4340933116998,15.7900646282429 3 11.281068834012496 33.22415793994269 17.434093311699836 EZB FALSE 1356 +Reddy_2147T N1 1 4.55319890857855 262 0.155 N1 6.45000978047359 4 29.368177492773345 6.450009780473595 6.450009780473595 Other FALSE 1357 +Reddy_2148T MCD 0.5813831135196665 0.5872700013066162 920,1358,2145,554,1366,254,934,1197 0.125,0.129,0.235,0.238,0.244,0.292,0.3,0.308 MCD,EZB,MCD,BN2,EZB,MCD,MCD,MCD 4.19865792311152,11.8334366751614,22.2656786546609 3 13.075965132615393 38.29777325293384 22.26567865466088 EZB FALSE 1358 +Reddy_2149T MCD 0.7510287992367203 2.259551550556059 194,1513,1480,311 0.188,0.203,0.21,0.21 MCD,EZB,MCD,MCD 4.92250423354,14.8488758234662 6 33.55180039092751 19.77138005700624 14.848875823466235 Other FALSE 1359 +Reddy_2150T BN2 0.7095488309320471 1.876102862474715 190,2218,189 0.123,0.182,0.189 BN2,ST2,BN2 13.4033312416204,5.48660368225803 4 25.146028109100747 18.8899349238784 13.403331241620368 Other FALSE 1360 +Reddy_2151T BN2 0.6198844070788248 2.4363614189700207 1714,1003,1854,459 0.147,0.255,0.279,0.28 ST2,BN2,BN2,BN2 11.0586485237019,6.78120741946537 6 26.942864609097036 17.83985594316725 11.05864852370188 Other FALSE 1361 +Reddy_2152T BN2 0.8103829886595751 0.32909290952540626 489,1362,1320,666,2041,582,922,1038,183 0.155,0.166,0.196,0.232,0.235,0.241,0.262,0.287,0.313 BN2,BN2,BN2,BN2,ST2,BN2,BN2,ST2,BN2 33.0857145042111,7.74154244368826 2 10.888274049917753 40.82725694789933 33.08571450421106 BN2 TRUE 1362 +Reddy_2153T ST2 0.7260835212097562 0.1180038422703941 1045,2082,1917,1394,1939,1487,1363,1472,1147,619 0.114,0.137,0.16,0.167,0.178,0.18,0.189,0.215,0.217,0.224 ST2,ST2,ST2,EZB,ST2,EZB,ST2,ST2,ST2,BN2 4.47364028309229,11.5440957532977,42.4589795928871 1 5.010322730840933 58.47671562927712 42.45897959288712 ST2 TRUE 1363 +Reddy_2154T Other 1 10 0 0 1 2 Other TRUE 1364 +Reddy_2155T EZB 1 2.669328455071931 2050,1300 0.124,0.131 EZB,EZB 15.6980346898584 5 41.903210686345304 15.698034689858401 15.698034689858401 Other FALSE 1365 +Reddy_2156T MCD 0.5505131035884784 0.40662017551070023 1358,920,2145,1366,554,934,1197,254 0.108,0.148,0.223,0.229,0.252,0.278,0.285,0.313 EZB,MCD,MCD,EZB,BN2,MCD,MCD,MCD 3.96268434458595,13.63311930301,21.5506181681946 2 8.76291614191537 39.14642181579059 21.550618168194593 EZB FALSE 1366 +Reddy_2157T EZB 1 0.9735709420537068 1294,360 0.106,0.143 EZB,EZB 16.4525208071791 2 16.0176961814036 16.452520807179134 16.452520807179134 Other FALSE 1367 +Reddy_2158T ST2 1 5.395149768113102 828,1188 0.206,0.224 ST2,ST2 9.32300850732705 9 50.29902718642203 9.323008507327053 9.323008507327053 Other FALSE 1368 +Reddy_2159T BN2 0.5059115449962679 11.690897650008287 19,641 0.288,0.295 BN2,MCD 3.47005047032946,3.38895582167967 8 40.56800488898485 6.859006292009131 3.4700504703294617 Other FALSE 1369 +Reddy_2160T MCD 0.8561898505127097 0.6715254050703173 2130,569,850,713,761,2028,755 0.172,0.219,0.236,0.248,0.26,0.283,0.289 MCD,MCD,BN2,MCD,MCD,MCD,MCD 4.23788768007091,25.2307395008323 4 16.943082563520033 29.46862718090316 25.23073950083225 Other FALSE 1370 +Reddy_2161T EZB 0.5452490389425476 2.8537054180983543 1099,1500 0.187,0.224 EZB,ST2 5.34308337840489,4.45626150217891 3 15.24758598630529 9.799344880583796 5.343083378404889 Other FALSE 1371 +Reddy_2163T EZB 1 0.11084837540619573 79,408,54,1372,1242,1872,595,1142,1253,1293 0.186,0.209,0.23,0.233,0.238,0.256,0.282,0.285,0.293,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.766254665414 1 4.518873101056386 40.76625466541397 40.76625466541397 EZB TRUE 1372 +Reddy_2164T Other 1 10 0 0 1 2 Other TRUE 1373 +Reddy_2165T Other 1 10 0 0 1 2 Other TRUE 1374 +Reddy_2166T BN2 1 0.0790368636996721 261,1158,1027,830,1375,1115,417,592,1126,981 0.15,0.193,0.213,0.247,0.265,0.27,0.288,0.296,0.299,0.307 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.5342644744036 1 3.282738000129567 41.534264474403564 41.534264474403564 BN2 TRUE 1375 +Reddy_2190T MCD 0.8989761038052664 0.21053547733948264 858,759,1009,1406,1904,1449,1376,764,479 0.116,0.118,0.135,0.135,0.138,0.154,0.154,0.156,0.172 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.49521139314863,57.7986006434458 2 12.168655976021979 64.29381203659437 57.79860064344575 BN2 FALSE 1376 +Reddy_2191T BN2 0.39336130467392777 1.8229054564910243 1377,1763,597,834,1185,827 0.173,0.197,0.279,0.372,0.459,0.46 MCD,EZB,BN2,BN2,ST2,BN2 8.44515994498309,5.07744240737617,5.76629815965052,2.18031835786256 5 15.394728144649122 21.469218869872346 8.445159944983095 MCD FALSE 1377 +Reddy_2192T N1 1 0.7092961645573458 814,891,745,2189,63 0.163,0.217,0.22,0.247,0.307 N1,N1,N1,N1,N1 22.6063889926101 4 16.034625006949764 22.60638899261013 22.60638899261013 Other FALSE 1378 +Reddy_2193T BN2 0.5851724788781775 2.6402180888550535 610,591,1666,2017 0.135,0.178,0.189,0.252 BN2,BN2,EZB,ST2 13.0390462293956,5.28090094236627,3.96245109548489 7 34.42592571626749 22.28239826724674 13.03904622939558 Other FALSE 1379 +Reddy_2194T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1380 +Reddy_2195T BN2 1 2.428977112203452 1381,52,987 0.143,0.305,0.331 BN2,BN2,BN2 13.2760996296091 8 32.24734213965329 13.276099629609124 13.276099629609124 BN2 TRUE 1381 +Reddy_2196T N1 1 3.708140931340138 913 0.234 N1 4.27140674203328 3 15.838978174535828 4.271406742033279 4.271406742033279 Other FALSE 1382 +Reddy_2198T EZB 0.843561018363707 0.7043544084179322 1453,62,2126,1053,1087,179 0.166,0.184,0.201,0.202,0.208,0.218 EZB,EZB,EZB,EZB,BN2,EZB 4.81441220967059,25.9606040894234 3 18.285465935578 30.77501629909403 25.96060408942344 Other FALSE 1383 +Reddy_2199T EZB 0.9032681823023524 0.0954827868352229 466,646,2176,1384,1031,1701,1550,337,336,1036 0.184,0.222,0.226,0.232,0.241,0.242,0.246,0.264,0.266,0.292 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.06384025644834,37.9475718432456 1 3.6233399132229276 42.011412099693956 37.94757184324561 EZB TRUE 1384 +Reddy_2200T Other 1 10 0 0 1 2 Other TRUE 1385 +Reddy_2201T ST2 1 3.0150065721469113 451,1667 0.245,0.304 ST2,ST2 7.3684133887695 6 22.215814793435328 7.368413388769497 7.368413388769497 Other FALSE 1386 +Reddy_2203T EZB 0.7762288247219821 1.9756825488656942 1488,1387,1087 0.113,0.16,0.23 EZB,EZB,BN2 4.3482175440256,15.0833179905368 6 29.799848132895534 19.4315355345624 15.083317990536804 EZB TRUE 1387 +Reddy_2204T ST2 0.516329586771963 2.3871455174145884 1797,1388,938,1841 0.209,0.21,0.384,0.464 ST2,EZB,ST2,BN2 2.15510248225072,4.75654417729525,7.37834601008487 5 17.613185603907912 14.289992669630838 7.37834601008487 EZB FALSE 1388 +Reddy_2205T ST2 1 3.2774085596396123 874,1057 0.243,0.282 ST2,ST2 7.6624051533523 5 25.112832237023493 7.662405153352297 7.662405153352297 Other FALSE 1389 +Reddy_2206T BN2 1 1.6630844347334914 1841,1148,70,1813 0.171,0.222,0.235,0.252 BN2,BN2,BN2,BN2 18.5858178459972 6 30.909784366469893 18.5858178459972 18.5858178459972 Other FALSE 1390 +Reddy_2208T ST2 0.6937804098815206 0.14886368089555532 1394,1147,1417,1687,2082,1139,1045,649,1484,235 0.14,0.15,0.165,0.169,0.179,0.193,0.2,0.22,0.224,0.224 EZB,ST2,ST2,ST2,ST2,BN2,ST2,EZB,ST2,ST2 5.18568445104786,11.656246666687,38.1575909938927 1 5.6802794494579585 54.99952211162753 38.1575909938927 Other FALSE 1391 +Reddy_2209T EZB 0.6520994407024013 0.4547073734376042 1392,1122,696,1703,1227,1493 0.15,0.192,0.199,0.204,0.273,0.279 EZB,BN2,EZB,ST2,EZB,EZB 5.20234887879093,18.9265860842459,4.8951446525803 2 8.606058246508159 29.02407961561712 18.92658608424589 EZB TRUE 1392 +Reddy_2210T MCD 1 0.9763197639990236 755,467,1897,986,761,1911 0.153,0.166,0.175,0.199,0.226,0.226 MCD,MCD,MCD,MCD,MCD,MCD 32.1580138155254 5 31.396504459051073 32.15801381552537 32.15801381552537 Other FALSE 1393 +Reddy_2211T ST2 0.7120722178246467 0.22809348737692808 1417,2082,545,1394,1687,1045,1147 0.17,0.197,0.203,0.204,0.217,0.222,0.234 ST2,ST2,BN2,EZB,ST2,ST2,ST2 4.92967154445075,4.9023473959875,24.3154984201837 1 5.5462068319678854 34.147517360621954 24.3154984201837 EZB FALSE 1394 +Reddy_2212T EZB 1 1.8460473974584566 1395,1601 0.182,0.242 EZB,EZB 9.62536636578023 4 17.768882529132757 9.62536636578023 9.62536636578023 EZB TRUE 1395 +Reddy_2213T ST2 0.8458719920635447 0.7988676500852534 1249,606,215,1625,1396 0.127,0.26,0.273,0.288,0.291 ST2,ST2,ST2,ST2,EZB 3.436523426033,18.8600304063491 4 15.06666817125657 22.296553832382145 18.860030406349146 EZB FALSE 1396 +Reddy_2214T EZB 1 1.4180142775061484 1397,136 0.133,0.315 EZB,EZB 10.7028745433669 4 15.176828912851427 10.702874543366946 10.702874543366946 EZB TRUE 1397 +Reddy_2215T MCD 0.8945521760063777 0.3891123524318337 1194,285,2030,1063,1398,952,1890,753 0.124,0.156,0.184,0.189,0.192,0.215,0.216,0.23 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 4.6415152585269,39.3756591386198 3 15.321555355982373 44.01717439714668 39.37565913861978 MCD TRUE 1398 +Reddy_2216T MCD 1 0.2841881937014713 69,2036,824,900,799,765,1225,1412,1526 0.158,0.187,0.22,0.228,0.244,0.259,0.274,0.296,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.8458546599226 2 11.039533268593273 38.84585465992255 38.84585465992255 MCD TRUE 1399 +Reddy_2217T EZB 0.613606964682863 1.6352850281684004 1400,781,2134,816,2138 0.155,0.223,0.286,0.314,0.331 EZB,MCD,MCD,EZB,EZB 12.675208258279,7.98167634020344 6 20.727578293680075 20.656884598482414 12.675208258278975 EZB TRUE 1400 +Reddy_2219T ST2 1 1.0348766694445515 1077,1427,271 0.186,0.205,0.232 ST2,ST2,ST2 14.5963074149697 2 15.10537800379268 14.596307414969726 14.596307414969726 BN2 FALSE 1401 +Reddy_2220T BN2 1 13.016844643694625 2170 0.322 BN2 3.10920869459573 9 40.47208654237721 3.109208694595732 3.109208694595732 Other FALSE 1402 +Reddy_2221T BN2 1 1.2383036437044421 1148,804,1980,70 0.119,0.143,0.169,0.202 BN2,BN2,BN2,BN2 26.2117352288616 5 32.458087241715376 26.211735228861574 26.211735228861574 Other FALSE 1403 +Reddy_2222T ST2 0.6129778524069809 1.6363451285119062 68,1152,1174,629,2066 0.207,0.229,0.236,0.252,0.259 ST2,BN2,ST2,ST2,BN2 8.23332607057671,13.0402008368152 5 21.338269114139504 21.273526907391954 13.040200836815242 ST2 TRUE 1404 +Reddy_2224T EZB 0.6108963786311047 2.0226284921204396 1037,1944,160,724,388 0.187,0.203,0.205,0.23,0.236 EZB,EZB,ST2,ST2,EZB 14.4916846670384,9.23031659856306 5 29.311294306376848 23.7220012656015 14.491684667038438 Other FALSE 1405 +Reddy_2225T MCD 0.8813055156133556 0.20613089407931223 1694,767,1376,1224,156,1310,788,837,759 0.119,0.126,0.138,0.139,0.153,0.154,0.161,0.176,0.186 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 7.24787735447243,53.815426403288 2 11.09302195976919 61.06330375776047 53.81542640328804 MCD TRUE 1406 +Reddy_2226T BN2 0.5230810370682542 0.923177371662163 897,1191,34,273,1112,274,1275 0.119,0.139,0.151,0.178,0.182,0.187,0.245 MCD,BN2,EZB,BN2,BN2,MCD,BN2 22.3523583555025,6.64012465334013,13.7396307010754 4 20.635191437083574 42.73211370991804 22.35235835550249 BN2 TRUE 1407 +Reddy_2227T BN2 1 1.9633201309259207 523,2069,1957,596 0.215,0.215,0.243,0.285 BN2,BN2,BN2,BN2 16.9029462135292 7 33.18589457298001 16.902946213529233 16.902946213529233 Other FALSE 1408 +Reddy_2228T BN2 1 0.07680249340082003 555,98,991,1409,1724,1001,976,857,796,935 0.183,0.197,0.198,0.201,0.222,0.234,0.268,0.284,0.287,0.301 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.3967148915893 1 3.3329759090785545 43.39671489158928 43.39671489158928 BN2 TRUE 1409 +Reddy_2229T EZB 0.6404275050790176 1.1374395294790742 1975,1425,994,77,282 0.142,0.169,0.187,0.251,0.307 EZB,N1,EZB,EZB,BN2 3.26252471151044,16.3677468830217,5.92726107421573 4 18.617322313256743 25.55753266874784 16.367746883021663 Other FALSE 1410 +Reddy_2231T ST2 1 0.6480295773846831 675,600,89,1747,1472,1939,1917 0.208,0.235,0.24,0.267,0.274,0.31,0.329 ST2,ST2,ST2,ST2,ST2,ST2,ST2 26.8804482748571 4 17.419325535466477 26.88044827485709 26.88044827485709 ST2 TRUE 1411 +Reddy_2232T MCD 1 0.11035778583012097 296,465,185,643,1526,481,246,739,900 0.142,0.17,0.177,0.192,0.203,0.212,0.216,0.22,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.9753680798596 1 5.184097609848244 46.97536807985958 46.97536807985958 MCD TRUE 1412 +Reddy_2233T ST2 0.5685479067338427 1.4691306576338745 1847,1121,1822,1082,2017,1863 0.122,0.151,0.187,0.203,0.221,0.227 ST2,ST2,EZB,BN2,ST2,EZB 4.92085814774325,9.7571000405399,19.341944408941 5 28.415843509425383 34.01990259722419 19.341944408941032 Other FALSE 1413 +Reddy_2234T ST2 0.8543680864666354 0.5511142064882205 955,1486,508,171,1038,648,1174 0.186,0.188,0.19,0.217,0.267,0.27,0.281 ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.59891861014878,26.9801391565759 3 14.869137982218119 31.579057766724723 26.980139156575945 BN2 FALSE 1414 +Reddy_2236T BN2 0.4911000880631006 2.8616768783884345 737,402,581,793 0.128,0.141,0.152,0.187 BN2,EZB,ST2,BN2 13.175313023465,7.09221275628505,6.56063683660882 6 37.703488644779945 26.828162616358917 13.175313023465048 EZB FALSE 1415 +Reddy_2237T MCD 0.692966724554949 0.48727355412793444 2014,291,236,790,749,1503,715,2084,267 0.154,0.239,0.261,0.277,0.313,0.324,0.325,0.332,0.336 MCD,BN2,MCD,MCD,MCD,BN2,MCD,BN2,MCD 10.2714222708099,23.1823532390963 2 11.296147655863667 33.45377550990619 23.18235323909626 ST2 FALSE 1416 +Reddy_2238T ST2 0.6918709941424603 0.18322808770199975 1147,1139,1394,235,1484,1687,234,2082,649,1417 0.107,0.138,0.156,0.169,0.17,0.184,0.191,0.207,0.209,0.209 ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2,EZB,ST2 7.23321506170511,11.196430700285,41.3818144109962 1 7.582310720165891 59.811460172986315 41.38181441099621 ST2 TRUE 1417 +Reddy_2239T EZB 0.5884559009616778 4.006141827552904 1480,632,1513 0.145,0.196,0.211 MCD,EZB,EZB 9.8451246563172,6.88531281610496 7 39.440965683144746 16.730437472422164 9.8451246563172 Other FALSE 1418 +Reddy_2240T ST2 0.5267484888336236 7.702938253054477 1071,999 0.185,0.206 ST2,N1 4.85204594792436,5.40052764865222 7 41.59993101148157 10.252573596576584 5.400527648652225 Other FALSE 1419 +Reddy_2241T MCD 0.8134104419513191 1.0535469664492718 467,1420,647,1897,755,986 0.152,0.179,0.214,0.224,0.227,0.24 MCD,BN2,MCD,MCD,MCD,MCD 5.57248165183957,24.2924352819672 5 25.593221498981805 29.864916933806775 24.2924352819672 BN2 FALSE 1420 +Reddy_2242T BN2 0.5289633175660765 11.703335770383738 1807,1603 0.288,0.324 BN2,MCD 3.46682051563525,3.08716990394084 9 40.57336455013418 6.553990419576084 3.4668205156352467 Other FALSE 1421 +Reddy_2243T MCD 1 0.09058716055405133 1422,247,895,950,1491,837,471,1968,156,739 0.105,0.13,0.135,0.139,0.142,0.157,0.161,0.169,0.18,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 68.5001684218594 1 6.205235754810534 68.5001684218594 68.5001684218594 MCD TRUE 1422 +Reddy_2244T EZB 1 0.10233387474315155 654,2073,25,1423,1160,210,396,72,1539,390 0.148,0.192,0.209,0.259,0.327,0.329,0.34,0.341,0.353,0.354 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.2099544658206 1 3.9101726942468042 38.20995446582055 38.20995446582055 EZB TRUE 1423 +Reddy_2246T BN2 0.5479445823552977 7.450314350364821 894,1631 0.258,0.313 BN2,MCD 3.87544986806494,3.19725418424381 6 28.873319766163657 7.072704052308746 3.8754498680649374 Other FALSE 1424 +Reddy_2247T EZB 0.6494500703402869 1.3477132943908763 1425,1975,994,77 0.132,0.166,0.223,0.279 N1,EZB,EZB,EZB 14.0664677574906,7.59257640909223 4 18.9575656018907 21.65904416658283 14.066467757490601 N1 FALSE 1425 +Reddy_2248T Other 1 10 0 0 1 2 Other TRUE 1426 +Reddy_2249T ST2 0.559274584081481 1.5462612531891657 1847,1121,1822,1082,1863,2017 0.134,0.147,0.191,0.203,0.217,0.223 ST2,ST2,EZB,BN2,EZB,ST2 4.93116912784049,9.84365170817747,18.7490475463526 5 28.990925755126447 33.52386838237057 18.749047546352614 ST2 TRUE 1427 +Reddy_2250T N1 0.38465701128763163 7.000422941715112 1425,286,1975 0.217,0.267,0.277 N1,BN2,EZB 3.75029110768896,3.60994967744901,4.60095958628349 6 32.208663041722986 11.961200371421455 4.600959586283486 Other FALSE 1428 +Reddy_2253T BN2 1 0.836101722059719 70,1841,1148,1813,804 0.124,0.157,0.165,0.181,0.184 BN2,BN2,BN2,BN2,BN2 31.5003044005115 4 26.337458754673044 31.500304400511542 31.500304400511542 Other FALSE 1429 +Reddy_2254T ST2 1 12.090711463623208 828 0.162 ST2 6.16359864630166 10 74.52229281001202 6.163598646301664 6.163598646301664 Other FALSE 1430 +Reddy_2255T ST2 0.5428665596004738 4.35326368060358 1500,1099,1226 0.144,0.307,0.389 ST2,EZB,EZB 5.82809382383447,6.92112403854905 7 30.129477905967946 12.74921786238352 6.921124038549049 Other FALSE 1431 +Reddy_2259T BN2 1 1.2085846375332705 1803,20,1096 0.198,0.242,0.272 BN2,BN2,BN2 12.8827262075181 3 15.569864983953591 12.88272620751807 12.88272620751807 Other FALSE 1432 +Reddy_2260T BN2 0.6631675486067201 1.2091689000872852 537,1433,1799 0.231,0.233,0.236 ST2,BN2,BN2 8.52237792712526,4.32863980592022 2 10.304994344270206 12.851017733045477 8.522377927125259 BN2 TRUE 1433 +Reddy_2261T Other 1 10 0 0 1 2 Other TRUE 1434 +Reddy_2262T EZB 1 0.8138153932178984 1435,2151,1297,288,1120,413 0.138,0.169,0.221,0.24,0.281,0.283 EZB,EZB,EZB,EZB,EZB,EZB 28.95428594214 4 23.563443599346147 28.954285942140018 28.954285942140018 EZB TRUE 1435 +Reddy_2263T ST2 0.6779905590507023 5.5772338848434275 1436,1901 0.129,0.272 ST2,BN2 3.67509933007958,7.73791800023604 9 43.15617846905634 11.413017330315622 7.737918000236042 ST2 TRUE 1436 +Reddy_2269T EZB 1 0.1972684977631123 1437,1241,1066,2003,1814,958,408,1372,1914,331 0.146,0.149,0.189,0.211,0.238,0.253,0.254,0.257,0.28,0.283 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7142857816463 1 9.215256980222074 46.71428578164626 46.71428578164626 EZB TRUE 1437 +Reddy_2270T MCD 0.4357055067954105 3.0703556298758303 150,1232,811,151,1278 0.226,0.287,0.298,0.313,0.331 MCD,BN2,BN2,MCD,EZB 6.83687156203137,3.01776021192802,7.60900094376504 6 23.362338885419483 17.463632717724433 7.609000943765036 Other FALSE 1438 +Reddy_2271T EZB 0.7388301828707027 1.4093083098481747 160,1944,1439,388,242 0.147,0.164,0.18,0.247,0.277 ST2,EZB,EZB,EZB,EZB 19.2778843813136,6.81455854841343 6 27.16848265487757 26.092442929727014 19.277884381313584 EZB TRUE 1439 +Reddy_2272T BN2 0.5413897234966527 10.172120628817899 1697,697 0.239,0.282 BN2,N1 4.18046594627739,3.54126530358302 9 42.52420389019896 7.721731249860408 4.180465946277389 Other FALSE 1440 +Reddy_2274T ST2 0.4708758859258911 3.971055152694913 1416,1441,879 0.136,0.225,0.264 ST2,EZB,MCD 4.44106838795731,3.79215970436892,7.32687940104173 6 29.095442198680992 15.560107493367957 7.326879401041733 EZB FALSE 1441 +Reddy_2278T ST2 0.7573270711574335 0.6876749829648888 271,1442,1955,1284,1077,1401,701 0.202,0.212,0.217,0.244,0.251,0.278,0.28 ST2,ST2,ST2,ST2,ST2,BN2,EZB 3.59508181459634,3.57085443570629,22.3632588044598 3 15.378653617396289 29.529195054762422 22.363258804459793 ST2 TRUE 1442 +Reddy_2279T ST2 1 5.685461791587608 828 0.14 ST2 7.16110049449274 5 40.71416324715758 7.161100494492736 7.161100494492736 Other FALSE 1443 +Reddy_2281T BN2 1 0 936,1549,964,865,973,2213,954,1213,872,454,903 0.138,0.159,0.19,0.204,0.204,0.206,0.207,0.244,0.251,0.258,0.266 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.02256322327 0 0 54.02256322327001 54.02256322327001 BN2 TRUE 1444 +Reddy_2286T ST2 1 4.763223607654686 451,1653 0.237,0.336 ST2,ST2 7.18374157299923 7 34.21776745180035 7.1837415729992316 7.1837415729992316 Other FALSE 1445 +Reddy_2290T EZB 0.8692530952551276 0.22513026210254955 1446,1292,1177,165,1959,1738,1527,109 0.143,0.162,0.198,0.21,0.216,0.245,0.251,0.338 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 33.555669898298,5.04720661886797 2 7.5543967592304595 38.60287651716596 33.55566989829799 EZB TRUE 1446 +Reddy_2291T Other 1 10 0 0 1 2 Other TRUE 1447 +Reddy_2293T Other 1 10 0 0 1 2 Other TRUE 1448 +Reddy_2297T MCD 0.913452063875619 0 759,1009,1449,1904,764,858,479,1376,1406,305 0.111,0.116,0.132,0.133,0.135,0.137,0.149,0.158,0.159,0.159 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.31775111979409,66.6793809055685 0 0 72.99713202536262 66.67938090556852 MCD TRUE 1449 +Reddy_2298T EZB 1 0.07424821260553922 1450,699,477,1542,1160,1539,520,72,1289,1964 0.117,0.126,0.162,0.188,0.194,0.196,0.21,0.216,0.279,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.5498728775652 1 4.050230559018599 54.549872877565214 54.549872877565214 EZB TRUE 1450 +Reddy_2300T ST2 1 7.998428374425588 1436 0.245 ST2 4.08984542699555 6 32.71233571029592 4.089845426995547 4.089845426995547 Other FALSE 1451 +Reddy_2302T N1 0.5029328863978856 2.452187876137287 175,2211 0.188,0.19 N1,BN2 5.25104014247307,5.31300643952703 2 13.02848997684751 10.564046582000099 5.313006439527027 Other FALSE 1452 +Reddy_2303T EZB 0.8789885460573211 0.8096415733557399 62,179,1053,2126,1453,993 0.148,0.149,0.167,0.194,0.198,0.245 EZB,EZB,EZB,EZB,EZB,BN2 4.08274221003877,29.6557352399776 4 24.01051613871676 33.73847745001641 29.65573523997764 EZB TRUE 1453 +Reddy_2306T MCD 0.5448904449759533 8.924634161593493 951,1664 0.258,0.309 MCD,ST2 3.87671337552375,3.23795235456596 6 34.59824862590566 7.11466573008971 3.8767133755237477 Other FALSE 1454 +Reddy_2307T Other 1 10 0 0 1 2 Other TRUE 1455 +Reddy_2449T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1456 +Reddy_2450T EZB 1 0.10464680234400632 1180,55,1305,1457,1517,1570,2003,1219,278,1853 0.191,0.195,0.207,0.221,0.234,0.246,0.256,0.268,0.276,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6872587761259 1 4.4670851317526985 42.68725877612592 42.68725877612592 EZB TRUE 1457 +Reddy_2451T ST2 1 0 1436 0.156 ST2 6.40881971552097 0 0 6.408819715520973 6.408819715520973 Other FALSE 1458 +Reddy_2453T MCD 1 0 880,1598,518,1459,771,2180,783,1239,1240,1647,803 0.138,0.139,0.144,0.163,0.165,0.17,0.174,0.177,0.18,0.182,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.2302973202112 0 0 67.230297320211207 67.230297320211207 MCD TRUE 1459 +Reddy_2455T ST2 0.5736626817548309 6.655072189198426 537,1433 0.198,0.267 ST2,BN2 3.74613687436559,5.04065403989198 7 33.54591651625581 8.786790914257569 5.0406540398919795 Other FALSE 1460 +Reddy_2459T BN2 0.5547325909063578 6.612489275076578 572,874 0.252,0.314 BN2,ST2 3.97117197583907,3.18754204410108 6 26.259332099720517 7.158714019940155 3.9711719758390704 ST2 FALSE 1461 +Reddy_2461T BN2 1 5.455628341398821 1971 0.25 BN2 3.99620773469909 4 21.801824175541547 3.996207734699092 3.996207734699092 Other FALSE 1462 +Reddy_2462T Other 1 10 0 0 1 2 Other TRUE 1463 +Reddy_2463T EZB 1 3.767997030195807 1300,2050 0.123,0.127 EZB,EZB 16.0260171571386 7 60.38598505396515 16.02601715713856 16.02601715713856 Other FALSE 1464 +Reddy_2464T ST2 0.67122509177991 4.1307163161040394 68,2134,629 0.25,0.303,0.365 ST2,MCD,ST2 3.30269040955504,6.74275505170303 7 27.852408307562637 10.045445461258073 6.742755051703029 Other FALSE 1465 +Reddy_2466T ST2 1 5.239935677999477 1436 0.217 ST2 4.60672732888584 4 24.13895488944415 4.6067273288858415 4.6067273288858415 Other FALSE 1466 +Reddy_2467T ST2 0.5235904460335199 3.761968820630553 2127,1131,65,2211 0.23,0.237,0.244,0.271 ST2,ST2,EZB,BN2 3.69498800495407,4.09466892085264,8.56110023460655 7 32.206592152882735 16.350757160413256 8.561100234606545 Other FALSE 1467 +Reddy_2468T EZB 1 0 1468,1483,385,102,112,448,5,1663,2061,1691,170 0.153,0.209,0.212,0.213,0.233,0.252,0.271,0.28,0.288,0.29,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5398206297599 0 0 46.53982062975985 46.53982062975985 EZB TRUE 1468 +Reddy_2469T ST2 0.660776260695788 0.6476152991018171 1211,1018,701,618,1713,1130,1401,1789 0.169,0.19,0.201,0.213,0.233,0.242,0.262,0.272 ST2,ST2,EZB,ST2,ST2,ST2,BN2,EZB 3.82127193027385,8.65411595067203,24.3008940695284 3 15.737630781279194 36.77628195047425 24.300894069528375 Other FALSE 1469 +Reddy_2470T Other 1 10 0 0 1 2 Other TRUE 1470 +Reddy_2471T EZB 1 1.065967803375348 1471 0.208 EZB 4.81494126286587 1 5.1325723613584575 4.814941262865872 4.814941262865872 EZB TRUE 1471 +Reddy_2473T ST2 0.801928770100425 0.15343440526953883 1917,1939,1045,2082,1472,1487,1363,1394,1884 0.158,0.158,0.166,0.187,0.187,0.198,0.22,0.231,0.25 ST2,ST2,ST2,ST2,ST2,EZB,ST2,EZB,ST2 9.37814889326025,37.9692063890546 1 5.825782600860974 47.3473552823149 37.96920638905465 ST2 TRUE 1472 +Reddy_2474T Other 1 10 0 0 1 2 Other TRUE 1473 +Reddy_2475T EZB 1 0 1474,2201,173,1673,1903,153,1054,287,1277,1554,1545 0.127,0.15,0.178,0.18,0.19,0.197,0.226,0.24,0.271,0.298,0.317 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.874804079031 0 0 54.87480407903098 54.87480407903098 EZB TRUE 1474 +Reddy_2476T EZB 1 0.5080305177731291 360,1294 0.126,0.14 EZB,EZB 15.0711874804765 1 7.656623179162371 15.071187480476485 15.071187480476485 Other FALSE 1475 +Reddy_2477T ST2 1 10.725339803911899 828 0.172 ST2 5.82344557021004 9 62.45843257008821 5.823445570210044 5.823445570210044 Other FALSE 1476 +Reddy_2478T BN2 0.8465176273404732 0.37454425823513726 2041,1362,582,922,489,1320,88,594 0.204,0.212,0.228,0.257,0.26,0.288,0.294,0.305 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.9852492581583,4.89270388374918 2 10.107170166687201 31.877953141907515 26.985249258158333 BN2 TRUE 1477 +Reddy_2479T EZB 0.5307592069336269 5.920733347924786 2050,262 0.137,0.155 EZB,N1 7.29448477755627,6.44900696458221 6 43.188699278407135 13.743491742138483 7.294484777556272 Other FALSE 1478 +Reddy_2480T ST2 0.5016679047297471 2.153686701611328 1479,951 0.176,0.177 ST2,MCD 5.64586876606981,5.68366192170973 2 12.240827097240931 11.329530687779535 5.68366192170973 ST2 TRUE 1479 +Reddy_2481T EZB 0.600937339262563 3.846004732780894 1480,632,1513 0.177,0.233,0.236 MCD,EZB,EZB 8.53013184306248,5.66457912885048 6 32.806927439663326 14.194710971912961 8.530131843062485 MCD FALSE 1480 +Reddy_2482T MCD 1 0.33248941508664565 1224,788,1310,767,156,471,1491,837 0.107,0.12,0.121,0.128,0.129,0.149,0.163,0.164 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.4911250160653 3 20.1126587745247 60.49112501606527 60.49112501606527 Other FALSE 1481 +Reddy_2483T Other 1 10 0 0 1 2 ST2 FALSE 1482 +Reddy_2485T EZB 1 0.20943481210568002 336,337,1384,1663,430,1483,112,646,2176 0.125,0.138,0.162,0.176,0.186,0.221,0.223,0.242,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7423660028022 2 10.417783077488846 49.74236600280221 49.74236600280221 EZB TRUE 1483 +Reddy_2487T ST2 0.5739515197943658 0.19873825477276166 619,1363,1484,1139,1487,1394,1147,235,1917,1045 0.137,0.154,0.158,0.163,0.173,0.186,0.19,0.199,0.199,0.201 BN2,ST2,ST2,BN2,EZB,EZB,ST2,ST2,ST2,ST2 13.4292182059548,11.1630306864605,33.1294659709865 1 6.584092248627443 57.72171486340172 33.12946597098645 ST2 TRUE 1484 +Reddy_2488T EZB 1 0.15960175941194046 276,1304,517,1085,375,142,1047,1237,372,1485 0.187,0.217,0.223,0.25,0.251,0.256,0.266,0.282,0.285,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6393060860701 1 6.486104752617162 40.63930608607006 40.63930608607006 EZB TRUE 1485 +Reddy_2522T EZB 0.362472607596188 2.5672169389356485 781,816,2138,2134,1486,508 0.166,0.174,0.193,0.222,0.23,0.231 MCD,EZB,EZB,MCD,ST2,ST2 10.9250802822891,10.5390901745054,8.67625961526267 5 28.047051159924465 30.140430072057143 10.925080282289112 ST2 FALSE 1486 +Reddy_2523T ST2 0.7723428798009497 0.35004510303901276 675,1472,1987,1487,1939,1363,1917,2004,89 0.232,0.239,0.254,0.269,0.272,0.276,0.285,0.298,0.303 ST2,ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2 3.93458007142065,3.71703397852422,25.9586417736144 2 9.086695434397686 33.61025582355931 25.958641773614435 EZB FALSE 1487 +Reddy_2524T EZB 0.5892075008856733 4.049974746861808 901,1387,1488 0.171,0.233,0.242 N1,EZB,EZB 8.40974814405618,5.86323400809732 6 34.0592676108955 14.272982152153503 8.409748144056184 EZB TRUE 1488 +Reddy_2525T EZB 0.8950607229507885 0.2657880461826261 367,1991,1745,6,44,565,196,625,433 0.207,0.261,0.271,0.276,0.281,0.286,0.287,0.289,0.295 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.50035342376207,29.8556360797731 2 7.9352711811824 33.35598950353513 29.855636079773063 Other FALSE 1489 +Reddy_2528T Other 1 10 0 0 1 2 Other TRUE 1490 +Reddy_2529T MCD 1 0.09517213760947686 739,481,1968,1422,643,837,247,1491,895,950 0.132,0.141,0.141,0.149,0.156,0.175,0.178,0.182,0.183,0.185 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.6076585624067 1 5.958504696108511 62.60765856240668 62.60765856240668 MCD TRUE 1491 +Reddy_2530T BN2 0.505859053517267 1.279408578369985 993,330,1356,207,1318,1053,1133 0.13,0.191,0.204,0.257,0.27,0.285,0.29 BN2,BN2,EZB,EZB,EZB,EZB,BN2 16.3959730430138,16.0161641501662 4 20.977148561954944 32.412137193180016 16.39597304301385 EZB FALSE 1492 +Reddy_2533T EZB 0.678191022440997 0.6996107649168282 1703,1493,1392,1227,397,1528,1122,696 0.133,0.177,0.268,0.285,0.301,0.306,0.31,0.314 ST2,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.22642433851507,22.6677609462375,7.52967347505518 3 15.858609574549014 33.42385875980774 22.66776094623749 EZB TRUE 1493 +Reddy_2534T EZB 0.5494525404749967 0.6729698867279226 1494,881,1036,466,1701,1509,1322,646 0.13,0.151,0.23,0.361,0.378,0.379,0.385,0.405 BN2,N1,EZB,EZB,EZB,EZB,EZB,EZB 7.72050060299465,17.4722795191303,6.60665337221408 3 11.758317968867694 31.79943349433899 17.47227951913026 BN2 FALSE 1494 +Reddy_2538T EZB 0.7475790098254507 1.904025235631564 1400,781,816,2138 0.238,0.261,0.275,0.284 EZB,MCD,EZB,EZB 11.3557566744305,3.83428548189211 5 21.6216472778073 15.19004215632264 11.355756674430532 Other FALSE 1495 +Reddy_2539T Other 1 10 0 0 1 2 BN2 FALSE 1496 +Reddy_2540T ST2 0.7804907161448323 0.11681637207150672 619,2004,1484,1176,235,1139,1363,1497,234,1147 0.154,0.155,0.158,0.174,0.186,0.19,0.212,0.223,0.224,0.235 BN2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2 11.7618512607155,41.8206262279176 1 4.885333833703835 53.582477488633096 41.8206262279176 ST2 TRUE 1497 +Reddy_2541T EZB 1 4.701005207337186 1099 0.234 EZB 4.28173750780703 4 20.128470320651807 4.281737507807033 4.281737507807033 Other FALSE 1498 +Reddy_2542T MCD 1 0.5407919301304858 185,296,246,465,643,481,739 0.15,0.162,0.162,0.179,0.187,0.212,0.218 MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.2639066701191 4 21.233603872596973 39.26390667011912 39.26390667011912 Other FALSE 1499 +Reddy_2543T EZB 0.5327654720570203 0 1099,1500 0.176,0.201 EZB,ST2 5.68400444781521,4.98486346111502 0 0 10.668867908930237 5.684004447815212 ST2 FALSE 1500 +Reddy_2544T MCD 0.8352023031675876 0.26496423285163195 1736,556,1640,188,792,708,1273,49 0.136,0.156,0.171,0.181,0.194,0.197,0.211,0.226 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.33209806144271,37.1594100261912 2 9.845914570808983 44.49150808763388 37.15941002619117 EZB FALSE 1501 +Reddy_2545T EZB 0.41549813559883514 3.5402113117457934 1441,1416,879,894 0.124,0.226,0.261,0.323 EZB,ST2,MCD,BN2 3.09644139572352,8.07652447684547,3.83232359056915,4.43288314190908 6 28.59260331252012 19.43817260504722 8.076524476845472 Other FALSE 1502 +Reddy_2547T MCD 0.8033945678927452 0.20622654540055071 790,749,883,1732,2084,1127,1503,758,2014 0.134,0.16,0.222,0.223,0.225,0.268,0.271,0.28,0.306 MCD,MCD,MCD,MCD,BN2,MCD,BN2,MCD,MCD 8.13759232533389,33.2528831977156 2 6.857627226472902 41.39047552304947 33.252883197715576 BN2 FALSE 1503 +Reddy_2548T ST2 1 3.3184306995278345 1667,566 0.127,0.195 ST2,ST2 12.9906263574111 7 43.10849331052858 12.990626357411141 12.990626357411141 Other FALSE 1504 +Reddy_2549T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1505 +Reddy_2550T MCD 0.5322567344028504 8.14461352520648 1603,2096 0.246,0.279 MCD,BN2 3.57941115915339,4.07310128179795 6 33.1738357892674 7.652512440951339 4.073101281797945 Other FALSE 1506 +Reddy_2551T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1507 +Reddy_2552T EZB 1 4.176841839782816 1508 0.227 EZB 4.4122619459927 3 18.429320304103868 4.412261945992703 4.412261945992703 EZB TRUE 1508 +Reddy_2553T BN2 0.5691274593514318 0.48192366876754156 1133,1012,1509,1850,1356,330,993 0.187,0.198,0.205,0.23,0.271,0.289,0.312 BN2,BN2,EZB,EZB,EZB,BN2,BN2 17.0717705822421,12.9246217930209 2 8.227290311351918 29.996392375263074 17.071770582242134 EZB FALSE 1509 +Reddy_2554T EZB 1 0.21802908985041808 354,1034,333,1705,443,149,332,404,121 0.153,0.171,0.195,0.212,0.237,0.245,0.278,0.283,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.0566257427255 2 8.95153874301569 41.056625742725515 41.056625742725515 EZB TRUE 1510 +Reddy_2555T EZB 1 0 137,1511,1539,1450,2104,364,699,520,435,9,2006 0.187,0.215,0.244,0.259,0.259,0.276,0.276,0.279,0.287,0.292,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8848343272181 0 0 42.88483432721813 42.88483432721813 EZB TRUE 1511 +Reddy_2556T EZB 1 14.931551488392413 65 0.252 EZB 3.97365214295476 10 59.3327915694898 3.9736521429547564 3.9736521429547564 Other FALSE 1512 +Reddy_2561T MCD 0.5643068444446601 1.5490362344349955 1513,847,194,1480,632 0.139,0.189,0.204,0.205,0.226 EZB,MCD,MCD,MCD,EZB 11.6279414464611,15.0604315476935 4 23.32915417360508 26.68837299415458 15.060431547693454 EZB FALSE 1513 +Reddy_2562T MCD 0.8994793232408971 0.3676194819787926 1224,1310,788,767,156,471,1694,1376 0.104,0.12,0.122,0.127,0.13,0.151,0.162,0.165 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 6.07847485899243,54.391421036749 3 19.995346025620073 60.46989589574144 54.39142103674901 MCD TRUE 1514 +Reddy_2565T ST2 0.682165852133179 4.758607340425692 1188,1638,198 0.365,0.405,0.412 ST2,ST2,N1 2.42759120630316,5.2103269425056 8 24.79390003462491 7.637918148808762 5.210326942505603 Other FALSE 1515 +Reddy_2566T EZB 1 0 375,1560,1081,1046,2063,1048,2047,1281,1163,1889,76 0.19,0.196,0.206,0.225,0.235,0.254,0.265,0.267,0.272,0.275,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0438272705843 0 0 46.04382727058427 46.04382727058427 EZB TRUE 1516 +Reddy_2567T EZB 1 0.3326032603535593 1457,55,1180,1517,1305,278,1570,1853 0.113,0.115,0.119,0.15,0.171,0.178,0.186,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2092477994132 3 18.030172559398853 54.209247799413234 54.209247799413234 EZB TRUE 1517 +Reddy_2570T EZB 0.5091294098174697 0.7933443297707798 940,2214,1093,24,384,826,132,972 0.217,0.247,0.272,0.297,0.298,0.302,0.33,0.338 ST2,EZB,EZB,EZB,EZB,BN2,BN2,BN2 9.30848117206121,14.4327414576072,4.60666138430276 3 11.450133598440372 28.34788401397122 14.43274145760725 Other FALSE 1518 +Reddy_2571T BN2 1 2.62891542367165 1108 0.199 BN2 5.03099839211949 2 13.226069269510194 5.030998392119488 5.030998392119488 Other FALSE 1519 +Reddy_2573T EZB 1 0 360,1294 0.113,0.142 EZB,EZB 15.8478603800918 0 0 15.847860380091774 15.847860380091774 Other FALSE 1520 +Reddy_2575T EZB 0.7880190644895925 1.0555346401452337 1002,1521,1260,628,1322 0.132,0.174,0.191,0.21,0.268 EZB,BN2,EZB,EZB,EZB 5.73309448085581,21.3122361148021 5 22.49580347812786 27.04533059565788 21.312236114802076 BN2 FALSE 1521 +Reddy_2576T Other 1 10 0 0 1 2 Other TRUE 1522 +Reddy_2577T MCD 0.7734120105217432 2.1868523218726046 1907,717,632,1480 0.155,0.16,0.202,0.241 MCD,MCD,EZB,MCD 4.94089891136615,16.8647533773677 7 36.880725081105346 21.805652288733818 16.864753377367673 Other FALSE 1523 +Reddy_2578T Other 1 10 0 0 1 2 Other TRUE 1524 +Reddy_2580T BN2 0.5773604226896474 0.22829799524140845 1112,34,274,1407,1191,897,273 0.115,0.145,0.156,0.165,0.17,0.184,0.203 BN2,EZB,MCD,BN2,BN2,MCD,BN2 25.6320595254365,6.90058829509507,11.862600828534 1 5.851747803565593 44.39524864906552 25.632059525436468 BN2 TRUE 1525 +Reddy_2581T MCD 1 0.18789415579200247 465,1526,2053,704,347,765,900,296,799 0.164,0.18,0.216,0.24,0.24,0.25,0.258,0.268,0.27 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.919261397135 2 7.500595920054945 39.91926139713495 39.91926139713495 MCD TRUE 1526 +Reddy_2582T EZB 1 0.19805919505062122 109,1959,165,1527,1292,1750,818,1446,1671 0.148,0.178,0.188,0.213,0.275,0.276,0.283,0.321,0.377 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9507428733464 2 7.714552780118715 38.95074287334643 38.95074287334643 EZB TRUE 1527 +Reddy_2585T EZB 1 0.08358025248781577 1528,2005,1554,695,1162,1493,173,1277,2201,2093 0.206,0.208,0.257,0.262,0.264,0.277,0.277,0.289,0.296,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.3390320183747 1 3.2043859762342137 38.33903201837474 38.33903201837474 EZB TRUE 1528 +Reddy_2588T ST2 1 2.6759382653812582 530,702 0.201,0.23 ST2,ST2 9.32991457132684 5 24.966275414151657 9.329914571326835 9.329914571326835 Other FALSE 1529 +Reddy_2591T EZB 1 2.1501193493461117 2050 0.117 EZB 8.55129274427453 2 18.38629999138767 8.551292744274527 8.551292744274527 Other FALSE 1530 +Reddy_2594T MCD 0.8902279058542917 0.28769842077416374 789,1531,997,1501,738,154,1826,1728,214 0.117,0.149,0.152,0.188,0.199,0.199,0.248,0.272,0.29 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 5.30832584954795,43.0493728065589 2 12.385236571765233 48.357698656106884 43.04937280655893 MCD TRUE 1531 +Reddy_2596T BN2 1 4.747619172432946 1980,804 0.174,0.222 BN2,BN2 10.2425043707233 9 48.62751012417404 10.242504370723267 10.242504370723267 BN2 TRUE 1532 +Reddy_2597T EZB 0.8767118711074726 0.19621133813253286 42,16,1533,155,308,1933,343,639 0.14,0.161,0.17,0.174,0.178,0.193,0.194,0.213 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 5.62847545386481,40.0245448687265 1 7.853269506838434 45.653020322591345 40.02454486872653 EZB TRUE 1533 +Reddy_2599T EZB 1 4.679889215388363 199,532 0.281,0.293 EZB,EZB 6.97943647342468 7 32.662989481468344 6.979436473424679 6.979436473424679 Other FALSE 1534 +Reddy_2600T ST2 0.6415155287959219 3.678813912326177 198,1638,1188 0.351,0.391,0.394 N1,ST2,ST2 2.84782625681855,5.09624520394311 6 18.748137756891463 7.944071460761661 5.096245203943108 EZB FALSE 1535 +Reddy_2602T EZB 0.5206327138745972 10.177487180820986 292,2080 0.199,0.216 EZB,ST2 5.02814654550866,4.62961488882136 8 51.17389701020368 9.657761434330013 5.028146545508657 Other FALSE 1536 +Reddy_2603T EZB 1 0.26618320998914613 639,1537,17,121,80,155,42,1933,233 0.149,0.179,0.181,0.184,0.186,0.195,0.196,0.205,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4464877182067 2 12.895641613531993 48.44648771820666 48.44648771820666 EZB TRUE 1537 +Reddy_2604T BN2 0.5395728318232095 5.636123248021563 1538,31 0.128,0.15 BN2,EZB 7.8320244670596,6.68320685138038 6 44.14225517786828 14.515231318439977 7.832024467059596 BN2 TRUE 1538 +Reddy_2605T EZB 1 0.09715820024879344 1539,1450,1160,699,477,137,72,1542,520,1511 0.128,0.208,0.214,0.219,0.255,0.256,0.262,0.28,0.284,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.9615922578198 1 4.271229183841067 43.96159225781983 43.96159225781983 EZB TRUE 1539 +Reddy_2606T EZB 1 0.28890671210341623 1926,1219,1726,1590,2072,1305,1853,56,2013 0.145,0.206,0.216,0.244,0.253,0.258,0.271,0.284,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.913394315458 2 11.242340808462743 38.913394315458014 38.913394315458014 EZB TRUE 1540 +Reddy_2608T EZB 0.8999669834942996 0.36515173743309165 178,1551,320,1218,259,56,1088,1541 0.151,0.163,0.178,0.188,0.189,0.22,0.222,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 38.0350311786474,4.22766498268163 3 13.888557718204904 42.26269616132901 38.03503117864739 ST2 FALSE 1541 +Reddy_2609T EZB 1 0.14159047646879844 477,1542,520,699,1450,1289,119,72,1668,1160 0.154,0.156,0.171,0.19,0.199,0.23,0.256,0.268,0.274,0.279 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.261472389571 1 6.833364870725124 48.26147238957104 48.26147238957104 EZB TRUE 1542 +Reddy_2611T MCD 0.8935923155588671 0.4200843404459202 1897,986,1184,1420,1543,1911,467,755 0.109,0.133,0.183,0.204,0.205,0.205,0.21,0.219 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 4.91015362163268,41.2345740587172 3 17.32199884702466 46.14472768034987 41.234574058717186 MCD TRUE 1543 +Reddy_2612T ST2 1 5.379710440432992 318,90 0.28,0.325 ST2,ST2 6.64791763472669 9 35.7638719066778 6.647917634726695 6.647917634726695 Other FALSE 1544 +Reddy_2613T EZB 1 0.08505731166740922 1545,1042,1032,340,1581,1244,1277,1579,1243,1245 0.123,0.214,0.236,0.249,0.266,0.266,0.272,0.287,0.294,0.305 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.3952568897911 1 3.6060265784948404 42.395256889791106 42.395256889791106 EZB TRUE 1545 +Reddy_2614T ST2 1 6.297719657656408 1436 0.216 ST2 4.63737022287487 5 29.20485761242954 4.6373702228748686 4.6373702228748686 Other FALSE 1546 +Reddy_2616T ST2 0.6586052527510067 1.8690605130054305 1123,293,234,1176,1776,722 0.211,0.221,0.231,0.238,0.244,0.251 EZB,ST2,ST2,ST2,BN2,ST2 4.09691855135075,4.72940030724461,17.0273854813779 5 31.82521384296534 25.853704339973234 17.02738548137787 Other FALSE 1547 +Reddy_2617T ST2 1 2.917278578329435 1667,451 0.25,0.256 ST2,ST2 7.91212911379731 5 23.08188477265756 7.912129113797313 7.912129113797313 Other FALSE 1548 +Reddy_2619T BN2 1 0.5238099567614671 2213,973,1210,284,936,903,1549 0.145,0.162,0.189,0.218,0.225,0.239,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.4822897960438 4 18.58597668386357 35.482289796043844 35.482289796043844 BN2 TRUE 1549 +Reddy_2620T EZB 0.9008634343333632 0.326051389084142 809,1031,1864,1011,1701,473,1550,466 0.139,0.178,0.181,0.197,0.207,0.229,0.248,0.252 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.02562172710331,36.5812088609174 2 11.927353963479238 40.60683058802069 36.581208860917386 BN2 FALSE 1550 +Reddy_2621T EZB 0.8967590963805577 0.3126640408903956 320,2059,319,1551,1088,1541,259,178,149 0.156,0.157,0.163,0.18,0.191,0.198,0.204,0.213,0.222 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 43.7595767948282,5.03789509193362 2 13.682046108324569 48.79747188676181 43.75957679482819 EZB TRUE 1551 +Reddy_2622T MCD 1 2.278927037283621 311 0.18 MCD 5.56291998975018 2 12.677488770887205 5.562919989750178 5.562919989750178 Other FALSE 1552 +Reddy_2624T EZB 1 9.076329961550293 1649 0.282 EZB 3.54428587789012 7 32.16910810579369 3.5442858778901214 3.5442858778901214 Other FALSE 1553 +Reddy_2626T EZB 1 0 1554,2005,1162,1277,173,1528,2201,2093,695,1474,1903 0.177,0.187,0.189,0.206,0.217,0.225,0.251,0.269,0.317,0.317,0.324 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2785127514732 0 0 47.278512751473194 47.278512751473194 EZB TRUE 1554 +Reddy_2628T ST2 0.729531331572305 0.45876603479375616 1555,722,1776,293,1644,1497,1341 0.154,0.166,0.175,0.188,0.21,0.212,0.226 ST2,ST2,BN2,ST2,ST2,ST2,BN2 10.1300208030882,27.3235624972499 2 12.535122423302742 37.45358330033817 27.32356249724995 ST2 TRUE 1555 +Reddy_2629T MCD 0.6637441989987397 1.5824129659766908 952,1398,1890,2030,285,1278 0.199,0.218,0.235,0.235,0.254,0.278 BN2,MCD,MCD,MCD,MCD,EZB 5.02726496766117,3.5991659076219,17.0279395462721 5 26.94523232188821 25.654370421555164 17.027939546272094 Other FALSE 1556 +Reddy_2630T BN2 1 13.690255154049348 1803 0.36 BN2 2.77673796172127 10 38.014251191899135 2.7767379617212726 2.7767379617212726 Other FALSE 1557 +Reddy_2632T ST2 0.6042170814523433 1.0696658900586657 1417,1687,649,545,1147,1394,2082 0.169,0.201,0.241,0.25,0.269,0.272,0.29 ST2,ST2,EZB,BN2,ST2,EZB,ST2 3.99846909965137,7.81539681103687,18.0354917979258 4 19.291950386674024 29.849357708614008 18.03549179792576 Other FALSE 1558 +Reddy_2634T MCD 0.7499589378043606 2.342609739822955 81,82,401 0.107,0.175,0.199 MCD,MCD,EZB 5.01506598356745,15.0419036178635 6 35.23730992068519 20.05696960143095 15.041903617863506 Other FALSE 1559 +Reddy_2636T EZB 1 0.08310371879914118 1081,1560,375,2063,1889,39,93,1485,1048,1046 0.16,0.166,0.209,0.226,0.238,0.249,0.256,0.264,0.269,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7733266826071 1 3.720829950333466 44.77332668260711 44.77332668260711 EZB TRUE 1560 +Reddy_2637T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1561 +Reddy_2639T MCD 0.7872076479954544 2.238163224136804 717,864,1907,632 0.152,0.196,0.215,0.226 MCD,MCD,MCD,EZB 4.41861283506361,16.346291511542 6 36.58566851215281 20.76490434660556 16.346291511541953 Other FALSE 1562 +Reddy_2640T EZB 1 0.2122376412539014 332,1083,589,333,122,231,1034,192,1705 0.148,0.174,0.179,0.202,0.208,0.227,0.255,0.257,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.8166452739923 2 9.299541440611032 43.8166452739923 43.8166452739923 EZB TRUE 1563 +Reddy_2642T MCD 1 0.20527239112514423 2053,1526,465,765,704,799,900,347 0.174,0.18,0.204,0.227,0.228,0.247,0.254,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.589308771467 2 7.510774901135239 36.589308771466975 36.589308771466975 Other FALSE 1564 +Reddy_2643T MCD 1 0.0906426671462638 1459,1647,518,803,1598,880,783,535,534,771 0.14,0.141,0.142,0.143,0.17,0.183,0.184,0.2,0.2,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.9786602498678 1 5.436625736907607 59.97866024986776 59.97866024986776 MCD TRUE 1565 +Reddy_2644T ST2 0.40766490186615534 2.8521806703605916 1082,2017,1863,1121,1983 0.15,0.162,0.173,0.192,0.244 BN2,ST2,EZB,ST2,EZB 6.6450354531488,9.87887293936333,11.372308537011 6 32.43587858663964 27.89621692952317 11.372308537011044 Other FALSE 1566 +Reddy_2646T MCD 1 0 1331,1699,305,798,21,23,709,479,2008,2034,1689 0.129,0.135,0.144,0.144,0.145,0.15,0.152,0.153,0.157,0.16,0.166 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.3456346006028 0 0 74.34563460060285 74.34563460060285 MCD TRUE 1567 +Reddy_2647T BN2 0.7186943152325345 1.259642143531561 1433,537,1799 0.115,0.184,0.191 BN2,ST2,BN2 13.9178057902567,5.44759824212884 3 17.53145471889486 19.36540403238549 13.917805790256653 Other FALSE 1568 +Reddy_2649T Other 1 10 0 0 1 2 Other TRUE 1569 +Reddy_2650T EZB 1 0.10466067224352066 2003,1180,1570,55,1517,1457,1172,278,1305,1066 0.193,0.194,0.197,0.203,0.205,0.216,0.241,0.246,0.248,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.8049412183837 1 4.793975939990991 45.804941218383746 45.804941218383746 EZB TRUE 1570 +Reddy_2651T ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1571 +Reddy_2652T MCD 1 0 798,23,2008,1331,21,2034,1699,59,846,905,709 0.112,0.115,0.118,0.119,0.135,0.139,0.143,0.151,0.173,0.178,0.178 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.7641773992319 0 0 79.7641773992319 79.7641773992319 MCD TRUE 1572 +Reddy_2654T EZB 1 0.09182068869879492 108,1573,117,172,2063,1048,76,2047,1138,2089 0.131,0.177,0.178,0.192,0.211,0.223,0.273,0.28,0.299,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2593228713454 1 4.339383573485643 47.259322871345375 47.259322871345375 EZB TRUE 1573 +Reddy_2655T BN2 1 5.349514031203395 2058,1793 0.254,0.293 BN2,BN2 7.34520774971009 8 39.293291919178024 7.345207749710087 7.345207749710087 Other FALSE 1574 +Reddy_2656T BN2 1 10.049853006443614 1697 0.263 BN2 3.79598875179198 8 38.149128969622765 3.7959887517919793 3.7959887517919793 Other FALSE 1575 +Reddy_2657T EZB 1 0 31 0.108 EZB 9.26325067277845 0 0 9.263250672778446 9.263250672778446 Other FALSE 1576 +Reddy_2659T EZB 1 4.949145569350474 1601,2166 0.187,0.345 EZB,EZB 8.25671264066838 9 40.86367278296397 8.25671264066838 8.25671264066838 Other FALSE 1577 +Reddy_2660T ST2 0.5888874432480815 2.614606940964133 1131,52 0.137,0.196 ST2,BN2 5.08931634696927,7.29005826318248 3 19.060636934949834 12.379374610151743 7.290058263182476 Other FALSE 1578 +Reddy_2662T EZB 1 0.12853560123499003 1579,1032,474,1054,1545,1079,1236,227,71,1474 0.149,0.151,0.213,0.231,0.239,0.248,0.281,0.291,0.317,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7083187198284 1 5.6180750256237095 43.70831871982837 43.70831871982837 EZB TRUE 1579 +Reddy_2663T ST2 0.33777527429436 5.165160893572753 1074,147,282,1425 0.186,0.269,0.273,0.314 ST2,MCD,BN2,N1 3.65733160054663,3.71840276196271,3.18959610520017,5.38896730703987 7 27.8348831910644 15.954297774749376 5.388967307039868 Other FALSE 1580 +Reddy_2664T EZB 1 0.10690180611234974 1243,105,1245,505,1581,341,1782,166,1244,1668 0.149,0.162,0.163,0.173,0.179,0.202,0.221,0.223,0.253,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.0655414307726 1 5.565900415166963 52.06554143077258 52.06554143077258 EZB TRUE 1581 +Reddy_2666T N1 0.5306670674802307 4.884252520449958 999,1071 0.206,0.233 N1,ST2 4.85621792550818,4.29493959509506 5 23.718994642517607 9.151157520603245 4.856217925508183 Other FALSE 1582 +Reddy_2667T BN2 0.5143500500683403 8.11282487052428 101,140 0.506,0.536 BN2,EZB 1.97452797887226,1.86435174634583 7 16.01899969454093 3.838879725218092 1.9745279788722623 Other FALSE 1583 +Reddy_2669T EZB 1 0.1342706709153777 1032,1579,474,1545,1054,1079,227,1236,1042,1017 0.136,0.166,0.208,0.224,0.249,0.271,0.295,0.304,0.332,0.337 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.0122300016137 1 5.775280979883214 43.012230001613744 43.012230001613744 Other FALSE 1584 +Reddy_2671T BN2 1 4.395274646953124 1538 0.157 BN2 6.35192419210641 4 27.91845136093351 6.35192419210641 6.35192419210641 Other FALSE 1585 +Reddy_2673T Other 1 10 0 0 1 2 Other TRUE 1586 +Reddy_2675T EZB 0.8893239549096854 0.12738173209796255 11,343,308,16,143,1533,139,575,218,1058 0.128,0.166,0.172,0.182,0.189,0.198,0.226,0.229,0.23,0.253 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.82168829210606,46.7794711309237 1 5.958850059283695 52.601159423029756 46.77947113092369 EZB TRUE 1587 +Reddy_2676T Other 1 10 0 0 1 2 Other TRUE 1588 +Reddy_2677T EZB 1 0 427,1020,366,689,161,645,78,1090,498,128,48 0.198,0.208,0.212,0.235,0.247,0.291,0.293,0.318,0.332,0.342,0.348 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6532134400376 0 0 41.65321344003758 41.65321344003758 EZB TRUE 1589 +Reddy_2678T EZB 1 0.10056136867352942 1305,1219,1926,55,1180,1726,1590,1853,1457 0.153,0.171,0.193,0.21,0.216,0.217,0.218,0.233,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7299395711652 1 4.498103943960637 44.72993957116521 44.72993957116521 EZB TRUE 1590 +Reddy_2680T ST2 0.5907706172975848 0.8878336485312514 1417,1687,545,649,1394,1147,2082 0.186,0.224,0.233,0.265,0.285,0.287,0.298 ST2,ST2,BN2,EZB,EZB,ST2,ST2 4.28994931935572,7.27263247296563,16.6919431295352 3 14.819668769771368 28.25452492185653 16.691943129535176 Other FALSE 1591 +Reddy_2681T Other 1 10 0 0 1 2 Other TRUE 1592 +Reddy_2684T ST2 0.39462092414400113 6.716429274097206 2216,1666,610 0.153,0.178,0.227 ST2,EZB,BN2 4.4097464488493,5.6058438748727,6.5287382187868 7 43.84980849557688 16.544328542508794 6.5287382187867955 Other FALSE 1593 +Reddy_2685T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1594 +Reddy_2687T MCD 1 0.2393934389149432 788,1224,156,1310,471,767,1491,950 0.108,0.123,0.128,0.13,0.134,0.141,0.15,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.474390479223 2 14.477172303106299 60.474390479223025 60.474390479223025 Other FALSE 1595 +Reddy_2688T MCD 0.5066868004818145 3.212910997462534 921,1835 0.146,0.15 MCD,EZB 6.66374324232442,6.84439570233151 3 21.9904342230062 13.508138944655927 6.844395702331506 Other FALSE 1596 +Reddy_2689T BN2 0.823957776263066 1.3846854047068649 523,596,2096,1603,2069 0.161,0.165,0.209,0.222,0.252 BN2,BN2,BN2,MCD,BN2 21.0532757922503,4.4981255037411 6 29.152163710797332 25.551401295991393 21.05327579225029 Other FALSE 1597 +Reddy_2690T MCD 1 0.23449752210180733 2180,1250,880,1239,771,929,647,1598,1240 0.159,0.175,0.191,0.193,0.196,0.205,0.212,0.213,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.3918376587509 2 10.878770976726388 46.391837658750866 46.391837658750866 MCD TRUE 1598 +Reddy_2691T EZB 1 12.269588240166984 1182 0.295 EZB 3.39002513756866 10 41.594212561782896 3.3900251375686605 3.3900251375686605 Other FALSE 1599 +Reddy_2692T EZB 0.8585612017961811 0.4044416920830485 1600,595,2083,1272,1318,1293,331,651 0.172,0.194,0.194,0.212,0.235,0.249,0.26,0.288 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.15800773576512,31.3101170027683 3 12.663116699917834 36.46812473853342 31.310117002768294 EZB TRUE 1600 +Reddy_2696T EZB 1 2.455421944244491 1601,1395 0.112,0.291 EZB,EZB 12.3350749400087 7 30.287813691597773 12.335074940008747 12.335074940008747 EZB TRUE 1601 +Reddy_2697T ST2 0.6950568658748894 2.576795800403351 702,530,1697 0.192,0.22,0.233 ST2,ST2,BN2 4.28543502115927,9.76779176636716 4 25.16960480278933 14.05322678752643 9.76779176636716 Other FALSE 1602 +Reddy_2698T Other 1 10 0 0 1 2 MCD FALSE 1603 +Reddy_2699T MCD 1 6.46149564720824 904 0.223 MCD 4.49302330720873 5 29.031650542334383 4.4930233072087304 4.4930233072087304 Other FALSE 1604 +Reddy_2700T Other 1 10 0 0 1 2 Other TRUE 1605 +Reddy_2704T EZB 0.9177480368194685 0.09377468707456524 404,2059,149,187,121,80,319,17,1541,1705 0.137,0.142,0.162,0.168,0.174,0.176,0.185,0.188,0.209,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 53.3538309849841,4.78176707075917 1 5.0032388048461245 58.135598055743245 53.35383098498407 EZB TRUE 1606 +Reddy_2705T ST2 1 0.4338125267388648 1946,90 0.112,0.149 ST2,ST2 15.6706762103353 1 6.798135642512174 15.6706762103353 15.6706762103353 Other FALSE 1607 +Reddy_2706T EZB 1 3.378054033367146 31 0.178 EZB 5.61925054539168 3 18.982131969360903 5.619250545391681 5.619250545391681 N1 FALSE 1608 +Reddy_2707T N1 0.4097569615441078 1.5006658744871442 1609,464,176,659,1008,1064 0.135,0.151,0.183,0.242,0.276,0.284 MCD,BN2,N1,MCD,N1,N1 6.61980212384529,11.5388820353255,12.6060737051064 4 18.917504620522962 30.764757864277268 12.606073705106448 MCD FALSE 1609 +Reddy_2709T ST2 0.6578496185079283 1.5173935453172025 572,718,419,1302,1461 0.122,0.233,0.248,0.258,0.278 BN2,ST2,ST2,ST2,ST2 8.21848753184048,15.8016158390831 4 23.977269879806695 24.020103370923536 15.801615839083052 Other FALSE 1610 +Reddy_2713T EZB 0.7853998336439457 1.801664739742062 696,1122,1392,1471,1227 0.225,0.266,0.292,0.327,0.352 EZB,BN2,EZB,EZB,EZB 3.7660132042874,13.7829629602465 5 24.832278374646975 17.548976164533897 13.782962960246492 Other FALSE 1611 +Reddy_2714T EZB 1 1.079726257691078 1294,360 0.143,0.166 EZB,EZB 13.0365361453503 2 14.07589038547356 13.03653614535031 13.03653614535031 Other FALSE 1612 +Reddy_2715T MCD 0.3560204941568707 6.9263229656692324 147,1074,1425 0.229,0.232,0.279 MCD,ST2,N1 4.36199293014096,3.58459395808733,4.30549691518042 7 30.212571808122163 12.25208380340871 4.361992930140961 Other FALSE 1613 +Reddy_2716T BN2 0.8256804075127272 1.319432644421487 1816,591,610,1666,1614 0.201,0.236,0.276,0.296,0.314 BN2,BN2,BN2,EZB,BN2 16.0280339804346,3.38387628726487 5 21.14791125968223 19.411910267699437 16.028033980434564 BN2 TRUE 1614 +Reddy_2718T BN2 1 11.755533336412224 2170 0.324 BN2 3.08190522870411 9 36.2294396556943 3.0819052287041098 3.0819052287041098 Other FALSE 1615 +Reddy_2719T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1616 +Reddy_2720T ST2 1 12.348920276119966 2107 0.268 ST2 3.73276379567117 9 46.09560252233029 3.7327637956711746 3.7327637956711746 Other FALSE 1617 +Reddy_2725T MCD 0.5429514395634154 4.033000247253929 768,140 0.195,0.232 MCD,EZB 4.31147421570963,5.12182147521476 4 20.656307275931603 9.433295690924384 5.121821475214758 Other FALSE 1618 +Reddy_2727T MCD 1 0.1039821355114704 846,707,798,2008,2018,1947,23,1699,1331,905 0.131,0.148,0.156,0.167,0.169,0.172,0.173,0.174,0.182,0.183 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.9475299674279 1 6.337454320162492 60.94752996742791 60.94752996742791 Other FALSE 1619 +Reddy_2728T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1620 +Reddy_2758T ST2 1 0.6024476223461437 1188,1638 0.27,0.28 ST2,ST2 7.27628921935511 1 4.383583139703363 7.2762892193551085 7.2762892193551085 Other FALSE 1621 +Reddy_2761T ST2 1 3.435494915738986 1667,566 0.216,0.245 ST2,ST2 8.71825574779564 6 29.951523295664103 8.718255747795638 8.718255747795638 Other FALSE 1622 +Reddy_2762T ST2 1 1.1605818816944742 271,2198,1955,1077 0.163,0.202,0.241,0.244 ST2,ST2,ST2,ST2 19.3395869134925 5 22.445174171254916 19.339586913492468 19.339586913492468 Other FALSE 1623 +Reddy_2763T EZB 0.37334294234849835 3.403102823869741 1123,854,234,293,1341,649 0.173,0.229,0.254,0.258,0.272,0.274 EZB,BN2,ST2,ST2,BN2,EZB 8.04049378000831,9.44757700441686,7.81728772854756 5 32.15107598245784 25.305358512972727 9.447577004416859 Other FALSE 1624 +Reddy_2764T ST2 0.9087841737961099 0 1442,1284,1955,2076,1942,1998,1401,1661,1625,726 0.144,0.166,0.184,0.192,0.201,0.216,0.217,0.22,0.239,0.252 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.60985592324989,45.9280399123446 0 0 50.53789583559446 45.92803991234457 ST2 TRUE 1625 +Reddy_2765T N1 1 1.3604232589713492 870,262 0.154,0.229 N1,N1 10.8453985418376 3 14.754332429129875 10.845398541837636 10.845398541837636 Other FALSE 1626 +Reddy_2767T MCD 0.8206372820234564 1.7355800484168311 1994,801,768,850 0.137,0.159,0.184,0.24 MCD,MCD,MCD,BN2 4.16191200626741,19.0419736909358 6 33.04886962046634 23.203885697203184 19.041973690935777 Other FALSE 1627 +Reddy_2768T ST2 0.7547185562884801 0.7359008789019338 1130,1789,1211,486,1018,1943,1713,701 0.196,0.218,0.231,0.237,0.245,0.263,0.265,0.275 ST2,EZB,ST2,ST2,ST2,ST2,ST2,EZB 8.22328911142449,25.3026433317001 3 18.620237466340267 33.5259324431246 25.302643331700114 Other FALSE 1628 +Reddy_2769T EZB 1 0.4278339027212356 651,1814,331,1241,1437,1600,408 0.157,0.197,0.202,0.255,0.348,0.362,0.369 EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.6676751627659 4 12.265003346830776 28.667675162765917 28.667675162765917 Other FALSE 1629 +Reddy_2770T ST2 1 2.2513131679494287 90,1946 0.2,0.238 ST2,ST2 9.21257389739265 4 20.74038892590727 9.212573897392653 9.212573897392653 Other FALSE 1630 +Reddy_2772T MCD 0.38889512607823035 7.816101811927579 1631,175,2211 0.254,0.301,0.348 MCD,N1,BN2 2.87176142262161,3.94085141630902,3.32084256342963 8 30.802095895550302 10.133455402360267 3.9408514163090205 MCD TRUE 1631 +Reddy_2773T BN2 1 1.6562123467136949 1113,2064,57,754,106 0.258,0.267,0.276,0.324,0.334 BN2,BN2,BN2,BN2,BN2 17.3227979831944 6 28.690231899393602 17.322797983194366 17.322797983194366 BN2 TRUE 1632 +Reddy_2775T EZB 0.4418704820795233 3.0880799133201076 1983,1863,1082,2017,1121 0.198,0.227,0.238,0.241,0.279 EZB,EZB,BN2,ST2,ST2 4.19895223192613,9.44748513496515,7.73422850267203 6 29.17458907667619 21.380665869563316 9.447485134965152 Other FALSE 1633 +Reddy_2776T EZB 1 2.9800149285383015 1535 0.225 EZB 4.4517726521168 2 13.2663489617666 4.451772652116796 4.451772652116796 Other FALSE 1634 +Reddy_2777T EZB 0.791005842500117 0.7027794310820751 1087,2126,1453,1059,179,62 0.156,0.181,0.188,0.208,0.229,0.239 BN2,EZB,EZB,EZB,EZB,EZB 6.39638352969326,24.2091779185824 3 17.013712284586042 30.60556144827562 24.209177918582355 Other FALSE 1635 +Reddy_2778T BN2 0.4827521434148294 2.3094539311683278 402,737,793,581 0.106,0.133,0.159,0.186 EZB,BN2,BN2,ST2 13.8486623784677,9.44897609992412,5.38926134665472 5 31.98284777137521 28.686899825046563 13.848662378467724 Other FALSE 1636 +Reddy_2779T EZB 1 1.034363076131765 1649 0.129 EZB 7.75122368385489 1 8.01757957341754 7.751223683854894 7.751223683854894 Other FALSE 1637 +Reddy_2781T Other 1 10 0 0 1 2 ST2 FALSE 1638 +Reddy_2783T ST2 0.6026927475184138 4.79780247451278 1436,1901 0.161,0.244 ST2,BN2 4.10448853661449,6.22627817095957 6 29.872452815634738 10.330766707574066 6.226278170959572 Other FALSE 1639 +Reddy_2784T MCD 1 0.2641436564595905 792,1640,1647,586,534,1811,803,1459,708 0.147,0.148,0.163,0.166,0.168,0.17,0.177,0.187,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8809777755642 2 14.232318483255458 53.88097777556419 53.88097777556419 MCD TRUE 1640 +Reddy_2785T EZB 1 1.0069293066985767 2162 0.163 EZB 6.15375037972859 1 6.196391603456207 6.153750379728585 6.153750379728585 Other FALSE 1641 +Reddy_2786T ST2 0.7212399084181357 3.365747526257225 1653,1535,451 0.144,0.237,0.251 ST2,EZB,ST2 4.22247611099327,10.9248718721137 7 36.770360478243866 15.147347983106982 10.924871872113712 Other FALSE 1642 +Reddy_2788T EZB 0.7544291670164479 2.2419480358963724 1488,1087,1387,1453 0.139,0.197,0.207,0.282 EZB,BN2,EZB,EZB 5.06497646174225,15.5603412936505 7 34.885476601176975 20.625317755392754 15.560341293650508 Other FALSE 1643 +Reddy_2789T ST2 0.6895006161669308 0.6072839722923535 649,234,1687,235,1147,1139,1176 0.133,0.152,0.175,0.184,0.191,0.204,0.216 EZB,ST2,ST2,ST2,ST2,BN2,ST2 4.90001803544506,7.52364603845578,27.5882158871237 3 16.753881332391494 40.01187996102455 27.588215887123695 ST2 TRUE 1644 +Reddy_2790T BN2 1 5.683194195600907 804,1980 0.179,0.186 BN2,BN2 10.9488294759925 9 62.22432412658487 10.948829475992529 10.948829475992529 Other FALSE 1645 +Reddy_2791T ST2 1 9.045939856512717 670 0.219 ST2 4.57644722025014 8 41.398266310887536 4.576447220250135 4.576447220250135 Other FALSE 1646 +Reddy_2792T MCD 0.8374322022534004 0.4776743542828774 647,1420,1184,986,1897,1543,467,1977 0.136,0.146,0.189,0.19,0.198,0.225,0.253,0.256 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.84306402019837,35.2505370191963 3 16.83827750876927 42.09360103939469 35.25053701919632 MCD TRUE 1647 +Reddy_2793T MCD 0.4613538147525473 2.373303086353738 847,194,1071,1513,999 0.195,0.221,0.251,0.264,0.286 MCD,MCD,ST2,EZB,N1 3.78545200298732,9.65346226518396,3.49563834801203,3.9896533977232 5 22.91059178796043 20.924206013906506 9.653462265183958 Other FALSE 1648 +Reddy_2794T ST2 0.5169079267545914 8.229788417663073 511,1649 0.279,0.299 ST2,EZB 3.34791363934738,3.58226349408419 7 29.48127061263132 6.93017713343157 3.5822634940841906 EZB FALSE 1649 +Reddy_2805T ST2 1 4.36076136873364 451,1653 0.256,0.318 ST2,ST2 7.04946438671377 6 30.74103196784499 7.04946438671377 7.04946438671377 Other FALSE 1650 +Reddy_2806T EZB 0.5082149061630302 11.96838523696239 1601,1901 0.27,0.279 EZB,BN2 3.58000598407284,3.69960868692334 9 44.27834199111111 7.279614670996176 3.6996086869233396 Other FALSE 1651 +Reddy_2808T MCD 0.6065376323818631 5.572109030761872 904,1733 0.209,0.322 MCD,EZB 3.10281871034226,4.7831164278146 5 26.652046242611203 7.8859351381568565 4.7831164278146 Other FALSE 1652 +Reddy_2809T ST2 0.7673576017134041 2.38887217980688 1653,451,1535 0.121,0.219,0.256 ST2,ST2,EZB 3.89937788467083,12.8618742060471 6 30.725373471001543 16.7612520907179 12.861874206047068 ST2 TRUE 1653 +Reddy_2810T ST2 1 5.647429359160402 530,702 0.293,0.322 ST2,ST2 6.52529301295635 8 36.85113133849395 6.525293012956354 6.525293012956354 Other FALSE 1654 +Reddy_2812T EZB 1 3.1817979157041347 2050,1300 0.127,0.139 EZB,EZB 15.0669927807216 6 47.940126225629385 15.066992780721648 15.066992780721648 Other FALSE 1655 +Reddy_2813T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1656 +Reddy_2815T BN2 0.49751821314280875 0.9026816798876667 1719,1848,2170,1657,114,511 0.156,0.178,0.237,0.254,0.263,0.269 EZB,BN2,BN2,BN2,EZB,ST2 13.7680861371244,10.1902431667101,3.71520258588623 3 12.428199123097546 27.673531889720735 13.768086137124396 BN2 TRUE 1657 +Reddy_2816T ST2 0.6018184833195189 0.9503111777578093 1687,649,1417,1147,234,1394,1139 0.133,0.147,0.168,0.207,0.224,0.24,0.244 ST2,EZB,ST2,ST2,ST2,EZB,BN2 4.10311887118668,10.9625615111487,22.7705318756655 4 21.639090964935413 37.836212258000906 22.770531875665494 Other FALSE 1658 +Reddy_2818T Other 1 10 0 0 1 2 Other TRUE 1659 +Reddy_2819T MCD 1 0 540,947,1572,587,483,888,516,515,387,1567,2094 0.125,0.131,0.136,0.138,0.141,0.142,0.143,0.164,0.173,0.175,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.7089354503145 0 0 74.70893545031447 74.70893545031447 MCD TRUE 1660 +Reddy_2820T ST2 0.8093596064673173 1.138882562724699 1249,215,1396,606,1057 0.183,0.254,0.269,0.3,0.329 ST2,ST2,EZB,ST2,ST2 3.71576689945033,15.7752068160089 5 17.9661079661284 19.49097371545927 15.77520681600894 ST2 TRUE 1661 +Reddy_2821T EZB 0.6465339657312897 1.949001153122374 1944,160,1439 0.224,0.229,0.283 EZB,ST2,EZB 7.99071397054456,4.36859643553182 3 15.573910742862417 12.359310406076386 7.9907139705445625 Other FALSE 1662 +Reddy_2823T EZB 1 0.2381622748595342 1663,430,1483,337,336,1468,112,1384,170 0.108,0.152,0.165,0.176,0.177,0.222,0.233,0.244,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0186431789981 2 11.912553844897522 50.01864317899815 50.01864317899815 EZB TRUE 1663 +Reddy_2824T BN2 0.635964971486329 4.510877077924508 833,286,834 0.251,0.278,0.297 ST2,BN2,BN2 6.96211898188142,3.98521191530634 7 31.405262929152023 10.94733089718776 6.962118981881423 ST2 FALSE 1664 +Reddy_2826T EZB 1 4.180538435438839 1395 0.208 EZB 4.80356846065454 4 20.08150257702808 4.803568460654539 4.803568460654539 Other FALSE 1665 +Reddy_2827T BN2 0.4545223976186366 2.0446031436970915 1666,115,1816,610,2216,591 0.178,0.186,0.218,0.24,0.245,0.262 EZB,EZB,BN2,BN2,ST2,BN2 12.5620712194674,10.9980394964404,4.07784790897721 5 25.68445030666979 27.63795862488497 12.562071219467393 EZB FALSE 1666 +Reddy_2828T ST2 0.5521682365889561 3.6924037508283263 1971,1667,566 0.119,0.191,0.193 BN2,ST2,ST2 8.43851868584952,10.4045366203999 6 38.41775004279539 18.843055306249454 10.404536620399933 ST2 TRUE 1667 +Reddy_2829T EZB 1 0.09403619661356857 1782,505,1668,119,105,1243,1245,166,341,1581 0.114,0.182,0.203,0.222,0.233,0.263,0.263,0.267,0.277,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.414148268417 1 4.364609972220181 46.414148268416966 46.414148268416966 EZB TRUE 1668 +Reddy_2830T BN2 0.4355315556625092 3.095197634503239 1763,834,1377,597 0.193,0.25,0.319,0.411 EZB,BN2,MCD,BN2 6.42584339965454,5.18982914568968,3.13835231487483 5 19.889255290298976 14.754024860219051 6.425843399654537 Other FALSE 1669 +Reddy_2831T EZB 1 0.5185698600740776 1289,1542,1964,477,520,699,72,119 0.201,0.254,0.26,0.284,0.313,0.317,0.327,0.331 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.7028097970705 3 14.884412060199718 28.702809797070515 28.702809797070515 Other FALSE 1670 +Reddy_2832T EZB 1 0.08302020726792979 1752,1671,398,1972,1750,397,172,818,1573 0.121,0.196,0.217,0.218,0.242,0.244,0.31,0.317,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.2736091896302 1 3.3435233823507042 40.273609189630236 40.273609189630236 EZB TRUE 1671 +Reddy_2833T EZB 0.7222289817173747 0.33337618684384734 1494,881,646,1036,625,466,1492,430,1181 0.24,0.28,0.289,0.309,0.349,0.364,0.376,0.387,0.389 BN2,N1,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.16146444785951,20.1188650429424,3.5762998500785 2 6.707150511642119 27.856629340880428 20.118865042942414 Other FALSE 1672 +Reddy_2834T EZB 1 0 287,676,382,1903,558,1138,2089,2016,174,93,153 0.144,0.163,0.184,0.19,0.195,0.213,0.22,0.226,0.233,0.242,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0347511670775 0 0 55.034751167077545 55.034751167077545 EZB TRUE 1673 +Reddy_2836T MCD 1 0.09498527115875009 1422,895,950,247,1491,471,837,156,788,1968 0.113,0.124,0.124,0.125,0.127,0.145,0.149,0.166,0.17,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.127259314003 1 6.851027283878058 72.127259314003 72.127259314003 Other FALSE 1674 +Reddy_2837T ST2 1 3.894696927234366 1057,874 0.206,0.319 ST2,ST2 7.99684996894071 7 31.145307001587632 7.9968499689407135 7.9968499689407135 Other FALSE 1675 +Reddy_2838T BN2 0.36525665787604533 1.5614042882939494 826,1190,24,636,476,1183,15 0.194,0.195,0.242,0.253,0.283,0.303,0.335 BN2,BN2,EZB,ST2,MCD,ST2,EZB 10.3009629790861,7.11725064970369,3.53481956958813,7.24895232869186 4 16.083967769102266 28.20198552706978 10.300962979086108 Other FALSE 1676 +Reddy_2839T EZB 0.3563671022839069 4.148747907391088 1266,1052,1608 0.146,0.15,0.175 EZB,ST2,N1 6.85682215551517,5.72029403477285,6.66377925592673 4 28.4472265690464 19.240895446214743 6.856822155515168 Other FALSE 1677 +Reddy_2840T Other 1 10 0 0 1 2 Other TRUE 1678 +Reddy_2841T ST2 0.4749898482935771 3.87344013663434 1388,938,2196,1797 0.194,0.215,0.32,0.352 EZB,ST2,BN2,ST2 3.12848554737313,5.16205329061835,7.50065836275792 7 29.053351153488524 15.791197200749398 7.500658362757916 Other FALSE 1679 +Reddy_2843T BN2 1 1.4731988985216309 2069,523,1957,596,1765 0.184,0.197,0.229,0.258,0.283 BN2,BN2,BN2,BN2,BN2 22.2874718151158 6 32.83387892886048 22.287471815115794 22.287471815115794 Other FALSE 1680 +Reddy_2844T BN2 1 8.090678402055051 915 0.275 BN2 3.64261388852699 6 29.47121751493107 3.6426138885269883 3.6426138885269883 Other FALSE 1681 +Reddy_2865T Other 1 10 0 0 1 2 Other TRUE 1682 +Reddy_2867T N1 0.6822202024497086 3.782891712875407 697,1697,2077 0.207,0.242,0.246 N1,BN2,N1 4.13757358823498,8.88268012245023 7 33.60221702334009 13.020253710685207 8.882680122450232 Other FALSE 1683 +Reddy_2871T ST2 1 4.627153467137141 670 0.193 ST2 5.19181997581784 4 24.023347801857383 5.191819975817839 5.191819975817839 Other FALSE 1684 +Reddy_2872T EZB 0.700703490714783 2.788652353536351 1521,628,1002,402 0.225,0.27,0.293,0.305 BN2,EZB,EZB,EZB 4.44107752907384,10.3973097935183 6 28.99448242614132 14.838387322592126 10.397309793518286 Other FALSE 1685 +Reddy_2873T BN2 1 0.5670930315822685 2058,1793 0.151,0.153 BN2,BN2 13.1561237702862 1 7.460746112763142 13.156123770286195 13.156123770286195 Other FALSE 1686 +Reddy_2874T ST2 0.7838323148075419 1.8022686607958118 1461,572,2060,1057,1302 0.258,0.261,0.295,0.299,0.307 ST2,BN2,ST2,ST2,ST2 3.82564723705955,13.8719435645143 6 25.000969150652217 17.697590801573824 13.871943564514272 ST2 TRUE 1687 +Reddy_2877T MCD 0.7989426364249776 1.7816304987856433 1890,254,952,1398,285 0.214,0.215,0.24,0.267,0.286 MCD,MCD,BN2,MCD,MCD 4.1716612818791,16.5769509932565 6 29.534001466460673 20.748612275135564 16.57695099325646 Other FALSE 1688 +Reddy_2879T MCD 1 0 479,305,764,1689,1449,310,1009,1699,709,1904,1331 0.12,0.121,0.136,0.141,0.147,0.149,0.155,0.159,0.165,0.17,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.0527960013992 0 0 75.05279600139924 75.05279600139924 MCD TRUE 1689 +Reddy_2881T EZB 1 1.049122495647007 2050 0.111 EZB 8.98354655319312 1 9.424840779647036 8.983546553193122 8.983546553193122 Other FALSE 1690 +Reddy_2882T EZB 1 0 47,1220,1691,2061,448,385,43,102,83,435,1796 0.109,0.148,0.166,0.205,0.24,0.25,0.271,0.306,0.31,0.33,0.354 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0649468953344 0 0 51.06494689533435 51.06494689533435 EZB TRUE 1691 +Reddy_2884T EZB 1 15.867027351533762 1508 0.392 EZB 2.55023021703118 10 40.46457260634155 2.5502302170311757 2.5502302170311757 Other FALSE 1692 +Reddy_2887T EZB 0.6343100350054522 1.4833323127466416 1392,1122,1703,696,1493,1528 0.196,0.217,0.225,0.266,0.274,0.314 EZB,BN2,ST2,EZB,EZB,EZB 4.61382645685843,15.7024102985149,4.43886632033306 5 23.29189258379279 24.755103075706394 15.702410298514902 Other FALSE 1693 +Reddy_2888T MCD 0.9180153501564746 0.10358687312834765 767,156,1224,788,837,1310,1694,471,1491,1376 0.11,0.118,0.126,0.132,0.145,0.147,0.15,0.155,0.16,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.90565320603732,66.127992327715 1 6.8499919514833625 72.03364553375233 66.12799232771502 MCD TRUE 1694 +Reddy_2889T EZB 0.7836008392778712 1.0502340584785088 1002,1521,1260,628,1322 0.124,0.172,0.202,0.22,0.286 EZB,BN2,EZB,EZB,EZB 5.81705629176935,21.064084431502 5 22.122218880630278 26.88114072327131 21.06408443150196 Other FALSE 1695 +Reddy_2891T ST2 1 8.93668740691894 318 0.237 ST2 4.21364573123317 7 37.65603474352925 4.213645731233173 4.213645731233173 Other FALSE 1696 +Reddy_2894T BN2 0.5471445698727676 9.929000885986547 1697,697 0.224,0.27 BN2,N1 4.47126560167524,3.70073472122842 9 44.39520012051465 8.172000322903664 4.471265601675243 BN2 TRUE 1697 +Reddy_2895T BN2 0.579337735605437 3.993175577144363 1538,31 0.113,0.156 BN2,EZB 8.85547744294063,6.4300406559041 5 35.36147624910333 15.285518098844728 8.855477442940629 Other FALSE 1698 +Reddy_2897T MCD 1 0 905,910,1699,798,305,479,823,764,1331,1904,23 0.113,0.117,0.128,0.155,0.155,0.164,0.167,0.167,0.171,0.175,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.3281692549772 0 0 73.32816925497723 73.32816925497723 MCD TRUE 1699 +Reddy_2898T ST2 1 2.5315231692589166 1057,874 0.271,0.272 ST2,ST2 7.35943036551187 4 18.63056848284091 7.359430365511868 7.359430365511868 Other FALSE 1700 +Reddy_2899T EZB 0.7021419394868738 0.3459685822134832 1550,2176,1031,1384,466,1701,1864,809,1049 0.106,0.158,0.217,0.241,0.245,0.257,0.276,0.28,0.294 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 9.4321893973258,30.2624780304445,3.40556107098647 2 10.46986661845957 43.10022849875677 30.26247803044451 EZB TRUE 1701 +Reddy_2900T EZB 0.6649675200963695 0.925012689190717 491,282,77,994,1074,1975 0.146,0.162,0.2,0.318,0.363,0.367 EZB,BN2,EZB,EZB,ST2,EZB 6.16437708179719,17.7011043419929,2.75402074291362 5 16.37374612903234 26.61950216670373 17.701104341992913 Other FALSE 1702 +Reddy_2901T EZB 0.8113963859599898 0.5830878866881399 1703,1493,397,1227,398,1392,1972,1528 0.165,0.181,0.269,0.283,0.285,0.298,0.312,0.316 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.0255628357552,6.04946644227579 3 15.17519043376991 32.07502927803102 26.025562835755228 ST2 FALSE 1703 +Reddy_2902T Other 1 10 0 0 1 2 Other TRUE 1704 +Reddy_2903T EZB 1 0.10145119805546597 121,1705,80,17,333,404,1537,187,639,149 0.155,0.162,0.172,0.179,0.184,0.185,0.204,0.205,0.205,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.4202961713613 1 5.521004245117893 54.42029617136131 54.42029617136131 EZB TRUE 1705 +Reddy_2904T EZB 0.4001690349620823 3.0057133895483674 2017,1082,1983,1863,1121 0.19,0.191,0.2,0.209,0.239 ST2,BN2,EZB,EZB,ST2 5.24405324590105,9.79594209296847,9.43951516072893 6 29.443794312075788 24.479510499598444 9.79594209296847 Other FALSE 1706 +Reddy_2905T EZB 0.5408518542236888 5.621222356837649 1471,724 0.26,0.306 EZB,ST2 3.8468370252431,3.26571513705771 4 21.623926289407354 7.112552162300813 3.8468370252431012 Other FALSE 1707 +Reddy_2908T BN2 0.5440147446472959 5.914921809189048 674,1664 0.233,0.278 BN2,ST2 4.29394238540273,3.59912012375159 6 25.39833346281987 7.893062509154321 4.293942385402733 Other FALSE 1708 +Reddy_2909T Other 1 10 0 0 1 2 Other TRUE 1709 +Reddy_2910T ST2 1 8.933605737266294 1710 0.141 ST2 7.11059967350489 8 63.52329403862715 7.110599673504892 7.110599673504892 ST2 TRUE 1710 +Reddy_2912T EZB 1 3.499326765134442 2166,1601,1395 0.242,0.257,0.297 EZB,EZB,EZB 11.3856596671 8 39.84214361179458 11.385659667099958 11.385659667099958 Other FALSE 1711 +Reddy_2915T N1 1 4.887798039971989 913 0.226 N1 4.43251666953964 4 21.665246289519043 4.432516669539644 4.432516669539644 Other FALSE 1712 +Reddy_2917T ST2 0.7425681476905392 0.14144209006833883 701,1211,1018,1401,1284,1713,618,1442,2076 0.156,0.16,0.161,0.174,0.204,0.214,0.224,0.229,0.236 EZB,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.73844175989872,6.42585046307247,35.088183000446 1 4.962945940283441 47.25247522341721 35.088183000446016 ST2 TRUE 1713 +Reddy_2918T ST2 1 3.3519944714199936 1714,35 0.181,0.308 ST2,ST2 8.78472031506366 7 29.446333929064277 8.784720315063655 8.784720315063655 ST2 TRUE 1714 +Reddy_2919T BN2 1 1.00168145271497 1148,804,1980,1841,70 0.109,0.158,0.186,0.195,0.196 BN2,BN2,BN2,BN2,BN2 31.0982449629422 5 31.150535191365933 31.098244962942193 31.098244962942193 Other FALSE 1715 +Reddy_2921T BN2 1 2.8582223326304224 19,894,2211 0.224,0.263,0.281 BN2,BN2,BN2 11.8204578924924 6 33.78549673023925 11.820457892492378 11.820457892492378 Other FALSE 1716 +Reddy_2922T ST2 1 4.682945546042907 1028 0.246 ST2 4.05728695721394 4 19.000053885302982 4.057286957213936 4.057286957213936 Other FALSE 1717 +Reddy_2923T EZB 1 0.20934970630803418 149,1705,404,319,121,2059,80,333,187 0.124,0.173,0.175,0.177,0.209,0.214,0.225,0.231,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.6636389651018 2 9.97836881891623 47.663638965101775 47.663638965101775 Other FALSE 1718 +Reddy_2925T EZB 0.4808154899640812 1.045721842588662 1725,1909,353,2183 0.241,0.254,0.276,0.307 ST2,EZB,BN2,EZB 3.61788132282793,7.194917833437,4.15119011532548 2 7.523882734055764 14.963989271590414 7.194917833437 EZB TRUE 1719 +Reddy_2928T EZB 1 9.868566832038194 1300 0.173 EZB 5.77438543630711 9 56.98490859214478 5.774385436307114 5.774385436307114 Other FALSE 1720 +Reddy_2931T BN2 1 2.28395452278329 1381,1108,52 0.219,0.269,0.305 BN2,BN2,BN2 11.5508097328653 6 26.381524131186925 11.55080973286529 11.55080973286529 Other FALSE 1721 +Reddy_2933T BN2 0.627777655019822 0.30087244428254567 960,1722,590,1076,1086,863,684,838,13 0.119,0.13,0.171,0.19,0.232,0.254,0.256,0.259,0.262 ST2,BN2,BN2,EZB,BN2,BN2,BN2,ST2,BN2 29.4964930581523,5.25631622848259,12.2327640645239 2 8.874681964169415 46.98557335115875 29.496493058152275 BN2 TRUE 1722 +Reddy_2934T EZB 0.6055207940138612 4.944795937033077 1480,1513,632 0.19,0.245,0.249 MCD,EZB,EZB 8.09808612462307,5.27566784828788 8 40.043383366780105 13.373753972910958 8.098086124623073 Other FALSE 1723 +Reddy_2935T BN2 1 0 1724,976,1001,1409,796,98,519,41,991,857,100 0.167,0.192,0.199,0.202,0.207,0.219,0.257,0.264,0.268,0.286,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.9087035473229 0 0 48.908703547322936 48.908703547322936 BN2 TRUE 1724 +Reddy_2936T EZB 0.4813877373861316 2.9777618668164703 353,1725,1738,1527,2183 0.175,0.214,0.265,0.328,0.356 BN2,ST2,EZB,EZB,EZB 5.70776920469082,9.62633837859005,4.66295015412584 5 28.664943340837347 19.99705773740671 9.626338378590052 ST2 FALSE 1725 +Reddy_2939T EZB 1 0.08386364933106205 958,1066,1914,1437,1372,468,1172,1242,2132,54 0.133,0.143,0.161,0.182,0.205,0.209,0.21,0.215,0.22,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3336825939969 1 4.556620903928184 54.33368259399688 54.33368259399688 EZB TRUE 1726 +Reddy_2944T ST2 0.7043277783856913 3.09698534143694 566,1667,1971 0.142,0.147,0.172 ST2,ST2,BN2 5.81363661704658,13.8488010150899 6 42.88953374021045 19.662437632136488 13.848801015089904 Other FALSE 1727 +Reddy_2945T MCD 1 0 1565,455,777,2010,783,1240,929,535,1239,771,534 0.144,0.15,0.159,0.161,0.171,0.173,0.182,0.182,0.197,0.2,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.6027131103941 0 0 63.60271311039409 63.60271311039409 MCD TRUE 1728 +Reddy_2946T ST2 1 5.91141195947365 1028 0.295 ST2 3.39458903893193 4 20.066814242240365 3.394589038931928 3.394589038931928 Other FALSE 1729 +Reddy_2947T EZB 1 7.498531278471929 2162 0.22 EZB 4.55021302856708 6 34.119914718420745 4.550213028567081 4.550213028567081 Other FALSE 1730 +Reddy_2948T MCD 1 0.1023393815148286 895,950,471,247,1491,1802,1422,788,156,837 0.125,0.126,0.133,0.135,0.139,0.158,0.159,0.159,0.17,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 68.5664633144784 1 7.0170494482629016 68.56646331447837 68.56646331447837 MCD TRUE 1731 +Reddy_2949T MCD 1 0.2495584861391479 1250,2180,1977,1239,929,771,880,1240,777 0.146,0.163,0.17,0.17,0.174,0.179,0.193,0.193,0.201 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4263416431414 2 12.833879968136998 51.42634164314144 51.42634164314144 MCD TRUE 1732 +Reddy_2950T MCD 1 4.530298620641546 904 0.206 MCD 4.8648752693004 4 22.039337722104793 4.864875269300405 4.864875269300405 EZB FALSE 1733 +Reddy_2951T EZB 0.5548594003860352 4.172379176028997 1983,1863,1082,2017 0.219,0.283,0.307,0.309 EZB,EZB,BN2,ST2 3.26113982208539,8.09551726002396,3.23355668085767 7 33.77756763490728 14.590213762967016 8.095517260023957 Other FALSE 1734 +Reddy_2952T BN2 1 1.601666852067858 158,594,1735,1414,903 0.164,0.227,0.232,0.266,0.269 BN2,BN2,BN2,BN2,BN2 22.2851157730587 6 35.693331228202766 22.28511577305874 22.28511577305874 BN2 TRUE 1735 +Reddy_2953T MCD 0.8700105215675332 0.23728971780420766 556,1736,1640,792,188,708,621,1273,1169 0.236,0.242,0.264,0.264,0.298,0.313,0.32,0.326,0.326 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 4.12708773582726,27.6223106431454 2 6.554490297612135 31.74939837897267 27.622310643145408 EZB FALSE 1736 +Reddy_2954T Other 1 10 0 0 1 2 Other TRUE 1737 +Reddy_2955T EZB 0.6825929595995339 1.7521603374336148 1738,1446,1177,353,1527 0.123,0.273,0.283,0.289,0.31 EZB,EZB,ST2,BN2,EZB 3.4555233553478,15.0366121617193,3.53653024963087 6 26.34655543913651 22.028665766697983 15.036612161719313 EZB TRUE 1738 +Reddy_2956T ST2 0.5502852717352847 1.122861096263296 1152,2066,1174,629,1198,268,171 0.16,0.194,0.205,0.205,0.228,0.235,0.274 BN2,BN2,ST2,ST2,ST2,ST2,BN2 15.0455546287933,18.4102200727572 4 20.672119893344654 33.45577470155048 18.41022007275717 Other FALSE 1739 +Reddy_2957T MCD 1 0.9339556957720057 1063,650,753,2053,1194,864 0.166,0.189,0.196,0.208,0.23,0.241 MCD,MCD,MCD,MCD,MCD,MCD 29.7031036248309 4 27.741382812516942 29.703103624830916 29.703103624830916 Other FALSE 1740 +Reddy_2995T EZB 1 8.239088760509306 199,1182 0.344,0.35 EZB,EZB 5.76339504832738 9 47.48512336504911 5.76339504832738 5.76339504832738 Other FALSE 1741 +Reddy_2996T EZB 0.5397668333631275 2.8411749547249934 1266,2042,1608,1052 0.244,0.249,0.263,0.321 EZB,EZB,N1,ST2 8.12089342180921,3.80932063329392,3.11497382946978 5 23.072879000035275 15.045187884572906 8.120893421809209 Other FALSE 1742 +Reddy_2998T EZB 0.5383335625881247 4.9024641482587334 1037,724 0.218,0.255 EZB,ST2 4.57684876832217,3.9250338680088 5 22.43783699870159 8.501882636330976 4.576848768322172 Other FALSE 1743 +Reddy_2999T BN2 0.5977692322833882 3.419943969191406 1232,811,150,1283 0.169,0.275,0.306,0.316 BN2,BN2,MCD,ST2 9.55714604703704,3.26699231094532,3.16388098274246 7 32.684903986245814 15.988019340724824 9.55714604703704 Other FALSE 1744 +Reddy_3382T EZB 1 0 1511,137,364,9,2104,2006,435,138,168,1041,1295 0.192,0.194,0.217,0.227,0.24,0.26,0.272,0.273,0.274,0.283,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.436701207514 0 0 45.43670120751402 45.43670120751402 EZB TRUE 1745 +Reddy_3384T ST2 0.501728693769021 4.323375478829048 1436,1901 0.226,0.228 ST2,BN2 4.39201760252347,4.42249278087698 3 19.12009684414201 8.814510383400446 4.422492780876977 Other FALSE 1746 +Reddy_3385T ST2 1 1.0251107417856036 1884,1747,2052,339,959,1472 0.14,0.177,0.23,0.253,0.267,0.269 ST2,ST2,ST2,ST2,ST2,ST2 28.5260780592981 5 29.242389039601072 28.526078059298065 28.526078059298065 ST2 TRUE 1747 +Reddy_3386T BN2 0.6259078764198323 1.2797865851521564 1816,1666,115,591,610 0.15,0.219,0.242,0.253,0.254 BN2,EZB,EZB,BN2,BN2 14.5604902039542,8.70250224669146 4 18.634320036260014 23.262992450645694 14.560490203954235 Other FALSE 1748 +Reddy_3387T MCD 0.5434242509630629 8.363846674268062 1603,2096 0.224,0.266 MCD,BN2 3.7554513537642,4.46980231265509 7 37.38474120733597 8.22525366641929 4.46980231265509 Other FALSE 1749 +Reddy_3388T EZB 1 0.2211886508565042 1671,1752,398,1750,1972,397,109,818,1227 0.146,0.184,0.236,0.257,0.26,0.262,0.305,0.335,0.38 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.9496691884915 2 8.172847477396576 36.9496691884915 36.9496691884915 EZB TRUE 1750 +Reddy_3389T EZB 1 5.210867297793099 2166,1601 0.192,0.323 EZB,EZB 8.30721602046627 9 43.287800296750596 8.307216020466267 8.307216020466267 Other FALSE 1751 +Reddy_3390T EZB 1 0 1752,1750,1972,1671,398,172,1573,397,818,108,117 0.173,0.235,0.244,0.252,0.256,0.268,0.274,0.28,0.297,0.306,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6246821953859 0 0 42.62468219538594 42.62468219538594 EZB TRUE 1752 +Reddy_3394T Other 1 10 0 0 1 2 Other TRUE 1753 +Reddy_3396T Other 1 10 0 0 1 2 Other TRUE 1754 +Reddy_3398T BN2 0.4801010146149372 3.015886069891635 1835,921,70,1813 0.202,0.213,0.22,0.23 EZB,MCD,BN2,BN2 8.91007719661954,4.95525490920034,4.6934230839933 5 26.871777698943973 18.558755189813173 8.910077196619536 Other FALSE 1755 +Reddy_3399T Other 1 10 0 0 1 2 Other TRUE 1756 +Reddy_3400T Other 1 10 0 0 1 2 Other TRUE 1757 +Reddy_3401T BN2 0.652963901181276 0.33788621773464045 1722,960,590,1076,684,1317,863,1086,838 0.133,0.141,0.204,0.22,0.231,0.238,0.243,0.265,0.268 BN2,ST2,BN2,EZB,BN2,BN2,BN2,BN2,ST2 28.8710145259245,4.53890965872755,10.8054060559539 2 9.755117900326503 44.21533024060598 28.87101452592453 BN2 TRUE 1758 +Reddy_3403T Other 1 10 0 0 1 2 Other TRUE 1759 +Reddy_3404T Other 1 10 0 0 1 2 Other TRUE 1760 +Reddy_3405T EZB 0.5105706397666298 9.568847509367924 1099,1500 0.341,0.356 EZB,ST2 2.93131422116171,2.80993682786365 7 28.04929878433802 5.741251049025358 2.9313142211617116 Other FALSE 1761 +Reddy_3406T Other 1 10 0 0 1 2 Other TRUE 1762 +Reddy_3407T BN2 0.39948909227543883 1.7988811535150435 1763,1377,597,834 0.149,0.278,0.289,0.294 EZB,MCD,BN2,BN2 6.86163869769704,6.71505394415153,3.59934252500422 3 12.343272535516713 17.1760351668528 6.86163869769704 EZB FALSE 1763 +Reddy_3408T MCD 0.6647648570179695 2.9527014287423388 311,1278,2030 0.28,0.286,0.298 MCD,EZB,MCD 3.49561546363027,6.93173840069189 5 20.467353879391087 10.42735386432216 6.931738400691893 Other FALSE 1764 +Reddy_3409T BN2 1 0.39929691111425397 687,1957,1765,41,1973,519,2069,99 0.138,0.173,0.175,0.202,0.221,0.223,0.229,0.242 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.1747146118128 3 16.440936360507774 41.17471461181276 41.17471461181276 BN2 TRUE 1765 +Reddy_3411T Other 1 10 0 0 1 2 Other TRUE 1766 +Reddy_3412T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1767 +Reddy_3417T EZB 0.8694942865169117 0.43028052064451977 1493,397,1703,1528,398,1972,2005,695 0.165,0.229,0.232,0.252,0.255,0.263,0.299,0.314 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 28.6622051131032,4.30201967544659 3 12.332788538886055 32.96422478854978 28.662205113103184 Other FALSE 1768 +Reddy_3418T BN2 1 5.147197936145942 1980,804 0.173,0.191 BN2,BN2 11.0267977492679 9 56.75711061733031 11.026797749267871 11.026797749267871 Other FALSE 1769 +Reddy_3419T BN2 0.6806928796406567 4.255624494661825 52,1131,1381 0.198,0.244,0.271 BN2,ST2,BN2 8.74771034882729,4.10347498081106 8 37.22697043267616 12.851185329638351 8.747710348827292 Other FALSE 1770 +Reddy_3422T BN2 0.5648284276222134 6.0280893580196695 793,402 0.138,0.179 BN2,EZB 7.24135412495611,5.57909500760685 6 43.65152973829975 12.820449132562961 7.241354124956108 Other FALSE 1771 +Reddy_3423T BN2 0.6705915115894011 0.3896943901903497 1722,960,1317,684,590,1076,915,863,838 0.149,0.165,0.208,0.224,0.232,0.244,0.249,0.253,0.268 BN2,ST2,BN2,BN2,BN2,EZB,BN2,BN2,ST2 28.2752774744421,4.10456738788488,9.78483659542889 2 11.018717012865647 42.164681457755876 28.275277474442106 Other FALSE 1772 +Reddy_3429T EZB 1 10.56860305144766 2162 0.392 EZB 2.55059522295502 8 26.956228456330216 2.550595222955017 2.550595222955017 Other FALSE 1773 +Reddy_3430T BN2 0.6030418370232846 3.0937091887354953 1603,2096,596 0.177,0.217,0.25 MCD,BN2,BN2 8.60626595859229,5.66515839410977 5 26.62528407679847 14.271424352702061 8.606265958592292 Other FALSE 1774 +Reddy_3431T ST2 1 4.436233154188841 90,1946 0.18,0.22 ST2,ST2 10.1197905054313 8 44.89375015363964 10.119790505431268 10.119790505431268 Other FALSE 1775 +Reddy_3432T ST2 0.5179541203152043 0.8181657185378559 1497,1987,2004,608,722,619,1776,1176 0.187,0.19,0.201,0.279,0.28,0.28,0.28,0.304 ST2,BN2,ST2,BN2,ST2,BN2,BN2,ST2 15.9886896962126,17.1797085207111 3 14.05584856611851 33.16839821692366 17.179708520711085 BN2 FALSE 1776 +Reddy_3433T BN2 0.5821264846024939 1.0533187534215587 2170,1719,1657,114,1848 0.171,0.172,0.173,0.179,0.234 BN2,EZB,BN2,EZB,BN2 15.8906272252087,11.4069234712858 3 16.737895659943465 27.29755069649442 15.890627225208657 Other FALSE 1777 +Reddy_3434T EZB 0.5164350459746586 8.966462287468586 1535,1653 0.202,0.216 EZB,ST2 4.94054299531643,4.62608698811592 8 44.2991924471219 9.566629983432351 4.940542995316434 Other FALSE 1778 +Reddy_3438T EZB 1 0.09343740137144997 1726,1590,499,2072,1853,2013,1219,1540,1926 0.141,0.156,0.163,0.169,0.174,0.175,0.187,0.187,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6432005703111 1 4.918843861165898 52.64320057031105 52.64320057031105 EZB TRUE 1779 +Reddy_3439T MCD 1 0 839,947,2039,416,483,587,1572,540,888,1947,707 0.144,0.146,0.155,0.165,0.167,0.174,0.178,0.178,0.19,0.196,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.7195270760112 0 0 64.71952707601118 64.71952707601118 Other FALSE 1780 +Reddy_3440T EZB 1 0 31 0.131 EZB 7.63894160747693 0 0 7.638941607476932 7.638941607476932 Other FALSE 1781 +Reddy_3441T EZB 0.9042572852695018 0.25356874159432297 1782,166,96,105,1967,341,505,1245,365 0.195,0.243,0.245,0.252,0.262,0.279,0.281,0.293,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.35178127606702,31.656430946678 2 8.027081358516718 35.008212222744994 31.65643094667798 EZB TRUE 1782 +Reddy_3444T Other 1 10 0 0 1 2 Other TRUE 1783 +Reddy_3446T EZB 1 12.711929835479935 1535 0.237 EZB 4.22380668385626 10 53.69273420381199 4.223806683856262 4.223806683856262 Other FALSE 1784 +Reddy_3447T MCD 1 4.82522514726196 751 0.204 MCD 4.90580540217528 4 23.671615594149745 4.905805402175282 4.905805402175282 Other FALSE 1785 +Reddy_3448T EZB 1 4.364856369419139 1397,2183 0.28,0.455 EZB,EZB 5.76655306611064 9 25.17017588020648 5.766553066110637 5.766553066110637 Other FALSE 1786 +Reddy_3449T EZB 0.7151193141451134 3.4924779790384464 1738,1177,1446 0.162,0.256,0.274 EZB,ST2,EZB 9.81585672719201,3.91032369198229 7 34.28166346511449 13.726180419174298 9.81585672719201 Other FALSE 1787 +Reddy_3450T EZB 1 0.1255902529728379 1788,1118,6,1142,196,438,367,1872,44,1991 0.135,0.145,0.148,0.151,0.168,0.196,0.206,0.208,0.227,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1105839999725 1 7.172532691983061 57.11058399997255 57.11058399997255 EZB TRUE 1788 +Reddy_3451T ST2 0.4926452712758102 0.8407625315010147 486,1789,1130,1943,1341,1123,854 0.138,0.146,0.18,0.224,0.243,0.277,0.307 ST2,EZB,ST2,ST2,BN2,EZB,BN2 7.36632141288011,10.4488126878806,17.2986296864799 4 14.544039686703433 35.11376378724064 17.298629686479888 EZB FALSE 1789 +Reddy_3453T EZB 0.5966161046015607 5.1255564414380395 1500,1226,1099 0.245,0.288,0.389 ST2,EZB,EZB 6.03523190092938,4.08053911896272 7 30.933921745380914 10.115771019892094 6.035231900929378 Other FALSE 1790 +Reddy_3454T BN2 0.4690616022673212 0.7003624529486885 1719,1848,2170,114,1657,511 0.108,0.168,0.187,0.24,0.241,0.248 EZB,BN2,BN2,EZB,BN2,ST2 15.4487233793519,13.46140010921,4.02525920888514 2 10.819705800888652 32.93538269744702 15.448723379351906 BN2 TRUE 1791 +Reddy_3455T N1 1 1.3525106916740341 63,2175,849,2189,745 0.142,0.161,0.199,0.217,0.242 N1,N1,N1,N1,N1 27.0029158632595 6 36.521732411432794 27.002915863259453 27.002915863259453 Other FALSE 1792 +Reddy_3456T ST2 0.5240945775249176 5.7458147371639425 874,1793 0.235,0.259 ST2,BN2 3.85881958430136,4.24955531975513 5 24.41715758264247 8.108374904056493 4.249555319755133 BN2 FALSE 1793 +Reddy_3457T BN2 1 1.911229720839522 1096,20 0.176,0.249 BN2,BN2 9.69421694761379 3 18.52787555054566 9.694216947613786 9.694216947613786 Other FALSE 1794 +Reddy_3458T ST2 0.6552778286262717 1.1849524874882384 1847,1121,1822,1082,2017 0.115,0.172,0.191,0.208,0.218 ST2,ST2,EZB,BN2,ST2 4.8091968033561,5.23137283064757,19.0859863806318 4 22.61598703789631 29.126556014635486 19.085986380631816 Other FALSE 1795 +Reddy_3459T EZB 1 0.08789586499027306 78,164,326,366,1020,1094,329,1796,1041,86 0.175,0.196,0.202,0.203,0.206,0.221,0.235,0.244,0.26,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0978538551811 1 4.051810738796336 46.09785385518109 46.09785385518109 EZB TRUE 1796 +Reddy_3460T Other 1 10 0 0 1 2 ST2 FALSE 1797 +Reddy_3461T MCD 1 0.5440544229754252 761,2130,2014,755,713,569,1911 0.207,0.254,0.266,0.283,0.29,0.302,0.31 MCD,MCD,MCD,MCD,MCD,MCD,MCD 26.0511705520176 4 14.17325456251232 26.05117055201759 26.05117055201759 Other FALSE 1798 +Reddy_3462T BN2 1 2.8069707491059015 1791,1799,1657 0.207,0.234,0.271 BN2,BN2,BN2 12.7971644447723 8 35.92126626797404 12.797164444772346 12.797164444772346 BN2 TRUE 1799 +Reddy_3463T BN2 1 2.4047250763090244 1980,1148,804 0.187,0.233,0.236 BN2,BN2,BN2 13.8692128641728 6 33.351643963144 13.869212864172782 13.869212864172782 Other FALSE 1800 +Reddy_3464T BN2 1 12.753867398548735 1807 0.32 BN2 3.12094190174053 9 39.80407917337329 3.1209419017405344 3.1209419017405344 Other FALSE 1801 +Reddy_3465T MCD 1 0.10324385995988875 156,788,767,1224,837,471,1491,1310,950,1694 0.103,0.121,0.125,0.129,0.136,0.14,0.144,0.148,0.158,0.166 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.247479058248 1 7.665596330264529 74.247479058248 74.247479058248 MCD TRUE 1802 +Reddy_3467T BN2 1 1.4483300980495688 20,1803,1096,523 0.16,0.209,0.23,0.35 BN2,BN2,BN2,BN2 18.2571970783188 7 26.442448034551703 18.257197078318754 18.257197078318754 BN2 TRUE 1803 +Reddy_3468T ST2 1 1.9931388527574783 1188,1638,828 0.193,0.236,0.38 ST2,ST2,ST2 12.0743251860937 7 24.06580664923154 12.07432518609371 12.07432518609371 Other FALSE 1804 +Reddy_3470T N1 1 3.380826041869617 262 0.149 N1 6.71030884901758 3 22.686386905746772 6.71030884901758 6.71030884901758 Other FALSE 1805 +Reddy_3471T BN2 1 0.08324418057570444 945,954,865,964,1996,638,1549,812,418,1213 0.148,0.166,0.185,0.185,0.187,0.188,0.21,0.221,0.244,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.711524316062 1 4.3046834680111985 51.711524316062025 51.711524316062025 BN2 TRUE 1806 +Reddy_3472T BN2 0.7886701804227201 2.610328530907274 1807,1854,1714,1003 0.203,0.251,0.309,0.315 BN2,BN2,ST2,BN2 12.0961675802608,3.24125467878462 7 31.57497134939025 15.33742225904537 12.096167580260754 BN2 TRUE 1807 +Reddy_3473T ST2 1 6.8253565072217555 90 0.238 ST2 4.20936908195414 5 28.730444654813752 4.209369081954139 4.209369081954139 Other FALSE 1808 +Reddy_3474T EZB 1 0.11722006976401672 227,474,1809,1579,1032,9,1017,364,168,1079 0.136,0.163,0.204,0.221,0.232,0.264,0.274,0.285,0.287,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.0045855410792 1 5.2754406568259595 45.00458554107918 45.00458554107918 EZB TRUE 1809 +Reddy_3475T Other 1 10 0 0 1 2 Other TRUE 1810 +Reddy_3476T MCD 0.8982305138735263 0.24219196373762825 1640,188,708,792,49,586,1736,1870,1273 0.123,0.15,0.153,0.158,0.176,0.177,0.178,0.187,0.19 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 5.62541223962583,49.650608635972 2 12.024978406314496 55.276020875597794 49.65060863597197 MCD TRUE 1811 +Reddy_3478T N1 1 1.368980322580875 891,814,745,2189,2175 0.2,0.236,0.246,0.259,0.32 N1,N1,N1,N1,N1 20.2839477996301 6 27.76832540195129 20.283947799630134 20.283947799630134 Other FALSE 1812 +Reddy_3479T EZB 1 10.041181541847445 2050 0.266 EZB 3.75288752583551 9 37.68342495304906 3.7528875258355114 3.7528875258355114 BN2 FALSE 1813 +Reddy_3480T EZB 1 0.2013864099479361 1814,651,1241,331,1437,2003,1066,408 0.147,0.178,0.217,0.218,0.308,0.332,0.352,0.353 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5733959805581 2 6.761225686285062 33.57339598055809 33.57339598055809 EZB TRUE 1814 +Reddy_3481T BN2 0.46121382021977475 2.199314010555821 1123,854,1341,293,486,1776 0.144,0.207,0.25,0.266,0.287,0.3 EZB,BN2,BN2,ST2,ST2,BN2 12.161766137262,6.9577208504758,7.24955401685905 5 26.74754265878365 26.369041004596838 12.161766137261994 Other FALSE 1815 +Reddy_3482T EZB 1 0 1471 0.183 EZB 5.45348509672235 0 0 5.453485096722345 5.453485096722345 BN2 FALSE 1816 +Reddy_3483T N1 0.5453759236187435 9.398512378707471 999,1071 0.217,0.261 N1,ST2 4.60393505383718,3.83782934105689 8 43.27014059425395 8.441764394894069 4.603935053837176 Other FALSE 1817 +Reddy_3484T ST2 0.571221742696048 3.6668107647804113 537,1433 0.161,0.215 ST2,BN2 4.65187261880413,6.19726102909563 4 22.724183453642002 10.849133647899762 6.1972610290956345 Other FALSE 1818 +Reddy_3485T EZB 0.8277382907872816 1.6911767694186275 1221,2114,1879,202,727 0.176,0.214,0.223,0.236,0.252 EZB,EZB,EZB,EZB,ST2 19.0807123614448,3.97091225688315 6 32.26885748963426 23.051624618327928 19.080712361444782 Other FALSE 1819 +Reddy_3486T BN2 0.5681870116463736 10.228832884428286 1807,1603 0.247,0.325 BN2,MCD 4.05281713794147,3.08007582664437 9 41.45558921515025 7.13289296458584 4.052817137941471 Other FALSE 1820 +Reddy_3487T Other 1 10 0 0 1 2 Other TRUE 1821 +Reddy_3488T ST2 0.6431489230586498 2.136916457681326 2198,1822,1121,1847,1863 0.14,0.21,0.22,0.222,0.237 ST2,EZB,ST2,ST2,EZB 8.97794956321389,16.1808635757736 5 34.577153674566986 25.15881313898752 16.180863575773632 EZB FALSE 1822 +Reddy_3489T ST2 1 12.958413690394229 90 0.247 ST2 4.04505224971663 9 52.41746045108789 4.045052249716625 4.045052249716625 Other FALSE 1823 +Reddy_3490T Other 1 10 0 0 1 2 Other TRUE 1824 +Reddy_3495T ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 1825 +Reddy_3496T MCD 1 0.15621014511657066 918,1399,1905,1826,2036,738,786,1225,789 0.125,0.231,0.251,0.255,0.322,0.331,0.336,0.342,0.383 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.853079733927 2 5.444404642996144 34.853079733927 34.853079733927 MCD TRUE 1826 +Reddy_3498T N1 0.2834905986825798 3.1580946121045703 175,2127,2211,65 0.187,0.192,0.237,0.245 N1,ST2,BN2,EZB 4.22241357569006,4.08393788667711,5.34231652074967,5.19610692503235 3 16.871541020336778 18.844774908149198 5.342316520749674 Other FALSE 1827 +Reddy_3500T EZB 0.5312128087370616 1.686885887884803 844,383,561,141,2042 0.117,0.158,0.178,0.194,0.279 BN2,EZB,EZB,BN2,EZB 13.6933301328226,15.516789913188 4 26.17505392983014 29.21012004601058 15.516789913188026 Other FALSE 1828 +Reddy_3502T N1 0.4304125367668938 1.841427440913497 1008,464,1609,1064,659,176,743 0.192,0.205,0.208,0.245,0.251,0.258,0.266 N1,BN2,MCD,N1,MCD,N1,MCD 4.87729609423079,12.5569343297047,13.1742916193252 4 24.25950210242219 30.608522043260713 13.174291619325231 Other FALSE 1829 +Reddy_3503T BN2 0.5488782697091588 3.368169044552817 1813,70,1835,921 0.252,0.291,0.323,0.334 BN2,BN2,EZB,MCD 7.40466437419966,3.09419537700584,2.99168047025975 7 24.940161330482347 13.490540221465253 7.404664374199659 Other FALSE 1830 +Reddy_3504T EZB 1 9.489375977983654 2162 0.201 EZB 4.97554329717075 8 47.21480104158974 4.975543297170754 4.975543297170754 Other FALSE 1831 +Reddy_3505T N1 1 0.5367504201710752 2189,63,745,2175,891,814 0.156,0.16,0.166,0.207,0.215,0.218 N1,N1,N1,N1,N1,N1 32.7376847926325 3 17.571966067873706 32.737684792632486 32.737684792632486 Other FALSE 1832 +Reddy_3506T EZB 0.6376145055125348 2.039564772044877 816,2138,781,1400,911 0.182,0.193,0.206,0.23,0.271 EZB,EZB,MCD,EZB,ST2 15.0264020443461,4.84784529976042,3.69234644325939 6 30.647320260231393 23.566593787365903 15.026402044346085 Other FALSE 1833 +Reddy_3507T BN2 0.8069262618708926 2.4420962190060744 1799,1433,537,1791 0.135,0.187,0.253,0.269 BN2,BN2,ST2,BN2 16.5099890976539,3.95035514665759 7 40.31898195121215 20.460344244311514 16.50998909765392 Other FALSE 1834 +Reddy_3508T EZB 0.47063159843347335 3.6456159914904585 1532,921,1835,1294 0.192,0.207,0.216,0.233 BN2,MCD,EZB,EZB 5.19746390115178,8.92209760406105,4.83814901350034 7 32.526541703003666 18.957710518713167 8.92209760406105 EZB TRUE 1835 +Reddy_3510T BN2 0.6386408058348632 4.0360123220050905 1901,2166 0.154,0.272 BN2,EZB 6.49573785530804,3.67545351856054 6 26.216878024538158 10.171191373868579 6.495737855308038 Other FALSE 1836 +Reddy_3511T Other 1 10 0 0 1 2 Other TRUE 1837 +Reddy_3512T ST2 1 11.085423855047718 1028 0.318 ST2 3.14540109677373 8 34.86810435186871 3.145401096773725 3.145401096773725 Other FALSE 1838 +Reddy_3513T Other 1 10 0 0 1 2 Other TRUE 1839 +Reddy_3514T EZB 0.5388034137612049 5.3179359906210095 1500,1226,1099 0.195,0.333,0.334 ST2,EZB,EZB 5.99889222844036,5.1348386931292 6 31.901724885479684 11.133730921569558 5.998892228440364 Other FALSE 1840 +Reddy_3515T BN2 1 1.8405392451780023 1841,1813,70,1148 0.188,0.252,0.252,0.261 BN2,BN2,BN2,BN2 17.071100249764 7 31.420029968058635 17.071100249763997 17.071100249763997 BN2 TRUE 1841 +Reddy_3517T EZB 0.507899318486165 12.808888014266365 1099,1500 0.364,0.375 EZB,ST2 2.74949508707294,2.66396972179515 9 35.21797466609288 5.4134648088680954 2.749495087072943 Other FALSE 1842 +Reddy_3519T MCD 0.6854500822305918 4.876192474540915 81,751,401 0.19,0.219,0.222 MCD,MCD,EZB 4.50577751192874,9.81874542510111 8 47.87809255131105 14.324522937029846 9.818745425101106 Other FALSE 1843 +Reddy_3520T Other 1 10 0 0 1 2 Other TRUE 1844 +Reddy_3521T EZB 1 8.359832319780965 1395 0.245 EZB 4.08843099873702 7 34.178597600436106 4.088430998737019 4.088430998737019 Other FALSE 1845 +Reddy_3522T BN2 0.5917839373952027 1.9873410484502416 834,1763,286,147 0.138,0.239,0.343,0.355 BN2,EZB,BN2,MCD 10.1452729214723,4.18345312313437,2.81481618488974 5 20.162117324572694 17.143542229496454 10.145272921472344 Other FALSE 1846 +Reddy_3523T ST2 0.5715445818318989 1.2635014521167374 1121,1847,1082,2017,1863,1822 0.118,0.145,0.171,0.192,0.199,0.221 ST2,ST2,BN2,ST2,EZB,EZB 5.84535648776268,9.54642316095806,20.5320971329749 4 25.942334542515685 35.92387678169564 20.532097132974897 ST2 TRUE 1847 +Reddy_3525T BN2 0.5018797194703165 1.7819853784508366 1848,511,1719,2170 0.139,0.179,0.201,0.291 BN2,ST2,EZB,BN2 10.6265923403532,4.96528722057496,5.58170432400846 5 18.93643217326708 21.17358388493663 10.626592340353211 BN2 TRUE 1848 +Reddy_3527T EZB 0.6652119983455392 4.474184783061411 1395,724,1037 0.251,0.265,0.285 EZB,ST2,EZB 7.48396734721593,3.76653229174043 7 33.484652821841976 11.25049963895636 7.483967347215927 Other FALSE 1849 +Reddy_3528T EZB 0.7620750708540039 1.9114183997508813 1387,1488,901 0.14,0.154,0.235 EZB,EZB,N1 13.6345124674107,4.25678589527088 5 26.06125800184153 17.891298362681542 13.634512467410662 EZB TRUE 1850 +Reddy_3529T BN2 0.5882890001232716 1.8066821654336094 1657,114,2170,1719,1848 0.148,0.158,0.175,0.197,0.259 BN2,EZB,BN2,EZB,BN2 16.3191045424467,11.4208405169156 6 29.483435132685084 27.73994505936227 16.319104542446716 Other FALSE 1851 +Reddy_3531T ST2 1 0 1028 0.187 ST2 5.35353677087316 0 0 5.353536770873159 5.353536770873159 Other FALSE 1852 +Reddy_3532T EZB 1 0 1305,1219,1926,55,1180,1590,1726,1853,1457 0.141,0.163,0.193,0.2,0.206,0.207,0.208,0.22,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.9567483521824 0 0 46.95674835218238 46.95674835218238 EZB TRUE 1853 +Reddy_3535T BN2 0.7479166791683285 2.0981160877189637 1854,1807,1714,1003 0.237,0.25,0.25,0.275 BN2,BN2,ST2,BN2 11.8505860574022,3.99420840630713 5 24.863905255933584 15.84479446370932 11.850586057402191 BN2 TRUE 1854 +Reddy_3536T BN2 0.6970953667880958 3.398044640029846 1793,2058,874 0.199,0.223,0.242 BN2,BN2,ST2 9.507616651958,4.13128715508472 6 32.3073058036444 13.63890380704272 9.507616651958001 Other FALSE 1855 +Reddy_3537T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 1856 +Reddy_3538T MCD 0.8952757531819948 0.44446386295225276 1184,1543,986,1897,1977,647,485,1420 0.129,0.158,0.171,0.194,0.207,0.208,0.211,0.217 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 4.6167480750623,39.4680576441472 3 17.542125363739842 44.08480571920947 39.46805764414717 Other FALSE 1857 +Reddy_3540T Other 1 10 0 0 1 2 Other TRUE 1858 +Reddy_3542T EZB 0.5559474585072289 7.445875082030368 1535,1653 0.233,0.292 EZB,ST2 4.29014930455404,3.42667579987848 8 31.943915804968853 7.716825104432521 4.290149304554042 Other FALSE 1859 +Reddy_3545T MCD 1 2.5945875873626414 768,801,1994 0.165,0.182,0.198 MCD,MCD,MCD 16.6237870783757 7 43.13187160851316 16.623787078375738 16.623787078375738 Other FALSE 1860 +Reddy_3546T EZB 1 0.1767609058362126 654,25,1160,1539,2073,72,1423,1450,699,47 0.162,0.256,0.261,0.262,0.281,0.293,0.35,0.355,0.355,0.385 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.7562782669588 1 6.320312135799317 35.75627826695879 35.75627826695879 Other FALSE 1861 +Reddy_3547T ST2 0.8082020612309335 1.0409414269323842 2052,1884,1970,1128,339 0.172,0.182,0.199,0.201,0.216 ST2,ST2,ST2,EZB,ST2 4.97521390015758,20.9646576755634 4 21.822980675949914 25.939871575720964 20.964657675563387 Other FALSE 1862 +Reddy_3548T EZB 0.41796463993914135 2.787476542508923 1863,1082,2017,1983,1121 0.188,0.208,0.217,0.226,0.242 EZB,BN2,ST2,EZB,ST2 4.80853433735013,9.72743530178664,8.73737545341226 5 27.11499772250346 23.273345092549032 9.727435301786638 EZB TRUE 1863 +Reddy_3549T EZB 0.8853978303134309 0.30641681192932374 1864,809,1031,1550,473,1701,1011,2176,466 0.165,0.195,0.207,0.216,0.22,0.247,0.255,0.269,0.277 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.63967163233938,35.8453527352502 2 10.98361870761743 40.48502436758958 35.845352735250195 EZB TRUE 1864 +Reddy_3551T EZB 0.5551557933471828 0.16888061315703431 1865,371,381,213,216,1049,400,380,1107 0.202,0.204,0.225,0.231,0.255,0.283,0.289,0.296,0.302 BN2,EZB,EZB,BN2,EZB,ST2,EZB,EZB,ST2 9.27370777038507,20.1129514161345,6.84272301062574 1 3.396687567554438 36.22938219714533 20.112951416134514 BN2 FALSE 1865 +Reddy_3552T MCD 0.8151225084834663 1.7086658324716437 2030,285,1398,1194,952 0.247,0.283,0.283,0.306,0.307 MCD,MCD,MCD,MCD,BN2 3.2615797872919,14.3802637953825 6 24.571065409099116 17.641843582674426 14.380263795382522 Other FALSE 1866 +Reddy_3555T EZB 1 0 135,679,513,617,103,1080,1867,162,1040,1287,468 0.114,0.122,0.149,0.167,0.171,0.181,0.215,0.221,0.231,0.249,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.4562763092819 0 0 62.45627630928195 62.45627630928195 EZB TRUE 1867 +Reddy_3556T EZB 1 3.318394760797893 2050 0.125 EZB 8.00292817653142 3 26.556874932043687 8.002928176531416 8.002928176531416 Other FALSE 1868 +Reddy_3558T Other 1 10 0 0 1 2 Other TRUE 1869 +Reddy_3559T MCD 1 0.11361571288216786 1647,803,1459,518,534,792,535,783,1811,1640 0.113,0.121,0.144,0.162,0.181,0.186,0.192,0.198,0.206,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.0420977866528 1 6.9353414558535595 61.04209778665281 61.04209778665281 MCD TRUE 1870 +Reddy_3560T ST2 1 13.35873775598508 833 0.289 ST2 3.46318563666242 10 46.26378872046751 3.4631856366624207 3.4631856366624207 Other FALSE 1871 +Reddy_3561T EZB 1 0.1874668053281026 1142,1118,6,1872,79,1788,1991,196,367 0.134,0.146,0.167,0.172,0.176,0.197,0.203,0.239,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1286830309182 2 9.3974640631113 50.1286830309182 50.1286830309182 EZB TRUE 1872 +Reddy_3562T MCD 0.4199125665363848 1.4722456665836505 1008,464,1609,659,176,869,1064 0.171,0.178,0.189,0.216,0.257,0.258,0.27 N1,BN2,MCD,MCD,N1,MCD,N1 5.61331750635051,13.7974450266916,13.4471372447585 4 20.31322865047279 32.857899777800604 13.79744502669156 Other FALSE 1873 +Reddy_3563T BN2 1 3.9961997444805237 1148,1841 0.239,0.254 BN2,BN2 8.11307605169048 6 32.421472444716535 8.113076051690475 8.113076051690475 Other FALSE 1874 +Reddy_3565T ST2 1 2.9910757620445962 1710,1479 0.238,0.269 ST2,ST2 7.91280567993489 6 23.667801279022072 7.912805679934894 7.912805679934894 BN2 FALSE 1875 +Reddy_3566T BN2 1 1.1834162322318615 1148,804,1980,70,1841 0.111,0.147,0.177,0.195,0.202 BN2,BN2,BN2,BN2,BN2 31.5271583085902 6 37.30975089852928 31.52715830859023 31.52715830859023 Other FALSE 1876 +Reddy_3580T ST2 0.675509184959186 0.9627413489827835 629,2066,1152,1198,1174,1101,268 0.135,0.167,0.209,0.215,0.283,0.291,0.296 ST2,BN2,BN2,ST2,ST2,ST2,ST2 10.7670254785886,22.4142695828274 4 21.57914413463505 33.181295061416066 22.41426958282743 Other FALSE 1877 +Reddy_3582T ST2 1 13.119001165221853 1667 0.304 ST2 3.29142945097823 10 43.180266802628864 3.2914294509782254 3.2914294509782254 Other FALSE 1878 +Reddy_3586T EZB 0.8786813784259708 0.19670357065637847 202,1221,727,1132,2114,2087,1879,1896,351 0.128,0.187,0.191,0.22,0.23,0.234,0.248,0.263,0.271 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 37.8340249873452,5.22370437425721 2 7.442087807313449 43.057729361602426 37.83402498734522 EZB TRUE 1879 +Reddy_3587T EZB 0.7039360332270582 5.858659300743166 1471,696,1122 0.314,0.342,0.389 EZB,EZB,BN2 2.57064706633212,6.11209502603337 8 35.80868237129645 8.68274209236549 6.112095026033371 Other FALSE 1880 +Reddy_3589T ST2 0.666760278165212 0.6064393276738677 701,618,1211,1018,1401,1284,1442 0.143,0.186,0.21,0.221,0.237,0.25,0.26 EZB,ST2,ST2,ST2,BN2,ST2,ST2 4.2257995100583,7.01283725482387,22.4867447802807 3 13.63684638612729 33.72538154516289 22.486744780280716 Other FALSE 1881 +Reddy_3590T EZB 0.5386540123283826 2.2305250173062015 1266,2042,1608,1052 0.165,0.192,0.202,0.213 EZB,EZB,N1,ST2 11.2634899061119,4.95506237825257,4.69188307547697 4 25.123496017758367 20.910435359841394 11.263489906111854 Other FALSE 1882 +Reddy_3592T BN2 0.6053772778656177 3.5424169127613623 1108,65 0.152,0.232 BN2,EZB 6.60023343512834,4.30244440966759 4 23.38077854877164 10.902677844795924 6.600233435128336 Other FALSE 1883 +Reddy_3593T ST2 0.8176928287459795 0.8608854486624752 1970,2052,1128,339,444,1884 0.156,0.165,0.168,0.203,0.206,0.222 ST2,ST2,EZB,ST2,ST2,ST2 5.96029378965802,26.7333942789981 4 23.014390128146125 32.693688068656115 26.733394278998098 ST2 TRUE 1884 +Reddy_3594T MCD 1 2.880664849275111 864,650,2030 0.227,0.242,0.266 MCD,MCD,MCD 12.3014137296049 7 35.43625012726297 12.301413729604862 12.301413729604862 Other FALSE 1885 +Reddy_3595T EZB 1 0.08827211228029339 349,2015,1886,413,1222,348,463,316,288,2001 0.119,0.139,0.154,0.158,0.159,0.178,0.206,0.211,0.211,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.120950724096 1 5.218731200435094 59.120950724096 59.120950724096 EZB TRUE 1886 +Reddy_3596T ST2 1 16.45731262983338 318 0.295 ST2 3.38706553465923 9 55.74199640162076 3.387065534659234 3.387065534659234 Other FALSE 1887 +Reddy_3597T BN2 0.7228770789152266 3.6123012675200195 2032,318 0.128,0.335 BN2,ST2 7.78707417562905,2.98526098723791 8 28.129257914897224 10.772335162866959 7.787074175629049 Other FALSE 1888 +Reddy_3600T EZB 1 0 2016,240,174,558,39,93,1138,382,153,1903,1673 0.112,0.128,0.149,0.18,0.191,0.21,0.227,0.229,0.241,0.259,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.5480543804017 0 0 59.54805438040171 59.54805438040171 EZB TRUE 1889 +Reddy_3601T MCD 1 1.0597773207335293 1063,753,650,1194,2053 0.145,0.177,0.203,0.214,0.221 MCD,MCD,MCD,MCD,MCD 26.7034850554977 4 28.29974784636316 26.70348505549767 26.70348505549767 MCD TRUE 1890 +Reddy_3602T MCD 1 0.6178677440746084 704,347,2053,717,465,864 0.148,0.244,0.268,0.269,0.29,0.3 MCD,MCD,MCD,MCD,MCD,MCD 25.0793403451142 3 15.495715441915035 25.07934034511422 25.07934034511422 Other FALSE 1891 +Reddy_3604T BN2 0.7774963695821668 1.1765252362064424 459,1003,1714,1854 0.177,0.178,0.234,0.273 BN2,BN2,ST2,BN2 14.9211961174052,4.27014251922919 4 17.55516378651286 19.19133863663444 14.921196117405248 Other FALSE 1892 +Reddy_3606T BN2 1 2.059048778465043 1803,20,1096 0.191,0.277,0.309 BN2,BN2,BN2 12.0724304871561 5 24.857723247683005 12.072430487156145 12.072430487156145 Other FALSE 1893 +Reddy_3607T BN2 0.579347624373424 9.835770330596299 1697,697 0.202,0.278 BN2,N1 4.96099016607975,3.6020727643071 9 48.79515988590724 8.563062930386852 4.960990166079753 Other FALSE 1894 +Reddy_3608T EZB 1 11.976788510795991 292 0.216 EZB 4.63367906823512 9 55.496594227154226 4.633679068235117 4.633679068235117 Other FALSE 1895 +Reddy_3609T EZB 0.9126129408660896 0 351,1896,527,1132,727,2001,1268,1516,348,2114,1879 0.145,0.159,0.165,0.208,0.215,0.225,0.232,0.241,0.25,0.258,0.259 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 48.6771507713303,4.66107027683347 0 0 53.33822104816373 48.67715077133026 EZB TRUE 1896 +Reddy_3610T MCD 1 0.5594109070752007 761,1911,755,1897,986,1543 0.145,0.163,0.218,0.228,0.239,0.27 MCD,MCD,MCD,MCD,MCD,MCD 29.9014711250353 4 16.7272090849389 29.90147112503526 29.90147112503526 MCD TRUE 1897 +Reddy_3612T MCD 1 5.522252714089882 879,1631 0.29,0.315 MCD,MCD 6.62635024290413 7 36.592380613387476 6.626350242904129 6.626350242904129 Other FALSE 1898 +Reddy_3613T EZB 0.6100818283601454 5.55170991161664 1471,724 0.21,0.328 EZB,ST2 4.76330546199823,3.04434466061179 6 26.444490145433264 7.807650122610018 4.7633054619982325 Other FALSE 1899 +Reddy_3614T Other 1 10 0 0 1 2 Other TRUE 1900 +Reddy_3616T BN2 0.546216492830402 2.2224569957537583 1901,2166 0.193,0.232 BN2,EZB 5.18000568408344,4.30342396711864 2 11.51233987063548 9.48342965120208 5.180005684083443 BN2 TRUE 1901 +Reddy_3617T Other 1 10 0 0 1 2 Other TRUE 1902 +Reddy_3618T EZB 1 0.13855175519850285 2063,108,172,1048,1138,117,1573,382,93,676 0.184,0.197,0.221,0.233,0.235,0.238,0.243,0.246,0.255,0.257 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7945617774797 1 6.067813402419071 43.79456177747965 43.79456177747965 EZB TRUE 1903 +Reddy_3620T MCD 0.9204254335524926 0 1009,759,1449,764,1904,479,858,305,1376,1406,1689 0.115,0.116,0.126,0.131,0.135,0.144,0.145,0.154,0.16,0.166,0.179 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.24802841727307,72.2698787006336 0 0 78.51790711790663 72.26987870063356 MCD TRUE 1904 +Reddy_3622T MCD 1 0 1171,896,2207,455,1728,2010,485,214,1977,1565,777 0.147,0.218,0.221,0.227,0.253,0.254,0.259,0.272,0.297,0.312,0.32 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 45.4626641358264 0 0 45.46266413582643 45.46266413582643 MCD TRUE 1905 +Reddy_3623T Other 1 10 0 0 1 2 Other TRUE 1906 +Reddy_3624T MCD 1 0.8997891988249243 717,1907,704,864 0.149,0.191,0.225,0.227 MCD,MCD,MCD,MCD 20.7897101670169 4 18.706356654982525 20.789710167016906 20.789710167016906 MCD TRUE 1907 +Reddy_3625T ST2 1 7.691733336522279 1667 0.273 ST2 3.66394561111704 6 28.182092600233457 3.6639456111170436 3.6639456111170436 Other FALSE 1908 +Reddy_3627T EZB 0.5019183978492316 0.539364779184855 1909,1725,353,2183 0.252,0.268,0.303,0.32 EZB,ST2,BN2,EZB 3.30222317932801,7.09054718899012,3.73412200975327 1 3.824391418889452 14.126892378071405 7.090547188990122 EZB TRUE 1909 +Reddy_3628T BN2 0.634034431537761 0.7234544977864404 1722,960,1317,915,838,590,1076,684 0.159,0.178,0.18,0.234,0.237,0.244,0.245,0.258 BN2,ST2,BN2,BN2,ST2,BN2,EZB,BN2 24.1150374495502,4.082166315296,9.837067325231 3 17.44613230716552 38.03427109007716 24.11503744955017 Other FALSE 1910 +Reddy_3629T MCD 1 0.8543271553798658 755,467,761,713,2028,1897 0.108,0.185,0.202,0.207,0.209,0.229 MCD,MCD,MCD,MCD,MCD,MCD 33.5572260702027 4 28.668849490995306 33.55722607020265 33.55722607020265 MCD TRUE 1911 +Reddy_3630T ST2 0.5025709964804748 6.750394278411023 724,1471 0.27,0.273 ST2,EZB 3.66638444578616,3.70428437296969 4 25.00538003690197 7.37066881875585 3.704284372969691 Other FALSE 1912 +Reddy_3631T BN2 0.5224084189762277 8.484853282196651 19,641 0.247,0.271 BN2,MCD 4.0409273105209,3.69426064536996 7 34.28667535379136 7.735187955890858 4.040927310520901 Other FALSE 1913 +Reddy_3632T EZB 1 0.1976065171841884 1779,278,2132,1172,1570,1517,733,162,1914 0.148,0.151,0.152,0.16,0.179,0.185,0.194,0.205,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1132296703206 2 10.297913814371771 52.113229670320635 52.113229670320635 EZB TRUE 1914 +Reddy_3634T Other 1 10 0 0 1 2 Other TRUE 1915 +Reddy_3635T EZB 1 2.719496554400507 1601,1395 0.177,0.294 EZB,EZB 9.05335100424728 6 24.620556861828845 9.05335100424728 9.05335100424728 Other FALSE 1916 +Reddy_3636T ST2 0.7178320048071831 0.2545992177095862 1045,1917,1939,2082,1487,1472,1363,1394,619 0.14,0.15,0.159,0.162,0.186,0.194,0.204,0.204,0.25 ST2,ST2,ST2,ST2,EZB,ST2,ST2,EZB,BN2 3.99964167427697,10.2810912775626,36.3300138200648 2 9.249593097966962 50.61074677190443 36.33001382006483 ST2 TRUE 1917 +Reddy_3637T EZB 0.3500157342474907 8.242879621322917 1733,1178,904 0.25,0.266,0.272 EZB,ST2,MCD 4.00667659399264,3.68119275930839,3.7592634523333 7 33.02655284585343 11.447132805634329 4.006676593992638 Other FALSE 1918 +Reddy_3638T MCD 1 2.5392616536040484 311,194 0.149,0.192 MCD,MCD 11.9440635049664 5 30.329102446372758 11.944063504966405 11.944063504966405 Other FALSE 1919 +Reddy_3639T EZB 1 0.11865389809301656 520,477,1450,699,1542,119,1668,1295,1539,1160 0.12,0.191,0.196,0.202,0.224,0.234,0.253,0.264,0.293,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.805271748772 1 5.553627944294737 46.80527174877198 46.80527174877198 Other FALSE 1920 +Reddy_3640T BN2 0.7131477208760549 3.867401354768396 1793,2058,874 0.188,0.229,0.257 BN2,BN2,ST2 9.67636592888127,3.89216362765225 7 37.42239070259016 13.568529556533516 9.676365928881266 Other FALSE 1921 +Reddy_3642T ST2 1 5.421524608805046 511 0.216 ST2 4.61921143570973 4 25.043168471973978 4.619211435709728 4.619211435709728 Other FALSE 1922 +Reddy_3643T Other 1 10 0 0 1 2 Other TRUE 1923 +Reddy_3644T ST2 0.748094092058749 2.9947509113455006 566,1667,1971 0.122,0.188,0.219 ST2,ST2,BN2 4.55939021922083,13.5401861523034 6 40.54948481939833 18.099576371524222 13.540186152303399 Other FALSE 1924 +Reddy_3645T EZB 0.6782179667063387 3.484515732458005 342,793,1257 0.193,0.217,0.22 EZB,BN2,EZB 4.61345984839668,9.7237602915059 6 33.88259571440273 14.33722013990258 9.723760291505897 Other FALSE 1925 +Reddy_3647T EZB 1 0.1432912890182315 1926,1219,1726,1590,1853,1305,2072,499,2013,1540 0.121,0.153,0.157,0.185,0.212,0.215,0.222,0.238,0.252,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5319706019267 1 7.527373782217918 52.531970601926695 52.531970601926695 EZB TRUE 1926 +Reddy_3648T BN2 1 0.40345520109556426 1927,519,687,1765,41,1001,1006,1409 0.165,0.178,0.198,0.198,0.21,0.24,0.246,0.262 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.5848390228326 3 15.567253987196889 38.58483902283257 38.58483902283257 BN2 TRUE 1927 +Reddy_3649T MCD 1 0.5313583690284062 1514,1731,1802,471,895,950,247 0.17,0.174,0.184,0.24,0.24,0.244,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.5665261914417 4 17.835854611033724 33.566526191441675 33.566526191441675 Other FALSE 1928 +Reddy_3651T BN2 1 0.23121366808099333 1014,889,106,1935,111,1982,1632,943,579 0.16,0.175,0.178,0.204,0.225,0.225,0.237,0.244,0.253 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.655747286719 2 10.093805462979162 43.655747286718956 43.655747286718956 BN2 TRUE 1929 +Reddy_3652T ST2 0.5582562300889267 10.149856753449912 1436,2166 0.245,0.309 ST2,EZB 3.23279480625978,4.08546303110727 8 41.46686453725406 7.318257837367047 4.085463031107269 Other FALSE 1930 +Reddy_3653T EZB 0.5591968776405299 9.227394132111657 1863,2198 0.212,0.269 EZB,ST2 4.71063609532383,3.71329523133136 9 43.46689586450444 8.423931326655183 4.710636095323825 Other FALSE 1931 +Reddy_3654T Other 1 10 0 0 1 2 Other TRUE 1932 +Reddy_3655T EZB 1 0.25112907340176294 639,42,121,17,80,333,1705,1537,155 0.18,0.193,0.194,0.203,0.203,0.204,0.209,0.209,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7705495068777 2 11.243186613349947 44.77054950687768 44.77054950687768 EZB TRUE 1933 +Reddy_3656T ST2 1 3.7037580637313154 35,566 0.244,0.304 ST2,ST2 7.39789147666651 7 27.40000021131275 7.3978914766665085 7.3978914766665085 Other FALSE 1934 +Reddy_3659T BN2 0.9101477863898948 0.08905047208115915 579,961,1935,692,260,1982,892,933,232,667 0.161,0.169,0.187,0.188,0.198,0.209,0.212,0.223,0.248,0.248 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 45.4484019262146,4.48678728793037 1 4.04720164686367 49.93518921414493 45.44840192621456 BN2 TRUE 1935 +Reddy_3660T Other 1 10 0 0 1 2 Other TRUE 1936 +Reddy_3661T EZB 1 0 1809,78,168,86,645,1041,227,1094,329,1020,138 0.2,0.204,0.228,0.23,0.234,0.248,0.263,0.266,0.285,0.29,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8421757373637 0 0 44.842175737363654 44.842175737363654 EZB TRUE 1937 +Reddy_3663T EZB 0.4699458248546141 4.807346469638401 1649,1848,511 0.183,0.322,0.327 EZB,BN2,ST2 3.10213797369911,5.46566527040595,3.06261239390353 6 26.275346641911277 11.630415638008596 5.465665270405953 Other FALSE 1938 +Reddy_3664T ST2 0.6264604777940087 0.1379647188286141 1917,1487,1363,1045,1939,2082,1394,619,1472,1139 0.141,0.142,0.15,0.154,0.161,0.175,0.185,0.191,0.192,0.215 ST2,EZB,ST2,ST2,ST2,ST2,EZB,BN2,ST2,BN2 9.8750826555099,12.4485577531196,37.4388186660998 1 5.165236090543932 59.76245907472932 37.43881866609982 ST2 TRUE 1939 +Reddy_3665T EZB 0.7416270271149038 2.6388338801240816 1178,2138,816,1733 0.261,0.27,0.272,0.275 ST2,EZB,EZB,EZB 11.0140989781048,3.83716530355456 6 29.064377542462847 14.851264281659313 11.014098978104753 Other FALSE 1940 +Reddy_3666T ST2 0.6093698926426129 0.19975117737644646 1139,1147,235,1484,234,1394,1176,619,1687,649 0.115,0.137,0.14,0.144,0.171,0.182,0.189,0.194,0.204,0.213 BN2,ST2,ST2,ST2,ST2,EZB,ST2,BN2,ST2,EZB 13.8311890171872,10.1934030776756,37.4775595375066 1 7.486186642812805 61.50215163236926 37.47755953750656 Other FALSE 1941 +Reddy_3667T ST2 0.9169468160855078 0.10664877189478816 1955,1442,1284,1998,1625,1942,2076,1661,1401,271 0.144,0.163,0.2,0.225,0.226,0.231,0.233,0.233,0.253,0.262 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 3.95621452468199,43.6784978152462 1 4.6582581502051985 47.63471233992822 43.67849781524623 ST2 TRUE 1942 +Reddy_3669T ST2 0.7786525602164894 0.5435441953860158 1130,1713,1943,1018,1211,1789,486,1401 0.165,0.19,0.19,0.198,0.198,0.199,0.21,0.256 ST2,ST2,ST2,ST2,ST2,EZB,ST2,BN2 3.90911287781137,5.03573833717399,31.4660576427538 3 17.103192983400625 40.41090885773919 31.466057642753828 ST2 TRUE 1943 +Reddy_3671T EZB 0.808511125339633 0.8649684107012838 160,388,1944,1439,1037,242 0.17,0.181,0.185,0.199,0.209,0.245 ST2,EZB,EZB,EZB,EZB,EZB 24.8400617323619,5.88315400808606 4 21.485868718362866 30.723215740447973 24.840061732361917 EZB TRUE 1944 +Reddy_3672T MCD 1 1.1219309992045825 755,467,2028,713,1897,761 0.132,0.146,0.201,0.212,0.223,0.241 MCD,MCD,MCD,MCD,MCD,MCD 32.7608623234953 5 36.75542700140282 32.76086232349528 32.76086232349528 Other FALSE 1945 +Reddy_3673T ST2 1 1.5326077420306168 1946,90 0.153,0.193 ST2,ST2 11.7466197453786 3 18.002960364456953 11.746619745378599 11.746619745378599 ST2 TRUE 1946 +Reddy_3674T MCD 1 0 798,2008,23,1331,1699,2034,21,846,59,707,905 0.116,0.126,0.13,0.139,0.148,0.154,0.154,0.154,0.16,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.3608957443108 0 0 75.36089574431075 75.36089574431075 MCD TRUE 1947 +Reddy_3675T Other 1 10 0 0 1 2 Other TRUE 1948 +Reddy_3677T ST2 1 5.106368790825767 2107 0.223 ST2 4.47838507032524 4 22.86828575640884 4.478385070325236 4.478385070325236 Other FALSE 1949 +Reddy_3679T Other 1 10 0 0 1 2 Other TRUE 1950 +Reddy_3680T N1 1 4.1916599646626675 901,262 0.216,0.231 N1,N1 8.94820584311807 7 37.507836188158585 8.948205843118075 8.948205843118075 Other FALSE 1951 +Reddy_3681T BN2 0.5718334639495825 4.123298535311059 1971,1667 0.13,0.174 BN2,ST2 7.68760481897778,5.75617786187557 4 31.69828969014131 13.443782680853353 7.6876048189777775 Other FALSE 1952 +Reddy_3682T ST2 0.5586431870017705 1.5624015688905857 1082,1121,2017,1847,1863,1822 0.157,0.159,0.16,0.165,0.231,0.25 BN2,ST2,ST2,ST2,EZB,EZB 6.38167104405022,8.32414331963691,18.6137446203182 5 29.082143797713883 33.31955898400534 18.61374462031822 Other FALSE 1953 +Reddy_3683T EZB 1 2.0470475273823494 1221,2087,202,2114 0.194,0.229,0.233,0.245 EZB,EZB,EZB,EZB 17.8971424365732 7 36.63630117199679 17.897142436573155 17.897142436573155 Other FALSE 1954 +Reddy_3684T ST2 1 0.3730553410793455 1955,271,1442,1625,1284,1998,606,1661 0.13,0.2,0.219,0.251,0.263,0.279,0.29,0.291 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.5209267872738 3 13.25127145808087 35.52092678727375 35.52092678727375 ST2 TRUE 1955 +Reddy_3685T EZB 0.5569145528153134 1.3105538211883916 1909,353,1725,2183 0.159,0.285,0.286,0.398 EZB,BN2,ST2,EZB 3.50905977600141,8.80626138501381,3.49726865280166 4 11.541079508513622 15.812589813816876 8.806261385013807 Other FALSE 1956 +Reddy_3686T BN2 1 1.28713954340516 523,2069,596,1096,20 0.197,0.274,0.282,0.29,0.298 BN2,BN2,BN2,BN2,BN2 19.0823537450307 6 24.56165208647458 19.082353745030716 19.082353745030716 BN2 TRUE 1957 +Reddy_3687T Other 1 10 0 0 1 2 Other TRUE 1958 +Reddy_3688T EZB 0.909035518975261 0.32676885008190487 1527,1959,165,1292,1446,109,1738,1177 0.144,0.197,0.207,0.246,0.257,0.268,0.279,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 32.1286777988413,3.21502124061908 3 10.498651098979396 35.343699039460375 32.128677798841295 EZB TRUE 1959 +Reddy_3689T EZB 0.7113274112417116 5.745369881448987 1471,696,1122 0.29,0.344,0.388 EZB,EZB,BN2 2.57881710547196,6.35454617839446 8 36.50921822362429 8.933363283866424 6.354546178394459 Other FALSE 1960 +Reddy_3691T EZB 1 3.2166968534257223 31 0.126 EZB 7.93225696065116 3 25.51566600589088 7.932256960651163 7.932256960651163 Other FALSE 1961 +Reddy_3692T EZB 1 0 1962,1033,1264,142,463,2215,1937,372,1163,316,1304 0.114,0.116,0.141,0.15,0.151,0.161,0.179,0.188,0.192,0.192,0.192 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 70.5698282736951 0 0 70.56982827369511 70.56982827369511 EZB TRUE 1962 +Reddy_3693T ST2 1 1.054964882659469 271,2198,1955,1077,1249 0.194,0.226,0.233,0.284,0.286 ST2,ST2,ST2,ST2,ST2 20.8859436114731 5 22.033937051309962 20.885943611473056 20.885943611473056 Other FALSE 1963 +Reddy_3694T EZB 1 0 72,1160,1964,1542,1289,699,477,1450,1539,520 0.126,0.172,0.19,0.194,0.205,0.21,0.216,0.224,0.26,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1220133408542 0 0 50.1220133408542 50.1220133408542 EZB TRUE 1964 +Reddy_3696T BN2 1 2.039219086253074 1089 0.174 BN2 5.74950966553007 2 11.724509846545454 5.7495096655300735 5.7495096655300735 Other FALSE 1965 +Reddy_3697T EZB 1 16.330712458768197 1535 0.326 EZB 3.07139462108422 10 50.15806240433362 3.0713946210842153 3.0713946210842153 Other FALSE 1966 +Reddy_3698T EZB 1 1.2434423774906191 1967,532,1782,1182,96 0.21,0.278,0.338,0.358,0.367 EZB,EZB,EZB,EZB,EZB 16.8323935676635 5 20.930111476633247 16.832393567663452 16.832393567663452 EZB TRUE 1967 +Reddy_3699T MCD 1 0.09814576498778148 1422,837,1491,950,895,247,471,1968,156,788 0.126,0.128,0.131,0.14,0.147,0.151,0.151,0.153,0.156,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.5728986530488 1 6.828285360720867 69.57289865304881 69.57289865304881 MCD TRUE 1968 +Reddy_3700T BN2 0.6944038158730992 4.294147870948459 1799,1791,1719 0.233,0.272,0.285 BN2,BN2,EZB 7.96738608973568,3.50632115036351 8 34.21313401426284 11.473707240099188 7.967386089735681 Other FALSE 1969 +Reddy_3701T ST2 0.7308948055427646 0.871754885009075 444,629,1970,2066,339,1128,1198 0.246,0.26,0.304,0.305,0.322,0.324,0.336 ST2,ST2,ST2,BN2,ST2,EZB,ST2 3.27614508691657,3.0854343121294,17.2781701489952 4 15.062329231404563 23.639749548041188 17.278170148995223 ST2 TRUE 1970 +Reddy_3703T ST2 0.4737998128267772 3.4801002388401545 1863,1121,1082,1847 0.139,0.155,0.189,0.21 EZB,ST2,BN2,ST2 5.29797286207813,7.18276536134954,11.2378740607581 7 39.10892820289996 23.71861228418581 11.237874060758134 BN2 FALSE 1971 +Reddy_3704T EZB 1 0.08409978036861997 2089,695,172,676,1972,108,397,1573,398,382 0.181,0.188,0.192,0.253,0.254,0.278,0.288,0.292,0.292,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.1983734965811 1 3.4647741626068442 41.1983734965811 41.1983734965811 EZB TRUE 1972 +Reddy_3706T BN2 1 1.620440397572186 1957,2069,99,1973 0.181,0.208,0.233,0.257 BN2,BN2,BN2,BN2 18.5111659758562 6 29.996241153441208 18.51116597585624 18.51116597585624 BN2 TRUE 1973 +Reddy_3707T EZB 1 0.6393752332215447 1601,1395 0.176,0.229 EZB,EZB 10.0353175014784 1 6.416333467959972 10.035317501478355 10.035317501478355 Other FALSE 1974 +Reddy_3708T EZB 0.6602101781733728 1.8174192207697926 561,141,2042 0.168,0.175,0.194 EZB,BN2,EZB 5.71306698164546,11.1004648390942 3 20.174198158049077 16.813531820739666 11.100464839094208 EZB TRUE 1975 +Reddy_3709T ST2 0.6960737903534137 0.4871034354070237 1987,675,608,457,89,1411,2004,1472 0.199,0.219,0.232,0.266,0.297,0.305,0.311,0.313 BN2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 9.33121713422224,21.371028470304 2 10.409901386066412 30.70224560452629 21.371028470304047 Other FALSE 1976 +Reddy_3711T MCD 1 0 880,1598,2180,771,1239,518,1250,1240,929,783,1459 0.125,0.127,0.146,0.161,0.171,0.175,0.18,0.184,0.189,0.195,0.195 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.0510023257962 0 0 67.05100232579622 67.05100232579622 MCD TRUE 1977 +Reddy_3712T EZB 1 0.1393136390810201 172,108,2063,1138,676,382,2089,1573,93,117 0.199,0.206,0.222,0.226,0.228,0.229,0.235,0.249,0.255,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6490366958355 1 6.080906144477835 43.64903669583554 43.64903669583554 Other FALSE 1978 +Reddy_3713T Other 1 10 0 0 1 2 Other TRUE 1979 +Reddy_3715T BN2 0.47756716559512885 4.291178056949658 1835,921,70,804 0.187,0.197,0.206,0.214 EZB,MCD,BN2,BN2 9.51101029242081,5.34019480006958,5.06433973054721 7 40.81343866625851 19.915544823037596 9.511010292420806 BN2 TRUE 1980 +Reddy_3716T MCD 1 0.5033064705444877 1286,2108,301,300,686,1325,223 0.105,0.193,0.247,0.25,0.272,0.28,0.281 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.5291538233315 4 16.87544007116421 33.529153823331534 33.529153823331534 Other FALSE 1981 +Reddy_3717T BN2 0.9250368615000537 0.0875572412805069 579,1935,1982,961,692,889,260,933,892,1014 0.135,0.149,0.161,0.203,0.206,0.212,0.248,0.267,0.269,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 46.2944189908856,3.75160719212929 1 4.053411613525852 50.046026183014895 46.29441899088561 BN2 TRUE 1982 +Reddy_3718T EZB 0.5584237776503348 2.0370013588074394 1983,2017,1082,1863 0.165,0.254,0.257,0.262 EZB,ST2,BN2,EZB 3.88697230586889,9.89596899047737,3.9383116060935 4 20.158102280318683 17.721252902439755 9.895968990477368 EZB TRUE 1983 +Reddy_3719T Other 1 10 0 0 1 2 Other TRUE 1984 +Reddy_3721T ST2 0.6769893575746951 1.4316783095114276 618,701,1211,1130,1018,1789 0.297,0.351,0.364,0.377,0.388,0.39 ST2,EZB,ST2,ST2,ST2,EZB 5.40907252206665,11.3367302832309 5 16.230550847283073 16.74580280529758 11.336730283230935 Other FALSE 1985 +Reddy_3724T Other 1 10 0 0 1 2 Other TRUE 1986 +Reddy_3725T ST2 0.5649700573742674 0.7872273609992654 1987,608,457,675,2004,1497,1411 0.131,0.183,0.266,0.286,0.29,0.307,0.327 BN2,BN2,ST2,ST2,ST2,ST2,ST2 13.0980324264527,17.0103144781219 3 13.390984976379478 30.108346904574624 17.010314478121874 BN2 FALSE 1987 +Reddy_3726T ST2 0.46326622273488227 0.5321864479856246 1789,486,1130,1943,1123,1341,854 0.177,0.177,0.208,0.266,0.267,0.281,0.284 EZB,ST2,ST2,ST2,EZB,BN2,BN2 7.07148013088333,9.38824885627481,14.2067386069391 2 7.5606337566871655 30.666467594097256 14.206738606939108 Other FALSE 1988 +Reddy_3727T ST2 1 13.341393549924467 1028 0.316 ST2 3.16916243018431 10 42.28104320472386 3.169162430184307 3.169162430184307 Other FALSE 1989 +Reddy_3728T EZB 0.7000711265710431 2.2809570032475452 1488,1387,901 0.184,0.201,0.224 EZB,EZB,N1 10.4137598957812,4.46152848639778 4 23.753338564420474 14.875288382178947 10.413759895781165 Other FALSE 1990 +Reddy_3729T EZB 0.8468368793471144 0 1272,2083,595,1600,1318,1293,207 0.152,0.172,0.179,0.179,0.2,0.207,0.222 EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.80003638282943,32.0683248656599 0 0 37.868361248489336 32.0683248656599 EZB TRUE 1991 +Reddy_3730T EZB 1 1.0383006714722411 1649 0.159 EZB 6.28523311064902 1 6.525961759146445 6.285233110649025 6.285233110649025 Other FALSE 1992 +Reddy_3731T BN2 1 2.418537382974467 871,19 0.191,0.3 BN2,BN2 8.56957688048072 5 20.725842041716326 8.569576880480716 8.569576880480716 Other FALSE 1993 +Reddy_3732T MCD 0.8243629222113816 2.0051383473347064 1994,801,768,850 0.14,0.148,0.185,0.243 MCD,MCD,MCD,BN2 4.11348702837051,19.3068925416036 7 38.71299060303981 23.420379569974113 19.306892541603602 MCD TRUE 1994 +Reddy_3733T MCD 1 8.75522903198767 904 0.234 MCD 4.26621639480479 7 37.3517016365367 4.266216394804794 4.266216394804794 Other FALSE 1995 +Reddy_3734T BN2 0.8818328739938658 0 1212,836,1477,294,872,1213,669,88,454,560,1193 0.141,0.145,0.16,0.169,0.178,0.193,0.193,0.197,0.229,0.235,0.239 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.910239200485,7.09006330678245 0 0 60.00030250726746 52.910239200485016 BN2 TRUE 1996 +Reddy_3736T MCD 1 2.6373178084416176 1480,864,717 0.2,0.223,0.225 MCD,MCD,MCD 13.9195159514473 6 36.71018730363919 13.919515951447321 13.919515951447321 Other FALSE 1997 +Reddy_3737T ST2 1 0.09874233166295932 1625,1998,1661,1955,1942,606,1442,1284,2076,726 0.163,0.171,0.183,0.189,0.21,0.216,0.219,0.242,0.249,0.27 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 48.5747151620265 1 4.796380634962604 48.57471516202655 48.57471516202655 ST2 TRUE 1998 +Reddy_3739T ST2 0.37736637427566616 6.62003132474622 938,1388,2196 0.213,0.231,0.292 ST2,EZB,BN2 3.42747206662328,4.33037629395983,4.7018840407277 7 31.126619634941676 12.459732401310799 4.701884040727696 Other FALSE 1999 +Reddy_3740T N1 1 1.4444251571952305 814,891,745,2189,3 0.267,0.279,0.322,0.347,0.355 N1,N1,N1,N1,N1 16.1213352908538 5 23.286062261688574 16.12133529085384 16.12133529085384 Other FALSE 2000 +Reddy_3741T EZB 1 0.12869289843010714 2001,349,2015,413,527,348,1886,1222,1268,288 0.147,0.163,0.173,0.179,0.212,0.217,0.227,0.234,0.247,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.2574137190742 1 6.4677722391086885 50.257413719074194 50.257413719074194 EZB TRUE 2001 +Reddy_3742T Other 1 10 0 0 1 2 Other TRUE 2002 +Reddy_3743T EZB 1 0.1045143717222697 2003,1437,1066,1241,1570,1814,1180,1517,1172,55 0.133,0.238,0.25,0.261,0.268,0.288,0.288,0.29,0.291,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.474684769417 1 4.230186249332531 40.47468476941695 40.47468476941695 EZB TRUE 2003 +Reddy_3745T ST2 0.6244725775671172 0.7102068760587007 1987,608,457,675,1411,2004,1482,1497 0.161,0.164,0.235,0.267,0.296,0.321,0.335,0.338 BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2 12.29407084584,20.4440731921956 3 14.5195213557447 32.738144038035614 20.44407319219565 ST2 TRUE 2004 +Reddy_3746T EZB 1 0.080828250679757 2005,1528,1554,1162,1277,2093,1493,173,2201 0.148,0.163,0.219,0.22,0.258,0.26,0.276,0.281,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0856995149384 1 3.2400569690668553 40.085699514938405 40.085699514938405 EZB TRUE 2005 +Reddy_3747T EZB 1 0 691,780,250,368,116,118,275,28,433,44,5 0.149,0.179,0.193,0.194,0.214,0.217,0.217,0.234,0.235,0.24,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9484857348115 0 0 52.94848573481151 52.94848573481151 EZB TRUE 2006 +Reddy_3748T ST2 0.5946471574745994 7.570664645537175 2216,292 0.137,0.202 ST2,EZB 4.96062391254049,7.27716843063184 9 55.09300175740374 12.23779234317233 7.277168430631844 Other FALSE 2007 +Reddy_3749T MCD 0.9096757616274521 0 1449,759,1009,764,1376,479,1904,858,305,310,1689 0.112,0.128,0.138,0.146,0.148,0.153,0.159,0.162,0.164,0.165,0.167 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.75252221968647,68.0061731355434 0 0 74.75869535522986 68.00617313554339 MCD TRUE 2008 +Reddy_3750T ST2 0.5185863937999452 11.661752299215479 1714,1971 0.317,0.342 ST2,BN2 2.92457683274495,3.15040068155825 9 36.73919239161199 6.074977514303206 3.1504006815582546 Other FALSE 2009 +Reddy_3753T MCD 1 0.2049532161899637 1728,214,997,1905,896,789,154,1531,738 0.139,0.228,0.251,0.251,0.258,0.258,0.259,0.271,0.316 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.0245257712155 2 7.793248850908769 38.024525771215465 38.024525771215465 MCD TRUE 2010 +Reddy_3754T ST2 1 4.0193769655082185 1653 0.222 ST2 4.50757968007316 3 18.117661936278953 4.507579680073158 4.507579680073158 Other FALSE 2011 +Reddy_3755T ST2 0.35286998421664073 6.737619590508876 1725,2183,353 0.237,0.243,0.276 ST2,EZB,BN2 3.61668401661341,4.11665399415288,4.21686955518162 6 28.411662925612163 11.950207565947922 4.216869555181624 Other FALSE 2012 +Reddy_3756T EZB 1 0.12630335128454967 354,1034,1060,443,205,662,660,149,319,548 0.188,0.214,0.227,0.24,0.269,0.287,0.288,0.3,0.303,0.308 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.1426544039348 1 4.9438484293899005 39.142654403934785 39.142654403934785 EZB TRUE 2013 +Reddy_3757T MCD 0.7695995475438903 0.5144387188982464 2014,291,761,2130,267,850,790,569 0.172,0.294,0.3,0.301,0.317,0.327,0.33,0.34 MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD 6.46714015848493,21.6019894354202 3 11.11289977081104 28.069129593905167 21.601989435420236 MCD TRUE 2014 +Reddy_3758T EZB 1 0 1516,1281,1046,1163,351,2215,1896,348,1264,463,2047 0.151,0.184,0.208,0.212,0.225,0.227,0.228,0.234,0.24,0.26,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8502225347506 0 0 50.85022253475059 50.85022253475059 EZB TRUE 2015 +Reddy_3759T EZB 1 0 463,1264,1886,316,348,1962,1033,1222,1937,2215,142 0.113,0.142,0.148,0.168,0.169,0.169,0.171,0.186,0.188,0.194,0.204 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1159237030782 0 0 67.11592370307821 67.11592370307821 EZB TRUE 2016 +Reddy_3760T ST2 0.6418584330957545 1.5812801844649582 1847,2017,1121,1082,1822 0.164,0.178,0.178,0.179,0.245 ST2,ST2,ST2,BN2,EZB 5.57714908096992,4.08755893588807,17.321011905013 5 27.3893729002787 26.985719921870995 17.321011905013005 ST2 TRUE 2017 +Reddy_3761T MCD 1 0.09172911799212197 253,823,905,910,2203,2018,1699,1904,764,305 0.132,0.138,0.155,0.155,0.162,0.175,0.188,0.194,0.207,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.7700483503636 1 5.4826538175253345 59.77004835036357 59.77004835036357 MCD TRUE 2018 +Reddy_3762T ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 2019 +Reddy_3763T BN2 0.4211002033327609 3.2244541547150756 1763,834,1377,597 0.192,0.27,0.303,0.404 EZB,BN2,MCD,BN2 6.17895925171209,5.19520863831705,3.29920249262387 5 19.923770830998208 14.673370382653005 6.178959251712092 Other FALSE 2020 +Reddy_3764T EZB 1 0.7773679546734579 1967,532,1037,1182,388,1269,242 0.221,0.238,0.245,0.266,0.268,0.286,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.1260718777164 4 21.086939013905607 27.126071877716406 27.126071877716406 Other FALSE 2021 +Reddy_3765T EZB 1 0.5499403168418484 1322,1011,1260,1701,1036,809,1031 0.134,0.187,0.229,0.238,0.249,0.251,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0387925789297 4 18.16936405892869 33.038792578929666 33.038792578929666 Other FALSE 2022 +Reddy_3766T EZB 0.5828447784460639 1.4262753161449115 1480,1513,632 0.127,0.167,0.2 MCD,EZB,EZB 10.9742589338997,7.85452591540172 2 15.652314630403879 18.828784849301393 10.974258933899675 Other FALSE 2023 +Reddy_3767T ST2 1 13.842707473384138 90 0.289 ST2 3.46037304352841 10 47.90093179034772 3.460373043528409 3.460373043528409 Other FALSE 2024 +Reddy_3770T Other 1 10 0 0 1 2 Other TRUE 2025 +Reddy_3771T Other 1 10 0 0 1 2 Other TRUE 2026 +Reddy_3775T Other 1 10 0 0 1 2 Other TRUE 2027 +Reddy_3776T MCD 0.8600974355676998 1.1230925149004276 467,755,2028,713,1420,1897 0.133,0.195,0.204,0.231,0.238,0.251 MCD,MCD,MCD,MCD,BN2,MCD 4.20648824452881,25.8607822275886 5 29.0440509492748 30.067270472117443 25.860782227588636 MCD TRUE 2028 +Reddy_3777T MCD 0.7556785713863908 3.702380418756605 311,194,1513 0.148,0.219,0.274 MCD,MCD,EZB 3.65551602180337,11.3063972354432 8 41.86058373118855 14.961913257246522 11.306397235443153 Other FALSE 2029 +Reddy_3778T MCD 0.7356975478320327 0.8990237003267655 2030,1398,285,952,1194,1890,1278 0.19,0.22,0.233,0.238,0.27,0.285,0.29 MCD,MCD,MCD,BN2,MCD,MCD,EZB 4.20187015228481,3.45118600569777,21.3026197928367 4 19.15156007281026 28.955675950819295 21.302619792836715 MCD TRUE 2030 +Reddy_3779T MCD 0.8799859539513479 0.45337115396694433 2030,285,1398,1194,952,1890,1063,650 0.148,0.171,0.183,0.194,0.211,0.243,0.269,0.274 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.72939841054999,34.677642400596 3 15.72184275201126 39.40704081114602 34.67764240059603 Other FALSE 2031 +Reddy_3780T BN2 1 1.695535013368328 2064,1113,57,2032,106 0.3,0.301,0.309,0.339,0.35 BN2,BN2,BN2,BN2,BN2 15.6898825806378 6 26.602745271109264 15.689882580637832 15.689882580637832 BN2 TRUE 2032 +Reddy_3781T EZB 1 12.711929835479935 1535 0.237 EZB 4.22380668385626 10 53.69273420381199 4.223806683856262 4.223806683856262 Other FALSE 2033 +Reddy_3782T MCD 1 0 479,1689,305,310,764,1449,1009,709,1699,759,1904 0.128,0.131,0.131,0.139,0.143,0.144,0.159,0.162,0.169,0.174,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.1053767037571 0 0 74.10537670375706 74.10537670375706 MCD TRUE 2034 +Reddy_3783T ST2 1 3.897396336812171 451,1653 0.239,0.298 ST2,ST2 7.53208047378096 6 29.355502847088413 7.532080473780964 7.532080473780964 Other FALSE 2035 +Reddy_3784T MCD 1 0.10688699605329603 296,643,481,185,739,1968,465,900,246,1526 0.127,0.151,0.16,0.169,0.169,0.212,0.231,0.239,0.244,0.247 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.9637130460121 1 5.768019183370295 53.963713046012096 53.963713046012096 MCD TRUE 2036 +Reddy_3785T MCD 1 0.2340830453565609 185,643,296,481,739,246,1968,1422,465 0.121,0.126,0.135,0.151,0.157,0.203,0.207,0.221,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.1883172134067 2 12.918649361418144 55.18831721340666 55.18831721340666 Other FALSE 2037 +Reddy_3786T EZB 0.523554648672809 8.740176291507833 1535,1653 0.194,0.214 EZB,ST2 5.1434505000305,4.6806442970818 8 44.95466411691068 9.824094797112297 5.143450500030499 Other FALSE 2038 +Reddy_3787T MCD 1 0 839,416,1947,947,707,483,587,846,1572,2039,888 0.112,0.138,0.146,0.148,0.151,0.155,0.166,0.171,0.173,0.175,0.186 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 71.5483508810204 0 0 71.54835088102041 71.54835088102041 MCD TRUE 2039 +Reddy_3811T ST2 0.6984889860131895 4.604050353063143 874,1793,35 0.245,0.33,0.34 ST2,BN2,ST2 3.02829530785288,7.01543499509835 8 32.29941596607409 10.043730302951232 7.015434995098351 Other FALSE 2040 +Reddy_3812T BN2 0.6336376618908068 0.27439748467993624 666,1038,489,648,1320,1362,922,2041 0.134,0.165,0.213,0.274,0.286,0.298,0.312,0.319 BN2,ST2,BN2,ST2,BN2,BN2,BN2,ST2 22.2122023889683,12.8428515083742 1 6.0949724647345676 35.05505389734245 22.212202388968283 ST2 FALSE 2041 +Reddy_3814T EZB 0.7676844995166651 2.6255404257008723 2042,561,141 0.115,0.235,0.255 EZB,EZB,BN2 3.92545168884171,12.9716200979085 7 34.05751295389264 16.8970717867502 12.971620097908488 EZB TRUE 2042 +Reddy_3815T BN2 0.5887760295901914 0.6830508261573515 1356,330,1133,993,1012,1492,1509 0.163,0.179,0.213,0.216,0.246,0.268,0.297 EZB,BN2,BN2,BN2,BN2,EZB,EZB 18.9736101505745,13.2519037919357 3 12.95994008853742 32.225513942510176 18.973610150574498 Other FALSE 2043 +Reddy_3816T ST2 0.6917262119901566 0.13217062031806592 619,2004,1484,1176,1139,235,1363,1487,1497,234 0.145,0.151,0.16,0.185,0.191,0.193,0.201,0.226,0.228,0.233 BN2,ST2,ST2,ST2,BN2,ST2,ST2,EZB,ST2,ST2 12.1266198651347,4.42391342522315,37.1372401567618 1 4.908452068420195 53.687773447119675 37.137240156761806 Other FALSE 2044 +Reddy_3817T ST2 0.6799391482106795 1.6721809542903316 1427,1077,1822,744,271 0.13,0.172,0.227,0.273,0.279 ST2,ST2,EZB,BN2,ST2 3.65661070646129,4.40147001024855,17.118633872596 6 28.625453525224398 25.176714589305863 17.118633872596014 Other FALSE 2045 +Reddy_3819T BN2 0.6627676118740473 4.0073229790182285 793,402,737 0.206,0.218,0.241 BN2,EZB,BN2 9.00751667883378,4.58324502625928 7 36.09602857098055 13.590761705093053 9.007516678833777 Other FALSE 2046 +Reddy_3820T EZB 1 0 1281,2047,76,1048,1896,351,1516,2063,117,1046,1163 0.143,0.156,0.167,0.219,0.236,0.247,0.275,0.282,0.289,0.3,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3256623339448 0 0 49.325662333944784 49.325662333944784 EZB TRUE 2047 +Reddy_3821T ST2 1 12.320296927630936 530 0.321 ST2 3.11466795360282 9 38.37363401936338 3.1146679536028214 3.1146679536028214 Other FALSE 2048 +Reddy_3822T EZB 0.7775103958891271 0.3537562863713823 2148,29,2091,4,1297,1103,1022,220,1563 0.106,0.206,0.224,0.23,0.232,0.248,0.265,0.274,0.276 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0666951279721,9.46224763961426 2 11.697551271046079 42.52894276758632 33.06669512797207 Other FALSE 2049 +Reddy_3823T Other 1 10 0 0 1 2 EZB FALSE 2050 +Reddy_3824T MCD 0.8734416895482868 0.6236090819031643 465,246,185,296,2185,347,643 0.15,0.168,0.187,0.19,0.213,0.217,0.223 MCD,MCD,MCD,MCD,ST2,MCD,MCD 32.3471157226408,4.68697150918069 4 20.171955138011448 37.0340872318215 32.34711572264081 Other FALSE 2051 +Reddy_3825T ST2 1 0.3648517478978073 339,959,2052,1404,1101,1884,1198 0.125,0.139,0.168,0.168,0.208,0.243,0.244 ST2,ST2,ST2,ST2,ST2,ST2,ST2 40.0882806635767 3 14.626279270323824 40.08828066357669 40.08828066357669 ST2 TRUE 2052 +Reddy_3826T MCD 1 0.5694150439719238 2053,765,799,753,1526,1063,900 0.151,0.2,0.21,0.222,0.233,0.236,0.267 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.1581954268722 4 18.880775307022077 33.158195426872204 33.158195426872204 MCD TRUE 2053 +Reddy_3828T EZB 1 9.811210712042895 1294 0.16 EZB 6.26897266649864 8 61.50621177905561 6.2689726664986445 6.2689726664986445 Other FALSE 2054 +Reddy_3829T Other 1 10 0 0 1 2 Other TRUE 2055 +Reddy_3830T Other 1 10 0 0 1 2 Other TRUE 2056 +Reddy_3831T ST2 0.7478223082518661 2.775664169807983 2198,1863,1121,1847 0.173,0.2,0.21,0.235 ST2,EZB,ST2,ST2 4.99761428809763,14.8202143764976 7 41.13593803371758 19.817828664595243 14.820214376497612 Other FALSE 2057 +Reddy_3832T ST2 1 12.067462989262273 1057 0.34 ST2 2.94182824866223 9 35.500403511497694 2.9418282486622287 2.9418282486622287 BN2 FALSE 2058 +Reddy_3833T EZB 1 0.0910028492426778 149,404,1705,319,121,80,2059,187,17,333 0.106,0.154,0.161,0.186,0.187,0.204,0.205,0.217,0.219,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.3054859561426 1 5.1239596500025595 56.305485956142626 56.305485956142626 EZB TRUE 2059 +Reddy_3834T ST2 0.7024816133801862 0.42765981724665214 701,1211,1018,618,1401,1713,1284,1442 0.127,0.153,0.166,0.194,0.203,0.227,0.236,0.261 EZB,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.91577689147235,7.86124935525195,30.1683069539814 3 12.901772638580608 42.94533320070574 30.168306953981443 ST2 TRUE 2060 +Reddy_3835T EZB 1 0.14203691126126566 654,47,25,1691,1220,1539,2061,385,1160,137 0.24,0.263,0.27,0.275,0.284,0.3,0.342,0.344,0.349,0.349 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.7212296602757 1 4.789659304877343 33.721229660275725 33.721229660275725 EZB TRUE 2061 +Reddy_3837T ST2 0.5100885034909277 0.7441534229226979 1152,268,1174,1198,2066,531,171,629 0.143,0.144,0.166,0.212,0.215,0.218,0.223,0.256 BN2,ST2,ST2,ST2,BN2,BN2,BN2,ST2 20.7040115583701,21.5567063588439 3 16.04149682387318 42.26071791721397 21.556706358843908 Other FALSE 2062 +Reddy_3838T EZB 1 0.16850063290653075 2063,1138,382,108,93,676,172,558,2089,1048 0.2,0.204,0.215,0.225,0.227,0.228,0.232,0.253,0.253,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.8411996637193 1 7.387269890718285 43.841199663719294 43.841199663719294 EZB TRUE 2063 +Reddy_3839T BN2 1 0.7230467193665451 2064,57,106,817,1113,1014 0.132,0.136,0.236,0.263,0.279,0.311 BN2,BN2,BN2,BN2,BN2,BN2 29.7508693125288 5 21.511268454726753 29.750869312528778 29.750869312528778 BN2 TRUE 2064 +Reddy_3840T BN2 1 4.625293239751589 1841,1148 0.233,0.248 BN2,BN2 8.32699185295415 8 38.51477912493538 8.326991852954148 8.326991852954148 Other FALSE 2065 +Reddy_3842T ST2 0.5147442186052977 0.6838674892414122 268,531,1152,1198,1101,2066,1174,171 0.17,0.188,0.191,0.196,0.222,0.226,0.23,0.268 ST2,BN2,BN2,ST2,ST2,BN2,ST2,BN2 18.7022052578452,19.8387168185329 3 13.567053460461498 38.54092207637815 19.83871681853294 BN2 FALSE 2066 +Reddy_3843T EZB 0.39152512696945546 2.4241001269933213 353,1725,1909,1738,2183 0.119,0.165,0.309,0.324,0.334 BN2,ST2,EZB,EZB,EZB 8.40734163000481,9.31882361687694,6.0751771829336 5 22.58976151309976 23.80134242981535 9.318823616876942 Other FALSE 2067 +Reddy_3845T EZB 0.8632165054867962 0.8460787045751229 1053,62,1453,2126,179,1087 0.174,0.185,0.192,0.224,0.226,0.25 EZB,EZB,EZB,EZB,EZB,BN2 4.00345129165954,25.2650748993648 4 21.376241841848028 29.268526191024346 25.265074899364805 Other FALSE 2068 +Reddy_3846T BN2 0.8643926213901894 1.6025995275045042 523,596,2069,2096,1603 0.123,0.188,0.189,0.25,0.28 BN2,BN2,BN2,BN2,MCD 22.7412292832006,3.56768257056584 6 36.44508330412882 26.308911853766404 22.741229283200568 BN2 TRUE 2069 +Reddy_3847T Other 1 10 0 0 1 2 Other TRUE 2070 +Reddy_3848T EZB 0.5850133457006871 2.002581333648861 1816,1909,115,1666,591 0.367,0.395,0.484,0.525,0.529 BN2,EZB,EZB,EZB,BN2 4.61720500352681,6.50894798402592 6 13.034697734501695 11.126152987552734 6.50894798402592 Other FALSE 2071 +Reddy_3849T EZB 1 0.21378634664867666 2072,2013,56,75,1726,499,1926,1590,178 0.13,0.181,0.188,0.215,0.217,0.234,0.234,0.241,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.4767079105922 2 9.5085128951658 44.47670791059219 44.47670791059219 EZB TRUE 2072 +Reddy_3851T EZB 1 0.07499392508992421 2073,1423,25,654,210,693,390,396,877,216 0.161,0.19,0.218,0.22,0.279,0.292,0.302,0.302,0.317,0.383 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0150638541714 1 3.0008867011482625 40.01506385417138 40.01506385417138 EZB TRUE 2073 +Reddy_3852T ST2 0.5237129885175699 9.859182935281646 1653,1535 0.217,0.239 ST2,EZB 4.19195920717422,4.60937088605436 9 45.44463078217121 8.801330093228582 4.6093708860543625 Other FALSE 2074 +Reddy_3854T EZB 0.8937323848497506 0.35679208764364745 207,1272,1745,1293,1318,595,2083,1600 0.137,0.187,0.205,0.209,0.237,0.242,0.245,0.272 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.08559048895775,34.3606518886587 2 12.259608720151164 38.446242377616414 34.360651888658666 Other FALSE 2075 +Reddy_3856T ST2 0.8746538067685165 0.3262916625965437 1713,1018,1401,2076,1943,1211,1284,726 0.119,0.181,0.185,0.196,0.198,0.202,0.232,0.234 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.39514415796127,37.646802461014 2 12.283837766447903 43.041946618975246 37.646802461013976 ST2 TRUE 2076 +Reddy_3859T N1 0.8458551560391431 1.653892697999731 2077,697,3,1697 0.149,0.15,0.183,0.291 N1,N1,N1,BN2 3.43280301751237,18.8371797422359 6 31.154674026592357 22.269982759748235 18.837179742235868 N1 TRUE 2077 +Reddy_3861T ST2 0.5350772389993116 9.551288370939043 2107,674 0.29,0.334 ST2,BN2 2.99374953658493,3.44549110231444 8 32.90887909770986 6.439240638899374 3.44549110231444 Other FALSE 2078 +Reddy_3865T N1 1 2.076967633341159 901 0.177 N1 5.6640931559225 2 11.764138157080218 5.664093155922504 5.664093155922504 Other FALSE 2079 +Reddy_3867T ST2 0.5937631884332601 3.624033326431361 1500,1099,1226,2080 0.165,0.326,0.371,0.422 ST2,EZB,EZB,ST2 5.76207382821553,8.42195298607746 7 30.52143829518284 14.184026814292988 8.421952986077462 ST2 TRUE 2080 +Reddy_3871T EZB 1 1.7280710161971964 2087,1221,202,2114 0.203,0.209,0.229,0.268 EZB,EZB,EZB,EZB 17.796949319512 6 30.754392295779073 17.79694931951198 17.79694931951198 Other FALSE 2081 +Reddy_3872T ST2 0.8408352331482096 0.9968576487857301 1045,2082,1128,1884,1917,1939 0.199,0.199,0.232,0.234,0.236,0.239 ST2,ST2,EZB,ST2,ST2,ST2 4.30607421568748,22.7481181213463 5 22.676635544745356 27.0541923370338 22.748118121346323 ST2 TRUE 2082 +Reddy_3873T EZB 0.7957379076644695 0.5616503312837695 2083,1318,1600,1272,207,595,1293 0.126,0.133,0.147,0.176,0.246,0.275,0.302 BN2,EZB,EZB,EZB,EZB,EZB,EZB 7.9552963175933,30.991217088945 4 17.40622734489319 38.94651340653832 30.991217088945017 BN2 FALSE 2083 +Reddy_3875T MCD 0.7696189712027001 0.1307718184078722 790,749,2084,1503,1732,883,758,1127,1271 0.175,0.179,0.2,0.233,0.238,0.239,0.248,0.27,0.273 MCD,MCD,BN2,BN2,MCD,MCD,MCD,MCD,MCD 9.31024348139475,31.1021269728858 1 4.067281700596802 40.412370454280534 31.102126972885777 BN2 FALSE 2084 +Reddy_3876T BN2 1 0.5768061123683302 2085,423,1496,1632,100,99,943 0.14,0.244,0.244,0.254,0.266,0.27,0.295 BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.1259904090527 4 17.376855409091313 30.12599040905274 30.12599040905274 BN2 TRUE 2085 +Reddy_3879T MCD 0.78200755096723 1.642304946514413 246,2185,185,643,296 0.118,0.158,0.185,0.225,0.227 MCD,ST2,MCD,MCD,MCD 22.6874529975698,6.32435509751664 6 37.25971628172214 29.011808095086444 22.687452997569807 Other FALSE 2086 +Reddy_3880T EZB 1 1.3887944252813162 2087,202,2183,1221,110 0.167,0.268,0.273,0.32,0.32 EZB,EZB,EZB,EZB,EZB 19.6378850079833 5 27.272985223402774 19.63788500798332 19.63788500798332 EZB TRUE 2087 +Reddy_3881T EZB 1 0.2327269277453198 2015,413,349,2001,1222,1886,288,348,527 0.136,0.139,0.153,0.18,0.204,0.21,0.215,0.221,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4469317146641 2 11.507632504386391 49.44693171466409 49.44693171466409 Other FALSE 2088 +Reddy_3882T EZB 1 0.09903500855490226 2089,676,172,695,382,1138,108,287,558,93 0.149,0.195,0.199,0.206,0.234,0.251,0.265,0.274,0.284,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3787663688231 1 4.395051506992403 44.378766368823086 44.378766368823086 EZB TRUE 2089 +Reddy_3883T BN2 0.6464910058905083 4.953757382900154 175,2211,894 0.264,0.275,0.304 N1,BN2,BN2 6.92205341803465,3.78506138319893 6 34.29017322441838 10.70711480123358 6.922053418034648 Other FALSE 2090 +Reddy_3885T EZB 0.9219512054213446 0 29,1103,2091,1022,1563,1279,1,2148,4,1110,2217 0.108,0.165,0.165,0.171,0.177,0.206,0.209,0.211,0.216,0.224,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 56.0447899040595,4.74453340773796 0 0 60.78932331179748 56.04478990405953 EZB TRUE 2091 +Reddy_3886T EZB 1 1.2481004710715147 2087,202,2183,1221,110 0.151,0.248,0.293,0.303,0.313 EZB,EZB,EZB,EZB,EZB 20.5335784904097 5 25.627968986664328 20.53357849040975 20.53357849040975 Other FALSE 2092 +Reddy_3887T EZB 1 0.2691915671587876 2093,2005,1162,1528,1554,1277,399,1042 0.13,0.195,0.211,0.225,0.232,0.266,0.318,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.2801407886664 3 9.766307955642548 36.28014078866635 36.28014078866635 EZB TRUE 2093 +Reddy_3888T MCD 1 0 540,2094,621,705,516,947,1169,1572,888,587,515 0.132,0.138,0.15,0.161,0.163,0.174,0.175,0.18,0.183,0.184,0.185 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.177139986775 0 0 67.17713998677503 67.17713998677503 MCD TRUE 2094 +Reddy_3889T EZB 1 1.1727551274641788 2087,202,110,1221,2183 0.158,0.238,0.293,0.301,0.316 EZB,EZB,EZB,EZB,EZB 20.43071267414 5 23.96022304634504 20.430712674139976 20.430712674139976 Other FALSE 2095 +Reddy_3892T BN2 0.6542733643457493 2.843389827301343 2096,1603,596 0.23,0.239,0.281 BN2,MCD,BN2 7.91212161624164,4.1808689400123 5 22.497246115992546 12.092990556253943 7.912121616241642 BN2 TRUE 2096 +Reddy_3893T EZB 1 0 1485,1889,1085,644,39,1081,1560,240,174,2016,1109 0.15,0.165,0.178,0.208,0.23,0.238,0.243,0.269,0.271,0.285,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.154232372604 0 0 50.154232372604014 50.154232372604014 EZB TRUE 2097 +Reddy_3894T MCD 0.8213000161913456 0.7619769924187101 1994,801,850,569,768,2130 0.131,0.156,0.175,0.227,0.251,0.259 MCD,MCD,BN2,MCD,MCD,MCD 5.72904208416154,26.3305136139296 4 20.06324557238199 32.05955569809116 26.330513613929618 Other FALSE 2098 +Reddy_3895T ST2 0.5069083357223804 2.3974616745825363 2216,115 0.165,0.169 ST2,EZB 5.9073913110664,6.07291932695816 2 14.55959133921376 11.980310638024562 6.07291932695816 Other FALSE 2099 +Reddy_3896T EZB 1 0.10368298776641943 660,289,443,192,1034,548,354,498,930,2100 0.149,0.151,0.198,0.2,0.257,0.26,0.294,0.316,0.354,0.355 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.3619291605728 1 4.4958943706840175 43.36192916057282 43.36192916057282 EZB TRUE 2100 +Reddy_3898T EZB 0.6432412657386634 1.7891985035583582 1392,1122,1703,1493,696,1528 0.236,0.247,0.262,0.297,0.303,0.306 EZB,BN2,ST2,EZB,EZB,EZB 4.04270596485868,14.1678989128371,3.81518803620791 5 25.349183533414188 22.025792913903665 14.167898912837076 Other FALSE 2101 +Reddy_3899T ST2 1 14.016899216734158 1028 0.257 ST2 3.88925809517028 10 54.51533874786924 3.889258095170277 3.889258095170277 Other FALSE 2102 +Reddy_3900T Other 1 10 0 0 1 2 Other TRUE 2103 +Reddy_3901T EZB 1 0.08399829957132808 43,83,435,2104,47,137,1511,2006,1220,138 0.186,0.2,0.204,0.225,0.233,0.247,0.257,0.277,0.28,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6023385724786 1 3.5785239978502057 42.60233857247863 42.60233857247863 EZB TRUE 2104 +Reddy_3902T EZB 1 0 1649 0.12 EZB 8.34708690234558 0 0 8.347086902345577 8.347086902345577 Other FALSE 2105 +Reddy_3903T Other 1 10 0 0 1 2 Other TRUE 2106 +Reddy_3904T ST2 1 12.517929401145567 2107 0.348 ST2 2.87482977861706 9 35.986916209039315 2.8748297786170616 2.8748297786170616 ST2 TRUE 2107 +Reddy_3905T MCD 1 0.5183825607303053 1286,2108,301,300,223,686,1325 0.137,0.169,0.232,0.24,0.302,0.304,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD 31.5114247885973 4 16.33497307417347 31.51142478859726 31.51142478859726 MCD TRUE 2108 +Reddy_3906T EZB 0.7910405699515841 0.9353420781409004 160,1439,1944,388,242,1037 0.153,0.161,0.175,0.206,0.243,0.262 ST2,EZB,EZB,EZB,EZB,EZB 24.6945286687276,6.52324903670543 5 23.097831763717732 31.21777770543305 24.694528668727617 Other FALSE 2109 +Reddy_3907T Other 1 10 0 0 1 2 Other TRUE 2110 +Reddy_3908T ST2 1 11.303636107032387 1710 0.141 ST2 7.07152247181114 10 79.93391674405528 7.071522471811137 7.071522471811137 Other FALSE 2111 +Reddy_3909T ST2 0.7259532558056461 3.2098197821623047 35,1971,1714 0.167,0.278,0.283 ST2,BN2,ST2 3.60148565790489,9.5403805901054 7 30.622902347477606 13.141866248010295 9.540380590105404 Other FALSE 2112 +Reddy_3910T EZB 1 3.2790067298217553 1099 0.278 EZB 3.59719503638313 2 11.795226732781707 3.597195036383133 3.597195036383133 Other FALSE 2113 +Reddy_3911T EZB 0.9003651192367199 0.39232640703814087 1268,136,1879,2114,2001,527,1132,727 0.156,0.163,0.184,0.202,0.206,0.215,0.216,0.244 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.0511067880796,4.10009509265241 3 14.536127602953755 41.15120188073204 37.051106788079636 EZB TRUE 2114 +Reddy_3912T N1 1 1.2817275503178773 63,2175,2189,745,849 0.148,0.195,0.205,0.224,0.242 N1,N1,N1,N1,N1 25.3467730251452 6 32.48765729798267 25.346773025145247 25.346773025145247 Other FALSE 2115 +Reddy_3913T Other 1 10 0 0 1 2 Other TRUE 2116 +Reddy_3914T EZB 0.6115935189150578 1.7745877170711999 628,1521,2196,1002,1260 0.213,0.28,0.336,0.344,0.369 EZB,BN2,BN2,EZB,EZB 6.55293849054757,10.3184032858387 6 18.31091173083648 16.87134177638628 10.318403285838707 Other FALSE 2117 +Reddy_3915T EZB 1 2.0787542309613727 2050 0.123 EZB 8.12902987177093 2 16.8982552395552 8.129029871770928 8.129029871770928 Other FALSE 2118 +Reddy_3916T MCD 1 0.10610230098041583 1911,1543,986,1897,1184,761,485,755 0.129,0.189,0.198,0.204,0.21,0.236,0.267,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.3561217794167 1 4.1757750784615695 39.3561217794167 39.3561217794167 Other FALSE 2119 +Reddy_3917T BN2 0.6171037559920607 1.775963846057334 52,1131 0.13,0.21 BN2,ST2 7.69107573073125,4.77210514614904 2 13.659072435067692 12.46318087688029 7.691075730731251 Other FALSE 2120 +Reddy_3918T EZB 0.37399782594101133 2.0952165041828446 597,1763,1185,1377,15 0.233,0.286,0.318,0.343,0.371 BN2,EZB,ST2,MCD,EZB 4.29147746280812,6.18731967504973,2.91945381713723,3.14548070836926 4 12.963774299819436 16.543731663364337 6.187319675049733 BN2 FALSE 2121 +Reddy_3919T Other 1 10 0 0 1 2 Other TRUE 2122 +Reddy_3920T Other 1 10 0 0 1 2 Other TRUE 2123 +Reddy_3922T EZB 0.8623736246640167 0.993062939302499 1967,96,166,1782,365,532 0.161,0.244,0.289,0.301,0.309,0.316 EZB,EZB,EZB,EZB,BN2,EZB 3.23538620555872,20.2730888063005 5 20.132453158725347 23.5084750118592 20.273088806300482 Other FALSE 2124 +Reddy_3923T EZB 0.7008764611318922 2.810864156889439 1521,628,1002,402 0.226,0.27,0.294,0.306 BN2,EZB,EZB,EZB 4.4268114000635,10.3724632301267 6 29.155585112216823 14.799274630190215 10.37246323012671 Other FALSE 2125 +Reddy_3924T EZB 0.8027642124665704 1.1350803563506915 1453,1087,2126,62,179,1053 0.16,0.162,0.189,0.203,0.224,0.24 EZB,BN2,EZB,EZB,EZB,EZB 6.16626577490018,25.0971568119113 5 28.48728969745347 31.263422586811483 25.097156811911308 EZB TRUE 2126 +Reddy_3925T BN2 1 3.565877763048783 1381,52 0.172,0.313 BN2,BN2 9.01359406846772 8 32.141374653897444 9.013594068467718 9.013594068467718 ST2 FALSE 2127 +Reddy_3926T N1 1 1.4303073254452472 3,2077,697 0.175,0.282,0.295 N1,N1,N1 12.6534011791262 4 18.098252398301778 12.65340117912623 12.65340117912623 Other FALSE 2128 +Reddy_3927T EZB 0.7228505035864103 2.322156988109942 1983,292,2080 0.181,0.254,0.276 EZB,EZB,ST2 9.46417272079986,3.62867659429853 5 21.977294820284882 13.092849315098395 9.464172720799862 Other FALSE 2129 +Reddy_3928T MCD 0.8717643597753499 1.1578708815959649 713,2028,2130,569,755,850 0.153,0.181,0.187,0.202,0.255,0.259 MCD,MCD,MCD,MCD,MCD,BN2 3.86459773837394,26.272092277005 5 30.419690646146325 30.136690015378942 26.272092277005004 MCD TRUE 2130 +Reddy_3929T EZB 1 1.203805831974895 1508 0.163 EZB 6.1522618556993 1 7.4061287017275035 6.152261855699297 6.152261855699297 Other FALSE 2131 +Reddy_3930T EZB 1 0.49574195257114145 1540,1853,1779,278,1457,499,1590,733 0.164,0.168,0.181,0.184,0.189,0.19,0.196,0.204 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5450469633972 3 21.587106606436596 43.545046963397226 43.545046963397226 EZB TRUE 2132 +Reddy_3931T ST2 0.5442257531388032 7.683818320494888 537,1433 0.245,0.292 ST2,BN2 3.4227246398789,4.08696829132629 7 31.403521832174622 7.509692931205185 4.086968291326288 Other FALSE 2133 +Reddy_3932T ST2 0.5921833296154126 2.9846536177165763 68,2134,1174,781 0.121,0.206,0.288,0.308 ST2,MCD,ST2,MCD 8.09201312909702,11.7502682603958 7 35.07048067253055 19.84228138949281 11.750268260395789 MCD FALSE 2134 +Reddy_3933T MCD 1 1.8481783388855781 717,864,650,1907 0.141,0.164,0.217,0.233 MCD,MCD,MCD,MCD 22.0806852425917 7 40.80904417310841 22.08068524259169 22.08068524259169 MCD TRUE 2135 +Reddy_3935T ST2 1 4.382196582478197 874,1057 0.214,0.313 ST2,ST2 7.87136773744597 8 34.49388079846485 7.871367737445967 7.871367737445967 Other FALSE 2136 +Reddy_3936T ST2 0.5322043486476992 9.739232916807579 1667,1971 0.239,0.272 ST2,BN2 3.67884496709224,4.18536872633877 9 40.76228086853551 7.864213693431006 4.1853687263387656 Other FALSE 2137 +Reddy_3937T EZB 0.5225508428941588 2.600025198297264 2138,816,1178,911 0.204,0.209,0.212,0.241 EZB,EZB,ST2,ST2 9.69514152632229,8.85834787697777 5 25.20761226949616 18.55348940330007 9.695141526322294 EZB TRUE 2138 +Reddy_3938T N1 0.5218505415626262 2.0973344947464163 999,1071 0.15,0.163 N1,ST2 6.68546337440957,6.12560577652138 2 14.021652948512962 12.811069150930944 6.685463374409568 Other FALSE 2139 +Reddy_3939T BN2 0.48041457590977493 0.56531687181669 24,132,1093,972,2140,133,826,384 0.195,0.198,0.203,0.206,0.209,0.282,0.295,0.297 EZB,BN2,EZB,BN2,ST2,BN2,BN2,EZB 16.8353748543467,13.4201489220648,4.7879068323811 2 9.517321448520653 35.043430608792605 16.835374854346725 ST2 FALSE 2140 +Reddy_3940T N1 1 1.4178570790120801 3,2077,697 0.165,0.281,0.288 N1,N1,N1 13.096077155336 4 18.568365701981545 13.096077155336008 13.096077155336008 Other FALSE 2141 +Reddy_3941T EZB 0.6015125760391237 2.184315438246913 1471,1122,696,1944,160 0.284,0.29,0.308,0.311,0.316 EZB,BN2,EZB,EZB,ST2 3.4494355547915,9.98237346941173,3.1636436181077 6 21.80465247958244 16.59545264231093 9.98237346941173 Other FALSE 2142 +Reddy_3942T Other 1 10 0 0 1 2 Other TRUE 2143 +Reddy_3943T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2144 +Reddy_3944T MCD 0.9261977735343647 0.08022218059277471 1197,786,815,2145,1399,1251,1826,1225,738,1358 0.153,0.19,0.213,0.24,0.259,0.295,0.297,0.313,0.325,0.334 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 2.98969590888138,37.5198666349191 1 3.009925517003304 40.509562543800506 37.519866634919126 MCD TRUE 2145 +Reddy_3945T Other 1 10 0 0 1 2 Other TRUE 2146 +Reddy_3946T EZB 1 0.26953411845778785 1926,2072,1726,1219,56,1590,2013,1853,499 0.175,0.187,0.198,0.217,0.223,0.229,0.234,0.255,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6472080425218 2 11.225343505969215 41.64720804252183 41.64720804252183 Other FALSE 2147 +Reddy_3948T EZB 0.8413978943988127 0.2807701252031316 2148,29,2091,1103,4,1297,1022,1563,2217 0.142,0.167,0.196,0.216,0.219,0.219,0.231,0.24,0.255 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.272762764275,7.02585387405967 2 10.465078267992109 44.29861663833465 37.27276276427499 ST2 FALSE 2148 +Reddy_3949T Other 1 10 0 0 1 2 Other TRUE 2149 +Reddy_3951T ST2 1 1.101408660842422 1653,451 0.213,0.222 ST2,ST2 9.201499772977 2 10.134611542696451 9.201499772977002 9.201499772977002 Other FALSE 2150 +Reddy_3952T EZB 1 0 287,2089,695,676,1903,2201,382,173,558,1138,1474 0.174,0.18,0.183,0.199,0.224,0.25,0.251,0.277,0.279,0.281,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4543524647701 0 0 48.45435246477009 48.45435246477009 EZB TRUE 2151 +Reddy_3953T Other 1 10 0 0 1 2 Other TRUE 2152 +Reddy_3956T BN2 0.8139352579173745 1.5022640304358752 523,596,2096,1603,20 0.188,0.204,0.238,0.24,0.261 BN2,BN2,BN2,MCD,BN2 18.2512036345549,4.17220591432552 6 27.418126732352366 22.42340954888044 18.25120363455492 Other FALSE 2153 +Reddy_3957T BN2 1 10.189072367543012 1096 0.317 BN2 3.15312332000267 8 32.12740169129468 3.153123320002669 3.153123320002669 Other FALSE 2154 +Reddy_3958T Other 1 10 0 0 1 2 Other TRUE 2155 +Reddy_3959T Other 1 10 0 0 1 2 Other TRUE 2156 +Reddy_3960T Other 1 10 0 0 1 2 Other TRUE 2157 +Reddy_3963T BN2 0.5035616081813236 12.725270988573078 1108,65 0.24,0.244 BN2,EZB 4.16542173048048,4.10649904903333 9 53.00612030205511 8.271920779513804 4.165421730480479 Other FALSE 2158 +Reddy_3964T BN2 0.5170618704320097 5.556200029620968 1131,2211,52 0.139,0.257,0.263 ST2,BN2,BN2 7.6918920742496,7.18426204599708 8 42.7376909707869 14.87615412024668 7.691892074249596 Other FALSE 2159 +Reddy_3965T BN2 0.5570823643073782 3.3412789332361097 811,150,1232,151 0.177,0.214,0.241,0.323 BN2,MCD,BN2,MCD 9.78626430544217,7.78073284337164 7 32.69863875885444 17.56699714881382 9.786264305442174 Other FALSE 2160 +Reddy_3966T ST2 1 4.09365025163088 833 0.208 ST2 4.81007093057937 3 19.690748075328614 4.810070930579369 4.810070930579369 Other FALSE 2161 +Reddy_3967T EZB 1 2.3061255259322126 2162 0.176 EZB 5.67828708430472 2 13.094842788686307 5.678287084304718 5.678287084304718 EZB TRUE 2162 +Reddy_3968T Other 1 10 0 0 1 2 Other TRUE 2163 +Reddy_3969T ST2 1 1.171508296925573 724 0.146 ST2 6.82858648551738 1 7.999745724057449 6.82858648551738 6.82858648551738 Other FALSE 2164 +Reddy_3971T BN2 0.6254863639530983 3.744660948629725 874,915,1317 0.263,0.307,0.324 ST2,BN2,BN2 6.35321424088118,3.80402436097837 6 23.79063326610599 10.157238601859543 6.353214240881178 Other FALSE 2165 +Reddy_3972T EZB 1 5.006947366885208 2166,1395 0.209,0.334 EZB,EZB 7.77890365343529 9 38.94856116482153 7.778903653435285 7.778903653435285 EZB TRUE 2166 +Reddy_3973T ST2 0.6950568658748894 2.576795800403351 702,530,1697 0.192,0.22,0.233 ST2,ST2,BN2 4.28543502115927,9.76779176636716 4 25.16960480278933 14.05322678752643 9.76779176636716 Other FALSE 2167 +Reddy_3974T ST2 0.5998279985637407 3.7211270438027024 1863,1121,2198 0.164,0.214,0.224 EZB,ST2,ST2 6.09321864116067,9.13328051243196 6 33.98609711344677 15.22649915359263 9.13328051243196 Other FALSE 2168 +Reddy_3975T Other 1 10 0 0 1 2 Other TRUE 2169 +Reddy_3977T ST2 1 5.747036865390891 511 0.286 ST2 3.49221455559903 5 20.069885792882317 3.4922145555990345 3.4922145555990345 BN2 FALSE 2170 +Reddy_3978T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2171 +Reddy_3979T Other 1 10 0 0 1 2 Other TRUE 2172 +Reddy_3980T Other 1 10 0 0 1 2 Other TRUE 2173 +Reddy_3981T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2174 +Reddy_3982T N1 1 0.8393233091659625 63,2189,745,2175,891,814 0.146,0.153,0.17,0.192,0.218,0.227 N1,N1,N1,N1,N1,N1 33.4693516504191 5 28.091606982869006 33.46935165041907 33.46935165041907 N1 TRUE 2175 +Reddy_3983T EZB 0.8802000235278724 0.11116713382236527 466,2176,1550,1031,1384,1701,646,336,337 0.178,0.193,0.206,0.214,0.222,0.227,0.255,0.273,0.273 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 4.85081379699174,35.6401273520687 1 3.9620108067935624 40.49094114906045 35.6401273520687 EZB TRUE 2176 +Reddy_3984T MCD 1 4.677792690100579 311,1480 0.211,0.285 MCD,MCD 8.24756692483167 9 38.5804082720929 8.247566924831672 8.247566924831672 Other FALSE 2177 +Reddy_3985T ST2 1 5.680303051713426 90,1946 0.212,0.228 ST2,ST2 9.09975724267867 8 51.68937883543899 9.099757242678669 9.099757242678669 Other FALSE 2178 +Reddy_3986T ST2 1 3.540455026856559 702,530 0.251,0.259 ST2,ST2 7.8374313788033 6 27.748073322727482 7.837431378803302 7.837431378803302 Other FALSE 2179 +Reddy_3987T MCD 0.8580498593792176 0.45526561114242214 647,1420,2180,1250,880,1239,771,1184 0.161,0.197,0.22,0.235,0.25,0.254,0.258,0.259 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 5.07589452709854,30.6823971160123 3 13.968640274335849 35.75829164311087 30.68239711601234 MCD TRUE 2180 +Reddy_3989T Other 1 10 0 0 1 2 Other TRUE 2181 +Reddy_3990T ST2 1 6.070797277611823 1946,90 0.249,0.285 ST2,ST2 7.52842329730477 9 45.70353165798725 7.528423297304775 7.528423297304775 Other FALSE 2182 +Reddy_3991T EZB 1 2.89742120606839 2183,2087,202 0.215,0.221,0.327 EZB,EZB,EZB 12.2508467092708 8 35.49586304773432 12.250846709270784 12.250846709270784 EZB TRUE 2183 +Reddy_3992T MCD 1 0.10752452644635288 1911,1543,986,1897,1184,761,755,485 0.124,0.188,0.19,0.195,0.206,0.232,0.268,0.27 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.367903613789 1 4.340539719704674 40.367903613788954 40.367903613788954 Other FALSE 2184 +Reddy_3993T MCD 0.8775597838624773 0.4478332892077526 465,347,246,1526,2185,296,185 0.134,0.193,0.212,0.216,0.237,0.237,0.244 MCD,MCD,MCD,MCD,ST2,MCD,MCD 30.2832422606694,4.2252240769529 3 13.561843989470795 34.508466337622295 30.283242260669397 ST2 FALSE 2185 +Reddy_3994T Other 1 10 0 0 1 2 Other TRUE 2186 +Reddy_3995T EZB 1 14.19381110526913 1738 0.255 EZB 3.91484950613711 9 55.566634395666256 3.914849506137108 3.914849506137108 Other FALSE 2187 +Reddy_3996T EZB 1 5.093090754441603 1471,696 0.267,0.37 EZB,EZB 6.44587551612189 7 32.829428995441916 6.445875516121895 6.445875516121895 Other FALSE 2188 +Reddy_3997T N1 1 0.4944415035299157 2189,745,63,891,814,2175 0.142,0.149,0.168,0.197,0.202,0.21 N1,N1,N1,N1,N1,N1 34.4686786267454 3 17.04274528489746 34.46867862674539 34.46867862674539 N1 TRUE 2189 +Reddy_3998T EZB 0.5980627290377105 8.96754987374837 1471,451 0.26,0.387 EZB,ST2 3.84758882659645,2.58583134788468 9 34.503444696180644 6.433420174481135 3.8475888265964513 Other FALSE 2190 +Reddy_4000T ST2 1 1.0120285044049695 1710 0.108 ST2 9.29083287316596 1 9.40258769730667 9.290832873165957 9.290832873165957 Other FALSE 2191 +Reddy_648T ST2 0.7625883532961555 2.050494505224302 2198,1822,1847,1121 0.144,0.198,0.214,0.218 ST2,EZB,ST2,ST2 5.04226008609761,16.196209702992 5 33.210239001445565 21.23846978908958 16.196209702991972 Other FALSE 2192 +Reddy_658T BN2 0.4768324944489882 3.589031506620371 1835,921,70,1813 0.197,0.208,0.214,0.229 EZB,MCD,BN2,BN2 9.02148372867564,5.08487268608788,4.81325127493592 6 32.37838933867988 18.91960768969943 9.021483728675637 Other FALSE 2193 +Reddy_683T BN2 1 4.224959356239236 1799,1433 0.24,0.302 BN2,BN2 7.48564017555709 7 31.62652549716025 7.4856401755570925 7.4856401755570925 Other FALSE 2194 +Reddy_684T MCD 0.8154425909986589 0.2754773545575484 2195,2203,823,1406,858,1904,253,1514 0.121,0.156,0.173,0.192,0.199,0.205,0.216,0.22 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.25121591100807,36.45691017104 2 10.043053169260284 44.708126082048096 36.45691017104002 BN2 FALSE 2195 +Reddy_689T EZB 0.38450464801078765 7.089700596491321 1388,938,2196 0.216,0.236,0.315 EZB,ST2,BN2 3.17205508559572,4.62861504885835,4.23719487655222 7 32.815494872819755 12.037865011006293 4.628615048858351 BN2 FALSE 2196 +Reddy_690T BN2 1 1.9387103750022947 1096,20,1803 0.15,0.222,0.362 BN2,BN2,BN2 13.9527118377507 6 27.050267199264557 13.95271183775067 13.95271183775067 Other FALSE 2197 +Reddy_695T ST2 0.8062325511483714 1.090784560699278 1822,1077,1427,2198,271,1847 0.167,0.174,0.181,0.187,0.24,0.241 EZB,ST2,ST2,ST2,ST2,ST2 5.9937942000712,24.9391320246053 5 27.203220169680378 30.932926224676493 24.93913202460529 ST2 TRUE 2198 +Reddy_702T EZB 1 0 2050 0.104 EZB 9.57867225560371 0 0 9.578672255603708 9.578672255603708 Other FALSE 2199 +Reddy_704T N1 0.4183428332643767 5.117714935883117 175,2211,2127 0.144,0.153,0.319 N1,BN2,ST2 6.52106281320116,6.94632060418925,3.13698996218041 8 35.549288705491975 16.604373379570823 6.9463206041892525 Other FALSE 2200 +Reddy_705T EZB 1 0 2201,173,1903,1474,287,1673,153,1277,1554,1054,676 0.122,0.164,0.171,0.175,0.198,0.216,0.226,0.27,0.284,0.291,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.9660442126533 0 0 53.966044212653344 53.966044212653344 EZB TRUE 2201 +Reddy_707T ST2 0.39064380794049275 1.8164261147449845 781,816,2134,2138,508,1486,955 0.167,0.193,0.205,0.21,0.222,0.224,0.227 MCD,EZB,MCD,EZB,ST2,ST2,ST2 9.94622470650091,10.8852754777466,13.354580889026 4 24.257609478301053 34.18608107327349 13.354580889025964 Other FALSE 2202 +Reddy_759T MCD 1 0 709,21,480,1331,2034,1233,23,1689,2008,59,798 0.117,0.134,0.139,0.143,0.147,0.151,0.156,0.163,0.165,0.165,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.2042672258608 0 0 74.20426722586076 74.20426722586076 MCD TRUE 2203 +Reddy_787T EZB 1 6.280889999832026 1182,532 0.303,0.326 EZB,EZB 6.37498404782281 9 40.040573555059 6.374984047822813 6.374984047822813 Other FALSE 2204 +Reddy_790T BN2 0.5203270020046952 1.9526121426261727 1275,869,871,1008 0.205,0.217,0.25,0.28 BN2,MCD,BN2,N1 8.87102629068039,4.60036172055561,3.57755670334243 4 17.321673652738536 17.048944714578425 8.871026290680385 Other FALSE 2205 +Reddy_793T EZB 0.597915783680905 6.684718393361021 2166,1901 0.183,0.272 EZB,BN2 3.68214061233606,5.47549468617042 8 36.602140041393916 9.157635298506474 5.475494686170417 Other FALSE 2206 +Reddy_799T MCD 1 0 1565,777,1240,783,929,1239,771,535,518,1250,455 0.104,0.122,0.135,0.138,0.149,0.161,0.162,0.172,0.179,0.18,0.19 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.738507111808 0 0 73.73850711180805 73.73850711180805 MCD TRUE 2207 +Reddy_800T Other 1 10 0 0 1 2 Other TRUE 2208 +Reddy_813T N1 1 2.3335292892514774 901,870,262 0.158,0.206,0.233 N1,N1,N1 15.4848262899365 6 36.13429568653818 15.48482628993653 15.48482628993653 Other FALSE 2209 +Reddy_816T BN2 1 3.5503051112518813 2210,459,1003 0.278,0.281,0.291 BN2,BN2,BN2 10.590374578741 8 37.599060996976064 10.590374578740972 10.590374578740972 BN2 TRUE 2210 +Reddy_823T ST2 0.6633975443396457 4.1390047533383125 1131,52 0.12,0.237 ST2,BN2 4.21904245280681,8.31515740776698 6 34.41647603550383 12.534199860573793 8.315157407766984 BN2 FALSE 2211 +Reddy_829T MCD 1 0.7952109721596647 1063,753,1194,285,650 0.101,0.149,0.182,0.229,0.234 MCD,MCD,MCD,MCD,MCD 30.7993101945123 4 24.491949401625174 30.79931019451227 30.79931019451227 Other FALSE 2212 +Reddy_830T BN2 1 0.5473385290095821 2213,973,284,1210,936,1549,903 0.121,0.136,0.189,0.218,0.251,0.266,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2 36.8455969133522 4 20.167014815034214 36.84559691335224 36.84559691335224 BN2 TRUE 2213 +SU-DHL-10 EZB 0.4334712893288 2.4020609860668145 2214,940,826,1190,1964,636 0.171,0.326,0.364,0.368,0.388,0.399 EZB,ST2,BN2,BN2,EZB,ST2 5.46270585573423,8.44305137615619,5.572006541645 5 20.28072431402252 19.477763773535422 8.443051376156193 EZB TRUE 2214 +SU-DHL-4 EZB 1 0 2215,1264,1163,1033,1962,463,142,1516,348,1046,1886 0.125,0.128,0.152,0.152,0.154,0.158,0.169,0.189,0.195,0.198,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.7598864327769 0 0 67.7598864327769 67.7598864327769 EZB TRUE 2215 +SU-DHL-5 ST2 0.5257855907234787 4.784629922395934 292,2080,2216 0.112,0.193,0.212 EZB,ST2,ST2 8.93986429609053,9.91208140865832 8 47.42564130109104 18.85194570474885 9.912081408658322 ST2 TRUE 2216 +SU-DHL-6 EZB 1 0.12302174542890365 4,2217,2091,144,1587,1103,1022,220,1110,1563 0.113,0.149,0.165,0.18,0.187,0.189,0.21,0.211,0.213,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.4685273359641 1 6.946856794670066 56.46852733596413 56.46852733596413 EZB TRUE 2217 +SU-DHL-9 ST2 0.648110905416895 3.5175107249960416 2218,190,581 0.179,0.186,0.23 ST2,BN2,ST2 5.38930150125847,9.92603956562898 6 34.91495062883499 15.31534106688745 9.92603956562898 ST2 TRUE 2218 +Toledo EZB 0.8580747539032058 0.7012936620260001 179,62,1053,2126,1453,993 0.167,0.169,0.174,0.213,0.219,0.225 EZB,EZB,EZB,EZB,EZB,BN2 4.44941124102979,26.9009747078891 3 18.865483064964334 31.35038594891886 26.90097470788907 Other FALSE 2219 +WSU-NHL EZB 1 0.3085673171661916 729,1867,135,679,2100,513,1040 0.183,0.202,0.234,0.253,0.254,0.257,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.1237837902919 2 9.295215147064788 30.123783790291917 30.123783790291917 Other FALSE 2220 From c255a4aa987f4d15e89c406fc10ae2f3e018cc0b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 26 May 2025 16:47:58 -0700 Subject: [PATCH 24/79] tsv file results 2 --- ...ass_accuracy_0.870optimize_for_other=F.tsv | 6 + ...edictions_acc0.870optimize_for_other=F.tsv | 1119 +++++++++++++++++ 2 files changed, 1125 insertions(+) create mode 100644 temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv create mode 100644 temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv diff --git a/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv b/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv new file mode 100644 index 0000000..77b63cd --- /dev/null +++ b/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv @@ -0,0 +1,6 @@ +true_label acc n +BN2 0.8406374501992032 251 +EZB 0.9176954732510288 486 +MCD 0.9223744292237442 219 +N1 0.34782608695652173 23 +ST2 0.762589928057554 139 diff --git a/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv b/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv new file mode 100644 index 0000000..81f4145 --- /dev/null +++ b/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv @@ -0,0 +1,1119 @@ +sample_id predicted_label confidence other_score neighbor distance label weighted_votes neighbors_other other_weighted_votes total_w pred_w true_label correct fold +00-14595_tumorC EZB 1 0 139,610,649,9,273,1072,664,648,112,374 0.119,0.145,0.148,0.17,0.172,0.204,0.218,0.22,0.221,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.6020551081224 0 0 56.60205510812236 56.60205510812236 EZB TRUE 1 +00-15201_tumorB BN2 0.6402838443240962 0 980,827,327,438,41,577,895,611,1104,523 0.275,0.338,0.374,0.396,0.467,0.471,0.481,0.483,0.489,0.494 BN2,BN2,BN2,N1,N1,BN2,BN2,BN2,N1,N1 15.5428504395784,8.73208727337596 0 0 24.27493771295438 15.542850439578416 N1 FALSE 2 +FL1015T2 EZB 0.694617059653598 0 478,55,230,771,54,782,800,40,806,733 0.164,0.179,0.203,0.224,0.282,0.294,0.371,0.38,0.381,0.399 EZB,MCD,EZB,EZB,MCD,EZB,EZB,EZB,N1,EZB 26.7276928671703,9.12881716631189,2.62180329861372 0 0 38.478313332095894 26.72769286717029 EZB TRUE 3 +00-23442_tumorB EZB 1 0 234,235,921,471,76,336,908,32,28,1100 0.133,0.174,0.175,0.224,0.238,0.291,0.292,0.326,0.337,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4449799067832 0 0 43.444979906783246 43.444979906783246 EZB TRUE 4 +00-23442_tumorA EZB 1 0 403,686,141,708,838,237,949,289,310,963 0.17,0.175,0.184,0.189,0.201,0.249,0.256,0.273,0.3,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3477487607725 0 0 45.347748760772475 45.347748760772475 EZB TRUE 5 +FL1002T2 EZB 0.8003116814545509 0 882,120,334,111,303,1092,942,883,182,156 0.157,0.171,0.172,0.188,0.193,0.22,0.233,0.234,0.259,0.262 EZB,EZB,EZB,ST2,EZB,ST2,EZB,EZB,EZB,EZB 39.462718712829,9.84646872912647 0 0 49.30918744195551 39.46271871282904 EZB TRUE 6 +FL1006T2 EZB 0.8977685904330628 0 861,1102,706,1094,680,885,71,1095,192,892 0.145,0.164,0.181,0.185,0.194,0.198,0.206,0.214,0.217,0.22 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 47.3655035636608,5.39364179785467 0 0 52.759145361515515 47.365503563660845 EZB TRUE 7 +01-12047_tumorB EZB 1 0 97,12,209,1000,98,977,27,171,129,208 0.158,0.159,0.173,0.185,0.212,0.218,0.247,0.248,0.261,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8311483267336 0 0 48.83114832673358 48.83114832673358 EZB TRUE 8 +01-12047_tumorC EZB 1 0 664,273,1,139,649,610,374,983,343,648 0.15,0.157,0.17,0.173,0.179,0.206,0.254,0.256,0.267,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.3974666306824 0 0 50.39746663068243 50.39746663068243 EZB TRUE 9 +01-14774_tumorB BN2 1 0 352,109,531,258,952,463,1098,611,553,187 0.197,0.205,0.227,0.241,0.269,0.281,0.287,0.293,0.308,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.1703465142347 0 0 39.17034651423474 39.17034651423474 BN2 TRUE 10 +01-16433_tumorB EZB 0.47541138489930945 0 1084,411,691,622,1096,858,90,608,899,909 0.285,0.301,0.313,0.372,0.448,0.455,0.469,0.484,0.526,0.527 BN2,EZB,ST2,ST2,EZB,EZB,EZB,EZB,ST2,ST2 3.50829938762364,11.9543221941359,9.68259490620444 0 0 25.145216487963992 11.954322194135912 EZB TRUE 11 +01-16433_tumorA EZB 1 0 8,209,97,98,977,129,1000,171,27,30 0.159,0.178,0.2,0.203,0.207,0.212,0.241,0.245,0.247,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.5991356431291 0 0 47.59913564312906 47.59913564312906 EZB TRUE 12 +01-20774T EZB 1 0 140,277,418,1037,84,83,102,222,107,1079 0.149,0.187,0.19,0.206,0.211,0.212,0.215,0.227,0.233,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0342577647599 0 0 49.03425776475989 49.03425776475989 EZB TRUE 13 +01-23117_tumorB BN2 0.40240358179816577 0 891,802,504,699,388,23,127,495,552,185 0.147,0.148,0.17,0.239,0.243,0.268,0.284,0.285,0.318,0.331 MCD,ST2,MCD,BN2,BN2,EZB,BN2,MCD,BN2,BN2 17.9711620261526,3.73048067587552,16.1852033773509,6.77270179503921 0 0 44.659547874418216 17.97116202615255 BN2 TRUE 14 +01-23942_tumorA BN2 1 0 175,341,563,282,1062,871,197,318,488,763 0.161,0.173,0.174,0.181,0.193,0.199,0.201,0.211,0.226,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.942930924542 0 0 51.942930924541976 51.942930924541976 BN2 TRUE 15 +01-28152_tumorA MCD 0.8867144864589598 0 900,1042,844,580,526,453,549,678,276,296 0.13,0.151,0.164,0.189,0.197,0.198,0.205,0.222,0.227,0.232 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.11402455442833,47.8560230122748 0 0 53.97004756670314 47.856023012274804 MCD TRUE 16 +02-11616_tumorA MCD 1 0 166,246,767,297,762,879,413,851,165,804 0.13,0.133,0.146,0.151,0.156,0.159,0.16,0.166,0.166,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.4037473314394 0 0 65.4037473314394 65.4037473314394 MCD TRUE 17 +02-13135T EZB 1 0 756,190,116,754,406,868,865,251,709,182 0.14,0.143,0.165,0.174,0.191,0.216,0.246,0.248,0.258,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.681808152646 0 0 51.68180815264596 51.68180815264596 EZB TRUE 18 +FL1008T2 EZB 0.9201372791303423 0 733,744,429,658,241,1050,643,645,454,254 0.167,0.173,0.22,0.276,0.285,0.296,0.307,0.32,0.326,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 36.0458408165768,3.12857547339804 0 0 39.17441628997488 36.04584081657684 EZB TRUE 19 +02-15745_tumorD EZB 1 0 239,852,25,266,746,72,603,1059,677,167 0.12,0.127,0.16,0.177,0.193,0.199,0.203,0.208,0.218,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1634002312649 0 0 57.16340023126486 57.16340023126486 EZB TRUE 20 +02-17305_tumorB EZB 1 0 250,597,816,709,938,251,284,868,1075,164 0.131,0.164,0.174,0.177,0.191,0.212,0.241,0.246,0.25,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2268447338511 0 0 51.22684473385111 51.22684473385111 EZB TRUE 21 +02-18356_tumorA BN2 0.8092793077730909 0 840,470,88,539,571,189,1106,462,186,570 0.147,0.166,0.208,0.214,0.229,0.235,0.235,0.236,0.24,0.264 BN2,BN2,BN2,EZB,EZB,BN2,BN2,BN2,BN2,BN2 38.3471093467462,9.03716080377411 0 0 47.38427015052035 38.347109346746244 EZB FALSE 22 +02-24492_tumorA BN2 0.6661187436097572 0 495,802,659,855,388,127,666,891,179,14 0.17,0.221,0.221,0.235,0.24,0.24,0.252,0.252,0.256,0.268 MCD,ST2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 28.7094151312575,9.85640196002366,4.53372845772802 0 0 43.09954554900917 28.70941513125749 EZB FALSE 23 +02-28397_tumorA ST2 0.8073244129140299 0 59,281,490,970,946,396,1032,759,422,412 0.231,0.233,0.328,0.347,0.36,0.368,0.375,0.421,0.436,0.498 ST2,ST2,ST2,BN2,BN2,ST2,ST2,ST2,ST2,ST2 5.66122957794121,23.7209545563426 0 0 29.382184134283833 23.72095455634262 ST2 TRUE 24 +03-11110T EZB 1 0 603,1059,239,20,384,605,852,149,61,833 0.143,0.148,0.148,0.16,0.178,0.179,0.187,0.19,0.219,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.580004420683 0 0 57.58000442068297 57.58000442068297 EZB TRUE 25 +03-19969_tumorB BN2 1 0 1022,569,566,498,482,353,401,951,957,399 0.142,0.146,0.192,0.241,0.264,0.273,0.278,0.288,0.316,0.325 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.0118273558491 0 0 44.011827355849086 44.011827355849086 BN2 TRUE 26 +03-23488_tumorA EZB 1 0 98,866,152,776,613,97,151,8,12,599 0.146,0.16,0.17,0.184,0.197,0.208,0.228,0.247,0.247,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.799401319636 0 0 50.799401319635976 50.799401319635976 EZB TRUE 27 +03-23488_tumorB EZB 1 0 32,85,336,306,921,402,76,79,4,1100 0.128,0.171,0.186,0.244,0.265,0.274,0.304,0.334,0.337,0.352 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6706165555349 0 0 42.67061655553495 42.67061655553495 EZB TRUE 28 +FL1001T2 EZB 1 0 80,805,876,46,1078,594,760,632,667,914 0.122,0.161,0.166,0.167,0.168,0.171,0.176,0.179,0.193,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.3896017451861 0 0 59.38960174518607 59.38960174518607 EZB TRUE 29 +03-33266_tumorA EZB 1 0 208,171,977,256,364,53,209,35,48,129 0.116,0.125,0.148,0.175,0.183,0.187,0.192,0.203,0.204,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.5924092330831 0 0 59.59240923308311 59.59240923308311 EZB TRUE 30 +04-11038_tumorB EZB 1 0 114,211,360,1034,939,57,51,233,927,210 0.225,0.292,0.345,0.346,0.351,0.357,0.362,0.366,0.369,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.1990376799793 0 0 30.199037679979284 30.199037679979284 EZB TRUE 31 +04-11038_tumorA EZB 1 0 28,85,336,921,306,402,76,79,1100,4 0.128,0.192,0.2,0.252,0.257,0.273,0.28,0.318,0.325,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4349279554029 0 0 42.434927955402856 42.434927955402856 EZB TRUE 32 +04-13783_tumorB MCD 1 0 451,902,1008,468,1021,276,465,568,1076,961 0.123,0.153,0.175,0.184,0.184,0.189,0.191,0.203,0.203,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5145679648155 0 0 56.51456796481549 56.51456796481549 MCD TRUE 33 +04-14093_tumorB BN2 0.5825659512681077 0 701,104,103,497,442,742,484,100,694,521 0.191,0.203,0.221,0.251,0.341,0.343,0.355,0.376,0.377,0.382 BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD,BN2,MCD 20.5444822041933,14.7210223442151 0 0 35.26550454840837 20.544482204193304 BN2 TRUE 34 +04-16803_tumorB EZB 1 0 48,125,635,364,931,256,30,53,208,820 0.133,0.135,0.151,0.153,0.162,0.185,0.203,0.204,0.212,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.773168789628 0 0 58.773168789627995 58.773168789627995 EZB TRUE 35 +04-21856_tumorA EZB 1 0 963,887,707,289,304,997,237,972,750,653 0.121,0.149,0.161,0.166,0.172,0.203,0.209,0.221,0.233,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5032651173225 0 0 55.50326511732253 55.50326511732253 EZB TRUE 36 +04-21856_tumorB EZB 1 0 761,89,880,430,159,113,1031,997,1063,931 0.134,0.147,0.168,0.174,0.19,0.203,0.213,0.228,0.233,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.6617502749463 0 0 53.661750274946264 53.661750274946264 EZB TRUE 37 +04-24061_tumorB BN2 1 0 342,763,654,501,518,563,333,63,871,488 0.158,0.178,0.217,0.217,0.227,0.228,0.237,0.241,0.241,0.254 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.4250740734587 0 0 46.42507407345866 46.42507407345866 BN2 TRUE 38 +04-24937T MCD 1 0 130,906,323,514,1027,332,311,367,1101,349 0.137,0.151,0.157,0.169,0.182,0.186,0.211,0.22,0.222,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.961429122224 0 0 54.96142912222402 54.96142912222402 MCD TRUE 39 +04-32952_tumorC EZB 0.4920784679186051 0 54,800,253,145,806,230,755,146,55,221 0.198,0.213,0.244,0.257,0.262,0.291,0.295,0.302,0.306,0.323 MCD,EZB,EZB,BN2,N1,EZB,EZB,BN2,MCD,EZB 7.19984280394886,18.7287284662599,8.32007821622435,3.8118016149506 0 0 38.06045110138368 18.728728466259867 EZB TRUE 40 +04-35039T N1 0.8150968867344255 0 1104,1099,523,137,325,496,486,516,174,438 0.189,0.223,0.231,0.235,0.256,0.257,0.349,0.356,0.42,0.422 N1,N1,N1,N1,ST2,N1,ST2,N1,N1,N1 29.8290251226189,6.76665522910569 0 0 36.59568035172454 29.829025122618855 N1 TRUE 41 +05-12963_tumorB BN2 0.6830045235143078 0 180,742,100,185,127,179,484,388,472,651 0.113,0.167,0.17,0.208,0.254,0.266,0.279,0.288,0.308,0.335 MCD,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.8005230478119,14.7592316141142 0 0 46.55975466192615 31.800523047811943 EZB FALSE 42 +05-18426T MCD 0.4042321096411385 0 525,803,124,716,779,747,1044,1045,205,554 0.213,0.239,0.252,0.254,0.272,0.343,0.369,0.386,0.39,0.398 MCD,BN2,N1,BN2,MCD,ST2,MCD,BN2,MCD,ST2 10.7132862983163,13.6455473791573,3.96961295295945,5.42826653931304 0 0 33.756713169746114 13.645547379157275 ST2 FALSE 43 +05-21634T MCD 1 0 856,1047,519,1054,475,198,697,790,449,616 0.161,0.217,0.229,0.229,0.253,0.271,0.274,0.287,0.316,0.332 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.515835968023 0 0 40.51583596802299 40.51583596802299 MCD TRUE 44 +05-22052T EZB 0.7047528478430924 0 1092,111,6,764,334,882,120,303,836,941 0.21,0.239,0.326,0.329,0.359,0.375,0.375,0.412,0.421,0.43 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 21.3516853798928,8.94501430034259 0 0 30.29669968023537 21.351685379892785 BN2 FALSE 45 +05-23110T EZB 1 0 760,876,914,632,80,667,29,984,637,594 0.123,0.138,0.144,0.144,0.148,0.154,0.167,0.181,0.188,0.194 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.4893246688809 0 0 64.48932466888091 64.48932466888091 EZB TRUE 46 +05-24006T EZB 1 0 870,727,593,726,725,784,344,219,839,220 0.19,0.21,0.242,0.252,0.264,0.289,0.322,0.335,0.408,0.409 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.3754073835793 0 0 36.37540738357926 36.37540738357926 EZB TRUE 47 +05-24401T EZB 1 0 35,256,931,125,635,53,364,30,208,880 0.133,0.162,0.162,0.167,0.17,0.178,0.181,0.204,0.209,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.079952722436 0 0 57.079952722436026 57.079952722436026 EZB TRUE 48 +05-24666T EZB 1 0 1012,101,392,313,302,604,655,986,183,1051 0.112,0.157,0.165,0.175,0.196,0.201,0.206,0.211,0.217,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.9846747775875 0 0 55.984674777587536 55.984674777587536 EZB TRUE 49 +05-24904T EZB 1 0 213,723,291,68,255,257,181,734,669,853 0.139,0.167,0.173,0.174,0.2,0.211,0.214,0.215,0.231,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.2537147120016 0 0 52.25371471200162 52.25371471200162 EZB TRUE 50 +05-25439T EZB 1 0 233,1034,211,939,719,360,31,960,537,948 0.108,0.12,0.257,0.266,0.291,0.357,0.362,0.372,0.413,0.439 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6334716727759 0 0 41.63347167277588 41.63347167277588 EZB TRUE 51 +05-25674T EZB 0.9191401313324873 0 1020,886,610,112,1072,649,885,1,139,1094 0.142,0.216,0.242,0.244,0.256,0.266,0.286,0.287,0.299,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.8746756283179,3.33196342182409 0 0 41.206639050142016 37.87467562831793 EZB TRUE 52 +05-27675_tumorB EZB 1 0 256,48,208,30,171,35,113,977,364,931 0.12,0.178,0.179,0.187,0.189,0.204,0.218,0.227,0.228,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6526118690831 0 0 52.652611869083145 52.652611869083145 EZB TRUE 53 +05-32150_tumorB EZB 0.7848002392431485 0 230,40,55,800,806,3,782,253,771,478 0.196,0.198,0.208,0.231,0.268,0.282,0.286,0.288,0.319,0.338 EZB,EZB,MCD,EZB,N1,EZB,EZB,EZB,EZB,EZB 31.0999107339748,4.80066756758947,3.72722655737303 0 0 39.62780485893725 31.099910733974756 MCD FALSE 54 +05-32150T EZB 0.8156829672885244 0 230,3,54,478,771,782,40,800,806,733 0.127,0.179,0.208,0.23,0.264,0.29,0.306,0.318,0.34,0.359 EZB,EZB,MCD,EZB,EZB,EZB,EZB,EZB,N1,EZB 34.2620925663313,4.80066756758947,2.94141788664598 0 0 42.004178020566776 34.262092566331326 MCD FALSE 55 +05-32762T EZB 1 0 247,826,294,236,718,156,1108,78,675,117 0.15,0.156,0.165,0.18,0.207,0.228,0.256,0.261,0.268,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0559485475516 0 0 49.05594854755164 49.05594854755164 EZB TRUE 56 +FL1018T2 EZB 1 0 1080,609,600,948,31,960,82,939,292,138 0.178,0.297,0.301,0.32,0.357,0.362,0.363,0.391,0.422,0.434 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.9738653586675 0 0 30.973865358667467 30.973865358667467 EZB TRUE 57 +06-11677_tumorA BN2 1 0 564,357,1028,393,158,172,589,64,695,337 0.207,0.226,0.267,0.288,0.318,0.365,0.433,0.459,0.466,0.467 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.1283257956809 0 0 31.128325795680873 31.128325795680873 BN2 TRUE 58 +06-11677_tumorB ST2 0.8609218875706557 0 281,24,396,490,412,759,970,946,830,529 0.104,0.231,0.24,0.314,0.368,0.377,0.387,0.402,0.422,0.43 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2 5.07077381823605,31.389124361732 0 0 36.459898179968036 31.389124361731994 ST2 TRUE 59 +06-14634T ST2 1 0 394,728,1081,822,508,656,911,339,904,483 0.222,0.238,0.249,0.257,0.272,0.319,0.319,0.336,0.366,0.378 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 34.9165957214667 0 0 34.91659572146672 34.91659572146672 ST2 TRUE 60 +06-15922T EZB 1 0 833,629,384,605,74,184,1059,603,25,163 0.105,0.136,0.147,0.147,0.153,0.186,0.188,0.198,0.219,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.9518296473584 0 0 61.951829647358394 61.951829647358394 EZB TRUE 61 +06-18449_tumorB EZB 1 0 912,307,220,818,316,219,676,252,585,593 0.247,0.267,0.28,0.298,0.314,0.345,0.354,0.359,0.37,0.402 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.6177078269457 0 0 31.617707826945725 31.617707826945725 EZB TRUE 62 +06-22057T BN2 1 0 518,333,542,488,559,342,654,763,1077,331 0.119,0.134,0.158,0.166,0.183,0.183,0.184,0.198,0.203,0.223 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.0854240068163 0 0 59.085424006816275 59.085424006816275 BN2 TRUE 63 +06-22314_tumorB BN2 1 0 589,65,584,681,695,1113,477,674,345,1060 0.137,0.182,0.218,0.262,0.264,0.301,0.32,0.336,0.34,0.347 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.2179813987399 0 0 40.217981398739866 40.217981398739866 BN2 TRUE 64 +06-22314_tumorA BN2 1 0 589,584,681,64,695,1113,674,652,1019,795 0.17,0.177,0.18,0.182,0.193,0.221,0.261,0.287,0.342,0.345 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.4222986658779 0 0 45.42229866587789 45.42229866587789 BN2 TRUE 65 +06-23907T MCD 1 0 460,888,730,1043,513,419,459,314,739,897 0.258,0.278,0.279,0.303,0.344,0.367,0.383,0.406,0.413,0.417 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.8901382810901 0 0 29.890138281090096 29.890138281090096 BN2 FALSE 66 +06-24255_tumorD EZB 0.9088784393223224 0 454,142,241,429,1050,645,581,658,669,643 0.135,0.212,0.235,0.242,0.244,0.251,0.257,0.261,0.265,0.278 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 39.7159925506065,3.9818121637616 0 0 43.697804714368125 39.71599255060652 EZB TRUE 67 +06-24255_tumorC EZB 1 0 291,669,255,734,50,181,213,257,723,136 0.145,0.164,0.172,0.172,0.174,0.187,0.201,0.216,0.228,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8797594575461 0 0 53.87975945754612 53.87975945754612 EZB TRUE 68 +06-24925T EZB 1 0 244,240,225,243,268,343,224,1115,1051,410 0.124,0.158,0.18,0.195,0.197,0.201,0.236,0.277,0.286,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8807276141989 0 0 49.88072761419892 49.88072761419892 EZB TRUE 69 +06-25674T BN2 1 0 515,998,815,945,591,354,796,75,973,331 0.116,0.205,0.208,0.232,0.249,0.259,0.265,0.266,0.276,0.277 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.2428135332005 0 0 45.24281353320046 45.24281353320046 BN2 TRUE 70 +06-29769_tumorB EZB 1 0 706,711,229,680,892,648,1095,374,861,7 0.126,0.133,0.134,0.139,0.147,0.151,0.159,0.185,0.187,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.4380497054113 0 0 65.43804970541133 65.43804970541133 EZB TRUE 71 +06-30025T EZB 1 0 746,266,852,677,20,167,239,191,96,25 0.125,0.126,0.175,0.19,0.199,0.199,0.217,0.228,0.254,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7803532782503 0 0 53.78035327825033 53.78035327825033 EZB TRUE 72 +06-30145T EZB 1 0 149,1059,603,74,231,605,384,735,25,61 0.169,0.189,0.189,0.193,0.197,0.202,0.203,0.223,0.23,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7446957577074 0 0 49.744695757707404 49.744695757707404 EZB TRUE 73 +06-33777T EZB 1 0 61,605,384,833,1059,629,73,603,231,163 0.153,0.155,0.156,0.157,0.185,0.188,0.193,0.194,0.215,0.229 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.8709872468122 0 0 55.87098724681222 55.87098724681222 EZB TRUE 74 +06-34043T BN2 1 0 305,354,559,945,998,542,702,331,796,1077 0.129,0.152,0.16,0.164,0.168,0.179,0.195,0.199,0.202,0.208 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 58.0998909883884 0 0 58.09989098838838 58.09989098838838 BN2 TRUE 75 +07-10483T EZB 1 0 471,921,1100,235,4,823,234,908,32,28 0.196,0.203,0.203,0.218,0.238,0.241,0.246,0.257,0.28,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.7005900875435 0 0 42.70059008754354 42.70059008754354 EZB TRUE 76 +FL1013T2 BN2 1 0 212,633,634,493,487,1069,390,898,399,636 0.139,0.155,0.158,0.175,0.185,0.186,0.244,0.248,0.251,0.257 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.4647122230314 0 0 52.46471222303142 52.46471222303142 EZB FALSE 77 +07-25012T EZB 1 0 117,675,287,1108,397,105,92,56,247,236 0.118,0.119,0.158,0.202,0.217,0.222,0.233,0.261,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9943005335278 0 0 52.994300533527785 52.994300533527785 EZB TRUE 78 +07-25994_tumorC EZB 1 0 402,271,1013,917,927,210,306,32,1100,28 0.23,0.235,0.253,0.267,0.268,0.273,0.311,0.318,0.326,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1317082790187 0 0 36.13170827901866 36.13170827901866 EZB TRUE 79 +07-30628T EZB 1 0 29,876,46,760,632,594,805,667,1078,914 0.122,0.144,0.148,0.154,0.158,0.163,0.166,0.171,0.186,0.191 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.3674507585309 0 0 63.367450758530936 63.367450758530936 EZB TRUE 80 +07-31833T EZB 1 0 270,819,168,908,269,235,234,471,4,766 0.23,0.261,0.329,0.33,0.33,0.335,0.341,0.353,0.374,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3292406338814 0 0 31.329240633881394 31.329240633881394 EZB TRUE 81 +07-32561_tumorB EZB 1 0 600,609,751,1080,138,292,726,57,725,727 0.162,0.226,0.237,0.305,0.328,0.329,0.352,0.363,0.408,0.432 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.5445969077382 0 0 34.5445969077382 34.5445969077382 EZB TRUE 82 +07-40648_tumorA EZB 0.9232595209116665 0 84,418,140,1088,13,644,1037,599,107,203 0.129,0.193,0.196,0.204,0.212,0.216,0.229,0.257,0.263,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.68127971311468,44.2892275973399 0 0 47.97050731045456 44.289227597339874 EZB TRUE 83 +07-40648_tumorB EZB 1 0 83,140,13,418,1088,107,644,1037,102,599 0.129,0.182,0.211,0.215,0.229,0.237,0.246,0.25,0.272,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.434779658514 0 0 46.434779658513975 46.434779658513975 EZB TRUE 84 +08-13706T EZB 1 0 28,306,32,336,402,921,79,360,76,927 0.171,0.181,0.192,0.233,0.239,0.335,0.336,0.359,0.372,0.393 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.051539178059 0 0 39.05153917805903 39.05153917805903 EZB TRUE 85 +08-15460T BN2 0.9118514132431504 0 670,355,321,363,187,558,553,258,262,531 0.182,0.297,0.358,0.364,0.374,0.394,0.423,0.434,0.435,0.445 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 26.2818559224175,2.54066443640032 0 0 28.822520358817844 26.281855922417527 BN2 TRUE 86 +08-17645_tumorA BN2 0.6883832189961813 0 189,539,88,576,926,561,470,636,95,358 0.315,0.347,0.372,0.373,0.409,0.411,0.412,0.418,0.435,0.438 BN2,EZB,BN2,EZB,ST2,BN2,BN2,BN2,BN2,BN2 17.6915734275757,5.56210908330486,2.44649863730897 0 0 25.7001811481895 17.691573427575662 BN2 TRUE 87 +08-17645_tumorB BN2 0.6514955328728309 0 539,189,22,358,570,470,840,571,487,462 0.129,0.161,0.208,0.218,0.233,0.237,0.24,0.295,0.309,0.317 EZB,BN2,EZB,BN2,BN2,BN2,BN2,EZB,BN2,BN2 29.8755685086253,15.9813361072608 0 0 45.856904615886144 29.87556850862532 BN2 TRUE 88 +08-24058_tumorB EZB 1 0 880,37,159,761,1063,931,430,820,1031,48 0.123,0.147,0.179,0.181,0.187,0.198,0.211,0.214,0.217,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.9413826993904 0 0 54.941382699390424 54.941382699390424 EZB TRUE 89 +08-24058_tumorA EZB 0.9259594706736657 0 858,608,411,143,786,884,252,409,614,954 0.204,0.224,0.298,0.362,0.387,0.398,0.412,0.433,0.442,0.453 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 27.5934156262546,2.20639365284861 0 0 29.7998092791032 27.59341562625459 EZB TRUE 90 +08-29440_tumorA EZB 1 0 1040,1026,317,1002,397,914,984,105,667,760 0.116,0.159,0.161,0.172,0.191,0.205,0.224,0.23,0.236,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3263666773265 0 0 54.32636667732646 54.32636667732646 EZB TRUE 91 +08-29440_tumorB EZB 1 0 105,738,1108,287,317,397,675,78,984,91 0.121,0.146,0.147,0.188,0.207,0.209,0.215,0.233,0.238,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.1182935495735 0 0 54.118293549573494 54.118293549573494 EZB TRUE 92 +08-33625T EZB 1 0 740,631,228,757,387,274,102,947,1117,1079 0.112,0.15,0.175,0.185,0.188,0.205,0.225,0.231,0.233,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2947773580224 0 0 54.2947773580224 54.2947773580224 EZB TRUE 93 +09-11467T MCD 0.4581242627616985 0 371,534,813,193,170,419,930,541,444,510 0.152,0.193,0.212,0.261,0.274,0.3,0.326,0.342,0.35,0.366 EZB,MCD,EZB,BN2,MCD,MCD,ST2,MCD,EZB,MCD 3.82702082951094,14.1714641594429,17.8122842316567,3.07013205337874 0 0 38.88090127398929 17.812284231656726 EZB FALSE 94 +09-12737T BN2 0.8373107169533547 0 926,575,561,636,399,1022,569,498,26,1069 0.182,0.192,0.281,0.293,0.303,0.366,0.391,0.392,0.398,0.417 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.222382872136,5.48360261294913 0 0 33.705985485085144 28.22238287213601 BN2 TRUE 95 +09-12864T EZB 1 0 226,191,199,1039,1030,677,986,167,604,72 0.116,0.129,0.141,0.169,0.2,0.226,0.227,0.231,0.237,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6207501192223 0 0 55.62075011922234 55.62075011922234 EZB TRUE 96 +09-15842_tumorB EZB 0.9020417044171873 0 1000,8,98,12,27,203,209,152,1088,866 0.153,0.158,0.189,0.2,0.208,0.21,0.231,0.25,0.265,0.267 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.75969043399267,43.8292565834541 0 0 48.58894701744674 43.82925658345407 EZB TRUE 97 +09-15842_tumorA EZB 0.923105987469731 0 27,97,866,12,8,776,152,613,1000,203 0.146,0.189,0.199,0.203,0.212,0.213,0.215,0.226,0.24,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.73076476392654,44.7875091713548 0 0 48.51827393528132 44.78750917135478 EZB TRUE 98 +09-16981T BN2 1 0 780,435,959,943,538,650,345,477,646,335 0.138,0.157,0.174,0.176,0.187,0.188,0.199,0.214,0.223,0.236 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.1696379635696 0 0 54.16963796356961 54.16963796356961 BN2 TRUE 99 +09-21480T BN2 0.6293379944502788 0 180,742,42,185,127,103,388,552,484,179 0.162,0.164,0.17,0.248,0.285,0.29,0.306,0.313,0.313,0.329 MCD,BN2,EZB,BN2,BN2,MCD,BN2,BN2,BN2,BN2 26.3210966504968,5.87524975825284,9.62712028578554 0 0 41.82346669453519 26.32109665049681 MCD FALSE 100 +09-27193T EZB 1 0 392,1012,49,604,313,183,986,981,302,655 0.128,0.145,0.157,0.167,0.178,0.206,0.207,0.214,0.223,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7729495502341 0 0 55.77294955023413 55.77294955023413 EZB TRUE 101 +09-27866_tumorB EZB 1 0 757,228,107,631,140,387,1079,13,277,93 0.164,0.164,0.17,0.181,0.197,0.202,0.207,0.215,0.221,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1235731005717 0 0 52.12357310057174 52.12357310057174 EZB TRUE 102 +09-31008_tumorB BN2 0.5571087481485455 0 104,34,497,742,100,701,180,42,484,552 0.146,0.221,0.253,0.285,0.29,0.309,0.342,0.345,0.368,0.429 MCD,BN2,BN2,BN2,MCD,BN2,MCD,EZB,BN2,BN2 20.279157188403,2.90201093464359,13.2195476363043 0 0 36.4007157593509 20.279157188403012 MCD FALSE 103 +09-31008_tumorA BN2 0.5868454522458296 0 103,34,497,701,742,100,180,42,442,484 0.146,0.203,0.207,0.294,0.328,0.336,0.388,0.39,0.391,0.398 MCD,BN2,BN2,BN2,BN2,MCD,MCD,EZB,BN2,BN2 21.2639118934461,2.56618310822947,12.4041670379639 0 0 36.23426203963946 21.263911893446117 MCD FALSE 104 +FL1020T2 EZB 1 0 92,1108,738,287,397,317,675,78,91,117 0.121,0.153,0.16,0.172,0.189,0.2,0.203,0.222,0.23,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.2111519711252 0 0 55.2111519711252 55.2111519711252 EZB TRUE 105 +09-31601_tumorA MCD 1 0 439,991,288,280,994,1090,966,897,729,660 0.143,0.165,0.177,0.202,0.203,0.21,0.213,0.218,0.23,0.231 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.2977997900922 0 0 51.297799790092164 51.297799790092164 MCD TRUE 106 +09-31895T EZB 1 0 102,757,140,387,873,228,13,84,631,83 0.17,0.185,0.191,0.206,0.208,0.227,0.233,0.237,0.237,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1094815474575 0 0 47.10948154745753 47.10948154745753 EZB TRUE 107 +09-32452_tumorA MCD 1 0 794,1035,413,290,297,528,165,446,17,798 0.149,0.158,0.163,0.168,0.177,0.191,0.204,0.217,0.22,0.235 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.3603233617405 0 0 54.360323361740484 54.360323361740484 MCD TRUE 108 +09-33003T BN2 1 0 352,10,463,258,1098,611,553,187,531,562 0.194,0.205,0.221,0.225,0.231,0.273,0.277,0.302,0.33,0.367 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.6547021982942 0 0 39.654702198294174 39.654702198294174 BN2 TRUE 109 +09-33003_tumorB BN2 0.8380287200511392 0 262,279,928,321,1049,773,355,196,936,704 0.184,0.206,0.222,0.275,0.284,0.304,0.325,0.384,0.392,0.441 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,ST2 29.941346592687,5.7869594620982 0 0 35.72830605478522 29.941346592687026 BN2 TRUE 110 +09-41082T EZB 0.7386410893571581 0 1092,6,882,334,120,45,303,764,883,182 0.145,0.188,0.236,0.237,0.238,0.239,0.279,0.29,0.317,0.317 ST2,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.1856560673677,31.3097154975455,6.89289514361024 0 0 42.38826670852343 31.309715497545504 ST2 FALSE 111 +09-41114T EZB 0.9096646246546373 0 1072,885,1095,892,886,680,648,1094,610,1102 0.121,0.159,0.168,0.181,0.191,0.192,0.193,0.196,0.202,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 51.4355021646982,5.10786642482584 0 0 56.54336858952401 51.435502164698164 EZB TRUE 112 +10-10826T EZB 1 0 761,37,53,89,256,430,880,48,931,159 0.198,0.203,0.218,0.23,0.234,0.248,0.252,0.26,0.286,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.9644858879228 0 0 41.96448588792279 41.96448588792279 EZB TRUE 113 +10-11584_tumorB EZB 1 0 31,210,927,271,360,211,917,1013,402,1034 0.225,0.262,0.271,0.301,0.311,0.317,0.332,0.382,0.42,0.431 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9908549888743 0 0 31.9908549888743 31.9908549888743 EZB TRUE 114 +10-11741_tumorB EZB 0.7971172766363178 0 1097,812,426,862,284,424,874,1006,753,407 0.138,0.203,0.212,0.27,0.298,0.319,0.324,0.339,0.35,0.364 EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB,EZB,EZB 30.8590417907428,7.85426012257421 0 0 38.71330191331705 30.85904179074284 EZB TRUE 115 +10-15025T EZB 1 0 406,754,18,182,756,190,1018,865,356,251 0.149,0.161,0.165,0.196,0.196,0.205,0.228,0.235,0.237,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1266283895568 0 0 51.126628389556835 51.126628389556835 EZB TRUE 116 +FL1016T2 EZB 1 0 78,675,287,1108,397,105,92,236,56,836 0.118,0.135,0.171,0.22,0.223,0.238,0.25,0.263,0.27,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1253711425695 0 0 50.12537114256953 50.12537114256953 EZB TRUE 117 +10-28165T EZB 1 0 831,596,379,269,732,687,979,369,136,592 0.252,0.28,0.292,0.295,0.324,0.325,0.355,0.364,0.389,0.398 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.164139250367 0 0 31.164139250367036 31.164139250367036 EZB TRUE 118 +10-31625T ST2 0.48908612704932675 0 1066,308,381,412,830,919,293,481,642,530 0.164,0.258,0.285,0.303,0.306,0.314,0.358,0.382,0.383,0.385 N1,ST2,ST2,ST2,ST2,BN2,MCD,BN2,ST2,N1 5.80373492317513,2.7949520204372,8.70494373831188,16.564368599424 0 0 33.86799928134819 16.564368599423968 BN2 FALSE 119 +10-32847T EZB 0.9156883588420189 0 882,6,182,303,942,1018,334,111,883,356 0.125,0.171,0.188,0.206,0.216,0.22,0.227,0.238,0.248,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 45.5768127602446,4.19646689341522 0 0 49.77327965365986 45.57681276024465 EZB TRUE 120 +10-36955_tumorB EZB 1 0 122,849,190,1014,601,756,868,18,164,639 0.116,0.122,0.25,0.26,0.272,0.274,0.279,0.291,0.318,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3010062961506 0 0 45.30100629615064 45.30100629615064 EZB TRUE 121 +10-36955_tumorA EZB 1 0 121,849,190,1014,756,868,18,601,639,754 0.116,0.135,0.241,0.244,0.269,0.281,0.283,0.287,0.305,0.323 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.9634903695642 0 0 44.96349036956424 44.96349036956424 EZB TRUE 122 +10-39294_tumorA BN2 0.6093653875735058 0 1061,506,455,1112,579,1045,293,530,799,919 0.271,0.272,0.301,0.313,0.337,0.337,0.341,0.37,0.385,0.405 BN2,BN2,MCD,BN2,N1,BN2,MCD,N1,BN2,BN2 18.5974942706987,6.25283521911367,5.66911688825311 0 0 30.519446378065467 18.59749427069869 N1 FALSE 123 +10-39294_tumorB MCD 0.5111309569432871 0 1044,43,747,779,525,205,935,554,696,803 0.235,0.252,0.276,0.288,0.293,0.336,0.338,0.353,0.368,0.369 MCD,ST2,ST2,MCD,MCD,MCD,EZB,ST2,MCD,BN2 2.71158531937857,2.96151348551784,16.8250491850829,10.4191483633889 0 0 32.91729635336824 16.825049185082886 N1 FALSE 124 +10-41170T EZB 1 0 35,635,364,48,931,820,256,30,1063,208 0.135,0.137,0.15,0.167,0.168,0.208,0.219,0.224,0.226,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.8364007862396 0 0 55.83640078623957 55.83640078623957 EZB TRUE 125 +1001-2027_FFPE BN2 0.5776108436900259 0 512,131,864,665,505,924,132,967,976,582 0.153,0.189,0.248,0.253,0.313,0.314,0.33,0.338,0.368,0.377 N1,BN2,BN2,BN2,N1,ST2,BN2,EZB,BN2,BN2 21.6884256419429,2.96183392196738,9.71437798489911,3.18387089571342 0 0 37.548508444522774 21.688425641942864 EZB FALSE 126 +1001-2035_FFPE BN2 0.5586507293263471 0 388,185,179,891,23,180,42,802,659,14 0.144,0.148,0.223,0.239,0.24,0.248,0.254,0.258,0.261,0.284 BN2,BN2,BN2,MCD,EZB,MCD,EZB,ST2,BN2,BN2 25.5374471165899,8.09446593546845,8.20742887118256,3.87338299772073 0 0 45.71272492096165 25.537447116589906 BN2 TRUE 127 +1001-2036_FFPE MCD 1 0 466,489,837,223,985,790,198,713,697,475 0.181,0.236,0.24,0.247,0.276,0.306,0.316,0.319,0.328,0.399 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.7280401446759 0 0 36.72804014467594 36.72804014467594 MCD TRUE 128 +1001-2038_FFPE EZB 1 0 977,364,30,12,209,171,208,125,8,35 0.196,0.21,0.212,0.212,0.218,0.224,0.225,0.257,0.261,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3304719863211 0 0 44.33047198632107 44.33047198632107 EZB TRUE 129 +1001-2039_FFPE MCD 1 0 39,1027,323,906,332,311,514,349,491,326 0.137,0.147,0.151,0.153,0.167,0.184,0.187,0.212,0.219,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.6891122685148 0 0 57.68911226851485 57.68911226851485 MCD TRUE 130 +1001-2040_FFPE BN2 0.4549356001892212 0 512,126,665,505,132,864,924,967,976,582 0.17,0.189,0.198,0.225,0.261,0.268,0.269,0.282,0.288,0.331 N1,EZB,BN2,N1,BN2,BN2,ST2,EZB,BN2,BN2 19.106740632711,8.84000637967936,10.3355857618702,3.71644820652874 0 0 41.998780980789306 19.10674063271103 BN2 TRUE 131 +1001-2040_plasma BN2 0.3642871626029762 0 976,971,505,131,665,126,512,782,806,967 0.159,0.221,0.241,0.261,0.325,0.33,0.33,0.333,0.368,0.382 BN2,EZB,N1,BN2,BN2,EZB,N1,EZB,N1,EZB 13.2144854006013,13.1706161074358,9.88981223357968 0 0 36.274913741616736 13.214485400601273 BN2 TRUE 132 +1003-2043_FFPE EZB 1 0 214,215,428,547,923,157,351,336,873,85 0.342,0.412,0.422,0.438,0.458,0.503,0.553,0.558,0.56,0.58 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 21.2906843974184 0 0 21.29068439741836 21.29068439741836 EZB TRUE 133 +1003-2043_plasma EZB 0.9115500096632847 0 389,398,615,1000,203,97,8,209,1088,171 0.14,0.232,0.245,0.292,0.314,0.342,0.345,0.357,0.377,0.379 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.18944559453203,32.869864105676 0 0 36.05930970020801 32.86986410567599 EZB TRUE 134 +1003-2046_FFPE BN2 0.5103307061540363 0 285,552,386,440,375,891,388,100,14,127 0.184,0.2,0.22,0.26,0.275,0.374,0.38,0.381,0.395,0.407 BN2,BN2,MCD,MCD,MCD,MCD,BN2,MCD,BN2,BN2 18.0472268251488,17.3165610236299 0 0 35.36378784877871 18.04722682514877 MCD FALSE 135 +1004-2033_FFPE EZB 1 0 181,257,732,255,979,291,853,272,68,596 0.153,0.158,0.166,0.168,0.176,0.198,0.219,0.231,0.24,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7439586219202 0 0 52.74395862192023 52.74395862192023 EZB TRUE 136 +1006-2010_FFPE N1 0.6823768850610115 0 325,523,41,174,1104,486,1099,496,516,854 0.157,0.167,0.235,0.299,0.311,0.326,0.338,0.376,0.426,0.477 ST2,N1,N1,N1,N1,ST2,N1,N1,N1,BN2 2.0967898783924,24.7625207510434,9.42931709691133 0 0 36.28862772634714 24.762520751043414 N1 TRUE 137 +1007-2013_FFPE EZB 1 0 292,427,219,220,82,600,593,307,1080,726 0.114,0.254,0.296,0.327,0.328,0.329,0.339,0.353,0.356,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5891867672658 0 0 36.5891867672658 36.5891867672658 EZB TRUE 138 +1011-2017_FFPE EZB 1 0 1,610,649,9,273,1072,648,374,664,112 0.119,0.159,0.167,0.173,0.187,0.2,0.206,0.214,0.218,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5057636819493 0 0 55.50576368194928 55.50576368194928 EZB TRUE 139 +1011-2019_FFPE EZB 1 0 13,84,107,83,102,418,277,1037,757,228 0.149,0.182,0.191,0.196,0.197,0.217,0.224,0.243,0.253,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.692749452467 0 0 48.692749452467005 48.692749452467005 EZB TRUE 140 +1011-2020_FFPE EZB 1 0 838,5,403,686,708,237,949,289,310,723 0.181,0.184,0.254,0.255,0.272,0.315,0.319,0.349,0.381,0.385 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.891187778204 0 0 36.89118777820397 36.89118777820397 EZB TRUE 141 +1012-2006_FFPE EZB 1 0 67,669,454,581,369,181,255,68,291,136 0.212,0.245,0.247,0.259,0.263,0.263,0.265,0.27,0.276,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.0639566692163 0 0 39.06395666921631 39.06395666921631 EZB TRUE 142 +1013-2006_FFPE EZB 0.9227801093225226 0 786,144,620,884,846,90,608,889,1058,545 0.197,0.211,0.308,0.334,0.349,0.362,0.38,0.399,0.407,0.408 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 29.2819365352409,2.4503648434037 0 0 31.732301378644593 29.28193653524089 EZB TRUE 143 +1013-2006_plasma EZB 0.7557408623342362 0 786,620,143,846,545,689,884,1118,1058,608 0.203,0.206,0.211,0.31,0.313,0.4,0.42,0.449,0.451,0.458 EZB,EZB,EZB,EZB,ST2,ST2,EZB,ST2,EZB,EZB 24.5101188333804,7.92179011180572 0 0 32.43190894518607 24.510118833380353 EZB TRUE 144 +1013-2010_FFPE EZB 0.7765758849314409 0 146,221,253,592,248,40,254,755,758,54 0.154,0.176,0.204,0.22,0.246,0.257,0.295,0.31,0.325,0.341 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,MCD 6.49315001006839,32.7735839112941,2.93594652137039 0 0 42.20268044273291 32.773583911294125 BN2 FALSE 145 +1013-2010_plasma EZB 0.8584052063199104 0 221,145,592,253,248,254,40,687,831,385 0.122,0.154,0.175,0.208,0.236,0.272,0.302,0.316,0.317,0.324 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 6.49315001006839,39.3641152276536 0 0 45.85726523772196 39.36411522765357 BN2 FALSE 146 +11-13204_tumorA MCD 1 0 520,739,314,202,513,729,459,897,288,1043 0.2,0.218,0.232,0.25,0.253,0.254,0.257,0.263,0.264,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.5458640316711 0 0 40.54586403167108 40.54586403167108 MCD TRUE 147 +11-14210T ST2 0.8303147028407138 0 1005,218,996,359,783,671,774,668,842,982 0.192,0.197,0.325,0.346,0.347,0.364,0.379,0.387,0.39,0.399 ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2,ST2,ST2 2.58279179762009,2.87801166052926,26.7211448282907 0 0 32.18194828644003 26.721144828290676 ST2 TRUE 148 +11-21727T EZB 1 0 603,73,1059,735,25,605,384,239,74,20 0.167,0.169,0.175,0.181,0.19,0.211,0.212,0.234,0.234,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.6213465191374 0 0 50.62134651913739 50.62134651913739 EZB TRUE 149 +11-31342_tumorA EZB 1 0 599,151,644,152,1037,866,418,613,776,27 0.132,0.158,0.181,0.189,0.197,0.205,0.216,0.221,0.223,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3283896857783 0 0 52.328389685778326 52.328389685778326 EZB TRUE 150 +11-31342_tumorB EZB 1 0 150,613,866,776,599,152,644,27,1037,98 0.158,0.168,0.17,0.173,0.184,0.191,0.227,0.228,0.254,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0921943200874 0 0 51.09219432008736 51.09219432008736 EZB TRUE 151 +11-31342_tumorC EZB 1 0 866,27,599,644,150,151,776,613,98,1088 0.16,0.17,0.181,0.183,0.189,0.191,0.196,0.205,0.215,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.8403648412573 0 0 52.840364841257305 52.840364841257305 EZB TRUE 152 +11-35935T BN2 0.7470874847535309 0 672,962,186,582,432,1106,967,924,571,840 0.189,0.226,0.233,0.253,0.26,0.267,0.306,0.309,0.315,0.328 BN2,BN2,BN2,BN2,BN2,BN2,EZB,ST2,EZB,BN2 28.5965893292218,6.44408338584206,3.23675785741589 0 0 38.27743057247976 28.59658932922181 BN2 TRUE 153 +12-17272_tumorA MCD 0.9033533116560285 0 457,383,445,1070,934,480,441,458,749,522 0.197,0.308,0.363,0.393,0.411,0.423,0.488,0.507,0.515,0.517 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 2.54329111049066,23.7720555823927 0 0 26.315346692883367 23.772055582392706 MCD TRUE 154 +12-17272_tumorB MCD 0.9027534489099935 0 450,350,312,176,778,990,918,417,500,1048 0.106,0.128,0.137,0.138,0.146,0.153,0.155,0.157,0.159,0.161 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.85539140481754,63.6395652592243 0 0 70.49495666404181 63.63956525922428 MCD TRUE 155 +13-26601T EZB 1 0 883,718,303,743,826,334,942,294,236,56 0.143,0.152,0.181,0.191,0.194,0.201,0.202,0.202,0.223,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.218712568929 0 0 53.21871256892903 53.21871256892903 EZB TRUE 156 +FL1017T2 EZB 1 0 719,537,265,351,377,206,133,647,233,51 0.33,0.355,0.399,0.422,0.47,0.497,0.503,0.508,0.515,0.518 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 22.6864684308848 0 0 22.686468430884805 22.686468430884805 EZB TRUE 157 +13-30451T BN2 1 0 357,564,393,968,58,651,1019,777,472,652 0.203,0.257,0.266,0.296,0.318,0.35,0.377,0.381,0.412,0.413 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 32.0969442966255 0 0 32.09694429662547 32.09694429662547 BN2 TRUE 158 +13-31210T EZB 1 0 1031,880,89,37,430,997,1063,761,820,972 0.143,0.174,0.179,0.19,0.195,0.201,0.206,0.207,0.236,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.7446138218305 0 0 51.744613821830505 51.744613821830505 EZB TRUE 159 +13-38657_tumorA ST2 0.8073254931380475 0 1029,161,366,602,583,842,668,671,359,425 0.131,0.161,0.193,0.194,0.218,0.218,0.22,0.235,0.24,0.243 ST2,ST2,BN2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 9.71636021554983,40.7125230539499 0 0 50.42888326949974 40.71252305394991 ST2 TRUE 160 +13-38657_tumorB ST2 0.7437683448936322 0 1029,160,602,842,671,359,668,366,996,785 0.141,0.161,0.161,0.166,0.177,0.179,0.181,0.211,0.231,0.255 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,EZB 10.2441118051117,3.91640138436852,41.1039824623232 0 0 55.264495651803436 41.103982462323174 ST2 TRUE 161 +13-42815T MCD 1 0 479,1046,260,769,916,956,920,467,169,918 0.266,0.274,0.286,0.297,0.311,0.312,0.325,0.326,0.331,0.337 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 32.8493693096532 0 0 32.84936930965318 32.84936930965318 MCD TRUE 162 +14-13959T EZB 1 0 184,833,61,629,74,1078,805,242,605,384 0.195,0.218,0.22,0.225,0.229,0.242,0.259,0.263,0.264,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4401721671438 0 0 42.4401721671438 42.4401721671438 EZB TRUE 163 +14-16281T EZB 1 0 1075,753,597,868,601,709,1006,21,874,250 0.15,0.184,0.203,0.226,0.233,0.235,0.25,0.255,0.261,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.446108644953 0 0 45.44610864495299 45.44610864495299 EZB TRUE 164 +14-20552_tumorA MCD 1 0 290,413,794,879,712,1015,17,297,1109,1035 0.148,0.151,0.16,0.16,0.161,0.162,0.166,0.18,0.186,0.186 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.5502334713295 0 0 60.55023347132949 60.55023347132949 MCD TRUE 165 +14-20552_tumorB MCD 1 0 246,767,762,17,851,879,434,804,712,297 0.103,0.116,0.129,0.13,0.136,0.16,0.169,0.173,0.175,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 70.3431710703099 0 0 70.34317107030985 70.34317107030985 MCD TRUE 166 +14-20962T EZB 1 0 677,266,852,72,191,20,746,239,96,226 0.11,0.184,0.197,0.199,0.203,0.22,0.221,0.226,0.231,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6689537617088 0 0 51.66895376170881 51.66895376170881 EZB TRUE 167 +14-24534_tumorB EZB 1 0 217,766,1064,216,908,823,471,249,819,235 0.169,0.184,0.204,0.217,0.218,0.285,0.292,0.3,0.324,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8637337256432 0 0 41.863733725643186 41.863733725643186 EZB TRUE 168 +14-25466T MCD 0.7646204231943748 0 1103,918,778,990,1046,433,312,286,260,479 0.119,0.131,0.143,0.153,0.158,0.164,0.164,0.167,0.175,0.177 ST2,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.99638221858212,49.9764376267813,8.38828758882389 0 0 65.36110743418733 49.97643762678132 MCD TRUE 169 +14-27873T MCD 0.7385450095896632 0 419,541,200,510,371,94,420,775,1082,567 0.154,0.169,0.234,0.25,0.263,0.274,0.275,0.285,0.295,0.303 MCD,MCD,MCD,MCD,EZB,EZB,MCD,EZB,MCD,MCD 10.9777653892699,31.0094438509937 0 0 41.98720924026358 31.009443850993662 MCD TRUE 170 +14-32442T EZB 1 0 208,30,977,209,256,53,364,48,129,35 0.114,0.125,0.139,0.175,0.182,0.189,0.209,0.223,0.224,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6563764071955 0 0 58.65637640719553 58.65637640719553 EZB TRUE 171 +14-33262T BN2 1 0 337,1028,58,999,646,404,261,517,548,543 0.203,0.237,0.365,0.383,0.425,0.428,0.43,0.434,0.438,0.441 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.344995503772 0 0 28.344995503772033 28.344995503772033 BN2 TRUE 172 +14-33436T BN2 0.7841564894834385 0 535,624,765,347,196,773,1049,301,506,1112 0.131,0.176,0.179,0.249,0.281,0.297,0.337,0.367,0.437,0.444 BN2,EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2 31.3879269174924,5.67635775308144,2.96334672500274 0 0 40.02763139557656 31.387926917492386 BN2 TRUE 173 +14-33798_tumorB N1 0.5301451158890936 0 523,137,325,346,41,828,486,1104,607,1099 0.292,0.299,0.327,0.415,0.42,0.483,0.487,0.505,0.516,0.534 N1,N1,ST2,ST2,N1,ST2,ST2,N1,ST2,N1 13.0055609845907,11.5265163557416 0 0 24.53207734033228 13.005560984590664 N1 TRUE 174 +14-35632T BN2 1 0 282,341,197,15,973,563,1077,488,331,315 0.119,0.123,0.144,0.161,0.184,0.188,0.188,0.192,0.199,0.211 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.6768950064588 0 0 60.67689500645882 60.67689500645882 BN2 TRUE 175 +14-36022T MCD 0.8178436665699491 0 450,155,990,350,500,312,778,1103,918,286 0.134,0.138,0.156,0.161,0.173,0.174,0.178,0.184,0.184,0.185 MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2,MCD,MCD 5.61934359233507,49.6816453789995,5.44612861333197 0 0 60.74711758466658 49.68164537899955 MCD TRUE 176 +14-37722T ST2 0.8254375915068647 0 682,1016,370,692,673,1053,1086,792,557,793 0.144,0.21,0.235,0.262,0.266,0.271,0.302,0.317,0.322,0.36 ST2,ST2,ST2,ST2,BN2,ST2,ST2,BN2,ST2,ST2 6.91120587582789,32.6803988430064 0 0 39.59160471883426 32.68039884300637 ST2 TRUE 177 +15-11617T ST2 0.463484873601641 0 787,361,770,965,1107,365,661,950,195,1009 0.143,0.163,0.195,0.201,0.218,0.236,0.239,0.243,0.267,0.311 EZB,ST2,BN2,EZB,ST2,ST2,EZB,EZB,ST2,ST2 5.13809841367045,20.2740357216887,21.9530432566587 0 0 47.36517739201783 21.953043256658685 ST2 TRUE 178 +15-13383T BN2 0.8262653653360833 0 659,472,185,127,666,651,23,1019,388,42 0.173,0.19,0.207,0.223,0.223,0.253,0.256,0.257,0.263,0.266 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB 36.4915954187678,7.67290299745813 0 0 44.16449841622588 36.49159541876775 BN2 TRUE 179 +15-13383_tumorB BN2 0.6776135139020748 0 42,100,742,185,127,179,388,484,472,552 0.113,0.162,0.17,0.203,0.248,0.269,0.28,0.29,0.317,0.339 EZB,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.6568681197668,8.88398185586137,6.17732708295643 0 0 46.71817705858462 31.656868119766823 MCD FALSE 180 +15-15757T EZB 1 0 255,291,257,136,68,213,50,732,723,853 0.115,0.146,0.148,0.153,0.187,0.209,0.214,0.216,0.222,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1699510405322 0 0 57.16995104053216 57.16995104053216 EZB TRUE 181 +15-21654T EZB 1 0 120,116,356,1018,882,406,941,754,942,251 0.188,0.196,0.2,0.201,0.206,0.21,0.231,0.241,0.248,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5647284792508 0 0 46.56472847925079 46.56472847925079 EZB TRUE 182 +15-21665_tumorA EZB 1 0 1051,655,1115,101,1012,410,49,392,981,588 0.174,0.183,0.184,0.206,0.211,0.215,0.217,0.234,0.242,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.8896251806646 0 0 47.88962518066464 47.88962518066464 EZB TRUE 183 +15-21665_tumorB EZB 1 0 629,833,61,163,384,605,74,1059,603,25 0.162,0.181,0.186,0.195,0.229,0.23,0.23,0.268,0.277,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.1122441930165 0 0 46.11224419301648 46.11224419301648 EZB TRUE 184 +15-24306T BN2 0.5382271924190616 0 127,388,180,179,42,100,659,742,23,891 0.148,0.188,0.203,0.207,0.208,0.248,0.264,0.273,0.276,0.286 BN2,BN2,MCD,BN2,EZB,MCD,BN2,BN2,EZB,MCD 24.3448333270245,8.43387140368231,12.4528139846644 0 0 45.231518715371166 24.344833327024464 BN2 TRUE 185 +15-26538T BN2 0.8226729299491123 0 1106,672,840,462,153,22,470,571,962,582 0.143,0.144,0.22,0.225,0.233,0.24,0.241,0.256,0.326,0.329 BN2,BN2,BN2,BN2,BN2,EZB,BN2,EZB,BN2,BN2 37.465409876498,8.07566545560936 0 0 45.5410753321074 37.465409876498036 BN2 TRUE 186 +15-31924T BN2 1 0 553,258,1098,355,109,10,562,531,86,352 0.15,0.178,0.203,0.254,0.302,0.308,0.354,0.356,0.374,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.6596818058629 0 0 38.6596818058629 38.6596818058629 BN2 TRUE 187 +15-34472T MCD 1 0 456,932,987,414,461,380,340,660,869,565 0.17,0.176,0.183,0.188,0.197,0.214,0.233,0.237,0.253,0.261 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 48.4184120023178 0 0 48.41841200231778 48.41841200231778 MCD TRUE 188 +15-38154T BN2 0.7218614567099477 0 539,88,470,22,358,840,570,462,87,487 0.133,0.161,0.235,0.235,0.265,0.278,0.293,0.307,0.315,0.343 EZB,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 30.6220221575306,11.7988909870742 0 0 42.420913144604796 30.622022157530587 BN2 TRUE 189 +POG721T EZB 1 0 18,756,754,116,868,406,122,849,1014,121 0.143,0.15,0.193,0.205,0.217,0.22,0.241,0.247,0.248,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.1034151532694 0 0 49.10341515326941 49.10341515326941 EZB TRUE 190 +15-43657T EZB 1 0 96,226,199,1039,677,167,1030,72,266,746 0.129,0.139,0.171,0.181,0.198,0.203,0.221,0.228,0.234,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.4595901602152 0 0 53.4595901602152 53.4595901602152 EZB TRUE 191 +15-43891T EZB 1 0 848,7,1117,706,71,877,740,229,861,93 0.201,0.217,0.224,0.225,0.23,0.231,0.239,0.239,0.247,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.638917555545 0 0 43.63891755554499 43.63891755554499 EZB TRUE 192 +16-12281_tumorB EZB 0.5435880830392247 0 813,534,444,94,371,510,420,170,775,567 0.173,0.201,0.212,0.261,0.309,0.312,0.324,0.325,0.356,0.363 EZB,MCD,EZB,EZB,EZB,MCD,MCD,MCD,EZB,MCD 20.3748240279078,17.107278070415 0 0 37.48210209832286 20.374824027907827 BN2 FALSE 193 +16-13732T ST2 0.5536546642246294 0 974,684,988,586,1091,810,1116,320,834,657 0.198,0.272,0.279,0.295,0.323,0.339,0.391,0.393,0.413,0.416 EZB,ST2,BN2,ST2,EZB,ST2,ST2,ST2,ST2,BN2 5.99082637655286,8.14334165140168,17.5322724948333 0 0 31.666440522787845 17.5322724948333 EZB FALSE 194 +16-16192T ST2 0.7872548063589758 0 1009,748,770,907,787,1065,875,1004,683,814 0.165,0.167,0.178,0.218,0.227,0.24,0.241,0.243,0.249,0.254 ST2,ST2,BN2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 5.60802482789287,4.40435392966649,37.0503942536785 0 0 47.06277301123787 37.050394253678505 ST2 TRUE 195 +16-16723T BN2 0.8067417558388709 0 773,535,173,765,279,1049,624,572,110,301 0.25,0.27,0.281,0.295,0.302,0.304,0.339,0.349,0.384,0.385 BN2,BN2,BN2,BN2,BN2,ST2,EZB,BN2,BN2,BN2 26.0296354687819,2.94767473474571,3.28782951483942 0 0 32.26513971836704 26.029635468781915 BN2 TRUE 196 +16-17861T BN2 1 0 341,282,175,1077,973,331,488,333,563,559 0.128,0.129,0.144,0.144,0.15,0.158,0.163,0.196,0.199,0.201 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.6527745673858 0 0 63.65277456738576 63.65277456738576 BN2 TRUE 197 +16-18029T MCD 1 0 837,790,489,697,44,475,128,466,223,856 0.195,0.235,0.238,0.246,0.271,0.296,0.316,0.324,0.33,0.331 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0314172605331 0 0 37.03141726053312 37.03141726053312 MCD TRUE 198 +16-23208T EZB 1 0 226,96,1039,191,1030,986,604,313,392,302 0.136,0.141,0.166,0.171,0.186,0.192,0.196,0.222,0.236,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7040259577729 0 0 54.70402595777287 54.70402595777287 EZB TRUE 199 +16-27074_tumorA MCD 0.9123866442886828 0 1082,541,201,419,170,775,510,420,567,532 0.164,0.166,0.181,0.228,0.234,0.252,0.254,0.268,0.268,0.277 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.9697600407878,41.3402273299507 0 0 45.309987370738526 41.34022732995072 MCD TRUE 200 +16-27074_tumorB MCD 0.9047126762047418 0 1082,200,532,541,775,567,510,420,448,859 0.167,0.181,0.196,0.238,0.242,0.251,0.269,0.27,0.282,0.292 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 4.12746397578924,39.1885177455343 0 0 43.31598172132354 39.1885177455343 MCD TRUE 201 +16-32248_tumorB MCD 1 0 147,729,324,520,280,739,288,376,314,897 0.25,0.262,0.279,0.313,0.329,0.346,0.347,0.353,0.358,0.375 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 31.7001005411927 0 0 31.700100541192725 31.700100541192725 MCD TRUE 202 +16-43741_tumorA EZB 1 0 1088,1000,97,615,152,644,27,8,98,83 0.175,0.184,0.21,0.224,0.243,0.248,0.256,0.262,0.268,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6581671066354 0 0 43.6581671066354 43.6581671066354 BN2 FALSE 203 +17-16667_tumorA MCD 1 0 467,881,920,956,494,678,260,526,580,433 0.151,0.158,0.174,0.183,0.184,0.196,0.208,0.213,0.222,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.0604630141775 0 0 53.06046301417747 53.06046301417747 MCD TRUE 204 +17-23504T MCD 0.7047096172690674 0 779,617,789,713,525,985,696,124,441,43 0.235,0.237,0.285,0.298,0.313,0.325,0.33,0.336,0.349,0.39 MCD,N1,MCD,MCD,MCD,MCD,MCD,N1,MCD,ST2 23.2918278052277,7.19773922450213,2.56210025911685 0 0 33.05166728884667 23.291827805227694 MCD TRUE 205 +17-33848_tumorB EZB 1 0 265,267,351,1083,877,720,619,983,848,374 0.206,0.212,0.248,0.292,0.314,0.315,0.348,0.395,0.408,0.412 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.6505128099552 0 0 33.65051280995519 33.65051280995519 EZB TRUE 206 +17-36275_tumorB ST2 0.9042774476401273 0 483,656,1057,728,362,807,822,263,60,797 0.129,0.211,0.213,0.253,0.274,0.344,0.366,0.387,0.391,0.396 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.64930984203628,34.4745152343841 0 0 38.12382507642033 34.47451523438406 ST2 TRUE 207 +17-40409_tumorB EZB 1 0 171,30,977,256,53,209,364,48,35,129 0.114,0.116,0.149,0.17,0.179,0.188,0.198,0.209,0.212,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.869713237662 0 0 59.86971323766204 59.86971323766204 EZB TRUE 208 +17-40409_tumorA EZB 1 0 977,8,171,12,208,30,129,97,1000,256 0.146,0.173,0.175,0.178,0.188,0.192,0.218,0.231,0.246,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3669279099246 0 0 51.366927909924634 51.366927909924634 EZB TRUE 209 +18-36878_tumorB EZB 1 0 927,271,917,1013,114,360,79,402,211,306 0.138,0.14,0.211,0.253,0.262,0.271,0.273,0.286,0.349,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4676364384655 0 0 43.46763643846554 43.46763643846554 EZB TRUE 210 +19-16466_tumorA EZB 1 0 360,233,1034,51,31,114,927,210,306,402 0.205,0.254,0.256,0.257,0.292,0.317,0.322,0.349,0.364,0.369 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.6265428777636 0 0 34.626542877763576 34.626542877763576 EZB TRUE 211 +19-25121_tumorA BN2 0.8577681528182167 0 77,487,634,633,390,493,1069,358,636,399 0.139,0.159,0.164,0.186,0.208,0.209,0.225,0.242,0.267,0.281 EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2628992219034,7.17368912631419 0 0 50.4365883482176 43.2628992219034 BN2 TRUE 212 +2001-2024_FFPE EZB 1 0 723,50,291,257,255,68,853,181,949,272 0.129,0.139,0.181,0.189,0.199,0.201,0.204,0.209,0.237,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8507952759654 0 0 53.85079527596538 53.85079527596538 EZB TRUE 213 +2001-2026_FFPE EZB 1 0 215,923,428,133,336,28,921,4,85,32 0.216,0.223,0.331,0.342,0.408,0.484,0.485,0.485,0.492,0.505 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.7110517439094 0 0 27.7110517439094 27.7110517439094 EZB TRUE 214 +2001-2026_plasma EZB 1 0 923,214,428,133,336,4,398,234,921,547 0.181,0.216,0.244,0.412,0.494,0.524,0.532,0.54,0.542,0.576 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.9322981051048 0 0 27.932298105104767 27.932298105104767 EZB TRUE 215 +2004-2030_FFPE EZB 1 0 217,766,1064,168,823,249,908,1100,471,847 0.148,0.171,0.2,0.217,0.222,0.225,0.252,0.311,0.313,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.498259145426 0 0 44.49825914542597 44.49825914542597 EZB TRUE 216 +2004-2030_plasma EZB 1 0 216,766,168,1064,908,823,249,471,1100,235 0.148,0.15,0.169,0.184,0.226,0.24,0.25,0.296,0.33,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6865835537842 0 0 46.68658355378417 46.68658355378417 EZB TRUE 217 +2004-2033_plasma ST2 1 0 1005,148,996,774,982,825,359,1068,671,940 0.185,0.197,0.281,0.288,0.322,0.323,0.327,0.327,0.331,0.345 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.733798343447 0 0 35.733798343447 35.733798343447 ST2 TRUE 218 +4002-2003_FFPE EZB 1 0 220,593,307,138,870,292,427,726,47,62 0.184,0.193,0.257,0.296,0.298,0.308,0.315,0.333,0.335,0.345 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5615203512308 0 0 36.561520351230754 36.561520351230754 EZB TRUE 219 +4002-2003_plasma EZB 1 0 307,219,818,593,62,427,585,138,292,870 0.175,0.184,0.268,0.272,0.28,0.284,0.306,0.327,0.337,0.356 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.7325524505238 0 0 37.73255245052384 37.73255245052384 EZB TRUE 220 +7004-2002_FFPE EZB 0.6969348321757893 0 146,592,145,253,248,254,831,687,385,975 0.122,0.159,0.176,0.219,0.235,0.269,0.295,0.296,0.311,0.322 BN2,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 13.8862463189714,31.9330948434083 0 0 45.819341162379715 31.93309484340834 EZB TRUE 221 +75-10141_tumorA EZB 0.9097921097897486 0 329,277,1037,418,13,245,368,227,587,150 0.171,0.198,0.201,0.227,0.227,0.238,0.243,0.249,0.265,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.01599212660049,40.503307872992 0 0 44.51929999959249 40.503307872992 EZB TRUE 222 +8005-2002_FFPE MCD 1 0 466,985,790,697,128,475,837,198,713,489 0.167,0.209,0.23,0.244,0.247,0.299,0.311,0.33,0.335,0.337 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.8020413714182 0 0 38.80204137141817 38.80204137141817 MCD TRUE 223 +8005-2003_FFPE EZB 1 0 225,268,588,1115,240,259,69,244,183,983 0.166,0.168,0.19,0.191,0.208,0.221,0.236,0.26,0.27,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1989553287302 0 0 47.19895532873021 47.19895532873021 EZB TRUE 224 +8005-2003_plasma EZB 1 0 224,268,240,69,1115,244,588,1051,183,343 0.166,0.173,0.179,0.18,0.2,0.203,0.24,0.253,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8493300148829 0 0 48.84933001488294 48.84933001488294 EZB TRUE 225 +8006-2002_plasma EZB 1 0 96,199,191,1039,1030,986,604,677,167,313 0.116,0.136,0.139,0.153,0.209,0.227,0.23,0.231,0.235,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7038613565271 0 0 55.703861356527064 55.703861356527064 EZB TRUE 226 +89-62169T EZB 0.9179515205692883 0 368,587,995,863,329,1074,618,1079,1056,867 0.114,0.143,0.15,0.171,0.179,0.185,0.19,0.199,0.2,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 54.522952713047,4.8733786740761 0 0 59.396331387123155 54.52295271304705 BN2 FALSE 227 +92-38267_tumorA EZB 1 0 631,102,757,1079,93,740,274,863,387,1074 0.126,0.164,0.17,0.174,0.175,0.186,0.199,0.201,0.203,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.2781176057449 0 0 56.2781176057449 56.2781176057449 EZB TRUE 228 +92-38267_tumorB EZB 1 0 711,71,374,648,706,892,680,1095,861,720 0.114,0.134,0.156,0.156,0.16,0.166,0.168,0.181,0.221,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.723218127388 0 0 61.72321812738798 61.72321812738798 EZB TRUE 229 +94-25764_tumorB EZB 0.6843097668846683 0 55,54,3,478,771,40,782,800,253,733 0.127,0.196,0.203,0.248,0.289,0.291,0.308,0.316,0.338,0.339 MCD,MCD,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.1615041614403,12.991648291207 0 0 41.153152452647355 28.161504161440327 EZB TRUE 230 +95-32814T EZB 1 0 242,953,73,74,805,605,384,594,149,61 0.154,0.174,0.197,0.215,0.249,0.26,0.261,0.266,0.266,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.9075740294213 0 0 44.90757402942135 44.90757402942135 EZB TRUE 231 +FL1007T2 EZB 0.610448973884648 0 1085,432,771,478,971,3,962,505,967,582 0.423,0.449,0.498,0.508,0.522,0.535,0.555,0.559,0.565,0.565 EZB,BN2,EZB,EZB,EZB,EZB,BN2,N1,EZB,BN2 5.79981450280603,11.8943822062147,1.79044937126293 0 0 19.484646080283643 11.89438220621468 BN2 FALSE 232 +96-31596T EZB 1 0 51,1034,211,939,719,360,31,960,537,114 0.108,0.127,0.254,0.274,0.289,0.352,0.366,0.38,0.415,0.443 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.0174418434535 0 0 41.017441843453454 41.017441843453454 EZB TRUE 233 +97-18502_tumorA EZB 1 0 4,235,921,471,76,908,336,81,823,1100 0.133,0.152,0.203,0.207,0.246,0.269,0.324,0.341,0.343,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4216932431238 0 0 43.421693243123755 43.421693243123755 EZB TRUE 234 +97-18502_tumorB EZB 1 0 234,471,4,76,908,921,823,766,1100,168 0.152,0.156,0.174,0.218,0.218,0.219,0.293,0.298,0.305,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5463363660062 0 0 45.54633636600621 45.54633636600621 EZB TRUE 235 +FL1004T2 EZB 1 0 294,826,718,56,156,247,764,334,883,117 0.125,0.138,0.175,0.18,0.223,0.228,0.246,0.262,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8881942482066 0 0 50.88819424820656 50.88819424820656 EZB TRUE 236 +98-28290T EZB 1 0 289,707,686,963,36,708,887,972,403,304 0.154,0.183,0.187,0.188,0.209,0.214,0.221,0.222,0.223,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6868524365022 0 0 49.68685243650221 49.68685243650221 EZB TRUE 237 +99-13280T EZB 1 0 981,737,259,588,101,1115,1039,604,392,183 0.198,0.25,0.258,0.288,0.313,0.315,0.317,0.318,0.319,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.32264384324 0 0 35.32264384323998 35.32264384323998 EZB TRUE 238 +FL1010T2 EZB 1 0 20,852,25,603,266,1059,746,384,605,72 0.12,0.143,0.148,0.191,0.194,0.195,0.213,0.215,0.216,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1534162947477 0 0 56.15341629474765 56.15341629474765 EZB TRUE 239 +CABN-0003P EZB 1 0 268,69,244,343,225,224,243,664,1115,710 0.148,0.158,0.174,0.177,0.179,0.208,0.212,0.267,0.276,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.360512962291 0 0 50.360512962291004 50.360512962291004 EZB TRUE 240 +CABN0001_2015-08-11 EZB 0.8700911833247835 0 1050,645,643,975,581,385,454,429,369,67 0.116,0.139,0.149,0.182,0.19,0.201,0.234,0.234,0.235,0.235 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.1696389587045,7.19195978160128 0 0 55.36159874030575 48.169638958704475 EZB TRUE 241 +CABN0002_2015-08-19 EZB 1 0 953,231,805,594,74,73,989,29,80,163 0.145,0.154,0.195,0.214,0.231,0.245,0.247,0.254,0.261,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2153619850813 0 0 47.21536198508126 47.21536198508126 EZB TRUE 242 +CABN0003_2016-07-05 EZB 0.9187334630845931 0 343,244,69,710,240,268,225,693,273,664 0.169,0.176,0.195,0.206,0.212,0.26,0.272,0.281,0.313,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.55363676975035,40.174531116245 0 0 43.728167885995404 40.17453111624505 EZB TRUE 243 +CABN0003_2013-11-28 EZB 1 0 69,240,243,343,225,268,224,710,1051,1115 0.124,0.174,0.176,0.197,0.203,0.218,0.26,0.281,0.298,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.2177276231064 0 0 48.21772762310637 48.21772762310637 EZB TRUE 244 +CAGJ0001_Biopsy EZB 0.8932674607461879 0 587,329,227,857,368,1056,222,595,995,277 0.182,0.193,0.209,0.211,0.219,0.23,0.238,0.252,0.259,0.275 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.7772188626374,39.9815669400415 0 0 44.75878580267893 39.98156694004153 EZB TRUE 245 +CAHN0001_2016-07-13 MCD 1 0 166,767,762,851,17,879,434,804,712,297 0.103,0.113,0.127,0.133,0.133,0.161,0.168,0.174,0.176,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 70.5422188593448 0 0 70.54221885934476 70.54221885934476 MCD TRUE 246 +CALM0002_Biopsy EZB 1 0 56,826,294,1108,236,718,78,675,156,92 0.15,0.206,0.215,0.225,0.228,0.254,0.262,0.262,0.267,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.055148526797 0 0 44.05514852679705 44.05514852679705 EZB TRUE 247 +CAMP-0009P EZB 0.7597697294960183 0 592,221,146,145,758,253,831,755,269,40 0.202,0.235,0.236,0.246,0.319,0.34,0.345,0.35,0.372,0.392 EZB,EZB,BN2,BN2,EZB,EZB,EZB,EZB,EZB,EZB 8.30660958332184,26.2710877480566 0 0 34.577697331378424 26.27108774805658 EZB TRUE 248 +CAMP0009_2016-07-04 EZB 1 0 1064,216,217,509,766,168,847,823,638,736 0.206,0.225,0.25,0.277,0.292,0.3,0.318,0.337,0.361,0.363 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.3022090524888 0 0 35.302209052488806 35.302209052488806 EZB TRUE 249 +CAMP0010_2017-07-18 EZB 1 0 21,709,938,597,251,816,941,868,356,164 0.131,0.163,0.164,0.168,0.181,0.184,0.23,0.235,0.241,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.2337357041391 0 0 53.23373570413906 53.23373570413906 EZB TRUE 250 +CAMP0010-Biopsy EZB 1 0 938,356,709,941,250,21,597,868,756,116 0.159,0.172,0.18,0.18,0.181,0.212,0.216,0.226,0.234,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9736434219218 0 0 50.97364342192176 50.97364342192176 EZB TRUE 251 +CARM0001_2016-03-24 EZB 1 0 316,912,409,858,884,62,889,90,818,1058 0.172,0.231,0.235,0.316,0.342,0.359,0.38,0.412,0.426,0.46 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.8506409489035 0 0 32.850640948903504 32.850640948903504 EZB TRUE 252 +CASA0002_2016-08-24 EZB 0.6722953399272144 0 145,254,146,221,40,592,54,643,385,975 0.204,0.205,0.208,0.219,0.244,0.277,0.288,0.303,0.303,0.303 BN2,EZB,BN2,EZB,EZB,EZB,MCD,EZB,EZB,EZB 9.71069038502009,27.0548079108881,3.47694621340101 0 0 40.24244450930916 27.054807910888062 EZB TRUE 253 +CASA0002_2015-03-10 EZB 0.8988241572910893 0 643,253,975,385,645,1050,241,581,369,221 0.198,0.205,0.206,0.212,0.225,0.233,0.245,0.248,0.265,0.269 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 39.3973445347426,4.4347490011942 0 0 43.83209353593685 39.39734453474264 EZB TRUE 254 +CATC0001_2016-01-11 EZB 1 0 181,291,257,136,68,213,50,723,669,853 0.115,0.131,0.154,0.168,0.172,0.199,0.2,0.215,0.223,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1069313667673 0 0 58.10693136676735 58.10693136676735 EZB TRUE 255 +CATC0003_Biopsy EZB 1 0 53,48,208,30,171,35,364,125,977,931 0.12,0.162,0.17,0.175,0.182,0.185,0.207,0.219,0.219,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.4521067599403 0 0 55.45210675994035 55.45210675994035 EZB TRUE 256 +DFCIDL009-Tumor EZB 1 0 181,255,136,291,853,213,723,272,979,50 0.148,0.154,0.158,0.17,0.176,0.189,0.192,0.201,0.209,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1045516586617 0 0 56.10455165866166 56.10455165866166 EZB TRUE 257 +DLBCL-DFCI_DLBCL_Goe08-Tumor BN2 1 0 1098,553,187,109,10,352,531,355,463,562 0.165,0.167,0.178,0.225,0.241,0.302,0.319,0.332,0.346,0.347 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.4763304409581 0 0 41.476330440958066 41.476330440958066 BN2 TRUE 258 +DLBCL-DFCI_DLBCL_Goe16-Tumor EZB 1 0 588,737,1115,224,183,238,981,225,619,268 0.131,0.179,0.193,0.221,0.256,0.258,0.262,0.269,0.277,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3055156884276 0 0 45.30551568842758 45.30551568842758 EZB TRUE 259 +DLBCL-MC_F089_MLB-Tumor MCD 0.8272826232142025 0 479,956,1046,169,1103,433,918,204,467,778 0.124,0.129,0.164,0.175,0.194,0.198,0.201,0.208,0.209,0.213 MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD,MCD,BN2 4.70522253769687,47.2399907048561,5.15738915627027 0 0 57.10260239882328 47.23999070485614 MCD TRUE 260 +DLBCL-MC_F132_MLM-Tumor BN2 1 0 404,499,543,517,527,555,1001,546,815,929 0.12,0.157,0.164,0.169,0.225,0.23,0.241,0.249,0.265,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.184891421207 0 0 51.184891421207 51.184891421207 BN2 TRUE 261 +DLBCL-MC_F139_AD-Tumor BN2 0.8977977572737798 0 928,110,321,1049,279,773,936,355,196,765 0.146,0.184,0.203,0.249,0.272,0.289,0.312,0.34,0.408,0.423 BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2 35.299798754862,4.01840901394787 0 0 39.3182077688099 35.29979875486203 BN2 TRUE 262 +DLBCL-MC_F139_LJF-Tumor ST2 0.873778215570892 0 362,822,656,807,483,1081,728,207,703,60 0.272,0.289,0.301,0.311,0.359,0.372,0.375,0.387,0.41,0.44 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 3.6731736349939,25.4277747599872 0 0 29.100948394981128 25.427774759987223 ST2 TRUE 263 +DLBCL-MC_F262_WLC-Tumor BN2 1 0 640,1111,957,578,871,318,401,1062,482,15 0.128,0.166,0.211,0.215,0.219,0.22,0.245,0.252,0.264,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.8873683987927 0 0 47.88736839879268 47.88736839879268 BN2 TRUE 264 +DLBCL-MC_F300_TBF-Tumor EZB 1 0 351,206,267,877,1083,157,720,848,1117,619 0.176,0.206,0.311,0.338,0.341,0.399,0.409,0.42,0.434,0.453 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.4684879755381 0 0 31.46848797553813 31.46848797553813 EZB TRUE 265 +DLBCL-MC_F344_CJS-Tumor EZB 1 0 72,746,852,677,20,167,239,191,25,96 0.126,0.139,0.152,0.176,0.177,0.184,0.194,0.234,0.236,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.0128318878684 0 0 56.01283188786842 56.01283188786842 EZB TRUE 266 +DLBCL-MC_F349_BJJ-Tumor EZB 1 0 720,206,619,877,983,374,265,229,351,711 0.205,0.212,0.281,0.29,0.298,0.303,0.311,0.321,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.6739610532123 0 0 35.67396105321234 35.67396105321234 EZB TRUE 267 +DLBCL-MC_F358_WDS-Tumor EZB 1 0 240,224,225,69,343,244,1115,664,588,243 0.148,0.168,0.173,0.197,0.215,0.218,0.251,0.256,0.258,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.3797695733563 0 0 48.37976957335629 48.37976957335629 EZB TRUE 268 +DLBCL-MC_F362_MJB1-Tumor EZB 1 0 819,118,81,831,270,248,592,379,687,221 0.239,0.295,0.33,0.336,0.338,0.372,0.379,0.422,0.435,0.437 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.8097878401432 0 0 28.809787840143194 28.809787840143194 EZB TRUE 269 +DLBCL-MC_F502_RFK-Tumor EZB 1 0 81,269,819,118,234,235,4,908,471,168 0.23,0.338,0.344,0.403,0.405,0.419,0.432,0.448,0.453,0.459 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.475270925855 0 0 26.475270925854957 26.475270925854957 EZB TRUE 270 +DLBCL-MC_F606_DMJ-Tumor EZB 1 0 210,927,917,1013,79,402,360,114,306,211 0.14,0.156,0.191,0.225,0.235,0.269,0.29,0.301,0.348,0.378 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.499558525961 0 0 43.49955852596099 43.49955852596099 EZB TRUE 271 +DLBCL-MC_F739_AMA3-Tumor EZB 1 0 853,724,257,979,723,136,213,181,255,949 0.136,0.142,0.201,0.213,0.219,0.231,0.24,0.249,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0412381999253 0 0 49.04123819992527 49.04123819992527 EZB TRUE 272 +DLBCL-MC_F815_BAT-Tumor EZB 1 0 649,9,1,610,139,664,343,710,1020,1072 0.142,0.157,0.172,0.183,0.187,0.197,0.259,0.265,0.266,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.2088435321814 0 0 50.20884353218145 50.20884353218145 EZB TRUE 273 +DLBCL-RICOVER_1013-Tumor EZB 1 0 947,1074,618,863,995,1079,631,228,93,740 0.126,0.147,0.169,0.177,0.181,0.185,0.196,0.199,0.205,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.8959446621376 0 0 56.89594466213762 56.89594466213762 EZB TRUE 274 +DLBCL-RICOVER_1018-Tumor ST2 0.45447717859059106 0 850,300,792,476,903,370,1089,1086,692,177 0.307,0.33,0.366,0.392,0.443,0.452,0.539,0.544,0.577,0.58 EZB,ST2,BN2,EZB,EZB,ST2,MCD,ST2,ST2,ST2 2.73368651261012,8.05935146403868,1.85467131081335,10.5368556676494 0 0 23.184564955111522 10.536855667649379 ST2 TRUE 275 +DLBCL-RICOVER_1032-Tumor MCD 1 0 1008,468,443,1076,451,33,900,328,1021,568 0.12,0.12,0.165,0.173,0.181,0.189,0.198,0.202,0.207,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.7629243689948 0 0 58.76292436899479 58.76292436899479 MCD TRUE 276 +DLBCL-RICOVER_1046-Tumor EZB 0.9060871287436413 0 1079,329,13,863,222,368,227,995,102,140 0.179,0.183,0.187,0.192,0.198,0.2,0.213,0.213,0.221,0.224 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.70099192496062,45.3559583319225 0 0 50.05695025688311 45.355958331922494 EZB TRUE 277 +DLBCL-RICOVER_1061-Tumor ST2 0.8333432133734309 0 910,911,904,843,394,372,1081,925,60,339 0.161,0.191,0.25,0.257,0.27,0.281,0.349,0.367,0.392,0.398 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.20752719071787,31.0398439867139 0 0 37.24737117743179 31.039843986713915 ST2 TRUE 278 +DLBCL-RICOVER_1081-Tumor BN2 0.8066679375393261 0 110,262,773,1049,928,196,704,321,560,355 0.206,0.272,0.28,0.291,0.292,0.302,0.347,0.373,0.412,0.414 BN2,BN2,BN2,ST2,BN2,BN2,ST2,BN2,BN2,BN2 26.3587755027982,6.31734099589823 0 0 32.676116498696416 26.358775502798185 BN2 TRUE 279 +DLBCL-RICOVER_1106-Tumor MCD 1 0 324,439,966,729,106,660,288,565,679,991 0.159,0.171,0.175,0.18,0.202,0.207,0.231,0.245,0.253,0.256 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.4299484611692 0 0 49.42994846116923 49.42994846116923 MCD TRUE 280 +DLBCL-RICOVER_1138-Tumor ST2 0.8607259529336008 0 59,24,396,490,412,759,970,946,830,529 0.104,0.233,0.238,0.312,0.365,0.379,0.386,0.401,0.418,0.427 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2 5.08685086914988,31.4371891533585 0 0 36.52404002250836 31.43718915335848 ST2 TRUE 281 +DLBCL-RICOVER_1150-Tumor BN2 1 0 175,341,197,973,1077,15,331,488,563,315 0.119,0.123,0.129,0.165,0.172,0.181,0.181,0.187,0.2,0.205 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 62.3723661255734 0 0 62.37236612557339 62.37236612557339 BN2 TRUE 282 +DLBCL-RICOVER_1192-Tumor MCD 1 0 447,550,1036,469,326,416,349,821,491,299 0.157,0.159,0.165,0.17,0.183,0.204,0.204,0.209,0.219,0.233 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.5772909016993 0 0 53.57729090169934 53.57729090169934 MCD TRUE 283 +DLBCL-RICOVER_1199-Tumor EZB 0.87665269547303 0 426,812,816,21,753,1097,1075,250,597,115 0.199,0.201,0.226,0.241,0.26,0.262,0.263,0.27,0.278,0.298 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.6619422926716,5.01772763457238 0 0 40.679669927243964 35.66194229267158 EZB TRUE 284 +DLBCL-RICOVER_120-Tumor MCD 0.713852345248543 0 386,135,375,440,552,330,100,891,845,388 0.144,0.184,0.218,0.222,0.283,0.396,0.441,0.457,0.461,0.464 MCD,MCD,MCD,MCD,BN2,BN2,MCD,MCD,BN2,BN2 10.3839271286763,25.9047754213957 0 0 36.288702550071974 25.904775421395662 BN2 FALSE 285 +DLBCL-RICOVER_1204-Tumor MCD 0.8040747505171887 0 433,990,1103,169,918,176,778,155,450,312 0.129,0.139,0.163,0.167,0.18,0.185,0.186,0.191,0.194,0.202 MCD,MCD,ST2,MCD,MCD,MCD,BN2,MCD,MCD,MCD 5.37139877917413,47.2178616938554,6.13396353504237 0 0 58.723224008071874 47.217861693855376 MCD TRUE 286 +DLBCL-RICOVER_1210-Tumor EZB 1 0 675,78,397,117,105,1108,92,738,317,91 0.14,0.158,0.165,0.171,0.172,0.177,0.188,0.231,0.247,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.5347508253772 0 0 54.53475082537723 54.53475082537723 EZB TRUE 287 +DLBCL-RICOVER_1219-Tumor MCD 1 0 991,897,520,106,314,739,459,729,439,280 0.152,0.155,0.17,0.177,0.18,0.184,0.192,0.207,0.208,0.231 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.7752429862631 0 0 54.775242986263116 54.775242986263116 MCD TRUE 288 +DLBCL-RICOVER_1235-Tumor EZB 1 0 963,237,36,887,707,304,686,708,403,750 0.149,0.154,0.166,0.168,0.173,0.191,0.199,0.209,0.225,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7756097354918 0 0 54.77560973549181 54.77560973549181 EZB TRUE 289 +DLBCL-RICOVER_1248-Tumor MCD 1 0 1035,794,165,413,108,297,1015,17,879,712 0.139,0.144,0.148,0.148,0.168,0.184,0.194,0.198,0.208,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.8695429461506 0 0 58.86954294615059 58.86954294615059 MCD TRUE 290 +DLBCL-RICOVER_126-Tumor EZB 1 0 255,68,181,257,50,213,136,723,669,734 0.131,0.145,0.146,0.17,0.173,0.181,0.198,0.203,0.205,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.9968801160118 0 0 57.996880116011795 57.996880116011795 EZB TRUE 291 +DLBCL-RICOVER_1263-Tumor EZB 1 0 138,427,219,600,82,220,1080,593,307,726 0.114,0.251,0.308,0.326,0.329,0.337,0.345,0.353,0.359,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.2853949408711 0 0 36.28539494087111 36.28539494087111 EZB TRUE 292 +DLBCL-RICOVER_1278-Tumor BN2 0.6215450220923513 0 919,530,1061,481,1066,123,799,119,506,698 0.192,0.219,0.252,0.263,0.338,0.341,0.35,0.358,0.363,0.388 BN2,N1,BN2,BN2,N1,N1,BN2,BN2,BN2,ST2 21.40069926386,10.4557300114065,2.57502541134223 0 0 34.43145468660875 21.40069926386003 MCD FALSE 293 +DLBCL-RICOVER_1283-Tumor EZB 1 0 826,236,718,56,156,247,883,334,764,303 0.114,0.125,0.159,0.165,0.202,0.215,0.243,0.254,0.262,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2689466252525 0 0 54.268946625252454 54.268946625252454 EZB TRUE 294 +DLBCL-RICOVER_1289-Tumor MCD 0.8661812929741975 0 621,878,452,978,961,1101,367,465,902,33 0.152,0.164,0.167,0.189,0.217,0.218,0.236,0.248,0.248,0.264 ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.6630267585941,6.59112719813996 0 0 49.254153956734015 42.66302675859406 MCD TRUE 295 +DLBCL-RICOVER_1293-Tumor MCD 1 0 549,453,494,298,920,526,916,580,1042,467 0.127,0.14,0.164,0.167,0.183,0.186,0.188,0.198,0.206,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.036788886276 0 0 58.03678888627597 58.03678888627597 MCD TRUE 296 +DLBCL-RICOVER_1299-Tumor MCD 1 0 413,794,528,17,804,108,166,246,165,290 0.136,0.143,0.145,0.151,0.166,0.177,0.177,0.18,0.18,0.184 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.7201160155384 0 0 61.72011601553839 61.72011601553839 MCD TRUE 297 +DLBCL-RICOVER_134-Tumor MCD 1 0 296,549,916,453,443,1076,494,328,900,526 0.167,0.171,0.18,0.19,0.2,0.229,0.229,0.234,0.24,0.241 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.0231623882177 0 0 49.02316238821772 49.02316238821772 MCD TRUE 298 +DLBCL-RICOVER_150-Tumor MCD 0.904411316246163 0 1110,690,447,721,469,937,722,283,511,550 0.12,0.144,0.179,0.182,0.185,0.193,0.219,0.233,0.235,0.251 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.1682365052103,48.8992148106766 0 0 54.06745131588692 48.899214810676625 MCD TRUE 299 +DLBCL-RICOVER_151-Tumor ST2 0.6760360713215876 0 792,275,370,1086,476,692,850,148,177,982 0.281,0.33,0.339,0.35,0.366,0.397,0.454,0.469,0.469,0.478 BN2,ST2,ST2,ST2,EZB,ST2,EZB,ST2,ST2,ST2 3.55482125265656,4.9366382750611,17.7196670083691 0 0 26.211126536086766 17.71966700836911 ST2 TRUE 300 +DLBCL-RICOVER_181-Tumor BN2 0.9134050190474167 0 777,535,572,694,173,196,624,765,393,968 0.276,0.336,0.343,0.362,0.367,0.385,0.436,0.444,0.454,0.464 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 24.2002948662234,2.29429883708438 0 0 26.494593703307824 24.200294866223448 BN2 TRUE 301 +DLBCL-RICOVER_208-Tumor EZB 1 0 313,986,1030,49,1012,392,604,101,199,752 0.152,0.161,0.194,0.196,0.201,0.205,0.207,0.223,0.251,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7704694544831 0 0 49.77046945448312 49.77046945448312 EZB TRUE 302 +DLBCL-RICOVER_267-Tumor EZB 1 0 883,942,334,156,882,6,743,120,718,1018 0.143,0.154,0.165,0.181,0.182,0.193,0.193,0.206,0.212,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.9464489506231 0 0 54.94644895062312 54.94644895062312 EZB TRUE 303 +DLBCL-RICOVER_269-Tumor EZB 1 0 887,750,36,963,289,310,653,707,237,708 0.126,0.161,0.172,0.183,0.191,0.205,0.224,0.229,0.245,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3960274822473 0 0 52.3960274822473 52.3960274822473 EZB TRUE 304 +DLBCL-RICOVER_290-Tumor BN2 1 0 75,702,542,559,354,998,796,945,1023,591 0.129,0.17,0.178,0.178,0.18,0.182,0.189,0.193,0.199,0.202 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.3395738819857 0 0 56.339573881985736 56.339573881985736 BN2 TRUE 305 +DLBCL-RICOVER_299-Tumor EZB 1 0 85,402,28,32,360,79,336,927,271,210 0.181,0.186,0.244,0.257,0.278,0.311,0.314,0.325,0.348,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.6222609151148 0 0 37.62226091511475 37.62226091511475 EZB TRUE 306 +DLBCL-RICOVER_320-Tumor EZB 1 0 220,818,585,219,427,62,912,593,138,292 0.175,0.193,0.238,0.257,0.259,0.267,0.337,0.347,0.353,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0360116843802 0 0 38.03601168438017 38.03601168438017 EZB TRUE 307 +DLBCL-RICOVER_336-Tumor ST2 0.7719313082927433 0 642,412,119,1066,381,830,396,915,793,557 0.24,0.251,0.258,0.296,0.332,0.339,0.346,0.366,0.397,0.43 ST2,ST2,BN2,N1,ST2,ST2,ST2,ST2,ST2,ST2 3.87872810019165,3.38192340836816,24.5747199061164 0 0 31.835371414676224 24.574719906116417 ST2 TRUE 308 +DLBCL-RICOVER_361-Tumor ST2 1 0 544,1067,1007,899,909,797,590,508,1057,339 0.126,0.283,0.348,0.371,0.388,0.422,0.446,0.448,0.478,0.482 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 30.6629817571299 0 0 30.66298175712993 30.66298175712993 ST2 TRUE 309 +DLBCL-RICOVER_401-Tumor EZB 1 0 750,304,949,708,887,403,289,686,36,724 0.152,0.205,0.211,0.216,0.22,0.231,0.248,0.249,0.268,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.2031433710589 0 0 45.2031433710589 45.2031433710589 EZB TRUE 310 +DLBCL-RICOVER_417-Tumor MCD 1 0 332,491,416,821,323,326,906,130,1027,1036 0.131,0.141,0.158,0.164,0.165,0.169,0.175,0.184,0.187,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.8226438039436 0 0 60.82264380394356 60.82264380394356 MCD TRUE 311 +DLBCL-RICOVER_431-Tumor MCD 0.7902525970156028 0 778,350,918,417,155,1048,450,1103,769,169 0.121,0.133,0.133,0.136,0.137,0.138,0.142,0.149,0.161,0.164 BN2,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 8.23838980603686,56.2739042308567,6.69772721881087 0 0 71.2100212557044 56.27390423085668 MCD TRUE 312 +DLBCL-RICOVER_440-Tumor EZB 1 0 986,302,604,392,1012,49,101,1030,199,226 0.136,0.152,0.156,0.156,0.174,0.175,0.178,0.198,0.222,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.28334776879 0 0 57.28334776878997 57.28334776878997 EZB TRUE 313 +DLBCL-RICOVER_449-Tumor MCD 1 0 739,459,897,520,513,288,991,147,106,729 0.115,0.127,0.139,0.147,0.175,0.18,0.2,0.232,0.252,0.26 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.9824032012004 0 0 58.98240320120037 58.98240320120037 MCD TRUE 314 +DLBCL-RICOVER_451-Tumor BN2 1 0 282,175,973,197,341,331,15,1062,1077,945 0.205,0.211,0.215,0.226,0.228,0.241,0.247,0.249,0.251,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.0203993926156 0 0 43.020399392615644 43.020399392615644 BN2 TRUE 315 +DLBCL-RICOVER_463-Tumor EZB 1 0 912,252,409,62,884,818,889,858,307,585 0.17,0.172,0.216,0.314,0.344,0.355,0.358,0.383,0.403,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.5461715307673 0 0 35.54617153076731 35.54617153076731 EZB TRUE 316 +DLBCL-RICOVER_469-Tumor EZB 1 0 91,984,914,1040,1026,738,105,1002,92,397 0.161,0.17,0.175,0.176,0.184,0.193,0.2,0.202,0.207,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7148444025887 0 0 53.714844402588696 53.714844402588696 EZB TRUE 317 +DLBCL-RICOVER_473-Tumor BN2 1 0 1062,15,264,1111,640,957,871,175,563,282 0.137,0.211,0.22,0.237,0.239,0.246,0.247,0.26,0.276,0.277 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.1775245841318 0 0 44.177524584131845 44.177524584131845 BN2 TRUE 318 +DLBCL-RICOVER_522-Tumor EZB 0.8493252373249097 0 322,1033,714,913,810,715,890,993,839,684 0.107,0.135,0.213,0.25,0.27,0.308,0.334,0.335,0.335,0.336 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 37.6854771452582,6.68560178788977 0 0 44.37107893314795 37.68547714525818 EZB TRUE 319 +DLBCL-RICOVER_554-Tumor ST2 0.6353633882204924 0 834,685,586,1091,1024,824,901,974,372,925 0.147,0.179,0.205,0.214,0.282,0.287,0.313,0.317,0.358,0.38 ST2,ST2,ST2,EZB,EZB,EZB,ST2,EZB,ST2,ST2 14.8723006752221,25.9143351007137 0 0 40.786635775935764 25.9143351007137 ST2 TRUE 320 +DLBCL-RICOVER_575-Tumor BN2 0.7413654855109897 0 262,928,110,936,1049,355,558,86,485,773 0.203,0.207,0.275,0.286,0.318,0.327,0.338,0.358,0.36,0.369 BN2,BN2,BN2,BN2,ST2,BN2,ST2,BN2,ST2,BN2 25.4568286132036,8.88092936275256 0 0 34.33775797595613 25.456828613203573 BN2 TRUE 321 +DLBCL-RICOVER_577-Tumor EZB 0.8523212170041123 0 319,1033,714,913,810,715,890,839,993,684 0.107,0.134,0.212,0.246,0.274,0.302,0.33,0.332,0.334,0.341 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 37.9639943751415,6.57789149810238 0 0 44.54188587324391 37.96399437514153 EZB TRUE 322 +DLBCL-RICOVER_585-Tumor MCD 1 0 906,332,130,39,311,1027,491,416,514,821 0.11,0.135,0.151,0.157,0.165,0.188,0.206,0.222,0.225,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.0842667712063 0 0 59.08426677120634 59.08426677120634 MCD TRUE 323 +DLBCL-RICOVER_597-Tumor MCD 1 0 280,729,966,439,679,565,1038,660,106,288 0.159,0.169,0.225,0.23,0.24,0.247,0.253,0.255,0.256,0.263 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.7992642406948 0 0 44.79926424069482 44.79926424069482 MCD TRUE 324 +DLBCL-RICOVER_623-Tumor N1 0.8299545044227933 0 137,523,41,486,1104,174,1099,496,516,854 0.157,0.224,0.256,0.273,0.315,0.327,0.335,0.372,0.395,0.433 N1,N1,N1,ST2,N1,N1,N1,N1,N1,BN2 2.30988619510898,29.1574794957368,3.66405336930486 0 0 35.13141906015064 29.1574794957368 ST2 FALSE 325 +DLBCL-RICOVER_685-Tumor MCD 1 0 416,1036,821,550,491,311,283,332,469,1027 0.123,0.128,0.131,0.135,0.137,0.169,0.183,0.198,0.202,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.5519000303537 0 0 64.55190003035372 64.55190003035372 MCD TRUE 326 +DLBCL-RICOVER_711-Tumor BN2 0.8435301812717451 0 827,611,463,980,895,352,438,391,348,2 0.146,0.216,0.272,0.293,0.307,0.312,0.321,0.322,0.328,0.374 BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2,N1 31.1950049308545,5.786487401552 0 0 36.98149233240654 31.195004930854537 BN2 TRUE 327 +DLBCL-RICOVER_720-Tumor MCD 1 0 1076,568,1021,443,468,1008,276,465,33,451 0.129,0.139,0.146,0.162,0.183,0.187,0.202,0.202,0.217,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.672500220392 0 0 57.67250022039203 57.67250022039203 MCD TRUE 328 +DLBCL-RICOVER_744-Tumor EZB 0.8896225824874139 0 222,368,227,277,245,587,995,863,1079,1074 0.171,0.176,0.179,0.183,0.193,0.195,0.216,0.217,0.227,0.252 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.57497569807806,44.9333241309368 0 0 50.508299829014845 44.93332413093679 EZB TRUE 329 +DLBCL-RICOVER_826-Tumor MCD 0.5754402612707918 0 845,436,444,400,386,193,285,420,375,567 0.257,0.304,0.307,0.318,0.378,0.392,0.396,0.396,0.403,0.403 BN2,MCD,EZB,MCD,MCD,BN2,BN2,MCD,MCD,MCD 8.96722324783725,3.25820290280215,16.5701119925499 0 0 28.795538143189304 16.570111992549904 BN2 FALSE 330 +DLBCL-RICOVER_829-Tumor BN2 1 0 1077,973,354,945,197,559,282,488,341,75 0.125,0.126,0.147,0.156,0.158,0.161,0.181,0.186,0.186,0.199 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 62.9982039096411 0 0 62.99820390964114 62.99820390964114 BN2 TRUE 331 +DLBCL-RICOVER_850-Tumor MCD 1 0 311,323,906,130,491,1027,39,416,821,326 0.131,0.135,0.145,0.167,0.172,0.186,0.186,0.188,0.194,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.0830581300369 0 0 60.083058130036854 60.083058130036854 MCD TRUE 332 +DLBCL-RICOVER_865-Tumor BN2 1 0 488,63,518,763,542,342,1077,559,197,563 0.133,0.134,0.144,0.177,0.179,0.182,0.184,0.184,0.196,0.196 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.6912050554779 0 0 59.69120505547791 59.69120505547791 BN2 TRUE 333 +DLBCL-RICOVER_866-Tumor EZB 0.8326174620262691 0 303,6,883,718,156,882,942,120,111,1092 0.165,0.172,0.189,0.2,0.201,0.203,0.22,0.227,0.237,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,ST2 40.9893515409563,8.2401607025223 0 0 49.22951224347857 40.989351540956264 EZB TRUE 334 +DLBCL-RICOVER_945-Tumor BN2 1 0 630,1060,943,477,650,99,780,345,538,435 0.131,0.158,0.173,0.179,0.185,0.236,0.249,0.259,0.271,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.1789731632918 0 0 50.178973163291815 50.178973163291815 BN2 TRUE 335 +DLBCL-RICOVER_948-Tumor EZB 1 0 28,32,85,921,4,76,306,234,235,402 0.186,0.2,0.233,0.233,0.291,0.313,0.314,0.324,0.351,0.358 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5167062029519 0 0 37.51670620295191 37.51670620295191 EZB TRUE 336 +DLBCL-RICOVER_977-Tumor BN2 1 0 172,1028,404,815,261,517,543,999,499,548 0.203,0.324,0.344,0.349,0.35,0.372,0.377,0.394,0.403,0.415 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.4003063997538 0 0 29.400306399753774 29.400306399753774 BN2 TRUE 337 +DLBCL-RICOVER_978-Tumor ST2 0.6846002394847925 0 606,689,1118,607,1052,736,620,346,545,781 0.184,0.219,0.233,0.285,0.346,0.355,0.413,0.419,0.446,0.45 ST2,ST2,ST2,ST2,EZB,EZB,EZB,ST2,ST2,EZB 10.344303512335,22.4531326538737 0 0 32.797436166208676 22.453132653873656 BN2 FALSE 338 +DLBCL-RICOVER_985-Tumor ST2 0.9169924459753512 0 508,909,911,394,60,899,910,278,728,1081 0.182,0.27,0.312,0.32,0.336,0.34,0.388,0.398,0.444,0.446 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2 2.57627636580456,28.460373202734 0 0 31.036649568538568 28.46037320273401 ST2 TRUE 339 +DLBCL-c_D_1104-Tumor MCD 1 0 869,414,536,461,456,987,415,511,1038,565 0.123,0.145,0.158,0.167,0.176,0.179,0.189,0.192,0.2,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.0330894758442 0 0 59.03308947584417 59.03308947584417 MCD TRUE 340 +DLBCL-c_D_1106-Tumor BN2 1 0 175,282,197,488,1077,15,973,563,331,333 0.123,0.123,0.128,0.169,0.171,0.173,0.177,0.178,0.186,0.202 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.2077382074819 0 0 63.207738207481874 63.207738207481874 BN2 TRUE 341 +DLBCL-c_D_1107-Tumor BN2 1 0 763,38,518,654,333,63,501,488,563,542 0.154,0.158,0.169,0.177,0.182,0.183,0.204,0.206,0.209,0.24 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.0468781270124 0 0 54.04687812701237 54.04687812701237 BN2 TRUE 342 +DLBCL-c_D_1110-Tumor EZB 1 0 243,240,244,69,268,710,664,225,273,9 0.169,0.177,0.197,0.201,0.215,0.219,0.248,0.255,0.259,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.4063919310712 0 0 46.406391931071234 46.406391931071234 EZB TRUE 343 +DLBCL-c_D_1113-Tumor EZB 1 0 725,727,688,726,1114,601,47,407,784,1006 0.179,0.222,0.228,0.285,0.293,0.321,0.322,0.323,0.332,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.495942338292 0 0 36.49594233829201 36.49594233829201 EZB TRUE 344 +DLBCL-c_D_1114-Tumor BN2 1 0 780,999,959,477,943,646,99,548,435,335 0.165,0.179,0.179,0.188,0.19,0.197,0.199,0.234,0.25,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.073183486102 0 0 50.073183486101975 50.073183486101975 BN2 TRUE 345 +DLBCL-c_D_1117-Tumor ST2 0.5687986415461891 0 607,606,1118,174,338,828,689,872,126,523 0.297,0.385,0.387,0.415,0.419,0.442,0.464,0.579,0.602,0.607 ST2,ST2,ST2,N1,BN2,ST2,ST2,BN2,EZB,N1 4.11080223821441,1.66169888074566,4.05478299760117,12.9631916644048 0 0 22.79047578096603 12.9631916644048 ST2 TRUE 346 +DLBCL-c_D_1118-Tumor BN2 0.6806375449766406 0 624,765,173,535,799,506,773,698,1049,936 0.174,0.219,0.249,0.277,0.306,0.31,0.356,0.357,0.367,0.396 EZB,BN2,BN2,BN2,BN2,BN2,BN2,ST2,ST2,BN2 24.0128077317185,5.7446948894366,5.52237270888214 0 0 35.279875330037214 24.01280773171848 BN2 TRUE 347 +DLBCL-c_D_1123-Tumor BN2 1 0 391,623,827,327,492,980,577,463,473,611 0.144,0.232,0.318,0.328,0.329,0.335,0.338,0.348,0.363,0.367 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.7766096175063 0 0 34.77660961750632 34.77660961750632 BN2 TRUE 348 +DLBCL-c_D_1124-Tumor MCD 1 0 1027,283,130,326,1036,550,416,311,491,821 0.166,0.204,0.212,0.212,0.222,0.224,0.232,0.232,0.235,0.242 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.3595459429224 0 0 46.35954594292242 46.35954594292242 MCD TRUE 349 +DLBCL-c_D_1125-Tumor MCD 0.9059114662666361 0 155,450,417,312,1048,500,778,176,918,1109 0.128,0.128,0.132,0.133,0.136,0.142,0.152,0.161,0.164,0.168 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 6.58003086862869,63.3544298731631 0 0 69.93446074179182 63.35442987316313 MCD TRUE 350 +DLBCL-c_D_1127-Tumor EZB 1 0 265,206,877,267,848,1117,720,1083,192,157 0.176,0.248,0.289,0.327,0.359,0.369,0.409,0.413,0.42,0.422 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3382526741385 0 0 31.338252674138477 31.338252674138477 EZB TRUE 351 +DLBCL-c_D_1128-Tumor BN2 1 0 463,109,611,10,895,258,531,952,327,1098 0.191,0.194,0.197,0.197,0.276,0.302,0.308,0.311,0.312,0.322 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.2217226292124 0 0 40.22172262921243 40.22172262921243 BN2 TRUE 352 +DLBCL-c_D_1129-Tumor BN2 1 0 951,566,482,578,401,26,957,569,1111,1022 0.142,0.187,0.214,0.232,0.249,0.273,0.282,0.289,0.293,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.7533396730781 0 0 42.75333967307813 42.75333967307813 BN2 TRUE 353 +DLBCL-c_D_1130-Tumor BN2 1 0 945,559,331,75,1077,973,305,542,998,197 0.127,0.142,0.147,0.152,0.161,0.17,0.18,0.188,0.188,0.203 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 61.449628232367 0 0 61.44962823236696 61.44962823236696 BN2 TRUE 354 +DLBCL-c_D_1132-Tumor BN2 1 0 187,553,86,110,321,258,262,1098,670,928 0.254,0.285,0.297,0.325,0.327,0.332,0.34,0.343,0.379,0.382 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.0601931558982 0 0 31.060193155898165 31.060193155898165 BN2 TRUE 355 +DLBCL-c_D_1133-Tumor EZB 1 0 941,251,938,182,116,250,709,120,21,18 0.135,0.172,0.187,0.2,0.237,0.241,0.252,0.254,0.271,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2008931484633 0 0 47.20089314846326 47.20089314846326 EZB TRUE 356 +DLBCL-c_D_1134-Tumor BN2 1 0 564,158,393,58,1028,968,572,777,651,1019 0.157,0.203,0.204,0.226,0.387,0.395,0.409,0.443,0.445,0.455 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.8819846449542 0 0 34.88198464495415 34.88198464495415 BN2 TRUE 357 +DLBCL-c_D_1135-Tumor BN2 0.7358929579167797 0 570,487,390,88,539,212,189,77,634,22 0.186,0.199,0.217,0.218,0.242,0.242,0.265,0.278,0.297,0.3 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,EZB 30.8570498589925,11.0743880315737 0 0 41.931437890566215 30.857049858992504 BN2 TRUE 358 +DLBCL-c_D_1139-Tumor ST2 0.8224306669178493 0 671,842,996,161,668,602,1029,160,1005,366 0.137,0.159,0.169,0.179,0.182,0.194,0.218,0.24,0.259,0.266 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,BN2 9.25577786142228,42.869089089225 0 0 52.12486695064726 42.869089089224985 ST2 TRUE 359 +DLBCL-c_D_1141-Tumor EZB 1 0 211,927,402,210,306,271,114,31,233,51 0.205,0.236,0.266,0.271,0.278,0.29,0.311,0.345,0.352,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.3795578652145 0 0 35.37955786521448 35.37955786521448 EZB TRUE 360 +DLBCL-c_D_1146-Tumor ST2 0.5500228445783244 0 787,178,770,950,1107,965,195,875,1065,1009 0.156,0.163,0.189,0.212,0.213,0.246,0.267,0.284,0.286,0.292 EZB,ST2,BN2,EZB,ST2,EZB,ST2,ST2,ST2,ST2 5.27744321538524,15.2000488325764,25.0303560755106 0 0 45.50784812347222 25.030356075510554 ST2 TRUE 361 +DLBCL-c_D_1148-Tumor ST2 1 0 807,483,656,263,207,1032,728,822,1057,1081 0.182,0.255,0.262,0.272,0.274,0.341,0.358,0.373,0.373,0.476 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 33.7216470100066 0 0 33.721647010006556 33.721647010006556 BN2 FALSE 362 +DLBCL-c_D_1150-Tumor BN2 0.5647264569925512 0 670,408,422,1007,86,808,946,797,970,531 0.287,0.301,0.312,0.337,0.364,0.408,0.442,0.45,0.455,0.482 BN2,N1,ST2,ST2,BN2,BN2,BN2,ST2,BN2,BN2 15.2074475613278,3.32180042932079,8.39962593233882 0 0 26.92887392298737 15.207447561327761 BN2 TRUE 363 +DLBCL-c_D_1157-Tumor EZB 1 0 125,35,48,30,635,208,256,171,129,931 0.15,0.153,0.181,0.183,0.187,0.198,0.207,0.209,0.21,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.6547909668106 0 0 53.65479096681058 53.65479096681058 EZB TRUE 364 +DLBCL-c_D_1158-Tumor EZB 0.5001846277972772 0 661,965,178,787,770,361,1107,195,950,1041 0.112,0.223,0.236,0.258,0.288,0.298,0.315,0.324,0.359,0.362 EZB,EZB,ST2,EZB,BN2,ST2,ST2,ST2,EZB,ST2 3.47113563866364,20.1117307917129,16.6257478972595 0 0 40.208614327636106 20.111730791712933 ST2 FALSE 365 +DLBCL-c_D_1159-Tumor ST2 0.7872797152835607 0 425,583,785,160,668,612,161,1029,359,602 0.158,0.187,0.188,0.193,0.197,0.21,0.211,0.215,0.266,0.27 ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.0749286103877,5.32513217890994,38.490719904989 0 0 48.89078069428662 38.49071990498897 BN2 FALSE 366 +DLBCL-c_D_1163-Tumor MCD 1 0 1101,452,978,961,514,878,39,465,295,906 0.12,0.169,0.184,0.197,0.2,0.207,0.22,0.223,0.236,0.255 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.8298518580067 0 0 51.829851858006734 51.829851858006734 MCD TRUE 367 +DLBCL-c_D_pair20-Tumor EZB 0.7757922606477001 0 227,995,587,863,329,1074,1079,618,277,867 0.114,0.144,0.157,0.159,0.176,0.18,0.186,0.191,0.2,0.213 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 8.73708271195448,46.5042978365312,4.70288571385791 0 0 59.94426626234357 46.50429783653118 EZB TRUE 368 +DLBCL-c_D_pair5-Tumor EZB 0.9062397238863048 0 581,385,687,975,379,645,643,1050,241,831 0.145,0.155,0.156,0.17,0.173,0.198,0.216,0.222,0.235,0.253 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 48.7434997238295,5.04304090010013 0 0 53.78654062392959 48.743499723829466 EZB TRUE 369 +DLBCL-c_D_pair9-Tumor ST2 0.7707104267350875 0 792,177,692,1086,682,1016,1053,300,673,557 0.186,0.235,0.258,0.26,0.276,0.292,0.331,0.339,0.353,0.386 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 8.20425652240359,27.5769454118191 0 0 35.7812019342227 27.576945411819114 ST2 TRUE 370 +DLBCL10452T MCD 0.4724939531329812 0 94,534,170,813,419,193,541,930,510,444 0.152,0.245,0.263,0.263,0.275,0.309,0.332,0.337,0.381,0.389 EZB,MCD,MCD,EZB,MCD,BN2,MCD,ST2,MCD,EZB 3.23649163374541,12.9604661939139,17.1685653869856,2.97052992031153 0 0 36.336053134956465 17.168565386985634 EZB FALSE 371 +DLBCL10456T ST2 0.6001594913715487 0 910,278,922,834,1024,843,657,901,1091,925 0.277,0.281,0.301,0.311,0.324,0.325,0.327,0.335,0.34,0.345 EZB,ST2,ST2,ST2,EZB,ST2,BN2,ST2,EZB,ST2 3.05762340988479,9.64407639562204,19.0652160807217 0 0 31.766915886228485 19.065216080721658 ST2 TRUE 372 +DLBCL10458T BN2 0.8933599346708686 0 905,395,958,540,702,841,929,1023,555,546 0.163,0.166,0.168,0.191,0.217,0.218,0.22,0.229,0.252,0.269 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 43.9156520317886,5.24219614053624 0 0 49.15784817232485 43.9156520317886 BN2 TRUE 373 +DLBCL10459T EZB 1 0 711,229,648,71,892,720,680,1095,706,139 0.152,0.156,0.17,0.185,0.192,0.199,0.207,0.208,0.21,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.5764531777289 0 0 53.57645317772892 53.57645317772892 EZB TRUE 374 +DLBCL10461T MCD 0.5269148827710218 0 440,386,285,135,552,330,193,813,444,534 0.14,0.178,0.218,0.275,0.369,0.403,0.418,0.438,0.442,0.456 MCD,MCD,BN2,MCD,BN2,BN2,BN2,EZB,EZB,MCD 12.1638732438502,4.54406320069403,18.6090410634534 0 0 35.31697750799766 18.609041063453404 MCD TRUE 375 +DLBCL10463T MCD 1 0 480,934,202,445,532,147,859,458,749,933 0.229,0.325,0.353,0.424,0.431,0.435,0.462,0.488,0.493,0.496 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 25.4998829050328 0 0 25.499882905032763 25.499882905032763 MCD TRUE 376 +DLBCL10464T EZB 1 0 378,626,745,537,647,1083,157,719,265,238 0.254,0.322,0.348,0.367,0.396,0.425,0.47,0.53,0.543,0.563 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 25.1435286500871 0 0 25.143528650087067 25.143528650087067 EZB TRUE 377 +DLBCL10465T EZB 1 0 745,377,626,647,537,1083,184,238,629,163 0.238,0.254,0.376,0.45,0.482,0.514,0.543,0.548,0.59,0.594 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.0793686458132 0 0 24.0793686458132 24.0793686458132 EZB TRUE 378 +DLBCL10466T EZB 0.91894137165257 0 687,369,831,581,385,975,645,142,643,118 0.141,0.173,0.191,0.218,0.221,0.239,0.271,0.283,0.287,0.292 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 41.8483496463054,3.69138872791514 0 0 45.53973837422051 41.84834964630537 EZB TRUE 379 +DLBCL10467T MCD 1 0 1090,994,660,932,188,966,987,439,461,106 0.165,0.181,0.19,0.195,0.214,0.218,0.244,0.245,0.259,0.269 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.0139349075279 0 0 47.01393490752792 47.01393490752792 MCD TRUE 380 +DLBCL10469T ST2 0.6552959114669504 0 830,412,1066,529,119,308,919,396,698,293 0.123,0.226,0.236,0.281,0.285,0.332,0.353,0.363,0.39,0.438 ST2,ST2,N1,ST2,BN2,ST2,BN2,ST2,ST2,MCD 6.33460240610028,2.28201227219954,4.23208093073474,24.4258712918528 0 0 37.274566900887386 24.425871291852822 ST2 TRUE 381 +DLBCL10470T EZB 0.5387402127784001 0 657,922,791,641,974,1091,372,586,194,910 0.242,0.245,0.278,0.386,0.437,0.438,0.439,0.476,0.498,0.512 BN2,ST2,EZB,EZB,EZB,EZB,ST2,ST2,EZB,EZB 4.13880981633157,14.7208699427755,8.46493717798169 0 0 27.32461693708872 14.720869942775453 EZB TRUE 382 +DLBCL10471T MCD 0.764460607077467 0 1070,457,445,154,749,934,933,772,859,533 0.207,0.246,0.284,0.308,0.375,0.381,0.397,0.431,0.433,0.44 BN2,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 4.83571753415705,2.31961667273875,23.2231690154893 0 0 30.378503222385092 23.223169015489294 MCD TRUE 383 +DLBCL10472T EZB 1 0 605,1059,61,603,833,74,629,25,73,149 0.102,0.141,0.147,0.151,0.151,0.156,0.17,0.178,0.203,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.5656649601506 0 0 64.56566496015056 64.56566496015056 EZB TRUE 384 +DLBCL10475T EZB 0.895258823244195 0 975,581,369,645,643,1050,687,241,254,379 0.12,0.141,0.155,0.162,0.168,0.186,0.189,0.201,0.212,0.221 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 52.7146338259534,6.16737041381348 0 0 58.882004239766935 52.71463382595345 EZB TRUE 385 +DLBCL10480T MCD 0.47608277860633613 0 285,375,440,135,552,330,845,193,444,100 0.144,0.178,0.192,0.22,0.32,0.378,0.462,0.464,0.465,0.485 BN2,MCD,MCD,MCD,BN2,BN2,BN2,BN2,EZB,MCD 17.025915281961,2.1484432954324,17.4237103434716 0 0 36.59806892086502 17.423710343471612 MCD TRUE 386 +DLBCL10481T EZB 1 0 757,93,740,631,102,228,107,873,1117,848 0.139,0.188,0.188,0.189,0.202,0.203,0.206,0.209,0.236,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.6849836277551 0 0 50.68498362775512 50.68498362775512 EZB TRUE 387 +DLBCL10482T BN2 0.5334332552248533 0 127,185,891,802,23,14,179,180,552,42 0.144,0.188,0.197,0.224,0.24,0.243,0.263,0.28,0.28,0.288 BN2,BN2,MCD,ST2,EZB,BN2,BN2,MCD,BN2,EZB 23.7223265780729,7.63951623004831,8.6406435794201,4.46854414105491 0 0 44.471030528596266 23.722326578072934 BN2 TRUE 388 +DLBCL10483T EZB 0.9112956873439079 0 134,615,398,1000,203,8,97,209,171,977 0.14,0.26,0.266,0.267,0.303,0.311,0.315,0.318,0.339,0.346 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.29623545639835,33.8635751288873 0 0 37.159810585285676 33.863575128887334 EZB TRUE 389 +DLBCL10487T BN2 0.9013695002652803 0 212,570,358,487,634,860,77,633,493,88 0.208,0.213,0.217,0.222,0.226,0.237,0.244,0.271,0.295,0.324 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 37.4273428529503,4.09540984939333 0 0 41.52275270234362 37.42734285295028 BN2 TRUE 390 +DLBCL10488T BN2 1 0 348,623,463,827,327,611,492,980,577,473 0.144,0.198,0.313,0.321,0.322,0.341,0.363,0.363,0.382,0.395 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.9994613839862 0 0 34.99946138398624 34.99946138398624 BN2 TRUE 391 +DLBCL10489T EZB 1 0 101,604,313,1012,49,986,302,981,183,199 0.128,0.141,0.156,0.156,0.165,0.18,0.205,0.222,0.234,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2476572819226 0 0 57.247657281922585 57.247657281922585 EZB TRUE 392 +DLBCL10490T BN2 0.9344932921556488 0 564,357,158,58,572,777,1028,968,301,704 0.182,0.204,0.266,0.288,0.306,0.405,0.416,0.42,0.454,0.47 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 30.363446523087,2.12843627368077 0 0 32.49188279676779 30.36344652308702 BN2 TRUE 393 +DLBCL10491T ST2 0.9131147031241165 0 911,60,1081,278,904,508,843,910,822,339 0.203,0.222,0.235,0.27,0.273,0.297,0.307,0.309,0.312,0.32 ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2 3.23564743671523,34.0048012129267 0 0 37.2404486496419 34.004801212926665 ST2 TRUE 394 +DLBCL10495T BN2 0.9194354170776942 0 958,702,905,373,1023,305,796,540,542,75 0.139,0.153,0.154,0.166,0.193,0.206,0.226,0.232,0.232,0.235 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 49.2689207820525,4.31713851795506 0 0 53.58605930000755 49.26892078205249 BN2 TRUE 395 +DLBCL10497T ST2 1 0 281,59,412,308,830,759,381,24,915,642 0.238,0.24,0.255,0.346,0.348,0.354,0.363,0.368,0.378,0.399 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 31.505575172095 0 0 31.50557517209504 31.50557517209504 ST2 TRUE 396 +DLBCL10498T EZB 1 0 287,105,91,1040,675,317,92,78,117,1108 0.165,0.189,0.191,0.198,0.201,0.209,0.209,0.217,0.223,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.719604706545 0 0 49.71960470654505 49.71960470654505 EZB TRUE 397 +DLBCL10501T EZB 0.9036932222899354 0 615,134,389,203,1000,1088,84,83,428,97 0.197,0.232,0.266,0.319,0.347,0.352,0.366,0.373,0.389,0.398 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 3.12998843627658,29.3702001350775 0 0 32.500188571354066 29.37020013507749 EZB TRUE 398 +DLBCL10506T BN2 0.8119780079193942 0 636,561,575,1069,498,926,77,493,898,633 0.196,0.199,0.212,0.214,0.227,0.239,0.251,0.26,0.266,0.266 BN2,BN2,BN2,BN2,BN2,ST2,EZB,BN2,BN2,BN2 35.262925123395,3.98781590721928,4.17768286691518 0 0 43.42842389752944 35.26292512339497 BN2 TRUE 399 +DLBCL10508T MCD 0.7254197197073472 0 436,567,420,775,448,533,772,510,444,933 0.15,0.202,0.218,0.22,0.222,0.233,0.242,0.243,0.272,0.28 MCD,MCD,MCD,EZB,MCD,MCD,EZB,MCD,EZB,MCD 12.3582643406458,32.6495720832742 0 0 45.00783642392006 32.64957208327422 MCD TRUE 400 +DLBCL10509T BN2 1 0 482,957,1111,578,951,264,353,566,640,26 0.134,0.14,0.179,0.186,0.218,0.245,0.249,0.261,0.269,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.3494627636994 0 0 49.349462763699414 49.349462763699414 BN2 TRUE 401 +DLBCL10512T EZB 1 0 306,79,85,927,360,271,32,28,210,917 0.186,0.23,0.239,0.257,0.266,0.269,0.273,0.274,0.286,0.347 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9877384252548 0 0 38.98773842525483 38.98773842525483 EZB TRUE 402 +DLBCL10514T EZB 1 0 708,686,5,949,237,289,310,141,838,750 0.12,0.137,0.17,0.214,0.223,0.225,0.231,0.254,0.26,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0927165113371 0 0 51.092716511337116 51.092716511337116 EZB TRUE 403 +DLBCL10515T BN2 1 0 261,499,543,517,527,555,815,1001,591,546 0.12,0.158,0.184,0.189,0.213,0.225,0.245,0.259,0.26,0.261 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.174425124833 0 0 50.17442512483296 50.17442512483296 BN2 TRUE 404 +DLBCL10516T EZB 1 0 1013,1058,917,638,889,847,271,210,79,846 0.289,0.309,0.317,0.354,0.365,0.378,0.407,0.426,0.427,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.4841857593101 0 0 27.48418575931005 27.48418575931005 EZB TRUE 405 +DLBCL10518T EZB 1 0 754,116,865,18,1018,182,190,756,1073,942 0.133,0.149,0.19,0.191,0.198,0.21,0.22,0.229,0.234,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3894651666958 0 0 51.389465166695835 51.389465166695835 EZB TRUE 406 +DLBCL10519T EZB 0.9145589582164062 0 890,1114,839,874,1006,715,688,344,424,784 0.242,0.247,0.25,0.257,0.265,0.281,0.284,0.323,0.325,0.339 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 32.9811266847614,3.08120301903589 0 0 36.06232970379732 32.981126684761435 EZB TRUE 407 +DLBCL10520T BN2 0.8248070994872702 0 808,363,531,952,1007,670,86,10,422,895 0.237,0.301,0.348,0.36,0.376,0.407,0.451,0.475,0.5,0.502 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,ST2,BN2 21.9502241379663,4.66233066619499 0 0 26.612554804161253 21.95022413796626 N1 FALSE 408 +DLBCL10522T EZB 1 0 316,884,252,889,912,1058,858,818,62,90 0.216,0.23,0.235,0.246,0.274,0.326,0.366,0.4,0.42,0.433 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.929207564884 0 0 33.92920756488404 33.92920756488404 EZB TRUE 409 +DLBCL10523T EZB 1 0 655,1051,752,183,49,1012,101,1115,225,392 0.133,0.142,0.16,0.215,0.226,0.231,0.263,0.271,0.278,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8255664904428 0 0 48.82556649044275 48.82556649044275 EZB TRUE 410 +DLBCL10524T EZB 0.5389361992022118 0 608,90,11,691,858,1084,622,954,614,893 0.284,0.298,0.301,0.329,0.332,0.342,0.353,0.43,0.459,0.488 EZB,EZB,EZB,ST2,EZB,BN2,ST2,ST2,EZB,N1 2.92049275456891,15.3957486386815,2.0509955199164,8.19968559356373 0 0 28.56692250673049 15.39574863868145 EZB TRUE 411 +DLBCL10527T ST2 0.8053814085684721 0 830,381,308,396,1066,119,529,281,59,642 0.219,0.226,0.251,0.255,0.292,0.303,0.361,0.365,0.368,0.369 ST2,ST2,ST2,ST2,N1,BN2,ST2,ST2,ST2,ST2 3.29531348994769,3.43018107896257,27.8318132372994 0 0 34.55730780620962 27.83181323729936 ST2 TRUE 412 +DLBCL10529T MCD 1 0 794,297,290,165,17,108,1035,528,166,246 0.115,0.136,0.148,0.151,0.16,0.163,0.176,0.178,0.19,0.193 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5550111912165 0 0 63.55501119121652 63.55501119121652 MCD TRUE 413 +DLBCL10530T MCD 1 0 456,340,461,987,869,188,932,536,565,1038 0.14,0.145,0.145,0.149,0.165,0.188,0.195,0.197,0.207,0.213 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.7456550628095 0 0 58.74565506280948 58.74565506280948 MCD TRUE 414 +DLBCL10532T MCD 1 0 1038,679,565,869,340,461,987,414,536,1087 0.13,0.136,0.148,0.171,0.189,0.19,0.205,0.213,0.22,0.234 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5806723289143 0 0 56.58067232891432 56.58067232891432 MCD TRUE 415 +DLBCL10533T MCD 1 0 821,491,326,1036,550,311,332,283,469,1027 0.111,0.118,0.123,0.142,0.15,0.158,0.188,0.204,0.213,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.2606306833547 0 0 65.26063068335466 65.26063068335466 MCD TRUE 416 +DLBCL10534T MCD 0.9068643553642043 0 1048,350,312,769,778,155,450,500,1015,918 0.105,0.132,0.136,0.153,0.156,0.157,0.159,0.159,0.16,0.167 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.39444310729711,62.2628699150414 0 0 68.65731302233851 62.262869915041406 MCD TRUE 417 +DLBCL10537T EZB 1 0 1037,644,13,599,83,84,150,140,222,1088 0.136,0.182,0.19,0.192,0.193,0.215,0.216,0.217,0.227,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0115245164913 0 0 51.011524516491264 51.011524516491264 EZB TRUE 418 +DLBCL10538T MCD 0.8258117612282001 0 170,541,200,371,1082,510,94,201,888,420 0.154,0.18,0.228,0.275,0.279,0.293,0.3,0.308,0.315,0.317 MCD,MCD,MCD,EZB,MCD,MCD,EZB,MCD,MCD,MCD 6.96564215460226,33.0235224624635 0 0 39.98916461706574 33.02352246246348 MCD TRUE 419 +DLBCL10540T MCD 0.7696136458940591 0 510,775,567,400,444,541,448,200,436,201 0.126,0.133,0.141,0.218,0.248,0.248,0.266,0.268,0.268,0.27 MCD,EZB,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 11.5508235754031,38.5859287519139 0 0 50.13675232731704 38.58592875191392 MCD TRUE 420 +DLBCL10542T MCD 0.39190983096977344 0 663,801,789,441,716,617,525,779,1045,579 0.254,0.275,0.31,0.317,0.379,0.387,0.399,0.4,0.402,0.422 ST2,ST2,MCD,MCD,BN2,N1,MCD,MCD,BN2,N1 5.12547887726848,11.3853118228385,4.95353361945763,7.5865208574133 0 0 29.050845176977873 11.385311822838457 MCD TRUE 421 +DLBCL10543T ST2 0.5437214228019601 0 946,970,363,797,1007,490,670,24,1067,558 0.291,0.299,0.312,0.33,0.369,0.408,0.416,0.436,0.456,0.479 BN2,BN2,BN2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 12.3924388071271,14.7673697537477 0 0 27.159808560874836 14.767369753747724 ST2 TRUE 422 +DLBCL10547T ST2 0.8308504416901662 0 875,1065,814,1004,1009,896,748,361,1025,770 0.266,0.268,0.277,0.317,0.355,0.369,0.383,0.391,0.393,0.395 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2 5.07943484679398,24.9498179490655 0 0 30.02925279585952 24.94981794906554 ST2 TRUE 423 +DLBCL10549T EZB 1 0 862,715,115,890,407,839,913,1097,322,1033 0.166,0.214,0.319,0.319,0.325,0.335,0.336,0.345,0.38,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.1166699365011 0 0 34.11666993650114 34.11666993650114 ST2 FALSE 424 +DLBCL10552T ST2 0.6585273568773167 0 366,612,583,785,160,668,161,1029,896,662 0.158,0.168,0.176,0.2,0.243,0.246,0.269,0.269,0.298,0.315 BN2,ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2,ST2 10.4012002201839,5.00018147750353,29.7014457406854 0 0 45.10282743837287 29.7014457406854 ST2 TRUE 425 +DLBCL10553T EZB 1 0 812,1097,284,115,753,816,874,1075,21,1006 0.163,0.186,0.199,0.212,0.315,0.316,0.332,0.333,0.34,0.343 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4426611915157 0 0 39.44266119151571 39.44266119151571 ST2 FALSE 426 +DLBCL10554T EZB 1 0 585,292,138,307,818,220,219,593,62,1080 0.233,0.251,0.254,0.259,0.273,0.284,0.315,0.399,0.427,0.448 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5194772473775 0 0 33.51947724737753 33.51947724737753 EZB TRUE 427 +DLBCL10555T EZB 1 0 215,923,214,398,133,134,547,615,389,873 0.244,0.321,0.331,0.389,0.422,0.45,0.47,0.486,0.488,0.57 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 25.3887809839328 0 0 25.388780983932783 25.388780983932783 EZB TRUE 428 +DLBCL10565T EZB 0.9178849934540938 0 744,658,454,19,241,733,67,1050,645,643 0.169,0.182,0.21,0.22,0.234,0.237,0.242,0.25,0.273,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 40.9469936081665,3.66316332890122 0 0 44.61015693706773 40.94699360816651 EZB TRUE 429 +DLBCL10780T EZB 1 0 761,997,37,1031,159,89,880,113,36,707 0.153,0.162,0.174,0.187,0.195,0.211,0.226,0.248,0.254,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8622173536659 0 0 49.86221735366593 49.86221735366593 EZB TRUE 430 +DLBCL10833T BN2 0.42424593363733454 0 442,801,894,497,521,34,663,701,104,421 0.231,0.289,0.333,0.358,0.367,0.438,0.443,0.447,0.457,0.463 BN2,ST2,MCD,BN2,MCD,BN2,ST2,BN2,MCD,MCD 11.6425321698843,10.0792654170336,5.72108694485672 0 0 27.442884531774602 11.642532169884282 BN2 TRUE 431 +DLBCL10834T BN2 0.5662880108352032 0 1085,582,153,967,962,924,672,665,505,186 0.189,0.236,0.26,0.264,0.286,0.291,0.347,0.349,0.363,0.387 EZB,BN2,BN2,EZB,BN2,ST2,BN2,BN2,N1,BN2 19.9216943986391,9.065529572408,2.75675641329894,3.43545837731364 0 0 35.179438761659696 19.92169439863911 BN2 TRUE 432 +DLBCL10835T MCD 0.804347365277495 0 286,990,169,1103,956,918,778,260,176,155 0.129,0.16,0.164,0.168,0.182,0.187,0.196,0.198,0.212,0.212 MCD,MCD,MCD,ST2,MCD,MCD,BN2,MCD,MCD,MCD 5.10673675613268,45.4255701144267,5.94275859913997 0 0 56.47506546969937 45.425570114426726 MCD TRUE 433 +DLBCL10836T MCD 1 0 804,851,246,166,767,551,17,798,528,762 0.133,0.161,0.168,0.169,0.169,0.177,0.181,0.182,0.185,0.187 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.9301251813661 0 0 58.93012518136605 58.93012518136605 MCD TRUE 434 +DLBCL10839T BN2 1 0 538,99,780,700,959,650,955,943,731,548 0.149,0.157,0.185,0.186,0.197,0.207,0.219,0.23,0.238,0.238 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.0771729854921 0 0 51.07717298549206 51.07717298549206 BN2 TRUE 435 +DLBCL10842T MCD 0.809343589910559 0 400,448,533,772,567,775,933,420,749,510 0.15,0.228,0.23,0.236,0.248,0.267,0.267,0.268,0.278,0.294 MCD,MCD,MCD,EZB,MCD,EZB,MCD,MCD,MCD,MCD 7.98472379387782,33.8954510721653 0 0 41.88017486604312 33.8954510721653 MCD TRUE 436 +DLBCL10843T N1 0.5354434012369208 0 854,516,486,496,964,1099,1104,893,590,41 0.155,0.222,0.331,0.372,0.372,0.379,0.407,0.409,0.409,0.472 BN2,N1,ST2,N1,BN2,N1,N1,N1,ST2,N1 9.15263717218876,16.8529403580155,5.46915970455062 0 0 31.47473723475491 16.852940358015527 BN2 FALSE 437 +DLBCL10844T BN2 0.6374514047226192 0 895,327,827,611,496,952,1099,1104,352,2 0.216,0.321,0.339,0.34,0.34,0.342,0.365,0.369,0.381,0.396 BN2,BN2,BN2,BN2,N1,BN2,N1,N1,BN2,N1 19.1834103387537,10.9105077177939 0 0 30.093918056547615 19.18341033875367 N1 FALSE 438 +DLBCL10848T MCD 1 0 106,966,280,660,1090,994,991,288,729,324 0.143,0.17,0.171,0.192,0.197,0.198,0.208,0.208,0.224,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.463178916591 0 0 52.46317891659099 52.46317891659099 MCD TRUE 439 +DLBCL10850T MCD 0.5322297728229195 0 375,386,285,135,552,330,193,813,891,444 0.14,0.192,0.222,0.26,0.347,0.44,0.451,0.465,0.477,0.48 MCD,MCD,BN2,MCD,BN2,BN2,BN2,EZB,MCD,EZB 11.8734202689629,4.23441786310853,18.327526062182 0 0 34.43536419425343 18.327526062182 MCD TRUE 440 +DLBCL10852T MCD 0.7878634875507154 0 789,617,713,421,205,779,985,525,457,801 0.173,0.219,0.27,0.317,0.349,0.387,0.406,0.439,0.442,0.456 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 25.1124522790484,4.56837815885319,2.19328566959463 0 0 31.87411610749622 25.11245227904841 MCD TRUE 441 +DLBCL10853T BN2 0.4565105087379326 0 431,521,497,701,34,801,104,894,103,579 0.231,0.251,0.321,0.327,0.341,0.389,0.391,0.401,0.433,0.476 BN2,MCD,BN2,BN2,BN2,ST2,MCD,MCD,MCD,N1 13.4455378742692,11.332844198898,2.10252023687844,2.57195280919506 0 0 29.452855119240734 13.44553787426921 BN2 TRUE 442 +DLBCL10854T MCD 1 0 1076,468,328,1008,276,1021,568,298,900,33 0.139,0.152,0.162,0.162,0.165,0.194,0.196,0.2,0.211,0.227 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5675773752687 0 0 56.567577375268684 56.567577375268684 MCD TRUE 443 +DLBCL10856T MCD 0.5911056699035582 0 193,420,510,400,567,775,813,330,436,534 0.212,0.248,0.248,0.272,0.28,0.28,0.284,0.307,0.308,0.311 BN2,MCD,MCD,MCD,MCD,EZB,EZB,BN2,MCD,MCD 7.96943819678449,7.09856159587112,21.782596274797 0 0 36.85059606745263 21.78259627479701 EZB FALSE 444 +DLBCL10857T MCD 0.9074464762991163 0 934,749,933,383,859,772,533,448,154,457 0.202,0.258,0.277,0.284,0.29,0.319,0.328,0.351,0.363,0.369 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.13743262668942,30.7611430432012 0 0 33.8985756698906 30.76114304320118 MCD TRUE 445 +DLBCL10858T MCD 1 0 1035,108,519,290,794,413,297,165,528,573 0.209,0.217,0.233,0.248,0.258,0.273,0.293,0.295,0.304,0.323 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.4374463516816 0 0 38.43744635168162 38.43744635168162 MCD TRUE 446 +DLBCL10860T MCD 1 0 469,283,299,1110,550,1036,690,326,817,721 0.153,0.157,0.179,0.188,0.195,0.203,0.215,0.228,0.232,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4418315752756 0 0 51.44183157527556 51.44183157527556 MCD TRUE 447 +DLBCL10863T MCD 0.7829501981955937 0 533,772,933,859,749,400,567,436,775,532 0.126,0.135,0.174,0.177,0.196,0.222,0.225,0.228,0.241,0.249 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 11.5851032559213,41.7902196405317 0 0 53.375322896453056 41.79021964053173 MCD TRUE 448 +DLBCL10864T MCD 1 0 829,573,856,798,519,44,528,1047,804,1054 0.15,0.223,0.255,0.284,0.304,0.316,0.319,0.327,0.34,0.351 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0579401773711 0 0 37.05794017737112 37.05794017737112 MCD TRUE 449 +DLBCL10866T MCD 0.9056753929140929 0 155,350,176,312,778,500,990,417,918,1048 0.106,0.128,0.134,0.142,0.152,0.155,0.157,0.159,0.161,0.163 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.57613586088433,63.1420009440223 0 0 69.718136804906635 63.1420009440223 MCD TRUE 450 +DLBCL10867T MCD 1 0 33,902,1008,276,468,1021,1076,465,568,961 0.123,0.145,0.17,0.181,0.181,0.202,0.211,0.214,0.221,0.227 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.2916670356899 0 0 55.291667035689876 55.291667035689876 MCD TRUE 451 +DLBCL10870T MCD 0.9074182587248952 0 978,1101,878,295,367,961,621,465,39,514 0.151,0.152,0.154,0.167,0.169,0.181,0.199,0.216,0.264,0.264 MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD 49.349544355056,5.03501742839545 0 0 54.384561783451474 49.34954435505602 MCD TRUE 452 +DLBCL10876T MCD 1 0 549,296,526,580,1042,494,298,678,920,16 0.119,0.14,0.151,0.161,0.166,0.17,0.19,0.19,0.194,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.0201743590276 0 0 61.020174359027635 61.020174359027635 MCD TRUE 453 +DLBCL10879T EZB 0.9111859995944036 0 67,429,658,241,1050,142,645,581,744,643 0.135,0.21,0.227,0.234,0.245,0.247,0.258,0.275,0.278,0.281 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 39.7911259847754,3.87847166322142 0 0 43.66959764799681 39.79112598477539 EZB TRUE 454 +DLBCL10880T BN2 0.6082094632070897 0 1112,579,123,506,521,1045,1061,663,347,799 0.167,0.293,0.301,0.347,0.381,0.404,0.424,0.459,0.463,0.486 BN2,N1,N1,BN2,MCD,BN2,BN2,ST2,BN2,BN2 17.9056664141258,2.62149760548025,6.73619542230644,2.17660737430436 0 0 29.439966816216796 17.90566641412575 MCD FALSE 455 +DLBCL10883T MCD 1 0 414,188,340,987,461,869,932,536,511,565 0.14,0.17,0.176,0.177,0.18,0.199,0.209,0.214,0.245,0.246 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.5691879907226 0 0 52.56918799072262 52.56918799072262 MCD TRUE 456 +DLBCL10887T MCD 0.8820616000241736 0 154,383,1070,445,441,934,480,749,789,713 0.197,0.246,0.309,0.369,0.442,0.443,0.5,0.501,0.514,0.52 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 3.24139883929494,24.2424303457654 0 0 27.483829185060337 24.2424303457654 MCD TRUE 457 +DLBCL10889T MCD 1 0 522,717,480,489,376,154,837,128,198,934 0.276,0.305,0.399,0.476,0.488,0.507,0.516,0.57,0.574,0.579 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 22.6979994369171 0 0 22.69799943691709 22.69799943691709 MCD TRUE 458 +DLBCL10890T MCD 1 0 314,897,739,513,520,288,991,147,106,730 0.127,0.14,0.14,0.166,0.174,0.192,0.198,0.257,0.257,0.265 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.6992742708396 0 0 55.69927427083961 55.69927427083961 MCD TRUE 459 +DLBCL10891T MCD 0.9183422431558526 0 730,888,1043,513,66,459,314,739,897,520 0.123,0.149,0.151,0.206,0.258,0.266,0.28,0.281,0.305,0.316 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 3.8807603297329,43.6439388502819 0 0 47.5246991800148 43.6439388502819 MCD TRUE 460 +DLBCL10893T MCD 1 0 987,414,932,340,565,869,1038,456,415,188 0.116,0.145,0.165,0.167,0.168,0.176,0.179,0.18,0.19,0.197 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.6736549086766 0 0 60.67365490867657 60.67365490867657 MCD TRUE 461 +DLBCL10894T BN2 0.7375877595772992 0 470,1106,186,22,840,672,539,189,88,571 0.181,0.184,0.225,0.236,0.258,0.267,0.307,0.307,0.317,0.335 BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2,EZB 29.4562359002625,10.4796707329271 0 0 39.93590663318955 29.45623590026248 BN2 TRUE 462 +DLBCL10896T BN2 1 0 611,352,109,327,10,827,391,623,895,1098 0.177,0.191,0.221,0.272,0.281,0.312,0.313,0.331,0.332,0.343 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 37.9976998467448 0 0 37.9976998467448 37.9976998467448 BN2 TRUE 463 +DLBCL10897T BN2 1 0 315,70,1062,515,973,282,175,318,197,331 0.329,0.407,0.411,0.42,0.432,0.434,0.438,0.439,0.454,0.456 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.8862017124927 0 0 23.886201712492745 23.886201712492745 BN2 TRUE 464 +DLBCL10899T MCD 1 0 961,1021,568,978,878,33,328,1076,451,452 0.134,0.159,0.165,0.165,0.184,0.191,0.202,0.213,0.214,0.216 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.5001686107268 0 0 55.50016861072684 55.50016861072684 MCD TRUE 465 +DLBCL10900T MCD 1 0 223,128,985,790,837,697,489,713,198,475 0.167,0.181,0.213,0.262,0.277,0.282,0.292,0.304,0.324,0.347 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.8497418387377 0 0 39.84974183873769 39.84974183873769 MCD TRUE 466 +DLBCL10901T MCD 1 0 920,494,204,881,956,526,296,678,260,453 0.127,0.145,0.151,0.18,0.196,0.207,0.208,0.208,0.209,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.9517818054681 0 0 55.95178180546813 55.95178180546813 MCD TRUE 467 +DLBCL10902T MCD 1 0 1008,276,443,1076,451,328,33,1021,568,900 0.111,0.12,0.152,0.154,0.181,0.183,0.184,0.189,0.201,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.9435661871563 0 0 61.94356618715625 61.94356618715625 MCD TRUE 468 +DLBCL10904T MCD 1 0 447,550,283,1036,1110,299,326,821,416,690 0.153,0.167,0.17,0.175,0.181,0.185,0.202,0.211,0.213,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8227545013049 0 0 53.82275450130492 53.82275450130492 MCD TRUE 469 +DLBCL10905T BN2 0.6922706337929736 0 22,462,840,1106,539,189,88,186,672,571 0.166,0.181,0.203,0.217,0.229,0.235,0.237,0.241,0.284,0.288 EZB,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,EZB 31.1994785728352,13.8688473821345 0 0 45.06832595496964 31.199478572835154 BN2 TRUE 470 +DLBCL10910T EZB 1 0 235,908,76,234,4,823,921,766,1100,168 0.156,0.175,0.196,0.207,0.224,0.238,0.244,0.248,0.262,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0906961946751 0 0 46.090696194675125 46.090696194675125 EZB TRUE 471 +DLBCL10912T BN2 0.9250408364804547 0 651,1019,179,659,666,652,185,1113,42,127 0.167,0.186,0.19,0.21,0.223,0.238,0.288,0.307,0.308,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2 40.0367255800668,3.2443102414396 0 0 43.281035821506435 40.036725580066836 BN2 TRUE 472 +DLBCL10913T BN2 1 0 492,577,348,391,623,464,95,980,507,827 0.138,0.293,0.363,0.395,0.416,0.47,0.482,0.485,0.506,0.551 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.3939058689247 0 0 28.393905868924712 28.393905868924712 BN2 TRUE 473 +DLBCL10915T BN2 1 0 795,1060,630,335,584,650,477,943,674,855 0.218,0.283,0.305,0.329,0.38,0.389,0.391,0.4,0.414,0.441 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3896963228852 0 0 29.389696322885243 29.389696322885243 N1 FALSE 474 +DLBCL10916T MCD 1 0 1054,697,1047,790,44,616,856,1093,502,198 0.169,0.172,0.19,0.195,0.253,0.271,0.288,0.29,0.29,0.296 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.4922070128011 0 0 43.49220701280112 43.49220701280112 MCD TRUE 475 +DLBCL10917T ST2 0.7086517158582633 0 300,275,783,792,148,850,1086,370,218,692 0.366,0.392,0.504,0.534,0.569,0.597,0.601,0.602,0.641,0.653 ST2,ST2,EZB,BN2,ST2,EZB,ST2,ST2,ST2,ST2 1.87347388784947,3.65846440866614,13.4554338543511 0 0 18.987372150866726 13.455433854351108 EZB FALSE 476 +DLBCL10918T BN2 1 0 943,335,345,630,780,1060,99,650,959,999 0.142,0.179,0.188,0.189,0.205,0.208,0.214,0.222,0.246,0.265 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.9465014986444 0 0 49.946501498644416 49.946501498644416 BN2 TRUE 477 +DLBCL10919T EZB 0.7830642452219093 0 3,55,230,771,54,782,733,19,800,40 0.164,0.23,0.248,0.268,0.338,0.353,0.39,0.391,0.434,0.435 EZB,MCD,EZB,EZB,MCD,EZB,EZB,EZB,EZB,EZB 26.4162342267493,7.31820632258688 0 0 33.73444054933623 26.416234226749346 EZB TRUE 478 +DLBCL10924T MCD 0.8220199636454484 0 260,1046,956,169,1103,918,778,433,769,467 0.124,0.147,0.153,0.177,0.195,0.198,0.208,0.214,0.216,0.225 MCD,MCD,MCD,MCD,ST2,MCD,BN2,MCD,MCD,MCD 4.79702005110951,45.79459401268,5.11821785999088 0 0 55.70983192378038 45.79459401267999 MCD TRUE 479 +DLBCL10925T MCD 1 0 376,934,445,458,154,859,202,532,749,933 0.229,0.297,0.373,0.399,0.423,0.478,0.48,0.489,0.489,0.498 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 25.5555629100752 0 0 25.555562910075214 25.555562910075214 MCD TRUE 480 +DLBCL10927T N1 0.3688937641437906 0 530,293,803,919,119,1066,1061,123,43,554 0.15,0.263,0.295,0.335,0.382,0.4,0.4,0.414,0.431,0.448 N1,MCD,BN2,BN2,BN2,N1,BN2,N1,ST2,ST2 11.4908343917032,3.80435216503759,11.6000768656191,4.55031249444391 0 0 31.44557591680384 11.600076865619098 BN2 FALSE 481 +DLBCL10928T BN2 1 0 401,957,578,951,1111,353,566,26,264,640 0.134,0.171,0.179,0.186,0.199,0.214,0.232,0.264,0.264,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.3720312093218 0 0 49.37203120932184 49.37203120932184 BN2 TRUE 482 +DLBCL10929T ST2 0.9000448207511254 0 207,656,728,1057,362,807,822,263,60,797 0.129,0.184,0.24,0.241,0.255,0.33,0.341,0.359,0.378,0.425 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.9158584869313,35.2602854243447 0 0 39.176143911275986 35.26028542434469 ST2 TRUE 483 +DLBCL10931T BN2 0.6901524035344244 0 742,968,42,180,651,100,694,472,701,34 0.25,0.262,0.279,0.29,0.311,0.313,0.324,0.338,0.349,0.355 BN2,BN2,EZB,MCD,BN2,MCD,BN2,BN2,BN2,BN2 22.7578783349603,3.58410158548734,6.6331690733284 0 0 32.97514899377603 22.757878334960285 BN2 TRUE 484 +DLBCL10932T ST2 0.5166418404777333 0 936,558,529,698,321,928,490,1049,262,946 0.241,0.246,0.297,0.357,0.36,0.391,0.41,0.424,0.425,0.448 BN2,ST2,ST2,ST2,BN2,BN2,ST2,ST2,BN2,BN2 14.0589104551165,15.0269965025974 0 0 29.085906957713938 15.026996502597438 ST2 TRUE 485 +DLBCL10934T N1 0.6678914274316028 0 325,854,516,137,437,1099,1104,41,496,893 0.273,0.277,0.291,0.326,0.331,0.339,0.343,0.349,0.364,0.368 ST2,BN2,N1,N1,BN2,N1,N1,N1,N1,N1 6.62764372014819,20.6972563418333,3.66405336930486 0 0 30.988953431286358 20.69725634183331 ST2 FALSE 486 +DLBCL10935T BN2 0.8817658472542491 0 212,77,358,390,634,636,633,561,493,1069 0.159,0.185,0.199,0.222,0.223,0.237,0.239,0.249,0.26,0.263 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.3724164083687,5.41345354121632 0 0 45.78586994958502 40.3724164083687 BN2 TRUE 487 +DLBCL10937T BN2 1 0 333,1077,197,63,341,518,563,763,559,331 0.133,0.161,0.163,0.166,0.169,0.178,0.18,0.182,0.184,0.186 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.3048899955353 0 0 59.30488999553529 59.30488999553529 BN2 TRUE 488 +DLBCL10938T MCD 1 0 837,128,198,466,522,790,697,223,717,985 0.147,0.236,0.238,0.292,0.305,0.316,0.336,0.337,0.34,0.403 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.473744099333 0 0 36.47374409933305 36.47374409933305 MCD TRUE 489 +DLBCL10939T ST2 0.7120159392322972 0 970,946,281,59,529,24,558,396,422,485 0.212,0.224,0.312,0.314,0.32,0.328,0.356,0.405,0.408,0.41 BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 9.18959640325409,22.720490490988 0 0 31.910086894242077 22.72049049098799 ST2 TRUE 490 +DLBCL10940T MCD 1 0 416,821,326,311,1036,550,332,323,1027,906 0.118,0.122,0.137,0.141,0.159,0.168,0.172,0.206,0.208,0.216 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.4261471207709 0 0 63.426147120770906 63.426147120770906 MCD TRUE 491 +DLBCL10942T BN2 1 0 473,577,348,391,623,980,95,464,827,507 0.138,0.264,0.329,0.363,0.392,0.448,0.498,0.505,0.513,0.514 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.4866807557847 0 0 29.486680755784736 29.486680755784736 BN2 TRUE 492 +DLBCL10947T BN2 0.8896151582630442 0 633,1069,634,77,898,212,498,487,399,390 0.125,0.149,0.17,0.175,0.179,0.209,0.254,0.26,0.26,0.295 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 46.0126335421799,5.70931961340554 0 0 51.72195315558544 46.0126335421799 BN2 TRUE 493 +DLBCL10949T MCD 1 0 920,467,296,453,549,526,881,204,580,678 0.124,0.145,0.164,0.17,0.176,0.177,0.181,0.184,0.192,0.194 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.6205911596787 0 0 59.62059115967871 59.62059115967871 MCD TRUE 494 +DLBCL10950T BN2 0.6646012771584993 0 23,855,802,659,666,699,14,891,674,388 0.17,0.178,0.241,0.266,0.277,0.284,0.285,0.287,0.288,0.302 EZB,BN2,ST2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 26.8111889771956,5.89548181743369,3.48243241611564,4.15266244433592 0 0 40.341765655080884 26.811188977195638 MCD FALSE 495 +DLBCL10952T N1 0.7106529536696489 0 1099,1104,516,41,438,854,486,437,325,137 0.137,0.167,0.252,0.257,0.34,0.362,0.364,0.372,0.372,0.376 N1,N1,N1,N1,N1,BN2,ST2,BN2,ST2,N1 5.44891373137968,26.7268608685611,5.43310429171755 0 0 37.608878891658364 26.726860868561126 N1 TRUE 496 +DLBCL10953T BN2 0.5541341867049107 0 104,34,103,894,442,701,431,845,521,742 0.207,0.251,0.253,0.309,0.321,0.324,0.358,0.396,0.434,0.436 MCD,BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD,BN2 17.7995768941426,14.3218430095553 0 0 32.12141990369792 17.799576894142575 BN2 TRUE 497 +DLBCL10957T BN2 0.9218291529946979 0 569,898,1022,1069,399,26,493,566,633,77 0.196,0.198,0.209,0.216,0.227,0.241,0.254,0.272,0.276,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 39.3622899343735,3.33791086368035 0 0 42.70020079805389 39.36228993437354 BN2 TRUE 498 +DLBCL10960T BN2 1 0 261,404,555,527,543,517,546,929,1001,1023 0.157,0.158,0.175,0.185,0.191,0.2,0.212,0.219,0.23,0.231 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.0052855476226 0 0 52.005285547622556 52.005285547622556 BN2 TRUE 499 +DLBCL10961T MCD 1 0 1109,350,450,712,417,155,1048,879,176,312 0.134,0.142,0.155,0.158,0.159,0.159,0.163,0.166,0.173,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5474141786596 0 0 63.54741417865959 63.54741417865959 MCD TRUE 500 +DLBCL10966T BN2 1 0 654,342,38,518,63,763,333,542,958,702 0.157,0.204,0.217,0.222,0.24,0.258,0.262,0.275,0.287,0.293 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.7666483388759 0 0 42.76664833887594 42.76664833887594 BN2 TRUE 501 +DLBCL10967T MCD 0.8654238359032304 0 616,574,1093,1047,1054,475,992,856,44,449 0.119,0.172,0.2,0.237,0.238,0.29,0.295,0.328,0.346,0.352 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.4243445385006,5.81960482584914 0 0 43.24394936434978 37.42434453850065 MCD TRUE 502 +DLBCL10968T BN2 1 0 562,705,560,524,1098,623,553,507,109,258 0.185,0.222,0.278,0.325,0.338,0.343,0.376,0.386,0.389,0.403 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 32.7860788910709 0 0 32.78607889107087 32.78607889107087 BN2 TRUE 503 +DLBCL10972T BN2 0.5850536612677033 0 14,699,802,891,495,23,388,127,552,855 0.17,0.187,0.2,0.217,0.307,0.312,0.313,0.352,0.37,0.373 BN2,BN2,ST2,MCD,MCD,EZB,BN2,BN2,BN2,BN2 22.6599068780435,3.20921246066632,7.86859671996845,4.99361472035434 0 0 38.731330779032646 22.659906878043536 MCD FALSE 504 +DLBCL10973T BN2 0.5235868345588223 0 665,976,131,971,132,967,924,512,1085,582 0.215,0.224,0.225,0.237,0.241,0.245,0.257,0.28,0.291,0.298 BN2,BN2,BN2,EZB,BN2,EZB,ST2,N1,EZB,BN2 21.0848994317616,11.7315311022638,3.56826310864624,3.88541637552205 0 0 40.27011001819369 21.084899431761553 N1 FALSE 505 +DLBCL10974T BN2 0.5476785882515381 0 1061,799,123,1112,347,698,455,293,919,624 0.212,0.239,0.272,0.308,0.31,0.343,0.347,0.363,0.374,0.376 BN2,BN2,N1,BN2,BN2,ST2,MCD,MCD,BN2,EZB 18.0344360763527,2.6600149608685,5.63689664923125,3.68263971595758,2.91488084845143 0 0 32.928868250861434 18.034436076352687 BN2 TRUE 506 +DLBCL10975T BN2 1 0 705,503,524,623,560,562,464,473,391,492 0.268,0.386,0.411,0.422,0.457,0.463,0.481,0.506,0.508,0.514 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.4285876607882 0 0 23.42858766078821 23.42858766078821 BN2 TRUE 507 +DLBCL10978T ST2 0.9251715480722383 0 339,60,394,909,911,728,899,1081,278,910 0.182,0.272,0.297,0.306,0.327,0.365,0.372,0.402,0.418,0.424 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 2.35919809821575,29.1688909833717 0 0 31.528089081587446 29.168890983371693 ST2 TRUE 508 +DLBCL10981T EZB 1 0 781,736,1052,1064,249,217,168,216,766,819 0.228,0.242,0.249,0.271,0.277,0.355,0.36,0.362,0.405,0.423 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0126352302327 0 0 33.01263523023268 33.01263523023268 EZB TRUE 509 +DLBCL10983T MCD 0.7780961693764602 0 420,775,567,541,400,444,170,200,201,448 0.126,0.148,0.162,0.227,0.243,0.248,0.25,0.254,0.269,0.288 MCD,EZB,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 10.7767846044287,37.7883283733258 0 0 48.56511297775457 37.78832837332585 MCD TRUE 510 +DLBCL10984T MCD 0.8952489312790529 0 722,536,721,937,1087,869,340,690,414,299 0.118,0.135,0.154,0.162,0.164,0.187,0.192,0.196,0.231,0.235 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.16378120860865,52.678398483407 0 0 58.84217969201565 52.678398483407 MCD TRUE 511 +DLBCL10985T BN2 0.5847639886806556 0 126,131,665,864,924,505,967,582,132,976 0.153,0.17,0.203,0.208,0.261,0.28,0.285,0.323,0.33,0.358 EZB,BN2,BN2,BN2,ST2,N1,EZB,BN2,BN2,BN2 24.5415521380212,10.0232586558481,3.56826310864624,3.83522888813999 0 0 41.96830279065556 24.54155213802123 N1 FALSE 512 +DLBCL10987T MCD 1 0 459,314,739,730,1043,897,460,520,888,147 0.166,0.175,0.175,0.201,0.205,0.205,0.206,0.21,0.237,0.253 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.9481428110507 0 0 49.948142811050694 49.948142811050694 MCD TRUE 513 +DLBCL10992T MCD 1 0 39,130,367,1027,1101,906,323,332,349,452 0.169,0.187,0.2,0.209,0.213,0.22,0.225,0.251,0.252,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.4853263116606 0 0 46.48532631166059 46.48532631166059 MCD TRUE 514 +DLBCL10993T BN2 1 0 70,815,998,945,591,796,354,527,75,305 0.116,0.192,0.203,0.239,0.239,0.256,0.264,0.265,0.267,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.8536118989926 0 0 45.85361189899258 45.85361189899258 BN2 TRUE 515 +DLBCL10995T N1 0.551849683347814 0 854,437,496,1099,1104,486,41,325,137,523 0.213,0.222,0.252,0.257,0.286,0.291,0.356,0.395,0.426,0.464 BN2,BN2,N1,N1,N1,ST2,N1,ST2,N1,N1 9.20485235171345,18.6828226444649,5.96723732546138 0 0 33.854912321639716 18.682822644464885 N1 TRUE 516 +DLBCL10996T BN2 1 0 543,261,404,1001,499,1071,548,546,646,555 0.11,0.169,0.189,0.195,0.2,0.229,0.233,0.235,0.267,0.272 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.7090996434564 0 0 50.709099643456376 50.709099643456376 BN2 TRUE 517 +DLBCL10998T BN2 1 0 63,333,654,342,542,488,763,559,1077,501 0.119,0.144,0.167,0.169,0.171,0.178,0.193,0.201,0.221,0.222 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 57.9419994487296 0 0 57.941999448729646 57.941999448729646 BN2 TRUE 518 +DLBCL10999T MCD 1 0 856,44,446,573,108,1035,528,449,794,798 0.219,0.229,0.233,0.253,0.258,0.296,0.298,0.304,0.305,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.5908891991375 0 0 37.59088919913755 37.59088919913755 MCD TRUE 519 +DLBCL11000T MCD 1 0 739,314,897,288,459,147,513,991,729,106 0.138,0.147,0.166,0.17,0.174,0.2,0.21,0.211,0.219,0.246 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.8784903783682 0 0 54.87849037836819 54.87849037836819 MCD TRUE 520 +DLBCL11001T BN2 0.7345594177764221 0 442,701,431,455,34,694,1112,579,497,104 0.251,0.318,0.367,0.381,0.382,0.398,0.412,0.423,0.434,0.469 BN2,BN2,BN2,MCD,BN2,BN2,BN2,N1,BN2,MCD 19.7088205614284,4.75578535650314,2.36619916668474 0 0 26.830805084616312 19.708820561428425 MCD FALSE 521 +DLBCL11004T MCD 1 0 717,458,489,837,198,128,466,446,790,154 0.189,0.276,0.305,0.343,0.398,0.417,0.489,0.492,0.513,0.517 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 27.9832236373133 0 0 27.98322363731327 27.98322363731327 MCD TRUE 522 +DLBCL11005T N1 0.7901927423032509 0 137,325,41,174,1104,1099,496,486,516,2 0.167,0.224,0.231,0.292,0.319,0.351,0.386,0.389,0.464,0.494 N1,ST2,N1,N1,N1,N1,N1,ST2,N1,N1 26.4921479813844,7.03403691391746 0 0 33.5261848953019 26.492147981384438 N1 TRUE 523 +DLBCL11181T BN2 0.8832873175972394 0 560,704,705,562,503,507,279,1098,553,110 0.195,0.299,0.3,0.322,0.325,0.411,0.45,0.495,0.504,0.505 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 25.2704586143783,3.33909810734642 0 0 28.609556721724747 25.270458614378324 BN2 TRUE 524 +DLBCL11182T MCD 0.4002364211795327 0 779,43,716,124,205,803,1045,789,617,421 0.179,0.213,0.22,0.293,0.313,0.332,0.368,0.369,0.384,0.399 MCD,ST2,BN2,N1,MCD,BN2,BN2,MCD,N1,MCD 10.2668369720563,13.997325508784,6.01254846251665,4.69593214477731 0 0 34.9726430881343 13.997325508783991 MCD TRUE 525 +DLBCL11187T MCD 0.9145816672953094 0 580,678,1042,453,881,549,494,296,844,16 0.115,0.139,0.148,0.151,0.165,0.17,0.177,0.186,0.187,0.197 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 5.35257853160209,57.3105332632288 0 0 62.66311179483094 57.31053326322885 MCD TRUE 526 +DLBCL11191T BN2 1 0 591,555,796,1023,499,404,998,815,261,905 0.15,0.152,0.158,0.173,0.185,0.213,0.221,0.225,0.225,0.233 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2185865310043 0 0 53.218586531004306 53.218586531004306 BN2 TRUE 527 +DLBCL11195T MCD 1 0 297,798,804,413,794,434,17,108,573,166 0.145,0.148,0.152,0.178,0.18,0.185,0.186,0.191,0.198,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.4270805330322 0 0 57.4270805330322 57.4270805330322 MCD TRUE 528 +DLBCL11196T ST2 0.7531326878151179 0 830,381,485,490,412,698,558,1066,970,946 0.266,0.281,0.297,0.32,0.361,0.361,0.365,0.408,0.415,0.421 ST2,ST2,ST2,ST2,ST2,ST2,ST2,N1,BN2,BN2 4.78700278570589,2.45308933110362,22.0877765780435 0 0 29.327868694853 22.087776578043492 ST2 TRUE 529 +DLBCL11203T BN2 0.7015701618727962 0 481,293,919,803,1061,123,119,1066,1045,506 0.15,0.219,0.299,0.315,0.351,0.37,0.385,0.392,0.45,0.452 BN2,MCD,BN2,BN2,BN2,N1,BN2,N1,BN2,BN2 23.0902119669446,4.56806360908603,5.25391656353637 0 0 32.912192139566955 23.09021196694456 N1 FALSE 530 +DLBCL11205T BN2 0.9176420618033492 0 952,808,10,352,258,895,109,408,187,553 0.196,0.22,0.227,0.308,0.319,0.323,0.33,0.348,0.356,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 31.987977957949,2.87090579361559 0 0 34.85888375156461 31.987977957949017 BN2 TRUE 531 +DLBCL11242T MCD 0.9035380869000501 0 201,859,1082,448,533,772,933,200,749,567 0.196,0.221,0.245,0.249,0.261,0.262,0.27,0.277,0.287,0.291 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.81974693188095,35.7787516788929 0 0 39.59849861077381 35.77875167889286 MCD TRUE 532 +DLBCL11256T MCD 0.7689056783881708 0 772,448,933,859,749,436,400,567,532,775 0.11,0.126,0.151,0.167,0.171,0.23,0.233,0.25,0.261,0.266 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 12.8839302653778,42.8678951170693 0 0 55.75182538244707 42.86789511706931 MCD TRUE 533 +DLBCL11257T EZB 0.5167621771356284 0 813,94,193,371,444,930,170,419,510,541 0.129,0.193,0.201,0.245,0.311,0.321,0.334,0.374,0.379,0.396 EZB,EZB,BN2,EZB,EZB,ST2,MCD,MCD,MCD,MCD 4.98437973770489,20.2428215580019,10.8329701648694,3.11224179576207 0 0 39.17241325633826 20.24282155800191 MCD FALSE 534 +DLBCL11258T BN2 0.7987519745556503 0 173,624,765,196,347,773,301,1049,572,1112 0.131,0.207,0.208,0.27,0.277,0.311,0.336,0.355,0.446,0.448 BN2,EZB,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2 30.3858705022205,4.8385874993476,2.81722636374981 0 0 38.041684365317884 30.38587050222047 BN2 TRUE 535 +DLBCL11353T MCD 0.90564133910778 0 511,722,869,340,1087,937,721,414,456,415 0.135,0.149,0.156,0.158,0.183,0.184,0.185,0.197,0.214,0.22 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.42655843220515,52.0833551347494 0 0 57.50991356695458 52.08335513474943 MCD TRUE 536 +DLBCL11428T EZB 1 0 647,719,157,377,51,939,233,1034,960,378 0.253,0.276,0.355,0.367,0.413,0.413,0.415,0.419,0.432,0.482 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.145267090822 0 0 27.145267090821957 27.145267090821957 EZB TRUE 537 +DLBCL11429T BN2 1 0 435,99,650,700,955,1010,780,731,943,959 0.149,0.187,0.187,0.198,0.213,0.214,0.222,0.223,0.242,0.244 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.045762267134 0 0 49.04576226713399 49.04576226713399 BN2 TRUE 538 +DLBCL11430T BN2 0.8288073907137542 0 88,189,22,470,358,840,570,462,571,487 0.129,0.133,0.214,0.229,0.242,0.253,0.261,0.307,0.316,0.326 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2 37.9506723509583,7.83882322566278 0 0 45.78949557662111 37.95067235095833 EZB FALSE 539 +DLBCL11432T BN2 1 0 929,841,546,905,373,555,1023,395,1001,499 0.133,0.137,0.179,0.184,0.191,0.215,0.231,0.232,0.248,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.469331659313 0 0 52.469331659313006 52.469331659313006 MCD FALSE 540 +DLBCL11433T MCD 0.84385197082676 0 200,170,419,510,1082,201,775,420,567,371 0.166,0.169,0.18,0.227,0.229,0.238,0.246,0.248,0.265,0.332 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,EZB 7.08546693957048,38.291134847119 0 0 45.37660178668948 38.291134847119 MCD TRUE 541 +DLBCL11434T BN2 1 0 559,63,518,305,702,75,333,354,488,1077 0.15,0.158,0.171,0.178,0.179,0.179,0.179,0.188,0.197,0.2 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.6440807790698 0 0 56.64408077906984 56.64408077906984 BN2 TRUE 542 +DLBCL11435T BN2 1 0 517,261,404,499,1001,546,1071,548,555,929 0.11,0.164,0.184,0.191,0.191,0.227,0.23,0.242,0.262,0.263 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.611235388721 0 0 51.611235388721006 51.611235388721006 BN2 TRUE 543 +DLBCL11437T ST2 0.9294686914386189 0 309,1067,899,909,1007,590,508,797,1096,339 0.126,0.302,0.345,0.363,0.373,0.424,0.432,0.444,0.459,0.463 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2 2.17678777842418,28.6859286920889 0 0 30.86271647051308 28.685928692088904 ST2 TRUE 544 +DLBCL11439T EZB 0.9059349678616722 0 846,620,144,736,1052,689,249,847,143,786 0.263,0.308,0.313,0.358,0.374,0.375,0.387,0.402,0.408,0.413 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 25.6639245612075,2.66473640414133 0 0 28.328660965348824 25.663924561207494 ST2 FALSE 545 +DLBCL11443T BN2 0.8890255889917776 0 929,841,1001,540,499,555,543,1071,517,905 0.151,0.166,0.169,0.179,0.212,0.224,0.227,0.227,0.235,0.248 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 44.8197316095137,5.59471333390202 0 0 50.414444943415766 44.819731609513745 BN2 TRUE 546 +DLBCL11444T EZB 1 0 873,387,107,757,1117,102,848,84,740,140 0.224,0.324,0.327,0.349,0.365,0.385,0.394,0.402,0.405,0.405 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.7777581046657 0 0 28.777758104665708 28.777758104665708 EZB TRUE 547 +DLBCL11445T BN2 1 0 646,959,999,780,1071,517,345,99,435,543 0.14,0.168,0.207,0.214,0.219,0.233,0.234,0.237,0.238,0.242 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.3216782531329 0 0 48.321678253132866 48.321678253132866 BN2 TRUE 548 +DLBCL11447T MCD 1 0 453,296,526,298,494,580,1042,920,16,678 0.119,0.127,0.17,0.171,0.176,0.18,0.181,0.198,0.205,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.4717947188702 0 0 59.47179471887016 59.47179471887016 MCD TRUE 549 +DLBCL11450T MCD 1 0 1036,326,416,821,283,469,491,447,311,349 0.108,0.135,0.15,0.152,0.159,0.167,0.168,0.195,0.204,0.224 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.6422637989661 0 0 62.64226379896612 62.64226379896612 MCD TRUE 550 +DLBCL11451T MCD 1 0 434,851,767,804,246,762,166,17,798,528 0.177,0.186,0.207,0.21,0.216,0.216,0.218,0.243,0.247,0.261 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.4263092771587 0 0 46.42630927715871 46.42630927715871 MCD TRUE 551 +DLBCL11452T MCD 0.5162070018973152 0 135,388,285,891,127,100,14,386,185,180 0.2,0.28,0.283,0.288,0.307,0.313,0.318,0.32,0.321,0.339 MCD,BN2,BN2,MCD,BN2,MCD,BN2,MCD,BN2,MCD 16.6194736455328,17.7329740143345 0 0 34.352447659867316 17.73297401433455 BN2 FALSE 552 +DLBCL11454T BN2 1 0 187,1098,258,109,355,562,10,352,503,531 0.15,0.158,0.167,0.277,0.285,0.307,0.308,0.363,0.376,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.631520643389 0 0 40.63152064338896 40.63152064338896 BN2 TRUE 553 +DLBCL11455T ST2 0.41283510615494007 0 747,673,1044,1089,124,935,682,803,43,177 0.178,0.247,0.292,0.306,0.353,0.381,0.383,0.397,0.398,0.41 ST2,BN2,MCD,MCD,N1,EZB,ST2,BN2,ST2,ST2 6.57172407355645,2.6235013567236,6.69713132456042,2.83079789870513,13.1642331140314 0 0 31.887387767576985 13.164233114031383 ST2 TRUE 554 +DLBCL11457T BN2 0.9131142736375694 0 527,1023,499,796,591,905,929,540,546,404 0.152,0.157,0.175,0.177,0.183,0.193,0.198,0.215,0.224,0.225 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 48.9428824767028,4.6570708804266 0 0 53.59995335712942 48.94288247670283 BN2 TRUE 555 +DLBCL11458T EZB 1 0 811,724,272,979,596,628,853,732,310,750 0.167,0.233,0.272,0.285,0.293,0.303,0.307,0.338,0.344,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1886409944813 0 0 36.18864099448135 36.18864099448135 EZB TRUE 556 +DLBCL11459T ST2 1 0 793,1053,982,1016,692,598,1086,642,177,940 0.147,0.156,0.205,0.212,0.244,0.276,0.287,0.299,0.322,0.33 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.5327255005947 0 0 43.53272550059469 43.53272550059469 ST2 TRUE 557 +DLBCL11460T BN2 0.6671704129248003 0 485,936,321,946,970,490,529,670,86,928 0.246,0.333,0.338,0.342,0.35,0.356,0.365,0.387,0.394,0.417 ST2,BN2,BN2,BN2,BN2,ST2,ST2,BN2,BN2,BN2 19.2581229216921,9.6072502252774 0 0 28.86537314696946 19.258122921692056 ST2 FALSE 558 +DLBCL11461T BN2 1 0 354,542,1077,75,331,945,305,63,488,333 0.142,0.15,0.157,0.16,0.161,0.169,0.178,0.183,0.184,0.184 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.4139160516377 0 0 60.4139160516377 60.4139160516377 BN2 TRUE 559 +DLBCL11464T BN2 0.9042039892467344 0 524,562,503,705,704,1098,553,279,110,187 0.195,0.244,0.278,0.31,0.339,0.405,0.41,0.412,0.444,0.448 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 27.8720719464608,2.95291033400912 0 0 30.82498228046997 27.87207194646085 BN2 TRUE 560 +DLBCL11473T BN2 0.8057727181268259 0 636,926,399,575,487,77,212,95,1069,633 0.113,0.199,0.199,0.221,0.249,0.27,0.281,0.281,0.283,0.311 BN2,ST2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 36.2517012809773,3.70270878555701,5.03557341820238 0 0 44.98998348473665 36.25170128097726 BN2 TRUE 561 +DLBCL11474T BN2 1 0 503,560,1098,705,553,524,258,187,109,623 0.185,0.244,0.282,0.294,0.307,0.322,0.347,0.354,0.367,0.408 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 33.7080507377908 0 0 33.708050737790785 33.708050737790785 BN2 TRUE 562 +DLBCL11477T BN2 1 0 763,15,341,488,871,175,333,197,282,342 0.156,0.174,0.178,0.18,0.184,0.188,0.196,0.199,0.2,0.209 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.980016215704 0 0 53.98001621570405 53.98001621570405 BN2 TRUE 563 +DLBCL11482T BN2 1 0 357,393,58,158,1028,572,968,172,777,651 0.157,0.182,0.207,0.257,0.35,0.381,0.444,0.466,0.468,0.502 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.5850922931558 0 0 34.585092293155796 34.585092293155796 BN2 TRUE 564 +DLBCL11489T MCD 1 0 1038,679,415,461,987,869,340,932,414,966 0.12,0.14,0.148,0.168,0.178,0.194,0.203,0.204,0.207,0.218 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.164803451732 0 0 58.16480345173203 58.16480345173203 MCD TRUE 565 +DLBCL11490T BN2 1 0 353,26,569,951,1022,482,401,498,578,957 0.187,0.192,0.203,0.213,0.223,0.232,0.261,0.272,0.288,0.301 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2706569829355 0 0 43.27065698293551 43.27065698293551 BN2 TRUE 566 +DLBCL11491T MCD 0.7582851704462675 0 775,420,510,400,448,436,533,201,772,541 0.119,0.141,0.162,0.202,0.225,0.248,0.25,0.251,0.259,0.265 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 12.2497723417704,38.4288408173255 0 0 50.678613159095846 38.42884081732545 MCD TRUE 567 +DLBCL11493T MCD 1 0 1021,328,1076,465,443,961,468,1008,33,276 0.119,0.139,0.158,0.165,0.196,0.199,0.201,0.201,0.203,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.5638731044218 0 0 57.56387310442182 57.56387310442182 MCD TRUE 568 +DLBCL11494T BN2 1 0 1022,26,498,566,898,399,353,482,1069,951 0.128,0.146,0.196,0.203,0.287,0.289,0.289,0.302,0.311,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8423845360634 0 0 44.84238453606339 44.84238453606339 BN2 TRUE 569 +DLBCL11496T BN2 0.7103487880525121 0 358,390,88,571,840,539,22,487,189,212 0.186,0.213,0.233,0.243,0.256,0.261,0.264,0.271,0.293,0.293 BN2,BN2,BN2,EZB,BN2,EZB,EZB,BN2,BN2,BN2 28.8046105565793,11.7453432703989 0 0 40.549953826978275 28.80461055657934 BN2 TRUE 570 +DLBCL11497T BN2 0.8039191730537639 0 840,22,570,186,672,1106,470,88,153,539 0.185,0.229,0.243,0.256,0.259,0.284,0.288,0.295,0.315,0.316 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 30.8586028278709,7.5266028771399 0 0 38.38520570501078 30.85860282787089 EZB FALSE 571 +DLBCL11498T BN2 0.8869845087558504 0 393,704,301,196,564,357,777,535,158,279 0.306,0.339,0.343,0.349,0.381,0.409,0.41,0.446,0.446,0.467 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.1766007247592,2.95305598961611 0 0 26.129656714375265 23.176600724759155 BN2 TRUE 572 +DLBCL11500T MCD 1 0 798,528,449,804,297,829,108,519,434,794 0.175,0.198,0.223,0.23,0.241,0.241,0.246,0.253,0.257,0.263 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.5923986199939 0 0 43.592398619993894 43.592398619993894 MCD TRUE 573 +DLBCL11503T MCD 0.9246001479721179 0 1093,502,616,1054,1047,475,1105,992,697,856 0.136,0.172,0.174,0.272,0.279,0.303,0.324,0.324,0.369,0.388 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.08943127580521,37.8845387349419 0 0 40.97397001074708 37.88453873494187 N1 FALSE 574 +DLBCL11506T BN2 0.8464013966349968 0 926,95,399,561,636,1022,498,569,1069,26 0.159,0.192,0.212,0.221,0.23,0.299,0.303,0.318,0.326,0.336 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.6106728250469,6.28088638391368 0 0 40.89155920896058 34.6106728250469 BN2 TRUE 575 +DLBCL11507T BN2 0.8111462473575551 0 462,87,470,189,539,1106,864,22,88,186 0.371,0.373,0.403,0.426,0.452,0.452,0.458,0.467,0.478,0.495 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2 18.7141863543878,4.35709877496143 0 0 23.071285129349206 18.714186354387774 EZB FALSE 576 +DLBCL11508T BN2 0.920412083072746 0 492,473,980,348,391,827,623,2,327,95 0.264,0.293,0.322,0.338,0.382,0.434,0.455,0.471,0.471,0.484 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 24.5651907620178,2.12414895200272 0 0 26.689339714020512 24.56519076201779 BN2 TRUE 577 +DLBCL11511T BN2 1 0 1111,482,401,957,951,264,640,353,566,318 0.171,0.179,0.186,0.188,0.19,0.215,0.227,0.232,0.288,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.4620665464375 0 0 47.46206654643749 47.46206654643749 BN2 TRUE 578 +DLBCL11513T BN2 0.40059562807807286 0 1045,663,455,123,1112,716,801,421,521,442 0.246,0.269,0.293,0.337,0.359,0.385,0.408,0.422,0.423,0.476 BN2,ST2,MCD,N1,BN2,BN2,ST2,MCD,MCD,BN2 11.5509938026914,8.14837695161104,2.96925821045842,6.16591887618615 0 0 28.83454784094702 11.550993802691412 N1 FALSE 579 +DLBCL11514T MCD 0.9069658329607718 0 526,678,1042,453,881,844,549,16,494,296 0.115,0.137,0.138,0.161,0.169,0.173,0.18,0.189,0.192,0.198 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.79682945971136,56.5117787021385 0 0 62.30860816184983 56.51177870213848 MCD TRUE 580 +DLBCL11516T EZB 0.8880059732313779 0 385,975,369,645,1050,643,241,687,379,254 0.141,0.142,0.145,0.155,0.177,0.178,0.19,0.199,0.218,0.248 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 51.2022343484644,6.45755161237081 0 0 57.65978596083525 51.202234348464444 EZB TRUE 581 +DLBCL11518T BN2 0.45528573827442087 0 967,924,665,432,1085,153,864,505,672,512 0.154,0.163,0.233,0.236,0.245,0.253,0.279,0.298,0.304,0.323 EZB,ST2,BN2,BN2,EZB,BN2,BN2,N1,BN2,N1 19.3674167229409,10.5952458263947,6.45202512543017,6.12434901095472 0 0 42.53903668572047 19.367416722940916 BN2 TRUE 582 +DLBCL11519T ST2 0.7148586036625073 0 612,425,366,160,1029,896,785,161,668,662 0.151,0.176,0.187,0.218,0.249,0.264,0.266,0.267,0.281,0.3 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2,ST2 8.91903782277529,3.7633127236905,31.7950585893686 0 0 44.47740913583443 31.795058589368644 ST2 TRUE 583 +DLBCL11520T BN2 1 0 65,681,64,589,674,695,1113,795,652,1060 0.177,0.192,0.218,0.229,0.231,0.25,0.255,0.268,0.324,0.326 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.9246079486202 0 0 41.92460794862025 41.92460794862025 BN2 TRUE 584 +DLBCL11526T EZB 1 0 818,427,307,220,912,62,219,292,138,316 0.173,0.233,0.238,0.306,0.366,0.37,0.375,0.382,0.384,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.2912011501147 0 0 33.29120115011468 33.29120115011468 EZB TRUE 585 +DLBCL11527T EZB 0.5242189729618344 0 1091,320,974,834,685,194,824,657,1024,372 0.149,0.205,0.212,0.218,0.282,0.295,0.339,0.344,0.379,0.384 EZB,ST2,EZB,ST2,ST2,EZB,EZB,BN2,EZB,ST2 2.90873502968123,20.4043614612649,15.6102590411522 0 0 38.92335553209832 20.404361461264916 ST2 FALSE 586 +DLBCL11530T EZB 0.784827254364737 0 227,368,1056,245,595,995,329,857,867,618 0.143,0.157,0.17,0.182,0.184,0.187,0.195,0.197,0.202,0.207 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 7.00202315498083,43.5743910116191,4.94458203897852 0 0 55.52099620557848 43.574391011619134 EZB TRUE 587 +DLBCL11532T EZB 1 0 259,1115,224,737,225,183,268,619,981,238 0.131,0.176,0.19,0.2,0.24,0.251,0.258,0.271,0.283,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.29302205437 0 0 46.293022054370006 46.293022054370006 EZB TRUE 588 +DLBCL11537T BN2 1 0 64,65,584,695,681,1113,674,652,477,345 0.137,0.17,0.229,0.236,0.249,0.279,0.331,0.336,0.357,0.373 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.8658326598655 0 0 40.86583265986549 40.86583265986549 BN2 TRUE 589 +DLBCL11538T BN2 0.38637406855241607 0 964,1096,899,622,893,691,1084,909,854,437 0.173,0.186,0.278,0.298,0.299,0.331,0.344,0.347,0.408,0.409 BN2,EZB,ST2,ST2,N1,ST2,BN2,ST2,BN2,BN2 13.5882476393233,5.38140869280194,3.34321936211639,12.8557568442536 0 0 35.16863253849516 13.58824763932326 ST2 FALSE 590 +DLBCL11539T BN2 1 0 796,527,1023,998,555,305,75,815,905,499 0.12,0.15,0.162,0.174,0.183,0.202,0.21,0.23,0.23,0.234 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.0425512582718 0 0 55.04255125827184 55.04255125827184 BN2 TRUE 591 +DLBCL11541T EZB 0.7536409408794107 0 221,146,248,145,831,253,687,254,379,385 0.159,0.175,0.202,0.22,0.263,0.277,0.293,0.315,0.32,0.332 EZB,BN2,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 10.2613796458082,31.3907507139908 0 0 41.652130359799 31.390750713990787 EZB TRUE 592 +DLBCL11542T EZB 1 0 219,870,47,726,220,727,138,725,307,292 0.193,0.223,0.242,0.261,0.272,0.301,0.339,0.344,0.347,0.353 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1787877353356 0 0 36.178787735335646 36.178787735335646 EZB TRUE 593 +DLBCL11558T EZB 1 0 805,876,80,632,29,667,760,46,953,989 0.138,0.16,0.163,0.166,0.171,0.172,0.182,0.194,0.203,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7798200970318 0 0 57.77982009703184 57.77982009703184 EZB TRUE 594 +DLBCL11559T EZB 0.7871826743970243 0 1056,867,587,857,618,809,227,995,368,1074 0.124,0.148,0.184,0.185,0.19,0.199,0.207,0.217,0.219,0.222 EZB,ST2,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.83826868343364,42.855089468438,6.7477402898143 0 0 54.441098441685966 42.85508946843803 EZB TRUE 595 +DLBCL11562T EZB 1 0 979,732,136,118,257,724,272,556,181,853 0.182,0.182,0.242,0.28,0.287,0.287,0.288,0.293,0.294,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.2412499646302 0 0 39.24124996463016 39.24124996463016 EZB TRUE 596 +DLBCL11563T EZB 1 0 709,21,250,868,164,251,1075,938,816,753 0.14,0.164,0.168,0.189,0.203,0.216,0.217,0.227,0.238,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1695079139727 0 0 51.16950791397265 51.16950791397265 EZB TRUE 597 +DLBCL11564T ST2 0.922048511983224 0 940,1068,825,1003,774,793,982,915,557,835 0.154,0.197,0.219,0.234,0.24,0.25,0.258,0.259,0.276,0.296 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 3.37767179500539,39.9527620548075 0 0 43.330433849812856 39.952762054807465 ST2 TRUE 598 +DLBCL11566T EZB 1 0 150,644,152,1037,151,418,866,1088,776,613 0.132,0.149,0.181,0.182,0.184,0.192,0.216,0.232,0.24,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.163480843763 0 0 53.16348084376302 53.16348084376302 EZB TRUE 599 +DLBCL11567T EZB 1 0 82,609,1080,751,57,292,138,726,427,725 0.162,0.196,0.244,0.279,0.301,0.326,0.329,0.407,0.469,0.469 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.1074343987878 0 0 35.107434398787845 35.107434398787845 EZB TRUE 600 +DLBCL11568T EZB 1 0 688,164,1114,849,1075,1006,753,121,874,122 0.217,0.233,0.241,0.254,0.262,0.269,0.269,0.272,0.285,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9257055848102 0 0 38.92570558481024 38.92570558481024 EZB TRUE 601 +DLBCL11571T ST2 0.777775812313285 0 842,161,1029,671,359,160,996,668,366,835 0.14,0.161,0.162,0.166,0.194,0.194,0.21,0.236,0.27,0.273 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,EZB 7.95008592918014,3.65963849473544,40.6335734131435 0 0 52.24329783705911 40.63357341314353 ST2 TRUE 602 +DLBCL11572T EZB 1 0 1059,25,384,605,149,73,239,74,61,833 0.11,0.143,0.151,0.151,0.167,0.189,0.191,0.194,0.198,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.0415565245514 0 0 61.04155652455141 61.04155652455141 EZB TRUE 603 +DLBCL11573T EZB 1 0 392,313,986,101,1012,199,49,302,1030,981 0.141,0.156,0.159,0.167,0.194,0.196,0.201,0.207,0.216,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8957603835297 0 0 54.89576038352974 54.89576038352974 EZB TRUE 604 +DLBCL11575T EZB 1 0 384,1059,61,833,603,74,629,25,73,149 0.102,0.141,0.147,0.151,0.151,0.155,0.171,0.179,0.202,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.5943218951677 0 0 64.59432189516767 64.59432189516767 EZB TRUE 605 +DLBCL11576T ST2 0.42550319020191063 0 338,1118,689,607,1052,346,736,781,872,620 0.184,0.291,0.3,0.307,0.376,0.385,0.389,0.451,0.471,0.496 BN2,ST2,ST2,ST2,EZB,ST2,EZB,EZB,BN2,EZB 7.57127708784354,9.46231772607042,12.6159950939926 0 0 29.649589907906588 12.615995093992627 ST2 TRUE 606 +DLBCL11579T ST2 0.6143157339109457 0 1118,689,338,346,606,828,620,174,614,1052 0.195,0.278,0.285,0.297,0.307,0.313,0.424,0.516,0.518,0.527 ST2,ST2,BN2,ST2,ST2,ST2,EZB,N1,EZB,EZB 3.51223589577449,6.1845404543512,1.93847919356281,18.532569713155 0 0 30.167825256843464 18.53256971315496 ST2 TRUE 607 +DLBCL11581T EZB 0.8230437310454322 0 90,411,614,858,954,786,143,944,144,622 0.224,0.284,0.323,0.325,0.329,0.36,0.38,0.427,0.458,0.467 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 24.0986572760463,5.18126597351923 0 0 29.27992324956557 24.098657276046342 EZB TRUE 608 +DLBCL11584T EZB 1 0 600,82,751,1080,57,292,138,948,639,122 0.196,0.226,0.261,0.276,0.297,0.421,0.425,0.427,0.43,0.443 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9961145215271 0 0 31.99611452152709 31.99611452152709 EZB TRUE 609 +DLBCL11585T EZB 1 0 649,1,139,273,1072,1020,112,9,648,1095 0.142,0.145,0.159,0.183,0.191,0.201,0.202,0.206,0.235,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.140700473744 0 0 54.140700473744005 54.140700473744005 EZB TRUE 610 +DLBCL11586T BN2 0.9239278035451852 0 463,352,327,827,895,109,10,438,391,952 0.177,0.197,0.216,0.261,0.267,0.273,0.293,0.34,0.341,0.364 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 35.7318539240647,2.94200542615586 0 0 38.67385935022053 35.73185392406468 BN2 TRUE 611 +DLBCL11587T ST2 0.7259186020031683 0 583,425,366,896,662,160,785,1029,668,161 0.151,0.168,0.21,0.232,0.258,0.265,0.268,0.296,0.306,0.309 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2,ST2 8.03845231305224,3.72631402607679,31.1595854232078 0 0 42.9243517623368 31.159585423207762 ST2 TRUE 612 +DLBCL11589T EZB 1 0 776,866,151,27,152,150,98,599,644,97 0.114,0.146,0.168,0.197,0.205,0.221,0.226,0.241,0.272,0.305 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6236067562591 0 0 51.62360675625911 51.62360675625911 EZB TRUE 613 +DLBCL11590T EZB 0.6205452692239313 0 954,944,828,608,786,90,620,411,143,893 0.171,0.264,0.31,0.323,0.379,0.442,0.454,0.459,0.459,0.464 ST2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,N1 18.3435429043736,2.1562808703383,9.06053840049969 0 0 29.560362175211573 18.34354290437358 EZB TRUE 614 +DLBCL11591T EZB 0.8823838198698618 0 398,203,134,1088,389,1000,83,84,97,644 0.197,0.224,0.245,0.257,0.26,0.267,0.297,0.298,0.314,0.34 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.46183498772074,33.473719310879 0 0 37.935554298599726 33.473719310878984 EZB TRUE 615 +DLBCL11646T MCD 0.8700873603391058 0 502,574,1093,1047,1054,475,992,856,44,697 0.119,0.174,0.196,0.22,0.22,0.271,0.314,0.317,0.332,0.343 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.542539287304,5.754781929134 0 0 44.297321216438036 38.54253928730404 MCD TRUE 616 +DLBCL11648T MCD 1 0 789,713,441,205,985,779,525,421,466,223 0.177,0.206,0.219,0.237,0.315,0.315,0.384,0.387,0.403,0.422 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.6899057138429 0 0 35.68990571384294 35.68990571384294 N1 FALSE 617 +DLBCL11651T EZB 0.798543626490755 0 1074,947,867,995,274,863,227,595,368,1056 0.137,0.143,0.149,0.156,0.169,0.18,0.19,0.19,0.191,0.205 EZB,EZB,ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB 5.27139314594622,47.5186212498303,6.71659197293695 0 0 59.50660636871351 47.518621249830346 EZB TRUE 618 +DLBCL11656T EZB 1 0 983,737,588,259,267,224,720,268,664,1083 0.197,0.246,0.271,0.277,0.281,0.283,0.29,0.305,0.311,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.4413430704149 0 0 36.4413430704149 36.4413430704149 EZB TRUE 619 +DLBCL11657T EZB 0.467016131205936 0 144,786,689,143,545,1118,846,828,338,607 0.206,0.25,0.297,0.308,0.308,0.343,0.377,0.398,0.413,0.424 EZB,EZB,ST2,EZB,ST2,ST2,EZB,ST2,BN2,ST2 2.42001322409297,14.7409029855385,14.4030962527953 0 0 31.564012462426692 14.740902985538463 EZB TRUE 620 +DLBCL11658T MCD 1 0 295,878,978,902,452,961,33,451,465,1101 0.152,0.162,0.193,0.198,0.199,0.21,0.222,0.231,0.233,0.249 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.9481863193142 0 0 49.948186319314225 49.948186319314225 ST2 FALSE 621 +DLBCL11661T ST2 0.337066436803223 0 691,1084,1096,893,964,590,411,11,899,944 0.163,0.206,0.254,0.287,0.291,0.298,0.353,0.372,0.402,0.433 ST2,BN2,EZB,N1,BN2,ST2,EZB,EZB,ST2,EZB 8.2987135204085,11.7748525985251,3.48270885831474,11.9771122051052 0 0 35.533387182353565 11.977112205105232 ST2 TRUE 622 +DLBCL11662T BN2 1 0 391,348,463,503,705,611,492,327,109,827 0.198,0.232,0.331,0.343,0.363,0.384,0.392,0.398,0.399,0.407 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.7001169422988 0 0 30.700116942298823 30.700116942298823 BN2 TRUE 623 +DLBCL11664T BN2 0.9235670722022774 0 765,347,173,535,773,1049,196,506,799,936 0.159,0.174,0.176,0.207,0.306,0.331,0.339,0.376,0.38,0.406 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 36.5130793294019,3.02176381126488 0 0 39.534843140666815 36.51307932940194 EZB FALSE 624 +DLBCL11665T ST2 0.7098139324637762 0 969,703,901,1025,925,1024,950,1107,1041,685 0.243,0.274,0.313,0.314,0.322,0.328,0.35,0.36,0.366,0.375 ST2,ST2,ST2,BN2,ST2,EZB,EZB,ST2,ST2,ST2 3.18548379763693,5.90339542938218,22.2319877745799 0 0 31.32086700159905 22.231987774579945 ST2 TRUE 625 +DLBCL11666T EZB 1 0 1083,377,745,238,737,378,206,259,619,981 0.245,0.322,0.344,0.36,0.369,0.376,0.434,0.44,0.445,0.459 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.2391912473473 0 0 27.239191247347275 27.239191247347275 EZB TRUE 626 +DLBCL11667T EZB 1 0 1073,743,865,942,1018,883,156,639,247,406 0.265,0.281,0.294,0.332,0.333,0.342,0.344,0.358,0.359,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.8601591683131 0 0 30.86015916831308 30.86015916831308 EZB TRUE 627 +DLBCL11669T EZB 1 0 653,811,556,750,304,887,310,36,724,430 0.231,0.236,0.303,0.323,0.334,0.357,0.362,0.381,0.382,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3608486352754 0 0 31.360848635275403 31.360848635275403 EZB TRUE 628 +DLBCL11670T EZB 1 0 833,61,184,384,605,74,1059,603,163,25 0.132,0.136,0.162,0.17,0.171,0.188,0.207,0.216,0.225,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5271600777563 0 0 56.527160077756285 56.527160077756285 EZB TRUE 629 +DLBCL11671T BN2 1 0 1060,335,477,943,650,99,780,345,538,795 0.127,0.131,0.189,0.196,0.215,0.264,0.274,0.275,0.301,0.303 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.2014540047202 0 0 48.20145400472025 48.20145400472025 BN2 TRUE 630 +DLBCL11672T EZB 1 0 228,93,740,757,102,387,1079,274,863,947 0.126,0.15,0.161,0.164,0.181,0.189,0.194,0.196,0.217,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1322440849661 0 0 57.13224408496606 57.13224408496606 EZB TRUE 631 +DLBCL11675T EZB 1 0 667,876,760,46,80,594,914,29,1002,1026 0.114,0.114,0.122,0.144,0.158,0.166,0.169,0.179,0.186,0.189 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1978715070976 0 0 67.1978715070976 67.1978715070976 EZB TRUE 632 +DLBCL11676T BN2 0.878411001149196 0 493,634,77,1069,212,898,487,399,390,498 0.125,0.147,0.155,0.163,0.186,0.203,0.239,0.266,0.271,0.276 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.5025407045576,6.43684717164143 0 0 52.939387876199035 46.5025407045576 BN2 TRUE 633 +DLBCL11677T BN2 0.871988312287511 0 633,77,212,493,1069,487,390,898,358,399 0.147,0.158,0.164,0.17,0.209,0.223,0.226,0.247,0.297,0.301 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2420224649849,6.34811751240415 0 0 49.59013997738908 43.24202246498493 BN2 TRUE 634 +DLBCL11678T EZB 1 0 125,931,35,48,820,364,1063,880,256,89 0.137,0.138,0.151,0.17,0.172,0.187,0.19,0.224,0.231,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5288362380621 0 0 56.52883623806206 56.52883623806206 EZB TRUE 635 +DLBCL11679T BN2 0.8095534640463232 0 561,399,926,575,487,77,212,1069,95,633 0.113,0.196,0.211,0.23,0.237,0.257,0.267,0.272,0.293,0.299 BN2,BN2,ST2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 36.6726005603819,3.89256824823629,4.73461951800364 0 0 45.2997883266218 36.67260056038187 BN2 TRUE 636 +DLBCL11680T EZB 1 0 984,914,46,760,1078,317,80,29,738,876 0.143,0.181,0.188,0.21,0.21,0.213,0.215,0.221,0.222,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1933706394934 0 0 50.19337063949339 50.19337063949339 EZB TRUE 637 +DLBCL11684T EZB 1 0 847,1100,823,216,405,1013,249,79,766,217 0.176,0.314,0.316,0.342,0.354,0.361,0.361,0.378,0.383,0.389 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.1474438504728 0 0 31.147443850472847 31.147443850472847 EZB TRUE 638 +DLBCL11685T EZB 1 0 1014,865,122,121,190,754,849,18,406,627 0.175,0.279,0.305,0.32,0.322,0.323,0.34,0.351,0.352,0.358 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.3439198997024 0 0 33.343919899702364 33.343919899702364 EZB TRUE 639 +DLBCL_P04-Tumor BN2 1 0 264,1111,871,578,957,318,1062,401,15,482 0.128,0.19,0.204,0.227,0.236,0.239,0.268,0.269,0.27,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.5103437265782 0 0 45.51034372657823 45.51034372657823 BN2 TRUE 640 +SP124957 EZB 0.7677939265927072 0 810,684,382,319,322,791,1033,676,194,714 0.336,0.341,0.386,0.393,0.399,0.406,0.408,0.415,0.434,0.442 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 19.5299070358298,5.90648463048493 0 0 25.43639166631473 19.5299070358298 EZB TRUE 641 +SP124969 ST2 0.9174542849756441 0 308,793,915,557,1053,598,1016,412,119,940 0.24,0.261,0.265,0.299,0.339,0.343,0.361,0.369,0.383,0.385 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.61434925878239,29.0571827887746 0 0 31.671532047556948 29.057182788774558 ST2 TRUE 642 +SP124971 EZB 0.867294206357812 0 645,1050,975,241,385,581,254,369,733,687 0.131,0.135,0.148,0.149,0.168,0.178,0.198,0.216,0.254,0.257 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.9383390249784,7.64112900201748 0 0 57.579468026995876 49.9383390249784 EZB TRUE 643 +SP124973 EZB 1 0 599,150,418,152,1088,1037,83,151,866,84 0.149,0.181,0.182,0.183,0.184,0.192,0.216,0.227,0.236,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2041833543833 0 0 51.20418335438326 51.20418335438326 EZB TRUE 644 +SP192765 EZB 1 0 1050,643,241,975,581,385,369,254,687,67 0.124,0.131,0.139,0.144,0.155,0.162,0.198,0.225,0.247,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.9610721666017 0 0 59.96107216660167 59.96107216660167 ST2 FALSE 645 +SP192767 BN2 1 0 548,959,999,780,345,99,435,1071,943,517 0.14,0.149,0.17,0.192,0.197,0.223,0.241,0.255,0.265,0.267 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.1101002016708 0 0 50.110100201670804 50.110100201670804 BN2 TRUE 646 +SP193976 EZB 1 0 537,960,939,948,719,377,1034,51,378,233 0.253,0.339,0.371,0.385,0.39,0.396,0.447,0.45,0.45,0.455 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.1681554975212 0 0 26.16815549752124 26.16815549752124 EZB TRUE 647 +SP192997 EZB 1 0 892,1095,711,680,71,229,706,374,1072,112 0.123,0.138,0.143,0.146,0.151,0.156,0.163,0.17,0.176,0.193 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.1058976126808 0 0 65.10589761268079 65.10589761268079 EZB TRUE 648 +SP192993 EZB 1 0 610,273,1,139,9,664,1020,1072,112,648 0.142,0.142,0.148,0.167,0.179,0.227,0.227,0.231,0.243,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.436897691134 0 0 53.43689769113404 53.43689769113404 EZB TRUE 649 +SP192970 BN2 1 0 943,335,538,99,435,630,780,477,1060,959 0.184,0.185,0.187,0.188,0.207,0.215,0.219,0.222,0.242,0.262 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.0217976138198 0 0 48.02179761381985 48.02179761381985 BN2 TRUE 650 +SP193005 BN2 0.9231821696216669 0 472,1019,179,652,659,666,968,484,1113,42 0.167,0.198,0.253,0.261,0.275,0.277,0.279,0.311,0.329,0.335 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 35.902994579756,2.98748203600182 0 0 38.8904766157578 35.90299457975598 BN2 TRUE 651 +SP193025 BN2 1 0 1019,1113,666,695,681,659,472,651,674,179 0.163,0.17,0.186,0.2,0.232,0.237,0.238,0.261,0.263,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2830759089026 0 0 46.28307590890263 46.28307590890263 BN2 TRUE 652 +SP193794 EZB 1 0 304,628,887,750,36,430,997,963,761,310 0.224,0.231,0.24,0.245,0.254,0.268,0.273,0.274,0.295,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.7840938931038 0 0 38.78409389310379 38.78409389310379 EZB TRUE 653 +SP192856 BN2 1 0 501,518,342,63,333,38,542,763,488,702 0.157,0.167,0.177,0.184,0.21,0.217,0.219,0.227,0.243,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.9716407108667 0 0 49.97164071086669 49.97164071086669 BN2 TRUE 654 +SP192850 EZB 1 0 1051,410,183,752,49,1012,101,1115,392,225 0.117,0.133,0.183,0.183,0.206,0.209,0.235,0.246,0.258,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5183991879829 0 0 52.51839918798294 52.51839918798294 EZB TRUE 655 +SP192833 ST2 0.9004890409902653 0 483,728,207,822,362,263,1057,60,807,1081 0.184,0.196,0.211,0.257,0.262,0.301,0.316,0.319,0.345,0.354 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.81263033588574,34.5010425884478 0 0 38.313672924333574 34.50104258844783 ST2 TRUE 656 +SP192815 ST2 0.5124147544078913 0 922,382,1091,372,974,586,834,320,194,791 0.204,0.242,0.302,0.327,0.333,0.344,0.374,0.404,0.416,0.419 ST2,EZB,EZB,ST2,EZB,ST2,ST2,ST2,EZB,EZB 15.2416638458944,16.0178215131947 0 0 31.259485359089037 16.017821513194683 BN2 FALSE 657 +SP193512 EZB 0.922656082539285 0 429,744,454,67,19,241,733,1050,645,643 0.182,0.204,0.227,0.261,0.276,0.308,0.312,0.323,0.345,0.355 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 34.6213094185951,2.90221649076885 0 0 37.52352590936392 34.621309418595075 EZB TRUE 658 +SP193337 BN2 0.8158234903840249 0 666,179,472,23,1019,652,127,185,495,651 0.157,0.173,0.21,0.221,0.234,0.237,0.261,0.264,0.266,0.275 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,MCD,BN2 36.6803888551595,4.52416494326226,3.75662872145554 0 0 44.9611825198773 36.68038885515951 BN2 TRUE 659 +SP192798 MCD 1 0 966,932,380,439,1090,280,987,994,461,565 0.131,0.166,0.19,0.192,0.192,0.207,0.207,0.208,0.216,0.219 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.9900263309803 0 0 52.99002633098027 52.99002633098027 MCD TRUE 660 +SP193546 ST2 0.6363818148929522 0 365,965,178,787,770,361,1107,195,1041,950 0.112,0.217,0.239,0.264,0.296,0.301,0.312,0.334,0.352,0.358 ST2,EZB,ST2,EZB,BN2,ST2,ST2,ST2,ST2,EZB 3.3787031745656,11.2000175880589,25.5147656462926 0 0 40.09348640891715 25.51476564629261 EZB FALSE 661 +SP193375 ST2 1 0 896,683,907,1004,748,612,814,1009,583,1065 0.157,0.176,0.203,0.22,0.246,0.258,0.26,0.266,0.3,0.304 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.7017733329819 0 0 43.70177333298188 43.70177333298188 ST2 TRUE 662 +SP193420 MCD 0.4489214942919664 0 421,579,1045,801,716,525,789,779,431,455 0.254,0.269,0.274,0.293,0.326,0.405,0.441,0.443,0.443,0.459 MCD,N1,BN2,ST2,BN2,MCD,MCD,MCD,BN2,MCD 8.97554460892989,13.1172283354386,3.71661011596333,3.41004283534057 0 0 29.219425895672433 13.117228335438648 ST2 FALSE 663 +SP194216 EZB 1 0 9,273,983,139,1,649,343,610,268,240 0.15,0.197,0.216,0.218,0.218,0.227,0.248,0.256,0.256,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5721640677076 0 0 45.57216406770764 45.57216406770764 EZB TRUE 664 +SP194228 BN2 0.37586977370337316 0 924,967,131,512,505,864,582,126,1085,132 0.171,0.186,0.198,0.203,0.215,0.227,0.233,0.253,0.312,0.325 ST2,EZB,BN2,N1,N1,BN2,BN2,EZB,EZB,BN2 16.8328243657202,12.5166861227402,9.59137509587787,5.8427749900931 0 0 44.78366057443131 16.83282436572017 BN2 TRUE 665 +SP193725 BN2 0.9121745972362711 0 659,652,1019,472,179,1113,23,674,855,681 0.157,0.186,0.205,0.223,0.223,0.228,0.252,0.258,0.272,0.274 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 41.1592080013435,3.96286416121032 0 0 45.1220721625538 41.15920800134347 BN2 TRUE 666 +SP193957 EZB 1 0 632,876,760,46,80,914,594,1002,1026,29 0.114,0.127,0.131,0.154,0.171,0.172,0.172,0.174,0.179,0.193 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.7479112259367 0 0 64.74791122593668 64.74791122593668 EZB TRUE 667 +SP193967 ST2 0.7840389071461353 0 161,359,785,366,671,1029,842,160,602,425 0.181,0.182,0.188,0.197,0.211,0.217,0.22,0.22,0.236,0.246 ST2,ST2,EZB,BN2,ST2,ST2,ST2,ST2,ST2,ST2 5.0749286103877,5.32159933925559,37.7442172756013 0 0 48.14074522524461 37.744217275601315 BN2 FALSE 668 +SP194035 EZB 1 0 734,68,291,255,50,181,142,213,67,257 0.147,0.164,0.205,0.223,0.231,0.235,0.245,0.263,0.265,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.2118064403822 0 0 46.21180644038216 46.21180644038216 EZB TRUE 669 +SP193934 BN2 0.736389145642204 0 86,363,355,558,408,321,422,187,946,808 0.182,0.287,0.379,0.387,0.407,0.415,0.416,0.442,0.442,0.446 BN2,BN2,BN2,ST2,N1,BN2,ST2,BN2,BN2,BN2 20.7893388689415,2.45524950324123,4.98686912820064 0 0 28.231457500383357 20.789338868941492 BN2 TRUE 670 +SP59304 ST2 0.8465934104819731 0 842,359,996,602,161,1029,668,160,1005,835 0.127,0.137,0.154,0.166,0.177,0.207,0.211,0.235,0.273,0.276 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,EZB 4.73516337722098,3.61963004742697,46.1069702511958 0 0 54.46176367584376 46.10697025119581 ST2 TRUE 671 +SP59312 BN2 0.8267306461517281 0 186,1106,153,840,571,462,22,470,962,582 0.144,0.184,0.189,0.246,0.259,0.267,0.275,0.284,0.285,0.304 BN2,BN2,BN2,BN2,EZB,BN2,EZB,BN2,BN2,BN2 35.7746406263335,7.4977852754491 0 0 43.27242590178265 35.77464062633354 BN2 TRUE 672 +SP59368 ST2 0.7559338889478932 0 682,554,177,747,370,1016,1089,792,1053,1044 0.237,0.247,0.266,0.324,0.353,0.362,0.378,0.408,0.424,0.425 ST2,ST2,ST2,ST2,ST2,ST2,MCD,BN2,ST2,MCD 2.4526252799782,4.99810471002978,23.0767773230302 0 0 30.52750731303818 23.0767773230302 BN2 FALSE 673 +SP59452 BN2 0.9152623867850839 0 681,1113,855,584,695,666,65,652,495,795 0.189,0.217,0.22,0.231,0.257,0.258,0.261,0.263,0.288,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 37.5002373762991,3.47187938250382 0 0 40.9721167588029 37.500237376299076 BN2 TRUE 674 +SP59460 EZB 1 0 78,117,287,1108,397,105,92,738,247,56 0.119,0.135,0.14,0.188,0.201,0.203,0.215,0.26,0.262,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2366634097355 0 0 54.23666340973549 54.23666340973549 EZB TRUE 675 +SP116618 EZB 1 0 870,62,791,641,593,47,220,219,912,307 0.349,0.354,0.376,0.415,0.43,0.434,0.441,0.445,0.472,0.479 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.1164265066532 0 0 24.11642650665322 24.11642650665322 EZB TRUE 676 +SP116620 EZB 1 0 167,266,72,852,191,746,20,239,96,226 0.11,0.176,0.19,0.194,0.198,0.213,0.218,0.226,0.226,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7235237385383 0 0 52.72352373853833 52.72352373853833 EZB TRUE 677 +SP116726 MCD 0.9087327459133048 0 580,881,526,1042,844,453,494,204,467,920 0.137,0.138,0.139,0.172,0.189,0.19,0.194,0.196,0.208,0.209 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 5.2874519859008,52.6462728622054 0 0 57.93372484810617 52.64627286220538 MCD TRUE 678 +SP116657 MCD 1 0 1038,415,565,461,869,987,340,414,324,932 0.125,0.136,0.14,0.204,0.206,0.216,0.221,0.237,0.24,0.244 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8327904179286 0 0 53.83279041792861 53.83279041792861 MCD TRUE 679 +SP116659 EZB 1 0 892,1095,706,71,648,861,711,229,885,1072 0.125,0.126,0.127,0.139,0.146,0.161,0.161,0.168,0.169,0.184 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.5479992992813 0 0 67.54799929928133 67.54799929928133 EZB TRUE 680 +SP116663 BN2 1 0 1113,695,65,674,584,652,589,64,666,1019 0.163,0.177,0.18,0.189,0.192,0.232,0.249,0.262,0.274,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.0288321644228 0 0 47.028832164422795 47.028832164422795 BN2 TRUE 681 +SP116668 ST2 0.8128109977730037 0 177,1016,673,370,1053,692,557,1086,792,793 0.144,0.225,0.237,0.276,0.288,0.297,0.333,0.342,0.356,0.365 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 7.03031211218044,30.5269804025533 0 0 37.55729251473371 30.52698040255327 ST2 TRUE 682 +SP116676 ST2 1 0 907,662,748,1004,896,1009,195,814,1065,875 0.132,0.176,0.192,0.217,0.219,0.222,0.249,0.258,0.289,0.292 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 46.9287240121868 0 0 46.928724012186805 46.928724012186805 ST2 TRUE 683 +SP116688 EZB 0.7336730592080931 0 810,194,714,1033,988,319,322,993,641,974 0.166,0.272,0.291,0.321,0.332,0.336,0.341,0.341,0.341,0.351 ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.01079924948865,24.849861274214,6.0098230143412 0 0 33.8704835380438 24.849861274213954 ST2 FALSE 684 +SP116690 ST2 0.693819752259605 0 320,834,1024,824,901,586,1091,925,625,372 0.179,0.2,0.239,0.256,0.267,0.282,0.292,0.337,0.375,0.387 ST2,ST2,EZB,EZB,ST2,ST2,EZB,ST2,ST2,ST2 11.5133084916083,26.0897327776321 0 0 37.60304126924042 26.089732777632094 ST2 TRUE 685 +SP116701 EZB 1 0 708,403,5,237,289,838,887,310,963,949 0.137,0.137,0.175,0.187,0.199,0.242,0.248,0.249,0.249,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8363385710439 0 0 50.836338571043854 50.836338571043854 EZB TRUE 686 +SP116709 EZB 0.9170639893512733 0 379,369,385,831,581,975,645,643,1050,254 0.141,0.156,0.189,0.199,0.199,0.209,0.247,0.257,0.271,0.276 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 44.7821296590744,4.0499367823885 0 0 48.83206644146293 44.78212965907443 EZB TRUE 687 +DOHH-2 EZB 1 0 1114,601,344,1006,874,407,753,725,164,1075 0.176,0.217,0.228,0.24,0.252,0.284,0.295,0.304,0.308,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4654531628054 0 0 39.4654531628054 39.4654531628054 EZB TRUE 688 +Farage ST2 0.530020852564546 0 1118,338,607,620,606,545,1052,736,828,144 0.183,0.219,0.278,0.297,0.3,0.375,0.386,0.387,0.39,0.4 ST2,BN2,ST2,EZB,ST2,ST2,EZB,EZB,ST2,EZB 4.56919284032616,11.0504547950865,17.6151197380862 0 0 33.23476737349884 17.615119738086214 ST2 TRUE 689 +HBL-1 MCD 0.8800839725431598 0 721,299,937,1110,722,511,447,536,1087,469 0.142,0.144,0.15,0.155,0.179,0.196,0.215,0.226,0.228,0.228 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.67963463142747,49.0229663726926 0 0 55.70260100412009 49.022966372692615 MCD TRUE 690 +HT ST2 0.3816649110847545 0 1084,622,1096,11,411,590,964,893,899,909 0.146,0.163,0.266,0.313,0.329,0.331,0.34,0.35,0.397,0.431 BN2,ST2,EZB,EZB,EZB,ST2,BN2,N1,ST2,ST2 9.81415588872676,10.002258964436,2.85685803884429,13.9949888619605 0 0 36.66826175396758 13.994988861960538 ST2 TRUE 691 +HTMCP-01-02-00013-01A-01D ST2 0.9280996478821996 0 1086,1053,1016,982,557,370,177,793,682,792 0.155,0.196,0.204,0.23,0.244,0.258,0.262,0.29,0.297,0.327 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 3.05355501343529,39.4157086757381 0 0 42.46926368917342 39.41570867573813 ST2 TRUE 692 +HTMCP-01-06-00105-01A-01D EZB 1 0 710,243,244,343,69,240,273,268,225,649 0.259,0.281,0.34,0.34,0.364,0.393,0.423,0.441,0.443,0.447 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.7054304246825 0 0 27.705430424682508 27.705430424682508 BN2 FALSE 693 +HTMCP-01-06-00136-01A-01D BN2 0.7576899720471828 0 777,701,484,968,301,34,521,742,103,104 0.247,0.3,0.324,0.33,0.362,0.377,0.398,0.451,0.469,0.472 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD,MCD 21.1468456047588,6.76278285135326 0 0 27.909628456112102 21.146845604758838 BN2 TRUE 694 +HTMCP-01-06-00175-01A-01D BN2 1 0 1113,681,65,652,589,1019,584,674,64,666 0.153,0.177,0.193,0.2,0.236,0.25,0.25,0.257,0.264,0.275 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.9331573098885 0 0 45.933157309888486 45.933157309888486 BN2 TRUE 695 +HTMCP-01-06-00185-01A-01D MCD 0.6395086923622119 0 1105,935,205,985,1044,124,223,1093,779,466 0.219,0.319,0.33,0.345,0.361,0.368,0.381,0.404,0.423,0.432 BN2,EZB,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD 4.5617337979395,3.13254765557608,18.4740954809057,2.71957361503291 0 0 28.887950549454196 18.474095480905692 MCD TRUE 696 +HTMCP-01-06-00242-01A-01D MCD 1 0 790,475,1054,223,198,1047,44,466,837,856 0.123,0.172,0.237,0.244,0.246,0.255,0.274,0.282,0.291,0.325 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.9706636501566 0 0 43.97066365015657 43.97066365015657 MCD TRUE 697 +HTMCP-01-06-00253-01A-01D BN2 0.6462952339838987 0 799,1061,919,506,485,347,529,936,293,381 0.204,0.315,0.32,0.343,0.357,0.357,0.361,0.377,0.388,0.39 BN2,BN2,BN2,BN2,ST2,BN2,ST2,BN2,MCD,ST2 19.5696951817581,2.57502541134223,8.13508676541353 0 0 30.279807358513825 19.569695181758068 ST2 FALSE 698 +HTMCP-01-06-00299-01A-01D BN2 0.42768483982800737 0 504,14,802,891,495,23,855,388,127,659 0.187,0.239,0.244,0.283,0.284,0.317,0.331,0.367,0.4,0.437 MCD,BN2,ST2,MCD,MCD,EZB,BN2,BN2,BN2,BN2 14.7027014893163,3.15414229212775,12.4166205061905,4.10395599976442 0 0 34.377420287398984 14.702701489316325 BN2 TRUE 699 +HTMCP-01-06-00443-01A-01D BN2 1 0 955,731,435,1071,538,1010,99,1001,959,548 0.146,0.172,0.186,0.19,0.198,0.206,0.241,0.25,0.253,0.257 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.2357136621826 0 0 49.23571366218262 49.23571366218262 BN2 TRUE 700 +HTMCP-01-06-00497-01A-01D BN2 0.6250262081277284 0 34,104,694,103,521,497,442,484,742,100 0.191,0.294,0.3,0.309,0.318,0.324,0.327,0.349,0.389,0.434 BN2,MCD,BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD 20.1582942966309,12.0936241581412 0 0 32.25191845477204 20.15829429663087 BN2 TRUE 701 +HTMCP-01-06-00634-01A-01D BN2 1 0 395,958,305,542,75,905,1023,559,373,63 0.153,0.161,0.17,0.179,0.195,0.201,0.211,0.214,0.217,0.226 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.7166083385059 0 0 52.71660833850587 52.71660833850587 BN2 TRUE 702 +HTMCP-01-10-00160-01A-01D ST2 0.7516381072818805 0 969,625,925,1025,901,843,904,1024,1081,950 0.132,0.274,0.29,0.296,0.335,0.346,0.357,0.364,0.386,0.408 ST2,ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,EZB 3.37360786759798,5.19451275253069,25.9304110441196 0 0 34.49853166424824 25.93041104411957 ST2 TRUE 703 +HTMCP-01-10-00778-01A-01D BN2 1 0 524,572,560,279,196,110,393,562,773,705 0.299,0.339,0.339,0.347,0.39,0.441,0.47,0.481,0.482,0.5 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 25.2415547107689 0 0 25.241554710768945 25.241554710768945 ST2 FALSE 704 +HTMCP-01-15-00366-01A-01E BN2 1 0 503,507,562,524,560,623,1098,391,348,553 0.222,0.268,0.294,0.3,0.31,0.363,0.46,0.461,0.492,0.497 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3261679409219 0 0 29.326167940921916 29.326167940921916 BN2 TRUE 705 +Karpas422 EZB 1 0 71,680,892,1095,711,229,861,648,7,885 0.126,0.127,0.149,0.153,0.159,0.16,0.161,0.163,0.181,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.7302291724027 0 0 64.73022917240269 64.73022917240269 EZB TRUE 706 +LY_RELY_028_tumorB EZB 1 0 963,36,972,289,237,997,887,304,1031,686 0.146,0.161,0.165,0.173,0.183,0.201,0.204,0.229,0.234,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7059188051094 0 0 52.705918805109434 52.705918805109434 EZB TRUE 707 +LY_RELY_109_tumorB EZB 1 0 403,686,5,289,237,310,949,887,750,304 0.12,0.137,0.189,0.209,0.214,0.216,0.216,0.241,0.242,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.986592076091 0 0 51.98659207609099 51.98659207609099 EZB TRUE 708 +LY_RELY_116_tumorA EZB 1 0 597,250,868,21,251,938,756,164,816,356 0.14,0.163,0.172,0.177,0.18,0.205,0.225,0.235,0.246,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.9076795597249 0 0 51.90767955972493 51.90767955972493 EZB TRUE 709 +LY_RELY_128_tumorA EZB 0.8963348963737848 0 243,343,693,273,244,649,240,69,9,664 0.206,0.219,0.259,0.265,0.281,0.292,0.294,0.297,0.307,0.316 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 3.86090935620508,33.3831508062807 0 0 37.24406016248581 33.383150806280725 EZB TRUE 710 +LY_RELY_128_tumorB EZB 1 0 229,71,648,374,892,706,680,1095,861,1072 0.114,0.133,0.143,0.152,0.155,0.159,0.161,0.171,0.218,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.8076917742234 0 0 63.80769177422335 63.80769177422335 EZB TRUE 711 +OCI-Ly10 MCD 1 0 879,1109,1015,500,165,762,17,166,246,417 0.115,0.125,0.156,0.158,0.161,0.169,0.173,0.175,0.176,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.3391042834941 0 0 64.33910428349405 64.33910428349405 MCD TRUE 712 +OCI-Ly3 MCD 0.8570465647048454 0 617,985,789,441,205,466,128,223,779,696 0.206,0.238,0.269,0.27,0.298,0.304,0.319,0.335,0.408,0.434 N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.1534576701244,4.8627310303862 0 0 34.016188700510554 29.15345767012435 MCD TRUE 713 +OZM073-003_LowGradeDiagnosis EZB 0.688675212295522 0 1033,913,322,319,993,810,684,715,424,988 0.178,0.183,0.212,0.213,0.225,0.235,0.291,0.365,0.406,0.424 EZB,EZB,EZB,EZB,EZB,ST2,ST2,EZB,ST2,BN2 2.35654548113571,27.6744480351661,10.1540567673006 0 0 40.18505028360234 27.674448035166066 EZB TRUE 714 +OZM073-003_Transformation EZB 0.8722752912269633 0 890,424,839,862,407,322,319,1033,913,784 0.214,0.214,0.229,0.279,0.281,0.302,0.308,0.317,0.321,0.353 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.8625571831999,4.66554065891742 0 0 36.5280978421173 31.862557183199876 EZB TRUE 715 +OZM073-007_Relapse MCD 0.39711281989472336 0 525,1045,43,803,779,663,421,579,124,205 0.22,0.248,0.254,0.292,0.294,0.326,0.379,0.385,0.39,0.427 MCD,BN2,ST2,BN2,MCD,ST2,MCD,N1,N1,MCD 7.44451078122729,12.9224590848372,5.15852144738232,7.01553591469203 0 0 32.541027228138816 12.922459084837179 BN2 FALSE 716 +OZM073-008_Diagnosis MCD 1 0 522,458,489,837,198,446,128,519,162,1035 0.189,0.305,0.34,0.366,0.394,0.42,0.469,0.491,0.503,0.51 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 27.2637066845663 0 0 27.263706684566277 27.263706684566277 MCD TRUE 717 +OZM073-011_LowGradeDiagnosis EZB 1 0 156,826,294,236,883,334,56,303,743,942 0.152,0.157,0.159,0.175,0.188,0.2,0.207,0.212,0.243,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9874049799795 0 0 52.987404979979466 52.987404979979466 EZB TRUE 718 +OZM073-012_Relapse EZB 1 0 537,233,51,1034,157,939,647,211,960,360 0.276,0.289,0.291,0.306,0.33,0.383,0.39,0.428,0.455,0.509 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.4955444541931 0 0 28.495544454193094 28.495544454193094 EZB TRUE 719 +OZM073-012_LowGradeDiagnosis EZB 1 0 374,267,229,711,983,71,648,892,706,619 0.199,0.205,0.228,0.233,0.252,0.261,0.267,0.286,0.287,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.545391041529 0 0 40.54539104152898 40.54539104152898 EZB TRUE 720 +OZM073-013_Relapse MCD 0.8655088455074126 0 937,722,690,511,299,536,1087,1110,447,869 0.128,0.137,0.142,0.154,0.182,0.185,0.194,0.196,0.24,0.24 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.81676994956836,50.3043011280034 0 0 58.12107107757179 50.30430112800343 MCD TRUE 721 +OZM073-013_Diagnosis MCD 0.8837104885889657 0 511,721,937,536,1087,690,869,340,299,1110 0.118,0.137,0.145,0.149,0.175,0.179,0.203,0.206,0.219,0.233 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.90293933182481,52.457008509265 0 0 59.35994784108978 52.457008509264966 MCD TRUE 722 +OZM073-014_LowGradeDiagnosis2 EZB 1 0 213,50,853,257,291,949,255,272,181,68 0.129,0.167,0.184,0.192,0.203,0.212,0.215,0.219,0.222,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1435331728248 0 0 52.14353317282475 52.14353317282475 EZB TRUE 723 +OZM073-014_LowGradeDiagnosis EZB 1 0 272,853,979,556,257,723,136,310,949,811 0.142,0.177,0.225,0.233,0.24,0.257,0.262,0.269,0.272,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3881759076937 0 0 44.388175907693686 44.388175907693686 EZB TRUE 724 +OZM073-015_PlasmaEx-T3 EZB 1 0 727,344,726,47,688,784,593,870,1114,601 0.154,0.179,0.206,0.264,0.304,0.338,0.344,0.352,0.372,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.994726340876 0 0 37.99472634087599 37.99472634087599 EZB TRUE 725 +OZM073-015_Relapse EZB 1 0 727,725,47,593,344,870,219,82,138,292 0.194,0.206,0.252,0.261,0.285,0.317,0.333,0.352,0.359,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.7857254189458 0 0 35.78572541894579 35.78572541894579 EZB TRUE 726 +OZM073-015_Diagnosis EZB 1 0 725,726,47,344,870,593,784,688,219,839 0.154,0.194,0.21,0.222,0.298,0.301,0.31,0.35,0.389,0.397 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.7539874987359 0 0 38.753987498735945 38.753987498735945 EZB TRUE 727 +OZM073-018_Diagnosis ST2 0.9221337629813409 0 656,60,483,207,822,1057,1081,394,362,508 0.196,0.238,0.24,0.253,0.254,0.322,0.323,0.356,0.358,0.365 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.79562178214264,33.1072533172759 0 0 35.90287509941855 33.10725331727591 ST2 TRUE 728 +OZM073-021_Diagnosis MCD 1 0 324,280,288,520,439,106,991,147,966,897 0.169,0.18,0.207,0.219,0.224,0.23,0.252,0.254,0.254,0.254 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 45.4871959288667 0 0 45.4871959288667 45.4871959288667 MCD TRUE 729 +OZM073-021_Relapse MCD 0.9273351644727906 0 460,1043,888,513,459,739,314,66,897,520 0.123,0.127,0.137,0.201,0.265,0.275,0.276,0.279,0.305,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 3.58597420487631,45.763538236166 0 0 49.349512441042286 45.76353823616598 MCD TRUE 730 +OZM073-022_Diagnosis BN2 1 0 955,1010,700,538,435,1071,1001,99,650,959 0.126,0.151,0.172,0.223,0.238,0.245,0.293,0.295,0.309,0.321 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2973173441344 0 0 46.29731734413443 46.29731734413443 BN2 TRUE 731 +OZM073-023_Relapse EZB 1 0 979,136,596,181,257,255,272,291,853,724 0.157,0.166,0.182,0.216,0.221,0.232,0.262,0.262,0.264,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3487347913874 0 0 46.34873479138737 46.34873479138737 EZB TRUE 732 +OZM073-026_Transformation EZB 0.9093600574375451 0 19,744,429,241,1050,643,645,254,975,658 0.167,0.222,0.237,0.244,0.252,0.254,0.273,0.281,0.302,0.312 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 36.8012725246639,3.66814574774731 0 0 40.469418272411176 36.801272524663865 EZB TRUE 733 +OZM073-029_Transformation EZB 1 0 669,68,50,291,255,213,181,723,257,142 0.147,0.172,0.215,0.217,0.243,0.253,0.258,0.281,0.287,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2959472854073 0 0 44.29594728540729 44.29594728540729 EZB TRUE 734 +OZM073-036_Transformation EZB 1 0 149,73,603,1059,25,605,384,20,239,231 0.181,0.223,0.248,0.256,0.262,0.292,0.293,0.294,0.297,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.6528070187518 0 0 38.652807018751794 38.652807018751794 EZB TRUE 735 +QC2-12T EZB 0.6271519507411953 0 1052,509,781,338,545,249,689,606,1064,1118 0.119,0.242,0.25,0.355,0.358,0.363,0.387,0.389,0.4,0.457 EZB,EZB,EZB,BN2,ST2,EZB,ST2,ST2,EZB,ST2 2.8149169760286,21.7855635046376,10.1368164039093 0 0 34.7372968845755 21.785563504637572 EZB TRUE 736 +QC2-15T EZB 1 0 259,588,619,238,1115,224,981,1083,983,183 0.179,0.2,0.246,0.25,0.272,0.28,0.299,0.324,0.327,0.331 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4183464162955 0 0 38.4183464162955 38.4183464162955 EZB TRUE 737 +QC2-17T EZB 1 0 92,105,1108,317,984,637,287,397,914,91 0.146,0.16,0.188,0.193,0.201,0.222,0.231,0.238,0.244,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8022868437089 0 0 49.802286843708885 49.802286843708885 EZB TRUE 738 +QC2-19T MCD 1 0 314,520,459,897,513,288,991,147,729,106 0.115,0.138,0.14,0.152,0.175,0.184,0.21,0.218,0.255,0.259 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.9623623402534 0 0 57.96236234025342 57.96236234025342 MCD TRUE 739 +QC2-20T EZB 1 0 93,631,228,387,757,274,1117,848,102,947 0.112,0.161,0.186,0.188,0.19,0.213,0.222,0.232,0.234,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.1303151734321 0 0 53.130315173432145 53.130315173432145 EZB TRUE 740 +QC2-22T MCD 1 0 817,349,283,1087,447,469,299,550,721,511 0.178,0.278,0.28,0.281,0.283,0.333,0.337,0.338,0.339,0.339 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.7171241116813 0 0 34.717124111681294 34.717124111681294 MCD TRUE 741 +QC2-25T MCD 0.44875970938574744 0 100,42,180,484,185,103,127,179,104,34 0.164,0.167,0.17,0.25,0.273,0.285,0.318,0.328,0.328,0.343 MCD,EZB,MCD,BN2,BN2,MCD,BN2,BN2,MCD,BN2 16.7629433226456,5.99640059539324,18.5281749835238 0 0 41.287518901562656 18.52817498352381 BN2 FALSE 742 +QC2-3T EZB 1 0 942,1073,883,156,303,1018,718,334,882,120 0.156,0.168,0.168,0.191,0.193,0.202,0.243,0.253,0.253,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3836668427075 0 0 49.38366684270749 49.38366684270749 EZB TRUE 743 +QC2-30T EZB 0.9245075338275891 0 429,19,658,733,454,241,1050,67,643,645 0.169,0.173,0.204,0.222,0.278,0.284,0.299,0.31,0.32,0.323 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.9300777979269,3.09725449528993 0 0 41.02733229321684 37.930077797926906 EZB TRUE 744 +QC2-32T EZB 1 0 378,626,377,238,184,1039,167,677,1083,629 0.238,0.344,0.348,0.436,0.454,0.469,0.474,0.483,0.489,0.491 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.863239297758 0 0 24.86323929775796 24.86323929775796 EZB TRUE 745 +QC2-34T EZB 1 0 72,266,852,20,677,239,167,25,191,96 0.125,0.139,0.175,0.193,0.213,0.213,0.221,0.246,0.252,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6703743694063 0 0 51.670374369406275 51.670374369406275 EZB TRUE 746 +QC2-36T MCD 0.3728785076813135 0 554,1044,124,1089,673,935,43,803,525,779 0.178,0.223,0.276,0.29,0.324,0.33,0.343,0.377,0.436,0.456 ST2,MCD,N1,MCD,BN2,EZB,ST2,BN2,MCD,MCD 5.73819121662709,3.03002751322344,12.4291642051222,3.61873751172437,8.51689271732521 0 0 33.33301316402235 12.42916420512223 ST2 FALSE 747 +QC2-39T ST2 0.9187649249365673 0 1009,907,195,1004,683,814,1065,875,770,662 0.133,0.164,0.167,0.186,0.192,0.21,0.219,0.221,0.235,0.246 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 4.25273119343869,48.0981922237912 0 0 52.35092341722986 48.09819222379117 ST2 TRUE 748 +QC2-40T MCD 0.8741541580054929 0 933,772,859,533,448,445,436,532,934,400 0.122,0.162,0.165,0.171,0.196,0.258,0.278,0.287,0.292,0.295 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.18518843220844,42.9637411961412 0 0 49.14892962834968 42.963741196141235 MCD TRUE 749 +QC2-42T EZB 1 0 310,304,887,289,36,963,708,653,403,949 0.152,0.161,0.183,0.232,0.233,0.24,0.242,0.245,0.26,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.850796624181 0 0 46.85079662418098 46.85079662418098 EZB TRUE 750 +QC2-7T EZB 1 0 82,609,600,121,122,849,601,726,725,1014 0.237,0.261,0.279,0.32,0.325,0.332,0.372,0.385,0.392,0.4 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.196742044702 0 0 31.196742044701967 31.196742044701967 EZB TRUE 751 +Reddy_1008T EZB 1 0 410,655,1051,49,1012,183,101,302,313,392 0.16,0.183,0.198,0.228,0.237,0.263,0.279,0.28,0.291,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1688026280634 0 0 43.16880262806339 43.16880262806339 EZB TRUE 752 +Reddy_1016T EZB 1 0 1075,1006,164,874,1114,597,284,812,601,21 0.139,0.184,0.184,0.19,0.236,0.253,0.26,0.264,0.269,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5099289689163 0 0 46.509928968916256 46.509928968916256 EZB TRUE 753 +Reddy_2043T EZB 1 0 406,116,18,865,190,756,1018,182,1073,1014 0.133,0.161,0.174,0.178,0.193,0.214,0.228,0.241,0.253,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2074677988847 0 0 51.20746779888472 51.20746779888472 EZB TRUE 754 +Reddy_2044T EZB 0.5413090163488783 0 758,872,800,40,806,145,248,146,221,54 0.171,0.283,0.291,0.295,0.31,0.31,0.35,0.361,0.381,0.383 EZB,BN2,EZB,EZB,N1,BN2,EZB,BN2,EZB,MCD 9.53276105660172,18.1466378211492,2.61406962273872,3.23015160018411 0 0 33.523620100673746 18.146637821149188 EZB TRUE 755 +Reddy_2045T EZB 1 0 18,190,868,116,754,709,406,251,597,849 0.14,0.15,0.176,0.196,0.214,0.225,0.229,0.234,0.256,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0022329568881 0 0 50.002232956888065 50.002232956888065 EZB TRUE 756 +Reddy_2047T EZB 1 0 387,102,631,228,93,107,740,873,1079,140 0.139,0.164,0.164,0.17,0.185,0.185,0.19,0.227,0.241,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8388337902169 0 0 53.83883379021688 53.83883379021688 EZB TRUE 757 +Reddy_2060T EZB 0.6307055086241361 0 755,248,872,145,40,800,146,806,221,781 0.171,0.319,0.32,0.325,0.353,0.362,0.367,0.381,0.384,0.392 EZB,EZB,BN2,BN2,EZB,EZB,BN2,N1,EZB,EZB 8.92152606748121,19.721094200426,2.62568788980486 0 0 31.268308157712113 19.72109420042604 EZB TRUE 758 +Reddy_2073T ST2 0.9132389659665567 0 832,915,396,1032,1003,59,281,940,1068,835 0.266,0.32,0.354,0.363,0.372,0.377,0.379,0.411,0.416,0.417 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 2.40060161773928,25.2685201773506 0 0 27.66912179508993 25.268520177350645 ST2 TRUE 759 +Reddy_2075T EZB 1 0 876,632,46,667,914,80,29,594,1026,1002 0.122,0.122,0.123,0.131,0.149,0.154,0.176,0.182,0.187,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1441114257294 0 0 67.14411142572938 67.14411142572938 EZB TRUE 760 +Reddy_2076T EZB 1 0 37,430,89,113,880,159,997,1031,1063,931 0.134,0.153,0.181,0.198,0.201,0.207,0.214,0.218,0.266,0.276 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0620716864679 0 0 51.062071686467895 51.062071686467895 EZB TRUE 761 +Reddy_2081T MCD 1 0 767,246,166,851,879,17,1109,712,434,500 0.118,0.127,0.129,0.131,0.155,0.156,0.167,0.169,0.187,0.191 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.0615193320854 0 0 67.06151933208537 67.06151933208537 MCD TRUE 762 +Reddy_2091T BN2 1 0 342,563,333,38,488,518,63,871,341,654 0.154,0.156,0.177,0.178,0.182,0.193,0.198,0.21,0.218,0.227 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.673181542264 0 0 53.67318154226399 53.67318154226399 BN2 TRUE 763 +Reddy_2092T EZB 0.8077429742852865 0 836,236,334,718,1092,294,826,111,6,156 0.217,0.246,0.252,0.26,0.261,0.262,0.275,0.29,0.296,0.303 EZB,EZB,EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB 30.6234333872313,7.28891541942687 0 0 37.91234880665817 30.623433387231305 EZB TRUE 764 +Reddy_2095T BN2 0.7516269168389094 0 624,173,535,347,773,1049,196,936,928,799 0.159,0.179,0.208,0.219,0.247,0.275,0.295,0.373,0.387,0.415 EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 30.0483965254431,6.28646975751859,3.64294165906435 0 0 39.97780794202602 30.04839652544308 BN2 TRUE 765 +Reddy_2100T EZB 1 0 217,216,908,168,823,1064,471,1100,249,235 0.15,0.171,0.182,0.184,0.201,0.234,0.248,0.289,0.292,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.9817050675576 0 0 46.981705067557584 46.981705067557584 EZB TRUE 766 +Reddy_2103T MCD 1 0 246,166,762,851,17,879,434,712,804,1109 0.113,0.116,0.118,0.122,0.146,0.165,0.169,0.179,0.181,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.5176999603356 0 0 69.51769996033563 69.51769996033563 MCD TRUE 767 +Reddy_2111T EZB 0.5051701949052829 0 1116,988,993,194,824,684,714,810,974,913 0.224,0.321,0.453,0.495,0.518,0.537,0.56,0.565,0.568,0.571 ST2,BN2,EZB,EZB,EZB,ST2,EZB,ST2,EZB,EZB 3.11556956991156,11.4533468571882,8.10333734538511 0 0 22.672253772484908 11.453346857188237 EZB TRUE 768 +Reddy_2112T MCD 0.8097531378645632 0 1048,417,312,778,1046,1015,918,350,1103,169 0.148,0.153,0.161,0.168,0.169,0.172,0.172,0.18,0.19,0.193 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,ST2,MCD 5.95939564874569,47.779371081998,5.26609394568315 0 0 59.00486067642683 47.779371081997994 MCD TRUE 769 +Reddy_2127T ST2 0.8586924734109942 0 787,195,361,178,1009,748,875,1065,814,365 0.152,0.178,0.189,0.195,0.216,0.235,0.246,0.247,0.282,0.288 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.58792373312298,40.0332569792393 0 0 46.62118071236233 40.03325697923935 BN2 FALSE 770 +Reddy_2146T EZB 0.6393122685550168 0 782,3,55,478,230,971,54,806,800,976 0.207,0.224,0.264,0.268,0.289,0.305,0.319,0.332,0.345,0.35 EZB,EZB,MCD,EZB,EZB,EZB,MCD,N1,EZB,BN2 2.85750713291883,22.6751152712665,6.9263015121815,3.00905689138011 0 0 35.46798080774691 22.675115271266474 EZB TRUE 771 +Reddy_2148T MCD 0.9347050891534401 0 533,448,933,859,749,436,400,567,532,775 0.11,0.135,0.141,0.161,0.162,0.236,0.242,0.259,0.262,0.275 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 3.6366387033469,52.0589531306451 0 0 55.69559183399205 52.05895313064514 EZB FALSE 772 +Reddy_2152T BN2 0.747411700856546 0 1049,765,196,928,279,262,173,110,624,535 0.155,0.247,0.25,0.262,0.28,0.289,0.297,0.304,0.306,0.311 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2 28.7560286399064,3.26448279887244,6.45363688797539 0 0 38.47414832675427 28.75602863990644 BN2 TRUE 773 +Reddy_2153T ST2 0.884073778516856 0 825,1068,835,1003,940,598,996,218,671,842 0.139,0.154,0.18,0.184,0.195,0.24,0.257,0.288,0.299,0.303 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.56037682624387,42.4044128055166 0 0 47.964789631760425 42.40441280551656 ST2 TRUE 774 +Reddy_2156T MCD 1 0 567,420,510,400,448,201,541,200,533,436 0.119,0.133,0.148,0.22,0.241,0.242,0.246,0.252,0.266,0.267 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.036413997916 0 0 51.03641399791599 51.03641399791599 EZB FALSE 775 +Reddy_2163T EZB 1 0 613,866,151,27,152,98,150,599,644,97 0.114,0.136,0.173,0.184,0.196,0.213,0.223,0.24,0.268,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9881269585051 0 0 52.98812695850515 52.98812695850515 EZB TRUE 776 +Reddy_2166T BN2 1 0 694,301,968,158,484,393,572,357,701,564 0.247,0.276,0.29,0.381,0.382,0.405,0.41,0.443,0.445,0.468 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 27.9087682695643 0 0 27.908768269564312 27.908768269564312 BN2 TRUE 777 +Reddy_2190T MCD 0.8907398976392183 0 918,312,1103,169,155,350,450,990,417,1048 0.112,0.121,0.128,0.143,0.146,0.152,0.152,0.153,0.156,0.158 MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.473686467694,7.78582108992427 0 0 71.25950755761829 63.47368646769402 BN2 FALSE 778 +Reddy_2191T MCD 0.5277687645580372 0 525,205,43,124,716,789,617,441,421,803 0.179,0.235,0.272,0.288,0.294,0.314,0.315,0.387,0.4,0.404 MCD,MCD,ST2,N1,BN2,MCD,N1,MCD,MCD,BN2 5.8832033720566,18.1209745207479,6.65067394074517,3.68021256898794 0 0 34.335064402537654 18.12097452074794 MCD TRUE 779 +Reddy_2195T BN2 1 0 99,959,345,943,435,646,477,548,650,999 0.138,0.147,0.165,0.179,0.185,0.192,0.205,0.214,0.219,0.219 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.9917987141525 0 0 54.991798714152516 54.991798714152516 BN2 TRUE 780 +Reddy_2199T EZB 0.847821324044056 0 509,1052,736,1064,758,249,248,819,338,606 0.228,0.241,0.25,0.387,0.392,0.405,0.429,0.444,0.45,0.451 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,ST2 2.2199958246875,24.7220703280361,2.21746266180695 0 0 29.159528814530503 24.722070328036054 EZB TRUE 781 +Reddy_2203T EZB 0.5187512166729139 0 771,806,800,971,54,55,3,976,230,132 0.207,0.232,0.256,0.284,0.286,0.29,0.294,0.304,0.308,0.333 EZB,N1,EZB,EZB,MCD,MCD,EZB,BN2,EZB,BN2 6.29598101757248,18.9142290967691,6.9386192509794,4.31224964299107 0 0 36.461079008312076 18.91422909676913 EZB TRUE 782 +Reddy_2204T ST2 0.5909576657905032 0 1017,148,1005,218,785,668,359,996,476,671 0.325,0.347,0.396,0.443,0.443,0.459,0.476,0.499,0.504,0.51 BN2,ST2,ST2,ST2,EZB,BN2,ST2,ST2,EZB,ST2 5.26220625652496,4.24077939823208,13.7292934029161 0 0 23.232279057673153 13.72929340291612 EZB FALSE 783 +Reddy_2209T EZB 1 0 839,890,47,727,344,870,725,407,715,322 0.228,0.244,0.289,0.31,0.332,0.336,0.338,0.339,0.353,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.5068314133115 0 0 32.50683141331149 32.50683141331149 EZB TRUE 784 +Reddy_2211T ST2 0.6483063658808581 0 366,668,425,1017,161,583,160,612,359,1029 0.188,0.188,0.2,0.236,0.255,0.266,0.267,0.268,0.269,0.279 BN2,BN2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2 14.8919043054871,27.4514959177943 0 0 42.34340022328141 27.451495917794286 EZB FALSE 785 +Reddy_2212T EZB 0.8485774942057832 0 143,144,620,608,614,90,846,545,828,884 0.197,0.203,0.25,0.36,0.379,0.387,0.404,0.413,0.423,0.429 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,ST2,EZB 26.8195036647217,4.78574611841463 0 0 31.605249783136316 26.819503664721687 EZB TRUE 786 +Reddy_2213T ST2 0.6166190952998081 0 178,770,361,195,965,1107,365,950,661,1009 0.143,0.152,0.156,0.227,0.244,0.249,0.258,0.262,0.264,0.268 ST2,BN2,ST2,ST2,EZB,ST2,ST2,EZB,EZB,ST2 6.58792373312298,11.7113337143352,29.4320124804701 0 0 47.73126992792832 29.432012480470096 EZB FALSE 787 +Reddy_2214T MCD 0.9121545052588331 0 992,829,449,502,616,574,573,1093,1047,856 0.232,0.327,0.361,0.396,0.413,0.443,0.467,0.478,0.483,0.484 MCD,MCD,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD 23.4531851922099,2.25867070171546 0 0 25.711855893925335 23.453185192209876 EZB FALSE 788 +Reddy_2215T MCD 0.7073911410177665 0 441,617,713,205,421,779,525,985,716,663 0.173,0.177,0.269,0.285,0.31,0.314,0.369,0.39,0.433,0.441 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2 2.30761283779795,24.6949510049741,5.63788887294008,2.26944305912608 0 0 34.90989577483826 24.694951004974143 MCD TRUE 789 +Reddy_2216T MCD 1 0 697,475,223,198,1054,466,837,1047,44,128 0.123,0.195,0.23,0.235,0.26,0.262,0.272,0.277,0.287,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.5989773124325 0 0 43.59897731243248 43.59897731243248 MCD TRUE 790 +Reddy_2217T EZB 0.7062032181433154 0 382,676,922,641,657,11,62,252,974,372 0.278,0.376,0.4,0.406,0.419,0.545,0.553,0.589,0.595,0.601 EZB,EZB,ST2,EZB,BN2,EZB,EZB,EZB,EZB,ST2 2.38584903834364,15.7434952076932,4.16380707814509 0 0 22.293151324181935 15.743495207693197 EZB TRUE 791 +Reddy_2219T ST2 0.8357323496193599 0 370,300,1086,177,692,682,850,275,1016,673 0.186,0.281,0.311,0.317,0.327,0.356,0.362,0.366,0.377,0.408 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2 2.4526252799782,2.76328442622912,26.5365972184431 0 0 31.752506924650373 26.536597218443053 BN2 FALSE 792 +Reddy_2222T ST2 1 0 557,1053,982,598,1016,642,692,940,915,1086 0.147,0.201,0.23,0.25,0.252,0.261,0.29,0.304,0.304,0.331 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 40.9865509007107 0 0 40.9865509007107 40.9865509007107 ST2 TRUE 793 +Reddy_2225T MCD 1 0 413,297,290,108,165,1035,17,528,166,246 0.115,0.143,0.144,0.149,0.16,0.165,0.174,0.18,0.204,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.7308385829719 0 0 62.73083858297188 62.73083858297188 MCD TRUE 794 +Reddy_2226T BN2 0.8610806312826945 0 474,584,1060,674,630,335,681,65,855,64 0.218,0.268,0.275,0.3,0.303,0.333,0.334,0.345,0.348,0.367 N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.4819782642465,4.59503825372905 0 0 33.077016517975544 28.481978264246496 BN2 TRUE 795 +Reddy_2228T BN2 1 0 591,1023,527,555,998,305,75,905,395,702 0.12,0.144,0.158,0.177,0.179,0.189,0.202,0.211,0.226,0.228 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.5502829510305 0 0 56.550282951030496 56.550282951030496 BN2 TRUE 796 +Reddy_2231T ST2 0.9196534911012629 0 1067,1057,1007,422,207,309,483,544,363,728 0.247,0.284,0.303,0.33,0.396,0.422,0.425,0.444,0.45,0.491 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.22247831609962,25.4386901224777 0 0 27.661168438577345 25.438690122477727 ST2 TRUE 797 +Reddy_2232T MCD 1 0 528,804,573,434,297,17,413,794,166,246 0.148,0.157,0.175,0.182,0.191,0.221,0.226,0.228,0.229,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.6490406876131 0 0 51.6490406876131 51.6490406876131 MCD TRUE 798 +Reddy_2234T BN2 0.6020700739159555 0 698,1061,506,347,919,293,624,123,765,936 0.204,0.232,0.239,0.306,0.313,0.35,0.38,0.385,0.415,0.437 ST2,BN2,BN2,BN2,BN2,MCD,EZB,N1,BN2,BN2 19.6612911942718,2.63230646526343,2.85985865731566,2.59906930052404,4.90362529607568 0 0 32.65615091345062 19.661291194271808 BN2 TRUE 799 +Reddy_2236T EZB 0.5486131980490389 0 806,40,54,782,755,230,55,872,771,253 0.15,0.213,0.231,0.256,0.291,0.316,0.318,0.329,0.345,0.356 N1,EZB,MCD,EZB,EZB,EZB,MCD,BN2,EZB,EZB 3.03868826174577,20.906892853978,7.47868838437516,6.68435225440215 0 0 38.1086217545011 20.90689285397802 EZB TRUE 800 +Reddy_2237T BN2 0.3934290334558426 0 421,431,663,442,579,441,1070,1045,894,789 0.275,0.289,0.293,0.389,0.408,0.456,0.465,0.465,0.47,0.472 MCD,BN2,ST2,BN2,N1,MCD,BN2,BN2,MCD,MCD 10.3390613243195,10.0809423008088,2.44930876022281,3.41004283534057 0 0 26.27935522069162 10.339061324319456 ST2 FALSE 801 +Reddy_2238T BN2 0.5586989161326144 0 14,891,504,23,388,495,699,127,185,855 0.148,0.152,0.2,0.221,0.224,0.241,0.244,0.258,0.306,0.317 BN2,MCD,MCD,EZB,BN2,MCD,BN2,BN2,BN2,BN2 25.6353294138779,4.53372845772802,15.7149212693041 0 0 45.88397914091004 25.635329413877926 ST2 FALSE 802 +Reddy_2241T BN2 0.31706831880259156 0 43,716,481,530,525,1045,124,747,554,779 0.239,0.292,0.295,0.315,0.332,0.355,0.369,0.377,0.397,0.404 ST2,BN2,BN2,N1,MCD,BN2,N1,ST2,ST2,MCD 9.62363730632558,5.49134118311939,5.88959801729201,9.3473594503755 0 0 30.351935957112488 9.623637306325584 BN2 TRUE 803 +Reddy_2243T MCD 1 0 434,528,798,297,17,166,246,767,851,762 0.133,0.152,0.157,0.166,0.172,0.173,0.174,0.181,0.181,0.199 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.9000389936201 0 0 59.900038993620086 59.900038993620086 MCD TRUE 804 +Reddy_2244T EZB 1 0 594,29,80,876,632,242,953,667,760,46 0.138,0.161,0.166,0.185,0.194,0.195,0.2,0.204,0.205,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7477134183057 0 0 54.74771341830569 54.74771341830569 EZB TRUE 805 +Reddy_2247T EZB 0.6540346895876159 0 800,782,40,54,872,755,771,55,230,976 0.15,0.232,0.262,0.268,0.305,0.31,0.332,0.34,0.343,0.367 EZB,EZB,EZB,MCD,BN2,EZB,EZB,MCD,EZB,BN2 6.00509617172903,23.9592403054928,6.66864444401901 0 0 36.63298092124089 23.959240305492845 N1 FALSE 806 +Reddy_2249T ST2 0.8195605852956611 0 362,1032,263,483,207,656,832,1057,728,822 0.182,0.279,0.311,0.33,0.344,0.345,0.411,0.43,0.44,0.447 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.48497928666127,24.9129207267518 0 0 30.397900013413064 24.912920726751796 ST2 TRUE 807 +Reddy_2260T BN2 0.861374235134684 0 531,952,408,10,895,363,352,258,670,109 0.22,0.225,0.237,0.345,0.372,0.408,0.416,0.432,0.446,0.45 BN2,BN2,N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.1837624879937,4.21389908578162 0 0 30.397661573775295 26.183762487993672 BN2 TRUE 808 +Reddy_2262T EZB 0.771373549606777 0 1094,867,1102,886,595,885,618,861,1056,7 0.173,0.18,0.186,0.193,0.199,0.21,0.216,0.221,0.222,0.234 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.2595612701123,11.3397039492044 0 0 49.59926521931668 38.25956127011226 EZB TRUE 809 +Reddy_2263T EZB 0.7623932160607518 0 684,714,1033,319,322,993,913,641,194,988 0.166,0.235,0.255,0.27,0.274,0.312,0.318,0.336,0.339,0.376 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 2.6584167982463,27.8132093652433,6.0098230143412 0 0 36.48144917783078 27.81320936524328 ST2 FALSE 810 +Reddy_2269T EZB 1 0 556,628,724,272,653,750,310,979,853,596 0.167,0.236,0.272,0.314,0.319,0.322,0.335,0.349,0.349,0.36 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8248826336618 0 0 34.82488263366183 34.82488263366183 EZB TRUE 811 +Reddy_2271T EZB 0.8563186429647514 0 426,1097,284,115,753,874,1006,1075,816,21 0.163,0.165,0.201,0.203,0.264,0.27,0.281,0.288,0.328,0.332 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5724740124581,6.13648054893819 0 0 42.708954561396276 36.57247401245809 EZB TRUE 812 +Reddy_2274T MCD 0.4764872313343525 0 534,193,94,371,444,170,930,510,419,420 0.129,0.173,0.212,0.263,0.284,0.332,0.348,0.362,0.376,0.378 MCD,BN2,EZB,EZB,EZB,MCD,ST2,MCD,MCD,MCD 5.79403607007985,12.0495242503798,18.856680595703,2.87412615112855 0 0 39.574367067291206 18.856680595702965 EZB FALSE 813 +Reddy_2278T ST2 1 0 1004,1065,875,1009,748,896,907,195,683,662 0.142,0.15,0.153,0.19,0.21,0.233,0.248,0.254,0.258,0.26 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 50.2682962395257 0 0 50.26829623952566 50.26829623952566 ST2 TRUE 814 +Reddy_2281T BN2 1 0 515,70,527,591,998,404,796,261,499,555 0.192,0.208,0.225,0.23,0.245,0.245,0.25,0.265,0.269,0.275 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.1068108921169 0 0 42.10681089211686 42.10681089211686 BN2 TRUE 815 +Reddy_2290T EZB 1 0 21,250,938,284,597,709,251,941,356,1075 0.174,0.184,0.198,0.226,0.238,0.246,0.249,0.257,0.283,0.307 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6201429364529 0 0 43.62014293645291 43.62014293645291 EZB TRUE 816 +Reddy_2297T MCD 1 0 741,1087,447,283,721,511,722,299,690,469 0.178,0.209,0.232,0.253,0.261,0.263,0.264,0.268,0.271,0.285 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.0415689531844 0 0 41.04156895318436 41.04156895318436 MCD TRUE 817 +Reddy_2298T EZB 1 0 585,307,220,427,62,912,219,316,409,138 0.173,0.193,0.268,0.273,0.298,0.302,0.348,0.355,0.4,0.407 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.658887064425 0 0 35.65888706442495 35.65888706442495 EZB TRUE 818 +Reddy_2303T EZB 1 0 269,81,168,270,1064,217,908,766,509,248 0.239,0.261,0.324,0.344,0.376,0.39,0.397,0.405,0.423,0.425 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.9217027281102 0 0 28.921702728110233 28.921702728110233 EZB TRUE 819 +Reddy_2450T EZB 1 0 1063,931,635,880,125,89,35,48,159,364 0.131,0.161,0.172,0.193,0.208,0.214,0.218,0.223,0.236,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.4796080519993 0 0 51.479608051999314 51.479608051999314 EZB TRUE 820 +Reddy_2453T MCD 1 0 416,491,326,1036,550,311,332,283,469,1027 0.111,0.122,0.131,0.145,0.152,0.164,0.194,0.209,0.211,0.225 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5734444656084 0 0 63.57344446560836 63.57344446560836 MCD TRUE 821 +Reddy_2459T ST2 0.9224169782822506 0 1081,728,60,656,263,394,483,904,207,362 0.203,0.254,0.257,0.257,0.289,0.312,0.341,0.35,0.366,0.373 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 2.67782981597975,31.8378381316005 0 0 34.51566794758026 31.837838131600513 ST2 TRUE 822 +Reddy_2468T EZB 1 0 1100,766,908,216,471,217,76,168,235,638 0.191,0.201,0.222,0.222,0.238,0.24,0.241,0.285,0.293,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8156108452647 0 0 41.815610845264686 41.815610845264686 EZB TRUE 823 +Reddy_2471T ST2 0.6474643504714818 0 685,320,834,586,1091,1024,901,974,625,194 0.256,0.287,0.331,0.339,0.376,0.385,0.408,0.417,0.426,0.446 ST2,ST2,ST2,ST2,EZB,EZB,ST2,EZB,ST2,EZB 9.89509265567754,18.1732535354417 0 0 28.06834619111925 18.173253535441713 EZB FALSE 824 +Reddy_2473T ST2 0.8896770161361672 0 1068,774,1003,940,835,598,996,218,915,671 0.123,0.139,0.148,0.168,0.178,0.219,0.291,0.323,0.325,0.328 ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 5.61481382040532,45.2795114032078 0 0 50.89432522361311 45.279511403207785 ST2 TRUE 825 +Reddy_2475T EZB 1 0 294,236,56,718,156,247,883,334,303,764 0.114,0.138,0.156,0.157,0.194,0.206,0.236,0.255,0.267,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3089970703821 0 0 54.30899707038213 54.30899707038213 EZB TRUE 826 +Reddy_2478T BN2 0.8351288885485473 0 327,980,611,463,348,391,2,438,895,352 0.146,0.248,0.261,0.312,0.318,0.321,0.338,0.339,0.344,0.357 BN2,BN2,BN2,BN2,BN2,BN2,N1,N1,BN2,BN2 29.9159626983247,5.90600814777857 0 0 35.82197084610323 29.915962698324652 BN2 TRUE 827 +Reddy_2480T ST2 0.5187177414276689 0 614,607,1118,954,689,944,620,786,346,338 0.31,0.313,0.333,0.363,0.39,0.395,0.398,0.423,0.442,0.465 EZB,ST2,ST2,ST2,ST2,EZB,EZB,EZB,ST2,BN2 2.14892598045675,10.6373327180634,13.7808097333167 0 0 26.567068431836844 13.78080973331673 ST2 TRUE 828 +Reddy_2481T MCD 0.9128836378695838 0 449,573,798,856,788,528,804,519,434,44 0.15,0.241,0.286,0.304,0.327,0.328,0.338,0.348,0.351,0.365 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 3.06184441178203,32.0847610812061 0 0 35.146605492988115 32.08476108120609 MCD TRUE 829 +Reddy_2483T ST2 0.7346769304299484 0 381,412,1066,529,119,308,396,919,698,281 0.123,0.219,0.259,0.266,0.306,0.339,0.348,0.375,0.398,0.418 ST2,ST2,N1,ST2,BN2,ST2,ST2,BN2,ST2,ST2 5.9407852295596,3.86463276679789,27.1511045263424 0 0 36.95652252269993 27.151104526342436 ST2 TRUE 830 +Reddy_2485T EZB 0.9184006380671825 0 379,687,118,369,592,385,221,581,975,146 0.191,0.199,0.252,0.253,0.263,0.285,0.295,0.297,0.305,0.317 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.15455895093763,35.5045540153482 0 0 38.65911296628584 35.504554015348205 EZB TRUE 831 +Reddy_2487T ST2 0.9010850514943948 0 759,1032,1003,835,807,825,915,1068,940,774 0.266,0.313,0.392,0.396,0.411,0.437,0.446,0.446,0.462,0.464 ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2 2.52842439928896,23.0331761220486 0 0 25.56160052133754 23.033176122048584 ST2 TRUE 832 +Reddy_2488T EZB 1 0 61,629,384,605,74,184,1059,603,163,25 0.105,0.132,0.151,0.151,0.157,0.181,0.192,0.201,0.218,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.5859293777597 0 0 61.58592937775968 61.58592937775968 EZB TRUE 833 +Reddy_2522T ST2 0.6368602490686615 0 320,685,1091,586,1024,901,372,974,824,925 0.147,0.2,0.207,0.218,0.262,0.292,0.311,0.328,0.331,0.354 ST2,ST2,EZB,ST2,EZB,ST2,ST2,EZB,EZB,ST2 14.7414306766258,25.8529428085647 0 0 40.59437348519055 25.852942808564723 ST2 TRUE 834 +Reddy_2523T ST2 1 0 1003,825,774,1068,940,996,842,602,671,598 0.177,0.178,0.18,0.2,0.243,0.254,0.269,0.273,0.276,0.296 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 44.2656634167762 0 0 44.26566341677617 44.26566341677617 EZB FALSE 835 +Reddy_2524T EZB 1 0 764,236,117,78,294,826,675,718,56,287 0.217,0.267,0.271,0.287,0.291,0.305,0.306,0.322,0.336,0.342 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.5068183278884 0 0 34.50681832788842 34.50681832788842 EZB TRUE 836 +Reddy_2529T MCD 1 0 489,198,128,790,466,697,223,522,475,44 0.147,0.195,0.24,0.272,0.277,0.291,0.311,0.343,0.358,0.364 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.506445348769 0 0 38.50644534876899 38.50644534876899 MCD TRUE 837 +Reddy_2530T EZB 1 0 141,5,686,403,237,708,289,707,963,949 0.181,0.201,0.242,0.26,0.268,0.273,0.314,0.346,0.356,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5166405660904 0 0 37.51664056609036 37.51664056609036 EZB TRUE 838 +Reddy_2533T EZB 0.9224521785311877 0 890,784,715,407,322,424,319,1033,344,1114 0.117,0.228,0.229,0.25,0.332,0.335,0.335,0.361,0.364,0.384 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 35.4579432082057,2.9808442253771 0 0 38.438787433582796 35.45794320820569 EZB TRUE 839 +Reddy_2534T BN2 0.6483344890235474 0 22,571,470,186,1106,88,672,539,570,462 0.147,0.185,0.203,0.22,0.23,0.24,0.246,0.253,0.256,0.258 EZB,EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2 29.8262942794532,16.1781907270045 0 0 46.00448500645764 29.826294279453165 BN2 TRUE 840 +Reddy_2539T BN2 0.8510224905322906 0 540,929,546,373,905,1001,555,499,1023,395 0.137,0.146,0.166,0.218,0.221,0.229,0.243,0.262,0.266,0.266 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.8087545245208,7.31891835092883 0 0 49.12767287544967 41.808754524520836 BN2 TRUE 841 +Reddy_2540T ST2 0.7837785855672981 0 671,602,359,161,996,1029,160,668,835,366 0.127,0.14,0.159,0.166,0.173,0.188,0.218,0.22,0.269,0.276 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,BN2 8.16039468535616,3.71579165089888,43.049854950703 0 0 54.926041286958004 43.04985495070296 ST2 TRUE 842 +Reddy_2543T ST2 0.8340745785992753 0 904,925,278,1081,901,911,394,1024,910,372 0.137,0.232,0.257,0.284,0.29,0.306,0.307,0.311,0.316,0.325 ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,EZB,ST2 6.38581296247114,32.100230397026 0 0 38.48604335949718 32.100230397026046 ST2 TRUE 843 +Reddy_2544T MCD 1 0 1042,16,580,526,678,900,453,881,549,296 0.152,0.164,0.173,0.187,0.189,0.191,0.218,0.227,0.234,0.258 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4824109514507 0 0 51.48241095145073 51.48241095145073 EZB FALSE 844 +Reddy_2547T MCD 0.5070185471913775 0 330,894,436,497,400,1070,444,104,285,386 0.257,0.265,0.364,0.396,0.403,0.454,0.455,0.458,0.461,0.462 BN2,MCD,MCD,BN2,MCD,BN2,EZB,MCD,BN2,MCD 10.7816048761234,2.19732586706452,13.3484912505689 0 0 26.32742199375689 13.348491250568939 BN2 FALSE 845 +Reddy_2552T EZB 0.8667784802130977 0 545,144,1058,847,143,620,889,884,786,638 0.263,0.31,0.328,0.341,0.349,0.377,0.385,0.397,0.404,0.407 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.7746730492698,3.80780057557023 0 0 28.582473624839984 24.77467304926975 EZB TRUE 846 +Reddy_2553T EZB 1 0 638,249,216,846,823,1100,405,217,766,1064 0.176,0.318,0.336,0.341,0.349,0.37,0.378,0.384,0.391,0.4 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.613744400946 0 0 30.613744400946008 30.613744400946008 EZB TRUE 847 +Reddy_2554T EZB 1 0 1117,877,192,740,93,387,631,757,7,228 0.13,0.194,0.201,0.232,0.243,0.261,0.291,0.291,0.308,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.3590367455545 0 0 43.35903674555452 43.35903674555452 EZB TRUE 848 +Reddy_2555T EZB 1 0 121,122,190,601,868,756,1014,18,164,751 0.122,0.135,0.247,0.254,0.262,0.265,0.277,0.287,0.296,0.332 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.6254444773833 0 0 44.62544447738334 44.62544447738334 EZB TRUE 849 +Reddy_2561T ST2 0.40063721238544353 0 275,1089,792,935,903,370,300,1044,673,554 0.307,0.338,0.362,0.41,0.415,0.427,0.454,0.487,0.501,0.507 ST2,MCD,BN2,EZB,EZB,ST2,ST2,MCD,BN2,ST2 4.75815281846706,4.84836289237955,5.0100980306325,9.77030857589187 0 0 24.386922317370992 9.770308575891875 EZB FALSE 850 +Reddy_2562T MCD 1 0 767,762,246,166,434,17,804,879,551,1109 0.122,0.131,0.133,0.136,0.161,0.166,0.181,0.184,0.186,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.3243748759028 0 0 64.32437487590278 64.32437487590278 MCD TRUE 851 +Reddy_2566T EZB 1 0 20,239,266,746,72,25,677,167,603,1059 0.127,0.143,0.152,0.175,0.175,0.187,0.194,0.197,0.23,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1006688713383 0 0 57.100668871338264 57.100668871338264 EZB TRUE 852 +Reddy_2567T EZB 1 0 272,257,724,723,213,136,979,181,255,291 0.136,0.176,0.177,0.184,0.204,0.219,0.224,0.225,0.228,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1324434887607 0 0 51.13244348876067 51.13244348876067 EZB TRUE 853 +Reddy_2575T N1 0.46802969757875285 0 437,516,486,964,1099,496,893,1104,590,325 0.155,0.213,0.277,0.36,0.362,0.362,0.374,0.386,0.408,0.433 BN2,N1,ST2,BN2,N1,N1,N1,N1,ST2,ST2 9.24429738709351,15.4938387240178,8.36625620452957 0 0 33.10439231564085 15.493838724017778 BN2 FALSE 854 +Reddy_2580T BN2 0.6501877074218705 0 495,674,23,666,659,681,1113,802,652,699 0.178,0.22,0.235,0.272,0.287,0.306,0.313,0.317,0.328,0.331 MCD,BN2,EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2 24.2465625183621,4.26140508823571,5.63284654365583,3.15082062165673 0 0 37.29163477191041 24.246562518362136 BN2 TRUE 855 +Reddy_2581T MCD 1 0 44,519,1047,1054,449,573,475,829,616,697 0.161,0.219,0.222,0.243,0.255,0.287,0.288,0.304,0.317,0.325 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.792178893401 0 0 39.792178893401044 39.792178893401044 MCD TRUE 856 +Reddy_2582T EZB 0.8106919674970949 0 1056,595,587,245,867,227,368,618,809,995 0.163,0.185,0.197,0.211,0.232,0.239,0.253,0.267,0.272,0.273 EZB,EZB,EZB,EZB,ST2,BN2,EZB,EZB,EZB,EZB 4.19130400117245,36.3933642156332,4.30706091675532 0 0 44.891729133561014 36.393364215633234 EZB TRUE 857 +Reddy_2585T EZB 1 0 90,252,608,411,409,884,316,143,912,11 0.204,0.316,0.325,0.332,0.366,0.372,0.383,0.419,0.447,0.455 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.0165273152415 0 0 29.016527315241483 29.016527315241483 EZB TRUE 858 +Reddy_2594T MCD 0.8729648769256337 0 933,772,749,533,448,532,934,445,567,201 0.15,0.161,0.165,0.167,0.177,0.221,0.29,0.29,0.292,0.292 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.2061062691189,42.6473613304351 0 0 48.85346759955398 42.647361330435075 MCD TRUE 859 +Reddy_2596T BN2 0.8189316178693105 0 390,570,634,1055,212,358,633,77,571,487 0.237,0.298,0.307,0.32,0.326,0.345,0.351,0.352,0.356,0.356 BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,EZB,BN2 25.5696000147738,5.6535197877081 0 0 31.22311980248194 25.56960001477384 BN2 TRUE 860 +Reddy_2597T EZB 0.8941734470795837 0 1102,7,885,1094,706,680,1095,892,71,648 0.135,0.145,0.154,0.155,0.161,0.161,0.174,0.183,0.187,0.206 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 54.567184889444,6.45809501308857 0 0 61.02527990253253 54.56718488944396 EZB TRUE 861 +Reddy_2600T EZB 0.73523282507832 0 424,115,715,1097,407,812,890,426,913,839 0.166,0.27,0.279,0.302,0.355,0.364,0.377,0.382,0.382,0.393 ST2,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 23.9784705995392,8.63496800337535 0 0 32.61343860291456 23.97847059953921 EZB TRUE 862 +Reddy_2603T EZB 0.904158615223084 0 995,1079,1074,368,227,274,947,618,277,228 0.132,0.133,0.147,0.159,0.171,0.177,0.178,0.18,0.192,0.201 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.84877126803628,55.1767583781609 0 0 61.0255296461972 55.17675837816092 EZB TRUE 863 +Reddy_2604T BN2 0.46998668646468716 0 512,665,924,126,967,131,582,1106,505,462 0.208,0.227,0.233,0.248,0.267,0.268,0.279,0.339,0.34,0.345 N1,BN2,ST2,EZB,EZB,BN2,BN2,BN2,N1,BN2 17.5758045115414,7.7839591589554,7.75123954709197,4.28538484554838 0 0 37.39638806313717 17.575804511541417 BN2 TRUE 864 +Reddy_2605T EZB 1 0 754,406,1073,1018,116,1014,18,190,639,756 0.178,0.19,0.219,0.233,0.235,0.245,0.246,0.251,0.279,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1949898403662 0 0 43.19498984036621 43.19498984036621 EZB TRUE 865 +Reddy_2606T EZB 1 0 776,613,27,152,151,98,150,599,644,97 0.136,0.146,0.16,0.16,0.17,0.199,0.205,0.216,0.236,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0742482341444 0 0 55.07424823414436 55.07424823414436 EZB TRUE 866 +Reddy_2608T EZB 0.9119038570895251 0 595,618,1056,809,1074,947,995,587,227,368 0.148,0.149,0.169,0.18,0.185,0.188,0.193,0.202,0.205,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.8733786740761,50.4454867503561 0 0 55.31886542443223 50.445486750356125 ST2 FALSE 867 +Reddy_2609T EZB 1 0 709,756,597,18,190,251,164,250,21,116 0.172,0.176,0.189,0.216,0.217,0.226,0.226,0.235,0.246,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0260880576312 0 0 47.026088057631156 47.026088057631156 EZB TRUE 868 +Reddy_2611T MCD 1 0 340,536,414,415,461,511,1038,987,565,456 0.123,0.156,0.165,0.171,0.176,0.187,0.187,0.19,0.194,0.199 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.2432903007032 0 0 58.24329030070322 58.24329030070322 MCD TRUE 869 +Reddy_2613T EZB 1 0 47,593,727,219,726,784,676,725,220,344 0.19,0.223,0.298,0.298,0.317,0.336,0.349,0.352,0.356,0.412 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5356420200717 0 0 33.5356420200717 33.5356420200717 EZB TRUE 870 +Reddy_2619T BN2 1 0 563,15,640,763,264,38,318,341,175,1062 0.184,0.199,0.204,0.21,0.219,0.241,0.247,0.25,0.25,0.255 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8215245392046 0 0 44.82152453920463 44.82152453920463 BN2 TRUE 871 +Reddy_2620T EZB 0.5392855872578031 0 755,806,758,800,132,40,782,976,54,606 0.283,0.305,0.32,0.329,0.394,0.409,0.413,0.431,0.459,0.471 EZB,N1,EZB,EZB,BN2,EZB,EZB,BN2,MCD,ST2 4.85996247795436,14.5664483436482,2.17810978426866,3.2817229883936,2.12439545825776 0 0 27.010639052522556 14.566448343648176 BN2 FALSE 872 +Reddy_2621T EZB 1 0 107,387,547,757,102,631,140,228,740,93 0.208,0.209,0.224,0.227,0.261,0.291,0.295,0.296,0.296,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.2269526064206 0 0 39.226952606420554 39.226952606420554 EZB TRUE 873 +Reddy_2626T EZB 1 0 1006,1114,753,1075,688,407,164,812,601,1097 0.116,0.176,0.19,0.226,0.252,0.257,0.261,0.27,0.285,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.2615212396805 0 0 46.261521239680526 46.261521239680526 EZB TRUE 874 +Reddy_2628T ST2 0.9195982438954846 0 1065,814,1004,1009,748,195,770,423,907,361 0.103,0.153,0.187,0.19,0.221,0.241,0.246,0.266,0.275,0.284 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.06251457188003,46.4651700050478 0 0 50.52768457692778 46.46517000504775 ST2 TRUE 875 +Reddy_2636T EZB 1 0 632,760,667,46,80,594,29,914,805,1002 0.114,0.122,0.127,0.138,0.144,0.16,0.166,0.171,0.185,0.199 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.5851344234991 0 0 67.58513442349906 67.58513442349906 EZB TRUE 876 +Reddy_2640T EZB 1 0 848,1117,192,351,267,720,229,206,740,71 0.194,0.219,0.231,0.289,0.29,0.302,0.312,0.314,0.321,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.8719077418726 0 0 36.871907741872576 36.871907741872576 EZB TRUE 877 +Reddy_2643T MCD 0.892659767296298 0 978,961,452,621,295,465,1101,367,33,902 0.131,0.154,0.154,0.162,0.164,0.184,0.196,0.207,0.217,0.221 MCD,MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD 51.3011691366063,6.16884465374083 0 0 57.470013790347146 51.30116913660632 MCD TRUE 878 +Reddy_2646T MCD 1 0 712,1109,762,17,166,165,246,767,500,1015 0.115,0.132,0.155,0.159,0.16,0.16,0.161,0.165,0.166,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.6982344768305 0 0 65.69823447683052 65.69823447683052 MCD TRUE 879 +Reddy_2650T EZB 1 0 89,1063,37,159,931,820,761,1031,48,635 0.123,0.165,0.168,0.174,0.186,0.193,0.201,0.215,0.221,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.058652235354 0 0 55.05865223535396 55.05865223535396 EZB TRUE 880 +Reddy_2652T MCD 1 0 678,204,526,580,467,494,920,453,1042,549 0.138,0.158,0.165,0.169,0.18,0.181,0.189,0.206,0.207,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.0295425845926 0 0 56.02954258459262 56.02954258459262 MCD TRUE 881 +Reddy_2654T EZB 0.9184391777962239 0 120,6,303,942,334,182,1018,883,111,743 0.125,0.157,0.182,0.198,0.203,0.206,0.217,0.224,0.236,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 47.6905613329553,4.23509959909229 0 0 51.92566093204755 47.690561332955255 EZB TRUE 882 +Reddy_2662T EZB 1 0 156,303,942,743,718,334,882,1073,6,1018 0.143,0.143,0.16,0.168,0.188,0.189,0.224,0.229,0.234,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.167041210681 0 0 54.16704121068098 54.16704121068098 EZB TRUE 883 +Reddy_2664T EZB 1 0 889,409,1058,143,252,316,858,846,90,912 0.192,0.23,0.253,0.334,0.342,0.344,0.372,0.397,0.398,0.404 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.5411193424041 0 0 32.54111934240414 32.54111934240414 EZB TRUE 884 +Reddy_2675T EZB 0.8840075754099747 0 1094,1102,861,112,1095,680,1072,892,706,886 0.142,0.146,0.154,0.159,0.161,0.169,0.17,0.176,0.188,0.193 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.821369528088,7.06201091480851 0 0 60.88338044289654 53.821369528088034 EZB TRUE 885 +Reddy_2677T EZB 0.8902971896098313 0 1094,112,809,885,1020,1102,1072,52,861,1095 0.19,0.191,0.193,0.193,0.211,0.211,0.213,0.216,0.24,0.245 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6397736037353,5.25409161493622 0 0 47.893865218671536 42.639773603735314 EZB TRUE 886 +Reddy_2678T EZB 1 0 304,36,963,289,750,707,310,237,653,708 0.126,0.149,0.158,0.168,0.183,0.204,0.22,0.221,0.24,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.647067520312 0 0 54.64706752031199 54.64706752031199 EZB TRUE 887 +Reddy_2690T MCD 0.9208264338187231 0 1043,730,460,513,66,1082,459,739,314,200 0.137,0.137,0.149,0.237,0.278,0.292,0.301,0.309,0.311,0.314 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 3.601271668708,41.8845115592759 0 0 45.485783227983866 41.88451155927587 MCD TRUE 888 +Reddy_2692T EZB 1 0 1058,884,409,316,405,252,846,143,912,858 0.18,0.192,0.246,0.358,0.365,0.38,0.385,0.399,0.403,0.457 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.780719918071 0 0 32.78071991807098 32.78071991807098 EZB TRUE 889 +Reddy_2696T EZB 0.9191759466075397 0 839,715,407,784,424,322,319,1033,344,862 0.117,0.214,0.242,0.244,0.319,0.33,0.334,0.358,0.372,0.377 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 35.6424270006966,3.13407398613225 0 0 38.7765009868289 35.64242700069665 EZB TRUE 890 +Reddy_2698T BN2 0.5878751604114065 0 14,802,388,504,127,23,699,185,495,552 0.147,0.152,0.197,0.217,0.239,0.252,0.283,0.286,0.287,0.288 BN2,ST2,BN2,MCD,BN2,EZB,BN2,BN2,MCD,BN2 26.5595451473892,3.96092014258998,8.08977710369399,6.56864410461383 0 0 45.17888649828698 26.559545147389183 MCD FALSE 891 +Reddy_2704T EZB 1 0 1095,648,680,71,706,711,229,1072,885,112 0.116,0.123,0.125,0.147,0.149,0.155,0.166,0.168,0.176,0.181 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.803165381908 0 0 67.80316538190803 67.80316538190803 EZB TRUE 892 +Reddy_2706T ST2 0.488781713557018 0 964,622,590,944,1096,691,486,854,1084,954 0.231,0.287,0.299,0.324,0.335,0.35,0.368,0.374,0.39,0.393 BN2,ST2,ST2,EZB,EZB,ST2,ST2,BN2,BN2,ST2 9.55774467896439,6.07459093954444,14.9462568009397 0 0 30.57859241944852 14.946256800939688 N1 FALSE 893 +Reddy_2707T BN2 0.7410876919765866 0 845,497,431,442,1070,104,330,103,34,801 0.265,0.309,0.333,0.401,0.402,0.406,0.422,0.446,0.458,0.47 BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD,BN2,ST2 19.5509014603763,4.70465241775135,2.12580648184712 0 0 26.38136035997481 19.550901460376345 MCD FALSE 894 +Reddy_2716T BN2 0.8647254888326197 0 438,952,611,352,327,10,531,463,827,109 0.216,0.249,0.267,0.276,0.307,0.31,0.323,0.332,0.344,0.367 N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.6051743594382,4.63132582677143 0 0 34.23650018620958 29.605174359438156 BN2 TRUE 895 +Reddy_2764T ST2 1 0 662,1004,683,612,814,907,748,583,1009,1065 0.157,0.202,0.219,0.232,0.233,0.238,0.26,0.264,0.27,0.282 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.5822963038685 0 0 43.58229630386845 43.58229630386845 ST2 TRUE 896 +Reddy_2772T MCD 1 0 314,459,739,288,991,520,513,106,729,439 0.139,0.14,0.152,0.155,0.161,0.166,0.205,0.218,0.254,0.256 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.9570572162648 0 0 56.95705721626479 56.95705721626479 MCD TRUE 897 +Reddy_2773T BN2 0.9073217939320305 0 1069,493,498,633,634,77,399,212,569,1022 0.172,0.179,0.198,0.203,0.247,0.248,0.266,0.285,0.287,0.305 BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 39.4217903436911,4.02673101591406 0 0 43.44852135960514 39.42179034369108 BN2 TRUE 898 +Reddy_2781T ST2 0.710957923549591 0 909,1096,590,339,544,964,309,508,1084,691 0.171,0.253,0.278,0.34,0.345,0.351,0.371,0.372,0.381,0.397 ST2,EZB,ST2,ST2,ST2,BN2,ST2,ST2,BN2,ST2 5.47834364111992,3.95362148816287,23.1998414405854 0 0 32.631806569868154 23.199841440585363 ST2 TRUE 899 +Reddy_2784T MCD 0.8990359593925075 0 16,1042,844,276,468,443,453,580,549,1008 0.13,0.178,0.191,0.198,0.21,0.211,0.212,0.215,0.216,0.216 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 5.22332438274205,46.5111778352133 0 0 51.7345022179553 46.51117783521325 MCD TRUE 900 +Reddy_2789T ST2 0.8081425029516552 0 1024,925,685,843,834,320,625,969,904,703 0.131,0.17,0.267,0.29,0.292,0.313,0.313,0.314,0.326,0.335 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 7.63068187137834,32.1419722535503 0 0 39.77265412492869 32.14197225355035 ST2 TRUE 901 +Reddy_2792T MCD 0.8980333199954622 0 451,33,621,1008,878,276,468,465,961,1021 0.145,0.153,0.198,0.215,0.221,0.223,0.225,0.23,0.232,0.236 MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.4586808247204,5.04803550173511 0 0 49.50671632645552 44.4586808247204 MCD TRUE 902 +Reddy_2794T MCD 0.39568149905334316 0 850,992,275,574,1093,788,502,616,1105,935 0.415,0.434,0.443,0.502,0.517,0.521,0.536,0.552,0.567,0.573 EZB,MCD,ST2,N1,MCD,EZB,MCD,MCD,BN2,EZB 1.76396462016698,6.0770685856743,7.91485085710135,1.99072593760113,2.25647550140012 0 0 20.003085501943882 7.91485085710135 EZB FALSE 903 +Reddy_2809T ST2 0.8408944753425914 0 843,278,1081,925,394,911,910,901,1024,372 0.137,0.25,0.251,0.265,0.273,0.283,0.311,0.326,0.347,0.348 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,EZB,ST2 6.0990088044219,32.2340963316474 0 0 38.33310513606929 32.234096331647386 ST2 TRUE 904 +Reddy_2815T BN2 0.8992649756790846 0 395,373,1023,540,958,555,929,702,796,841 0.154,0.163,0.168,0.184,0.187,0.193,0.198,0.201,0.211,0.221 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 48.418255634082,5.42377863120083 0 0 53.84203426528286 48.41825563408203 BN2 TRUE 905 +Reddy_2819T MCD 1 0 323,332,39,130,311,1027,491,514,416,821 0.11,0.145,0.151,0.153,0.175,0.193,0.216,0.22,0.232,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.7330158489373 0 0 57.73301584893731 57.73301584893731 MCD TRUE 906 +Reddy_2820T ST2 1 0 683,748,1009,662,1004,195,896,814,1065,875 0.132,0.164,0.196,0.203,0.21,0.218,0.238,0.248,0.272,0.275 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 48.5969695481761 0 0 48.596969548176084 48.596969548176084 ST2 TRUE 907 +Reddy_2823T EZB 1 0 471,766,168,235,823,217,216,76,234,1100 0.175,0.182,0.218,0.218,0.222,0.226,0.252,0.257,0.269,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3785273220184 0 0 44.37852732201845 44.37852732201845 EZB TRUE 908 +Reddy_2824T ST2 0.7431425696535922 0 899,339,508,1096,590,544,309,1084,964,691 0.171,0.27,0.306,0.309,0.347,0.363,0.388,0.406,0.419,0.431 ST2,ST2,ST2,EZB,ST2,ST2,ST2,BN2,BN2,ST2 4.84579313648566,3.23388585757011,23.3762106921379 0 0 31.455889686193654 23.376210692137878 ST2 TRUE 909 +Reddy_2827T ST2 1 0 278,911,372,394,904,843,922,339,1081,925 0.161,0.21,0.277,0.309,0.311,0.316,0.372,0.388,0.405,0.416 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 34.332109210429 0 0 34.332109210429024 34.332109210429024 EZB FALSE 910 +Reddy_2828T ST2 0.8712991113742121 0 278,394,910,904,843,339,60,1081,508,372 0.191,0.203,0.21,0.283,0.306,0.312,0.319,0.321,0.327,0.37 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 4.75060977277442,32.1614102101438 0 0 36.912019982918245 32.16141021014383 ST2 TRUE 911 +Reddy_2829T EZB 1 0 316,252,62,409,818,307,585,220,889,884 0.17,0.231,0.247,0.274,0.302,0.337,0.366,0.387,0.403,0.404 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.4435053296903 0 0 34.443505329690254 34.443505329690254 EZB TRUE 912 +Reddy_2832T EZB 0.7632623981595662 0 714,993,1033,322,319,810,715,424,684,862 0.183,0.218,0.22,0.246,0.25,0.318,0.321,0.336,0.372,0.382 EZB,EZB,EZB,EZB,EZB,ST2,EZB,ST2,ST2,EZB 28.3871970417288,8.80472687615066 0 0 37.191923917879485 28.387197041728825 EZB TRUE 913 +Reddy_2834T EZB 1 0 46,760,984,632,876,667,1026,317,637,1002 0.144,0.149,0.153,0.169,0.171,0.172,0.174,0.175,0.181,0.185 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.1613822929174 0 0 60.16138229291742 60.16138229291742 EZB TRUE 914 +Reddy_2874T ST2 1 0 598,642,940,1003,793,1068,759,825,557,774 0.259,0.265,0.272,0.3,0.304,0.307,0.32,0.325,0.35,0.361 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 33.0083270707261 0 0 33.008327070726075 33.008327070726075 ST2 TRUE 915 +Reddy_2879T MCD 1 0 298,296,549,494,920,453,467,526,443,580 0.18,0.188,0.212,0.222,0.225,0.228,0.25,0.273,0.276,0.286 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.729984725873 0 0 43.72998472587304 43.72998472587304 MCD TRUE 916 +Reddy_2882T EZB 1 0 1013,271,210,927,79,405,114,402,360,638 0.15,0.191,0.211,0.243,0.267,0.317,0.332,0.347,0.379,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.6107869790581 0 0 38.610786979058105 38.610786979058105 EZB TRUE 917 +Reddy_2888T MCD 0.7516878724908354 0 778,1103,169,312,990,155,450,350,1046,417 0.112,0.119,0.131,0.133,0.151,0.155,0.161,0.164,0.165,0.167 BN2,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.94758568490785,52.5900839998871,8.42499157994203 0 0 69.962661264737 52.59008399988713 MCD TRUE 918 +Reddy_2894T BN2 0.46533870502583385 0 293,1061,1066,530,799,119,698,481,381,506 0.192,0.267,0.275,0.299,0.313,0.314,0.32,0.335,0.353,0.374 MCD,BN2,N1,N1,BN2,BN2,ST2,BN2,ST2,BN2 15.7936657049429,5.2164926646182,6.97512604238055,5.9548661372505 0 0 33.94015054919217 15.793665704942928 BN2 TRUE 919 +Reddy_2897T MCD 1 0 494,467,204,296,881,453,549,526,678,580 0.124,0.127,0.174,0.183,0.189,0.194,0.198,0.198,0.209,0.212 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.2369247997978 0 0 57.23692479979776 57.23692479979776 MCD TRUE 920 +Reddy_2899T EZB 1 0 4,76,234,235,336,471,32,28,1100,908 0.175,0.203,0.203,0.219,0.233,0.244,0.252,0.265,0.303,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6970051430655 0 0 42.69700514306547 42.69700514306547 EZB TRUE 921 +Reddy_2901T EZB 0.4810768984124146 0 657,382,372,910,1091,791,278,834,586,974 0.204,0.245,0.301,0.372,0.38,0.4,0.416,0.426,0.427,0.434 BN2,EZB,ST2,EZB,EZB,EZB,ST2,ST2,ST2,EZB 4.90302645642044,14.2071039888414,10.4217469650353 0 0 29.531877410297085 14.207103988841371 ST2 FALSE 922 +Reddy_2903T EZB 1 0 215,214,428,336,4,133,234,921,235,28 0.181,0.223,0.321,0.434,0.446,0.458,0.461,0.469,0.513,0.52 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.0410038997816 0 0 28.041003899781597 28.041003899781597 EZB TRUE 923 +Reddy_2910T BN2 0.585855223315257 0 967,582,665,864,505,512,131,1085,432,153 0.134,0.163,0.171,0.233,0.257,0.261,0.269,0.275,0.291,0.309 EZB,BN2,BN2,BN2,N1,N1,BN2,EZB,BN2,BN2 26.6411732878545,11.1121692659796,7.72064526366205 0 0 45.47398781749612 26.641173287854468 ST2 FALSE 924 +Reddy_2917T ST2 0.8673758662805816 0 901,1024,843,904,969,703,625,685,372,834 0.17,0.199,0.232,0.265,0.276,0.29,0.322,0.337,0.345,0.354 ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.02161621708375,32.8419013513518 0 0 37.86351756843557 32.84190135135182 ST2 TRUE 925 +Reddy_2918T BN2 0.930469217628049 0 575,95,561,636,399,487,498,1069,1022,77 0.159,0.182,0.199,0.211,0.239,0.348,0.35,0.351,0.357,0.361 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 37.093093830193,2.77184004129033 0 0 39.86493387148335 37.09309383019303 ST2 FALSE 926 +Reddy_2925T EZB 1 0 210,271,360,917,402,79,114,1013,211,306 0.138,0.156,0.236,0.243,0.257,0.268,0.271,0.28,0.322,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.0893160899486 0 0 43.089316089948625 43.089316089948625 EZB TRUE 927 +Reddy_2933T BN2 0.8175055815553613 0 262,321,1049,110,773,936,279,355,765,485 0.146,0.207,0.215,0.222,0.262,0.269,0.292,0.382,0.387,0.391 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 32.3028463343704,7.21106899928639 0 0 39.51391533365677 32.302846334370386 BN2 TRUE 928 +Reddy_2935T BN2 0.8585480099683959 0 540,841,546,555,905,499,373,1001,1023,527 0.133,0.146,0.151,0.198,0.198,0.219,0.22,0.22,0.228,0.249 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.6420750299092,7.51986175169178 0 0 53.16193678160095 45.642075029909165 BN2 TRUE 929 +Reddy_2936T EZB 0.4539506949065433 0 534,94,371,813,193,170,419,444,541,375 0.321,0.326,0.337,0.348,0.42,0.496,0.512,0.532,0.565,0.57 MCD,EZB,EZB,EZB,BN2,MCD,MCD,EZB,MCD,MCD 2.37994833219075,10.7952901402122,10.6055162688904 0 0 23.7807547412934 10.795290140212213 ST2 FALSE 930 +Reddy_2939T EZB 1 0 635,820,48,35,1063,125,880,89,364,256 0.138,0.161,0.162,0.162,0.166,0.168,0.186,0.198,0.212,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.4036767302892 0 0 57.40367673028922 57.40367673028922 EZB TRUE 931 +Reddy_2945T MCD 1 0 987,461,660,188,966,380,414,565,456,1038 0.151,0.165,0.166,0.176,0.192,0.195,0.195,0.204,0.209,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.0093604592706 0 0 54.00936045927056 54.00936045927056 MCD TRUE 932 +Reddy_2948T MCD 0.8646521237265254 0 749,772,859,533,448,436,532,445,400,567 0.122,0.141,0.15,0.151,0.174,0.267,0.27,0.277,0.28,0.3 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.07204533268048,45.1788323862369 0 0 52.25087771891736 45.178832386236884 MCD TRUE 933 +Reddy_2949T MCD 0.9111065314251034 0 445,859,749,480,933,376,532,772,533,448 0.202,0.29,0.292,0.297,0.303,0.325,0.339,0.34,0.349,0.365 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 2.94016703916405,30.1350080698692 0 0 33.07517510903328 30.13500806986923 MCD TRUE 934 +Reddy_2950T MCD 0.4692982156896029 0 1044,1089,696,747,124,1105,554,850,205,43 0.222,0.226,0.319,0.33,0.338,0.345,0.381,0.41,0.476,0.484 MCD,MCD,MCD,ST2,N1,BN2,ST2,EZB,MCD,ST2 2.8995290364209,2.43716092556622,14.1629274429509,2.96151348551784,7.71781923007554 0 0 30.178950120531425 14.162927442950924 EZB FALSE 935 +Reddy_2952T BN2 0.5832237443722503 0 485,928,1049,321,262,773,558,765,698,110 0.241,0.269,0.283,0.286,0.312,0.333,0.333,0.373,0.377,0.392 ST2,BN2,ST2,BN2,BN2,BN2,ST2,BN2,ST2,BN2 18.6566283416339,13.3321727345549 0 0 31.98880107618874 18.65662834163387 BN2 TRUE 936 +Reddy_2953T MCD 1 0 721,722,690,511,536,299,1110,1087,340,869 0.128,0.145,0.15,0.162,0.184,0.193,0.203,0.215,0.237,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.1361260886133 0 0 56.13612608861333 56.13612608861333 EZB FALSE 937 +Reddy_2955T EZB 1 0 251,250,941,356,21,816,709,597,868,182 0.159,0.164,0.168,0.187,0.191,0.198,0.205,0.227,0.27,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4953534057211 0 0 50.49535340572106 50.49535340572106 EZB TRUE 938 +Reddy_3382T EZB 1 0 960,1034,51,948,233,31,211,647,719,57 0.208,0.249,0.266,0.273,0.274,0.351,0.371,0.371,0.383,0.391 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.296239237016 0 0 33.29623923701597 33.29623923701597 EZB TRUE 939 +Reddy_3385T ST2 0.9132889607945325 0 1068,598,825,1003,774,835,915,982,793,557 0.145,0.154,0.168,0.181,0.195,0.243,0.272,0.299,0.304,0.33 ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2 4.11787905717574,43.3717957859333 0 0 47.48967484310905 43.371795785933315 ST2 TRUE 940 +Reddy_3388T EZB 1 0 356,938,251,250,182,709,816,21,116,120 0.135,0.168,0.18,0.23,0.231,0.255,0.257,0.258,0.271,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6593373273434 0 0 46.65933732734341 46.65933732734341 EZB TRUE 941 +Reddy_3390T EZB 1 0 303,743,883,1018,1073,882,156,120,334,6 0.154,0.156,0.16,0.174,0.185,0.198,0.202,0.216,0.22,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7660168966717 0 0 53.766016896671744 53.766016896671744 EZB TRUE 942 +Reddy_3401T BN2 1 0 477,335,99,780,650,345,630,1060,959,435 0.142,0.173,0.176,0.179,0.184,0.19,0.196,0.221,0.224,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2432703274432 0 0 53.243270327443234 53.243270327443234 BN2 TRUE 943 +Reddy_3407T ST2 0.5904200655911392 0 954,614,893,486,828,608,622,964,325,691 0.207,0.264,0.324,0.395,0.395,0.427,0.433,0.456,0.46,0.485 ST2,EZB,N1,ST2,ST2,EZB,ST2,BN2,ST2,ST2 2.19529788468264,6.12577317291429,3.0860783400432,16.4437008011223 0 0 27.85085019876246 16.443700801122326 EZB FALSE 944 +Reddy_3409T BN2 1 0 354,331,75,559,973,998,1077,305,197,542 0.127,0.156,0.164,0.169,0.17,0.174,0.177,0.193,0.214,0.215 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 58.1013648048504 0 0 58.10136480485038 58.10136480485038 BN2 TRUE 945 +Reddy_3432T ST2 0.6130181182059324 0 970,490,422,558,24,281,59,529,363,670 0.115,0.224,0.291,0.342,0.36,0.401,0.402,0.421,0.442,0.442 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2 13.2341823898224,20.9642724020869 0 0 34.19845479190937 20.964272402086934 BN2 FALSE 946 +Reddy_3438T EZB 0.8292691958464723 0 274,1074,618,995,863,867,1079,368,227,631 0.126,0.134,0.143,0.171,0.178,0.188,0.195,0.214,0.218,0.222 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,BN2,EZB 4.5972440269371,48.1272709520412,5.31124855003537 0 0 58.035763529013636 48.127270952041165 EZB TRUE 947 +Reddy_3441T EZB 1 0 960,939,57,647,1080,1034,609,51,31,233 0.174,0.273,0.32,0.385,0.396,0.422,0.427,0.439,0.44,0.447 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1334407731161 0 0 29.133440773116092 29.133440773116092 EZB TRUE 948 +Reddy_3450T EZB 1 0 310,723,403,708,213,853,686,272,5,750 0.211,0.212,0.214,0.216,0.237,0.238,0.25,0.255,0.256,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8176214127012 0 0 42.817621412701165 42.817621412701165 EZB TRUE 949 +Reddy_3451T ST2 0.5362670501593513 0 1107,361,1025,178,965,787,770,625,1041,661 0.154,0.212,0.231,0.243,0.255,0.262,0.301,0.35,0.351,0.358 ST2,ST2,BN2,ST2,EZB,EZB,BN2,ST2,ST2,EZB 7.64980266158617,10.5388586911943,21.0336137929269 0 0 39.22227514570734 21.033613792926918 EZB FALSE 950 +Reddy_3454T BN2 1 0 353,482,578,566,401,957,1111,26,264,569 0.142,0.186,0.19,0.213,0.218,0.247,0.253,0.288,0.305,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.9571232151621 0 0 44.95712321516207 44.95712321516207 BN2 TRUE 951 +Reddy_3456T BN2 0.8332009649901869 0 531,808,895,10,352,438,408,611,109,258 0.196,0.225,0.249,0.269,0.311,0.342,0.36,0.364,0.369,0.396 BN2,BN2,BN2,BN2,BN2,N1,N1,BN2,BN2,BN2 28.4581911695735,5.69706352328284 0 0 34.15525469285638 28.458191169573546 BN2 TRUE 952 +Reddy_3459T EZB 1 0 242,231,805,594,989,29,80,876,632,667 0.145,0.174,0.2,0.203,0.205,0.26,0.261,0.262,0.265,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.483084709872 0 0 46.48308470987196 46.48308470987196 EZB TRUE 953 +Reddy_3460T EZB 0.6808494113715429 0 614,944,608,828,893,411,786,622,90,691 0.171,0.207,0.329,0.363,0.393,0.43,0.441,0.445,0.453,0.485 EZB,EZB,EZB,ST2,N1,EZB,EZB,ST2,EZB,ST2 20.5014058187869,2.5472759182153,7.06283114142957 0 0 30.111512878431764 20.5014058187869 ST2 FALSE 954 +Reddy_3462T BN2 1 0 731,700,1010,538,435,1071,1001,99,959,650 0.126,0.146,0.17,0.213,0.219,0.221,0.273,0.276,0.297,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.4652601126088 0 0 48.46526011260879 48.46526011260879 BN2 TRUE 955 +Reddy_3465T MCD 0.9096944730633607 0 260,479,169,433,204,1046,467,1103,286,918 0.129,0.153,0.18,0.182,0.183,0.188,0.196,0.198,0.208,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 50.7854123096467,5.041476622226 0 0 55.82688893187272 50.78541230964672 MCD TRUE 956 +Reddy_3467T BN2 1 0 401,1111,482,578,264,640,318,951,353,1062 0.14,0.148,0.171,0.188,0.211,0.236,0.246,0.247,0.282,0.282 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.2208622266044 0 0 49.22086222660436 49.22086222660436 BN2 TRUE 957 +Reddy_3471T BN2 0.91990875403960215 0 395,702,373,905,305,1023,542,540,75,654 0.139,0.161,0.168,0.187,0.228,0.232,0.232,0.252,0.255,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 45.5589997557648,3.96656411750114 0 0 49.5255638732659 45.558999755764766 BN2 TRUE 958 +Reddy_3472T BN2 1 0 780,646,548,99,345,999,435,943,538,477 0.147,0.149,0.168,0.174,0.179,0.197,0.197,0.224,0.244,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.6115876029108 0 0 53.611587602910824 53.611587602910824 BN2 TRUE 959 +Reddy_3474T EZB 1 0 948,939,647,1034,57,51,233,31,537,1080 0.174,0.208,0.339,0.356,0.362,0.372,0.38,0.419,0.432,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3550327166385 0 0 31.35503271663849 31.35503271663849 EZB TRUE 960 +Reddy_3476T MCD 0.9166536190396003 0 978,465,878,452,1021,1101,367,568,33,621 0.131,0.134,0.154,0.181,0.193,0.197,0.197,0.199,0.205,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 52.3445957549339,4.7594124196878 0 0 57.104008174621754 52.34459575493395 MCD TRUE 961 +Reddy_3479T BN2 0.8167812535340672 0 153,672,432,571,1055,186,582,1106,840,1085 0.226,0.285,0.286,0.321,0.323,0.326,0.354,0.368,0.375,0.375 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,EZB 25.7965604828608,5.78663314608657 0 0 31.583193628947363 25.79656048286079 BN2 TRUE 962 +Reddy_3480T EZB 1 0 36,707,289,887,304,237,972,997,750,686 0.121,0.146,0.149,0.158,0.183,0.188,0.21,0.212,0.24,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5810047636073 0 0 56.58100476360732 56.58100476360732 EZB TRUE 963 +Reddy_3482T ST2 0.5099301876748444 0 590,893,1096,622,691,899,854,1084,437,909 0.173,0.231,0.238,0.291,0.34,0.351,0.36,0.365,0.372,0.419 ST2,N1,EZB,ST2,ST2,ST2,BN2,BN2,BN2,ST2 8.20512434377919,4.19650862405857,4.31967840112604,17.3989526187911 0 0 34.120263987754896 17.39895261879109 BN2 FALSE 964 +Reddy_3488T ST2 0.6080515553998823 0 178,1107,661,365,787,361,950,1041,770,195 0.201,0.203,0.217,0.223,0.244,0.246,0.255,0.274,0.295,0.362 ST2,ST2,EZB,ST2,EZB,ST2,EZB,ST2,BN2,ST2 3.3942542562872,12.6328867726065,24.8638007510776 0 0 40.890941779971264 24.86380075107756 EZB FALSE 965 +Reddy_3496T MCD 1 0 660,439,280,932,1090,106,994,380,565,324 0.131,0.17,0.175,0.192,0.205,0.213,0.216,0.218,0.218,0.225 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.2025503188142 0 0 52.2025503188142 52.2025503188142 MCD TRUE 966 +Reddy_3508T BN2 0.5773112831300916 0 924,582,665,1085,505,432,864,131,512,153 0.134,0.154,0.186,0.242,0.245,0.264,0.267,0.282,0.285,0.306 ST2,BN2,BN2,EZB,N1,BN2,BN2,BN2,N1,BN2 26.2160006656731,4.13066871399604,7.58150094427909,7.48234118300526 0 0 45.41051150695352 26.21600066567313 EZB FALSE 967 +Reddy_3515T BN2 0.9193034952396054 0 484,651,777,158,694,472,1019,357,742,42 0.262,0.279,0.29,0.296,0.33,0.341,0.367,0.395,0.401,0.408 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 27.9473388507928,2.45321874037427 0 0 30.400557591167114 27.947338850792846 BN2 TRUE 968 +Reddy_3523T ST2 0.7505479543801752 0 703,625,925,1025,901,1024,843,904,950,1081 0.132,0.243,0.276,0.296,0.314,0.342,0.35,0.365,0.398,0.409 ST2,ST2,ST2,BN2,ST2,EZB,ST2,ST2,EZB,ST2 3.38062557992967,5.43430361524854,26.5222402125745 0 0 35.3371694077527 26.52224021257449 ST2 TRUE 969 +Reddy_3525T ST2 0.6840178633704107 0 946,490,422,24,558,281,59,529,485,363 0.115,0.212,0.299,0.347,0.35,0.386,0.387,0.415,0.451,0.455 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 10.906351724118,23.6093707165558 0 0 34.5157224406738 23.60937071655583 BN2 FALSE 970 +Reddy_3528T BN2 0.46268213157599014 0 976,132,505,782,771,131,665,1085,967,806 0.163,0.221,0.237,0.284,0.305,0.333,0.352,0.357,0.373,0.377 BN2,BN2,N1,EZB,EZB,BN2,BN2,EZB,EZB,N1 16.4996992363554,12.2893757026023,6.87190721341988 0 0 35.66098215237753 16.499699236355376 EZB FALSE 971 +Reddy_3532T EZB 1 0 707,1031,997,963,36,237,289,159,430,887 0.165,0.203,0.207,0.21,0.221,0.222,0.233,0.242,0.265,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5510399219586 0 0 45.55103992195856 45.55103992195856 EZB TRUE 972 +Reddy_3535T BN2 1 0 331,1077,197,282,354,945,341,175,559,488 0.126,0.14,0.15,0.165,0.17,0.17,0.177,0.184,0.187,0.197 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 61.0382754881278 0 0 61.03827548812781 61.03827548812781 BN2 TRUE 973 +Reddy_3548T ST2 0.552476753660187 0 194,586,1091,320,834,657,684,988,685,810 0.198,0.212,0.229,0.317,0.328,0.333,0.351,0.363,0.392,0.416 EZB,ST2,EZB,ST2,ST2,BN2,ST2,BN2,ST2,ST2 5.75171271782127,9.41483179758239,18.7234145860447 0 0 33.88995910144834 18.723414586044687 EZB FALSE 974 +Reddy_3549T EZB 0.8845872746814908 0 385,581,645,643,1050,369,241,254,687,379 0.12,0.142,0.144,0.148,0.167,0.17,0.182,0.206,0.209,0.239 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.2767223749045,6.9510515257404 0 0 60.227773900644856 53.27672237490445 EZB TRUE 975 +Reddy_3551T EZB 0.3967442876917855 0 132,971,505,131,782,665,771,512,806,126 0.159,0.163,0.224,0.288,0.304,0.328,0.35,0.358,0.367,0.368 BN2,EZB,N1,BN2,EZB,BN2,EZB,N1,N1,EZB 12.824315824578,15.005938777292,9.99244188758654 0 0 37.82269648945653 15.005938777292029 BN2 FALSE 976 +Reddy_3555T EZB 1 0 171,209,30,208,129,12,364,8,256,53 0.139,0.146,0.148,0.149,0.196,0.207,0.217,0.218,0.219,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.647831624429 0 0 55.64783162442902 55.64783162442902 EZB TRUE 977 +Reddy_3559T MCD 0.9112489803826599 0 878,961,452,465,1101,367,295,621,33,1021 0.131,0.131,0.151,0.165,0.177,0.184,0.189,0.193,0.223,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 53.2943471387362,5.19059856332248 0 0 58.48494570205865 53.29434713873617 MCD TRUE 978 +Reddy_3561T EZB 1 0 732,136,596,257,272,853,724,181,255,291 0.157,0.176,0.182,0.209,0.213,0.224,0.225,0.227,0.242,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.1779452346527 0 0 48.17794523465269 48.17794523465269 EZB TRUE 979 +Reddy_3565T BN2 0.7975365505539574 0 827,2,327,577,348,391,611,438,463,492 0.248,0.275,0.293,0.322,0.335,0.363,0.406,0.444,0.446,0.448 BN2,N1,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 23.2132700420771,5.89294462100448 0 0 29.106214663081566 23.213270042077088 BN2 TRUE 980 +Reddy_3586T EZB 1 0 238,101,392,604,183,1012,259,49,1115,313 0.198,0.214,0.222,0.227,0.242,0.258,0.262,0.27,0.272,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.4418737186389 0 0 41.44187371863885 41.44187371863885 EZB TRUE 981 +Reddy_3593T ST2 1 0 557,1053,793,692,1086,598,1016,940,218,1068 0.205,0.215,0.23,0.23,0.242,0.258,0.274,0.299,0.322,0.325 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 39.4332448870612 0 0 39.43324488706124 39.43324488706124 ST2 TRUE 982 +Reddy_3595T EZB 1 0 619,664,720,9,268,224,374,267,139,240 0.197,0.216,0.252,0.256,0.265,0.278,0.284,0.298,0.3,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4675500488925 0 0 38.46755004889251 38.46755004889251 EZB TRUE 983 +Reddy_3600T EZB 1 0 637,914,317,46,760,738,1026,876,632,80 0.143,0.153,0.17,0.181,0.196,0.201,0.216,0.217,0.218,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3382489257165 0 0 53.33824892571647 53.33824892571647 EZB TRUE 984 +Reddy_3601T MCD 0.9087090360684633 0 223,466,713,128,617,205,790,696,697,837 0.209,0.213,0.238,0.276,0.315,0.325,0.339,0.345,0.352,0.39 MCD,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD,MCD 31.6454101544821,3.17916943965973 0 0 34.82457959414182 31.645410154482086 MCD TRUE 985 +Reddy_3609T EZB 1 0 313,604,302,1030,392,199,101,1012,49,96 0.136,0.159,0.161,0.164,0.18,0.192,0.207,0.209,0.211,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.4437893513866 0 0 55.443789351386606 55.443789351386606 EZB TRUE 986 +Reddy_3610T MCD 1 0 461,414,932,456,565,340,188,869,1038,415 0.116,0.149,0.151,0.177,0.178,0.179,0.183,0.19,0.192,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.6181157884448 0 0 59.6181157884448 59.6181157884448 MCD TRUE 987 +Reddy_3616T EZB 0.5679561935225336 0 1116,194,768,684,974,993,810,586,714,824 0.213,0.279,0.321,0.332,0.363,0.367,0.376,0.423,0.424,0.454 ST2,EZB,EZB,ST2,EZB,EZB,ST2,ST2,EZB,EZB 16.7422666281874,12.7358283712699 0 0 29.478094999457298 16.742266628187398 BN2 FALSE 988 +Reddy_3618T EZB 1 0 1011,953,594,667,632,805,876,242,760,1002 0.189,0.205,0.206,0.224,0.23,0.235,0.237,0.247,0.253,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2882510352007 0 0 44.28825103520068 44.28825103520068 EZB TRUE 989 +Reddy_3620T MCD 0.7909445214862874 0 286,1103,918,778,155,169,176,450,433,312 0.139,0.139,0.151,0.153,0.153,0.153,0.156,0.157,0.16,0.165 MCD,ST2,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.54663536289882,52.0286401112864,7.20511581484959 0 0 65.78039128903477 52.02864011128635 MCD TRUE 990 +Reddy_3622T MCD 1 0 288,897,106,459,314,439,739,520,994,729 0.152,0.161,0.165,0.198,0.2,0.208,0.21,0.211,0.247,0.252 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.2505009887633 0 0 51.250500988763335 51.250500988763335 MCD TRUE 991 +Reddy_3624T MCD 0.6679912043408849 0 788,502,616,574,1093,829,449,1047,1054,903 0.232,0.295,0.314,0.324,0.359,0.367,0.38,0.412,0.422,0.434 EZB,MCD,MCD,N1,MCD,MCD,MCD,MCD,MCD,EZB 6.61415875611843,19.5099687418123,3.08279710680716 0 0 29.206924604737846 19.50996874181226 MCD TRUE 992 +Reddy_3627T EZB 0.6380202880537215 0 913,714,1033,810,322,319,684,1116,988,715 0.218,0.225,0.3,0.312,0.334,0.335,0.341,0.352,0.367,0.439 EZB,EZB,EZB,ST2,EZB,EZB,ST2,ST2,BN2,EZB 2.7260236476154,20.6254046575764,8.97576417405384 0 0 32.32719247924565 20.62540465757641 EZB TRUE 993 +Reddy_3629T MCD 1 0 1090,380,439,106,660,966,991,932,280,288 0.118,0.181,0.198,0.203,0.208,0.216,0.247,0.251,0.264,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 48.844405321937 0 0 48.84440532193701 48.84440532193701 MCD TRUE 994 +Reddy_3632T EZB 0.8119241375885005 0 863,1074,368,227,618,1079,947,274,587,867 0.132,0.137,0.144,0.15,0.156,0.165,0.171,0.181,0.187,0.193 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,ST2 6.65888522925204,51.0642429078415,5.16974638626728 0 0 62.892874523360845 51.064242907841525 EZB TRUE 995 +Reddy_3636T ST2 0.8323738882460746 0 671,359,842,602,161,1005,668,835,774,1029 0.154,0.169,0.173,0.21,0.231,0.233,0.251,0.254,0.257,0.26 ST2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 3.98431521315886,3.93586781073616,39.3289176145653 0 0 47.249100638460334 39.32891761456531 ST2 TRUE 996 +Reddy_3647T EZB 1 0 430,1031,159,707,36,972,963,761,37,887 0.162,0.166,0.201,0.201,0.203,0.207,0.212,0.214,0.228,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.730074517588 0 0 49.73007451758803 49.73007451758803 EZB TRUE 997 +Reddy_3648T BN2 1 0 75,591,945,796,305,354,515,70,1023,559 0.168,0.174,0.174,0.179,0.182,0.188,0.203,0.205,0.219,0.219 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.7674522884134 0 0 52.767452288413374 52.767452288413374 BN2 TRUE 998 +Reddy_3651T BN2 1 0 646,345,959,548,780,99,477,943,435,517 0.17,0.179,0.197,0.207,0.219,0.257,0.265,0.268,0.293,0.316 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9388375985121 0 0 43.93883759851213 43.93883759851213 BN2 TRUE 999 +Reddy_3655T EZB 0.879693679482106 0 97,203,8,98,12,209,27,1088,615,389 0.153,0.184,0.185,0.24,0.241,0.246,0.252,0.254,0.267,0.267 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.4371610133822,39.7571478972082 0 0 45.19430891059041 39.757147897208206 EZB TRUE 1000 +Reddy_3659T BN2 0.9154925626789516 0 1071,546,543,517,929,841,499,261,540,700 0.164,0.169,0.191,0.195,0.22,0.229,0.23,0.241,0.248,0.25 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 43.7637210242575,4.03974872343145 0 0 47.80346974768892 43.76372102425747 BN2 TRUE 1001 +Reddy_3661T EZB 1 0 1026,1040,91,667,914,632,760,876,317,46 0.118,0.166,0.172,0.174,0.185,0.186,0.188,0.199,0.202,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.0198684444714 0 0 57.019868444471356 57.019868444471356 EZB TRUE 1002 +Reddy_3664T ST2 0.8780135241128382 0 825,1068,835,940,774,598,915,996,842,671 0.148,0.153,0.177,0.181,0.184,0.234,0.3,0.32,0.345,0.349 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.66121413600272,40.7473249649988 0 0 46.40853910100149 40.74732496499876 ST2 TRUE 1003 +Reddy_3667T ST2 1 0 814,1009,1065,748,875,896,907,683,662,195 0.142,0.179,0.184,0.186,0.187,0.202,0.21,0.217,0.22,0.243 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 51.7670552745133 0 0 51.767055274513275 51.767055274513275 ST2 TRUE 1004 +Reddy_3669T ST2 0.9158998581840754 0 218,148,996,359,671,842,668,774,161,602 0.185,0.192,0.233,0.259,0.273,0.299,0.312,0.312,0.338,0.338 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 3.20532629828837,34.9078828958658 0 0 38.11320919415417 34.907882895865804 ST2 TRUE 1005 +Reddy_3671T EZB 1 0 874,1114,753,1075,688,164,407,601,812,1097 0.116,0.166,0.184,0.218,0.24,0.25,0.265,0.269,0.281,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1039648020711 0 0 47.10396480207114 47.10396480207114 EZB TRUE 1006 +Reddy_3673T ST2 0.6435633466181373 0 1067,797,363,309,422,544,408,1057,808,670 0.291,0.303,0.337,0.348,0.369,0.373,0.376,0.467,0.512,0.524 ST2,ST2,BN2,ST2,ST2,ST2,N1,ST2,BN2,BN2 6.83106728889076,2.66150390618913,17.139289207083 0 0 26.631860402162907 17.139289207083014 ST2 TRUE 1007 +Reddy_3674T MCD 1 0 468,276,1076,443,451,33,328,1021,568,902 0.111,0.12,0.16,0.162,0.17,0.175,0.187,0.188,0.201,0.215 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.7184536242275 0 0 61.718453624227465 61.718453624227465 MCD TRUE 1008 +Reddy_3684T ST2 0.9126011322915903 0 748,195,1004,1065,875,814,907,770,683,662 0.133,0.165,0.179,0.189,0.19,0.19,0.196,0.216,0.222,0.266 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2 4.62914443457599,48.336581048508 0 0 52.96572548308396 48.33658104850796 ST2 TRUE 1009 +Reddy_3686T BN2 1 0 731,955,700,538,435,650,1071,99,780,959 0.151,0.17,0.206,0.214,0.245,0.289,0.29,0.297,0.33,0.339 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.4067799947274 0 0 42.406779994727366 42.406779994727366 BN2 TRUE 1010 +Reddy_3688T EZB 1 0 989,1002,667,1026,632,594,876,1040,760,953 0.189,0.241,0.258,0.259,0.27,0.277,0.281,0.286,0.288,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4179588620152 0 0 38.41795886201519 38.41795886201519 EZB TRUE 1011 +Reddy_3692T EZB 1 0 49,101,392,313,604,302,655,986,183,1051 0.112,0.145,0.156,0.174,0.194,0.201,0.209,0.209,0.211,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.0235743544542 0 0 57.0235743544542 57.0235743544542 EZB TRUE 1012 +Reddy_3694T EZB 1 0 917,271,210,79,927,405,402,638,114,360 0.15,0.225,0.253,0.253,0.28,0.289,0.357,0.361,0.382,0.414 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.64950505175 0 0 36.64950505175 36.64950505175 EZB TRUE 1013 +Reddy_3698T EZB 1 0 639,122,865,190,121,754,849,18,406,756 0.175,0.244,0.245,0.248,0.26,0.266,0.277,0.281,0.298,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4064560582144 0 0 39.40645605821436 39.40645605821436 EZB TRUE 1014 +Reddy_3699T MCD 1 0 712,1048,417,165,879,1109,769,350,500,290 0.156,0.157,0.16,0.162,0.17,0.171,0.172,0.186,0.187,0.194 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.6648490130582 0 0 58.66484901305817 58.66484901305817 MCD TRUE 1015 +Reddy_3701T ST2 1 0 1053,692,177,557,682,793,1086,982,370,642 0.163,0.204,0.21,0.212,0.225,0.252,0.259,0.274,0.292,0.361 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 42.6572076984569 0 0 42.65720769845686 42.65720769845686 ST2 TRUE 1016 +Reddy_3703T ST2 0.5421503562397456 0 785,668,425,366,783,359,161,1005,612,671 0.236,0.291,0.323,0.323,0.325,0.352,0.372,0.382,0.387,0.388 EZB,BN2,ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2 6.52637115707369,7.32647223656603,16.4034723694747 0 0 30.256315763114443 16.403472369474727 BN2 FALSE 1017 +Reddy_3704T EZB 1 0 1073,942,406,182,743,882,120,303,754,116 0.169,0.174,0.198,0.201,0.202,0.217,0.22,0.222,0.228,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0560004892979 0 0 49.056000489297944 49.056000489297944 EZB TRUE 1018 +Reddy_3706T BN2 1 0 652,472,651,666,1113,659,695,179,681,674 0.163,0.186,0.198,0.205,0.231,0.234,0.25,0.257,0.294,0.323 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.4259064392884 0 0 44.42590643928836 44.42590643928836 BN2 TRUE 1019 +Reddy_3708T EZB 1 0 52,610,112,886,1072,649,1,139,885,273 0.142,0.201,0.211,0.211,0.22,0.227,0.245,0.257,0.26,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.9978503087693 0 0 45.9978503087693 45.9978503087693 EZB TRUE 1020 +Reddy_3711T MCD 1 0 568,328,1076,465,33,1008,468,961,443,451 0.119,0.146,0.155,0.159,0.184,0.188,0.189,0.193,0.194,0.202 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.3653356559226 0 0 59.36533565592265 59.36533565592265 MCD TRUE 1021 +Reddy_3715T BN2 1 0 569,26,498,566,399,575,898,482,353,401 0.128,0.142,0.209,0.223,0.283,0.299,0.305,0.306,0.308,0.32 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9117679681085 0 0 43.911767968108535 43.911767968108535 BN2 TRUE 1022 +Reddy_3717T BN2 1 0 796,555,591,905,527,395,305,702,998,75 0.144,0.157,0.162,0.168,0.173,0.193,0.199,0.211,0.219,0.221 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.2264212946487 0 0 55.2264212946487 55.2264212946487 BN2 TRUE 1023 +Reddy_3718T ST2 1 0 901,925,685,834,320,843,372,625,969,904 0.131,0.199,0.239,0.262,0.282,0.311,0.324,0.328,0.342,0.347 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 39.3637089883367 0 0 39.36370898833669 39.36370898833669 EZB FALSE 1024 +Reddy_3725T ST2 0.6952590063018006 0 950,1107,969,703,625,361,178,787,965,423 0.231,0.281,0.296,0.296,0.314,0.32,0.367,0.375,0.384,0.393 EZB,ST2,ST2,ST2,ST2,ST2,ST2,EZB,EZB,ST2 9.59806421609289,21.897745059893 0 0 31.49580927598584 21.89774505989295 BN2 FALSE 1025 +Reddy_3729T EZB 1 0 1002,1040,91,914,667,317,760,632,46,876 0.118,0.155,0.159,0.174,0.179,0.184,0.187,0.189,0.201,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.592535764587 0 0 58.59253576458696 58.59253576458696 EZB TRUE 1026 +Reddy_3732T MCD 1 0 130,349,39,332,311,323,906,326,491,514 0.147,0.166,0.182,0.186,0.187,0.188,0.193,0.205,0.208,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.0598289693713 0 0 54.059828969371296 54.059828969371296 MCD TRUE 1027 +Reddy_3734T BN2 1 0 172,58,337,564,357,393,158,999,589,64 0.237,0.267,0.324,0.35,0.387,0.416,0.484,0.497,0.512,0.526 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.8345543624395 0 0 26.83455436243952 26.83455436243952 BN2 TRUE 1028 +Reddy_3737T ST2 0.8244213715521092 0 160,161,602,842,671,366,668,359,583,996 0.131,0.141,0.162,0.188,0.207,0.215,0.217,0.218,0.249,0.26 ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2,ST2 9.25748369679233,43.4680887639802 0 0 52.72557246077255 43.46808876398022 ST2 TRUE 1029 +Reddy_3741T EZB 1 0 986,199,302,313,96,226,604,191,392,1039 0.164,0.186,0.194,0.198,0.2,0.209,0.216,0.221,0.244,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.6968502214526 0 0 48.69685022145262 48.69685022145262 EZB TRUE 1030 +Reddy_3743T EZB 1 0 159,997,430,972,37,880,89,761,707,1063 0.143,0.166,0.187,0.203,0.213,0.215,0.217,0.218,0.234,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1331889762957 0 0 50.13318897629574 50.13318897629574 EZB TRUE 1031 +Reddy_3745T ST2 0.8886043974135162 0 807,832,362,759,24,207,483,59,281,1057 0.279,0.313,0.341,0.363,0.375,0.435,0.436,0.443,0.447,0.473 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 2.93477398943019,23.4107362577246 0 0 26.34551024715481 23.410736257724615 ST2 TRUE 1032 +Reddy_3746T EZB 0.8402588327841515 0 322,319,714,913,810,993,715,684,890,839 0.134,0.135,0.178,0.22,0.255,0.3,0.317,0.321,0.358,0.361 EZB,EZB,EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB 37.0689243386599,7.0471538176792 0 0 44.11607815633907 37.06892433865987 EZB TRUE 1033 +Reddy_3747T EZB 1 0 51,233,939,211,719,31,960,360,537,948 0.12,0.127,0.249,0.256,0.306,0.346,0.356,0.358,0.419,0.422 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6445452622684 0 0 40.64454526226835 40.64454526226835 EZB TRUE 1034 +Reddy_3749T MCD 1 0 290,108,794,413,165,297,446,1015,17,528 0.139,0.158,0.165,0.176,0.186,0.208,0.209,0.226,0.232,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.2211884976349 0 0 53.221188497634884 53.221188497634884 MCD TRUE 1035 +Reddy_3753T MCD 1 0 550,326,416,821,491,283,469,311,447,349 0.108,0.128,0.142,0.145,0.159,0.165,0.175,0.196,0.203,0.222 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.6462295241017 0 0 63.64622952410171 63.64622952410171 MCD TRUE 1036 +Reddy_3756T EZB 1 0 418,599,644,150,222,13,83,140,277,84 0.136,0.182,0.192,0.197,0.201,0.206,0.229,0.243,0.249,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4782058861783 0 0 49.47820588617825 49.47820588617825 EZB TRUE 1037 +Reddy_3757T MCD 1 0 565,679,415,461,869,987,340,414,932,966 0.12,0.125,0.13,0.179,0.187,0.192,0.2,0.213,0.223,0.236 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.5901578064597 0 0 58.590157806459715 58.590157806459715 MCD TRUE 1038 +Reddy_3758T EZB 1 0 226,199,96,191,604,677,167,986,1030,392 0.153,0.166,0.169,0.181,0.232,0.247,0.247,0.251,0.252,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.0699102562458 0 0 48.06991025624585 48.06991025624585 EZB TRUE 1039 +Reddy_3759T EZB 1 0 91,1026,1002,317,397,914,667,984,760,632 0.116,0.155,0.166,0.176,0.198,0.212,0.234,0.236,0.238,0.244 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3572688271898 0 0 53.35726882718977 53.35726882718977 EZB TRUE 1040 +Reddy_3760T ST2 0.4982685254872068 0 965,1107,950,661,365,625,178,361,787,1025 0.274,0.306,0.351,0.352,0.362,0.366,0.372,0.404,0.415,0.441 EZB,ST2,EZB,EZB,ST2,ST2,ST2,ST2,EZB,BN2 2.26975866931073,11.7452743960266,13.918301352135 0 0 27.933334417472356 13.918301352134995 ST2 TRUE 1041 +Reddy_3761T MCD 0.8904327969103821 0 580,526,16,844,453,678,900,549,296,881 0.138,0.148,0.151,0.152,0.166,0.172,0.178,0.181,0.206,0.207 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.56105394452313,53.3204960039288 0 0 59.88154994845188 53.32049600392875 MCD TRUE 1042 +Reddy_3776T MCD 0.9305406130218533 0 730,888,460,513,459,739,314,1082,66,520 0.127,0.137,0.151,0.205,0.271,0.275,0.278,0.298,0.303,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 3.29905046137707,44.1970563271124 0 0 47.49610678848949 44.19705632711242 MCD TRUE 1043 +Reddy_3778T MCD 0.34519807942599007 0 935,747,124,1089,554,696,43,779,673,525 0.222,0.223,0.235,0.252,0.292,0.361,0.369,0.419,0.425,0.428 EZB,ST2,N1,MCD,ST2,MCD,ST2,MCD,BN2,MCD 2.35195967063854,4.49582982647557,11.4515654949311,4.2488321811637,10.6257097209258 0 0 33.17389689413471 11.451565494931117 MCD TRUE 1044 +Reddy_3780T MCD 0.3314626245953946 0 579,716,663,123,803,525,43,421,455,779 0.246,0.248,0.274,0.337,0.355,0.368,0.386,0.402,0.404,0.437 N1,BN2,ST2,N1,BN2,MCD,ST2,MCD,MCD,MCD 6.83870761093021,9.97042591859341,7.02935423711851,6.24160325648297 0 0 30.0800910231251 9.970425918593413 BN2 FALSE 1045 +Reddy_3782T MCD 0.8003461252493761 0 479,169,260,918,769,1103,778,312,956,1048 0.147,0.158,0.164,0.165,0.169,0.17,0.172,0.185,0.188,0.202 MCD,MCD,MCD,MCD,MCD,ST2,BN2,MCD,MCD,MCD 5.82086970655386,46.9030911935053,5.87954789920065 0 0 58.60350879925981 46.903091193505304 MCD TRUE 1046 +Reddy_3784T MCD 0.9219836610423915 0 1054,475,44,616,856,502,697,790,574,1093 0.124,0.19,0.217,0.22,0.222,0.237,0.255,0.277,0.279,0.283 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,N1,MCD 42.3098912859479,3.58017496328745 0 0 45.89006624923535 42.3098912859479 MCD TRUE 1047 +Reddy_3787T MCD 0.9067495283408784 0 417,350,312,769,1015,778,155,500,450,918 0.105,0.136,0.138,0.148,0.157,0.158,0.161,0.163,0.163,0.168 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 6.33869215364071,61.636215006206 0 0 67.97490715984668 61.63621500620597 MCD TRUE 1048 +Reddy_3812T BN2 0.9219372469649395 0 773,928,262,765,936,110,279,196,321,624 0.155,0.215,0.249,0.275,0.283,0.284,0.291,0.304,0.318,0.331 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 35.6876551341278,3.02176381126488 0 0 38.70941894539263 35.68765513412775 ST2 FALSE 1049 +Reddy_3814T EZB 0.8615156188660725 0 241,645,643,975,581,385,369,254,67,454 0.116,0.124,0.135,0.167,0.177,0.186,0.222,0.233,0.244,0.245 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0324607723073,8.04247098361379 0 0 58.07493175592106 50.03246077230726 EZB TRUE 1050 +Reddy_3820T EZB 1 0 655,410,183,752,49,1012,1115,101,225,392 0.117,0.142,0.174,0.198,0.218,0.219,0.23,0.241,0.253,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.7326303348028 0 0 51.732630334802764 51.732630334802764 EZB TRUE 1051 +Reddy_3823T EZB 0.6239461608870523 0 736,781,509,338,545,606,249,689,1064,1118 0.119,0.241,0.249,0.346,0.374,0.376,0.38,0.386,0.412,0.453 EZB,EZB,EZB,BN2,ST2,ST2,EZB,ST2,EZB,ST2 2.88937748752595,21.6191786657259,10.1405530503216 0 0 34.6491092035735 21.61917866572592 EZB TRUE 1052 +Reddy_3825T ST2 1 0 557,1016,692,793,982,1086,177,682,598,370 0.156,0.163,0.196,0.201,0.215,0.247,0.271,0.288,0.325,0.331 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 44.5819353198369 0 0 44.58193531983688 44.58193531983688 ST2 TRUE 1053 +Reddy_3826T MCD 0.9210811176075658 0 1047,475,616,44,697,502,856,790,1093,574 0.124,0.169,0.22,0.229,0.237,0.238,0.243,0.26,0.271,0.272 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,N1 42.9745795264285,3.68209240497697 0 0 46.65667193140544 42.97457952642847 MCD TRUE 1054 +Reddy_3832T BN2 0.803698127049192 0 860,962,571,570,390,153,840,672,186,22 0.32,0.323,0.357,0.418,0.429,0.431,0.442,0.446,0.472,0.486 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,EZB 19.8817586108756,4.85608504178068 0 0 24.737843652656263 19.88175861087558 BN2 TRUE 1055 +Reddy_3833T EZB 0.797792399026438 0 595,857,867,587,227,618,368,995,809,245 0.124,0.163,0.169,0.17,0.2,0.205,0.214,0.221,0.222,0.23 EZB,EZB,ST2,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.99415479190506,42.9953221119483,5.90339317665423 0 0 53.89287008050754 42.995322111948255 EZB TRUE 1056 +Reddy_3834T ST2 0.9143274548143769 0 207,483,797,1067,656,728,362,807,60,822 0.213,0.241,0.284,0.313,0.316,0.322,0.373,0.43,0.446,0.461 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 2.6774941046211,28.5751563077121 0 0 31.25265041233322 28.57515630771212 ST2 TRUE 1057 +Reddy_3835T EZB 1 0 889,884,405,409,846,143,847,316,144,638 0.18,0.253,0.309,0.326,0.328,0.407,0.433,0.434,0.451,0.458 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.3457380592031 0 0 30.345738059203075 30.345738059203075 EZB TRUE 1058 +Reddy_3838T EZB 1 0 603,384,605,25,149,74,61,73,833,239 0.11,0.141,0.141,0.148,0.175,0.185,0.188,0.189,0.192,0.195 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.1133766460426 0 0 62.11337664604259 62.11337664604259 EZB TRUE 1059 +Reddy_3839T BN2 0.9212995778879212 0 630,335,477,943,650,795,474,99,345,780 0.127,0.158,0.208,0.221,0.242,0.275,0.283,0.291,0.296,0.3 BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2,BN2 41.4122657334192,3.53757113544901 0 0 44.94983686886824 41.412265733419225 BN2 TRUE 1060 +Reddy_3842T BN2 0.5973463502966281 0 506,799,293,919,123,698,530,347,481,1112 0.212,0.232,0.252,0.267,0.271,0.315,0.351,0.396,0.4,0.401 BN2,BN2,MCD,BN2,N1,ST2,N1,BN2,BN2,BN2 20.2963579863355,3.96817423671017,6.54103044345662,3.17197478100502 0 0 33.97753744750731 20.2963579863355 BN2 TRUE 1061 +Reddy_3846T BN2 1 0 318,15,175,282,315,264,341,871,563,640 0.137,0.193,0.232,0.248,0.249,0.252,0.253,0.255,0.265,0.268 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.1737298809272 0 0 44.173729880927155 44.173729880927155 BN2 TRUE 1062 +Reddy_3849T EZB 1 0 820,880,931,89,635,159,48,125,35,37 0.131,0.165,0.166,0.187,0.19,0.206,0.225,0.226,0.228,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7644420370538 0 0 52.76444203705383 52.76444203705383 EZB TRUE 1063 +Reddy_3851T EZB 1 0 217,216,168,249,766,509,908,823,819,471 0.184,0.2,0.204,0.206,0.234,0.271,0.304,0.318,0.376,0.377 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.9096548051495 0 0 39.90965480514946 39.90965480514946 EZB TRUE 1064 +Reddy_3856T ST2 0.920424252407817 0 875,814,1004,1009,748,195,770,423,907,896 0.103,0.15,0.184,0.189,0.219,0.24,0.247,0.268,0.272,0.282 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.04777459471645,46.8191630991756 0 0 50.86693769389203 46.819163099175576 ST2 TRUE 1065 +Reddy_3859T ST2 0.4944574306438026 0 119,381,830,919,412,308,293,530,481,529 0.164,0.236,0.259,0.275,0.292,0.296,0.338,0.392,0.4,0.408 BN2,ST2,ST2,BN2,ST2,ST2,MCD,N1,BN2,ST2 12.2404632967172,2.95661934164728,2.55405788574168,17.361907515967 0 0 35.11304804007315 17.36190751596698 N1 FALSE 1066 +Reddy_3867T ST2 1 0 797,309,1007,544,1057,207,483,728,422,508 0.247,0.283,0.291,0.302,0.313,0.412,0.437,0.446,0.456,0.481 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 28.7643297644871 0 0 28.764329764487137 28.764329764487137 ST2 TRUE 1067 +Reddy_3872T ST2 0.9014214305480986 0 825,940,1003,774,598,835,915,996,982,218 0.123,0.145,0.153,0.154,0.197,0.2,0.307,0.311,0.325,0.327 ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2 5.00277977814569,45.7463820930524 0 0 50.74916187119812 45.74638209305243 ST2 TRUE 1068 +Reddy_3873T BN2 0.8924627961415962 0 493,633,898,77,634,399,498,212,487,636 0.149,0.163,0.172,0.186,0.209,0.214,0.216,0.225,0.263,0.272 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 44.6555066938467,5.38076023728682 0 0 50.03626693113356 44.65550669384674 BN2 TRUE 1069 +Reddy_3875T MCD 0.7587891738237165 0 383,457,445,154,894,749,933,845,801,772 0.207,0.309,0.375,0.393,0.402,0.424,0.444,0.454,0.465,0.47 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2,EZB 2.20165819578298,2.12738658498657,20.3849416045069,2.15110645966016 0 0 26.865092844936566 20.384941604506853 BN2 FALSE 1070 +Reddy_3876T BN2 1 0 1001,700,548,955,546,517,543,435,731,959 0.164,0.19,0.219,0.221,0.227,0.229,0.23,0.243,0.245,0.254 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.7067555615328 0 0 45.706755561532816 45.706755561532816 BN2 TRUE 1071 +Reddy_3880T EZB 0.9169177288653769 0 112,1095,892,885,648,680,610,139,1,1094 0.121,0.158,0.168,0.17,0.176,0.184,0.191,0.2,0.204,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 52.6202266422725,4.76793915029695 0 0 57.38816579256941 52.620226642272456 EZB TRUE 1072 +Reddy_3882T EZB 1 0 743,1018,942,865,883,406,303,754,156,627 0.168,0.169,0.185,0.219,0.229,0.234,0.239,0.253,0.259,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.243775450377 0 0 46.243775450377036 46.243775450377036 EZB TRUE 1073 +Reddy_3885T EZB 0.8267969864659138 0 947,995,618,274,863,1079,368,227,867,587 0.134,0.137,0.137,0.147,0.147,0.173,0.18,0.185,0.185,0.215 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,ST2,EZB 5.41572978647013,51.6445271841165,5.4031143642164 0 0 62.463371334802986 51.644527184116455 EZB TRUE 1074 +Reddy_3887T EZB 1 0 753,164,597,1006,874,21,709,1114,601,284 0.139,0.15,0.217,0.218,0.226,0.25,0.255,0.261,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.852592821777 0 0 46.85259282177695 46.85259282177695 EZB TRUE 1075 +Reddy_3888T MCD 1 0 328,443,468,1021,568,1008,276,33,451,465 0.129,0.139,0.154,0.155,0.158,0.16,0.173,0.203,0.211,0.213 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.5849141496637 0 0 60.584914149663724 60.584914149663724 MCD TRUE 1076 +Reddy_3892T BN2 1 0 331,973,197,559,488,354,341,282,945,333 0.125,0.14,0.144,0.157,0.161,0.161,0.171,0.172,0.177,0.184 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.6125782734105 0 0 63.612578273410534 63.612578273410534 BN2 TRUE 1077 +Reddy_3893T EZB 1 0 29,80,637,46,805,876,760,594,632,163 0.168,0.186,0.21,0.211,0.218,0.227,0.23,0.238,0.241,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6325327152737 0 0 46.63253271527374 46.63253271527374 EZB TRUE 1078 +Reddy_3896T EZB 0.911666526062844 0 863,995,1074,228,277,274,368,631,947,227 0.133,0.165,0.173,0.174,0.179,0.185,0.186,0.194,0.195,0.199 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 5.01993692642395,51.8094472546549 0 0 56.82938418107883 51.80944725465488 EZB TRUE 1079 +Reddy_3901T EZB 1 0 57,600,609,82,292,138,31,948,751,960 0.178,0.244,0.276,0.305,0.345,0.356,0.385,0.396,0.416,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.1030809145896 0 0 32.103080914589555 32.103080914589555 EZB TRUE 1080 +Reddy_3904T ST2 1 0 822,394,60,904,843,911,728,278,656,263 0.203,0.235,0.249,0.251,0.284,0.321,0.323,0.349,0.354,0.372 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.2899176567983 0 0 35.2899176567983 35.2899176567983 ST2 TRUE 1081 +Reddy_3905T MCD 0.9167051277176349 0 200,201,541,532,419,888,775,170,1043,567 0.164,0.167,0.229,0.245,0.279,0.292,0.293,0.295,0.298,0.306 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD 3.41355990517298,37.5680733171122 0 0 40.98163322228518 37.56807331711221 MCD TRUE 1082 +Reddy_3911T EZB 1 0 626,206,737,619,265,267,238,259,351,588 0.245,0.292,0.324,0.336,0.341,0.346,0.396,0.402,0.413,0.421 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1878389352069 0 0 29.187838935206912 29.187838935206912 EZB TRUE 1083 +Reddy_3918T ST2 0.5607197320443262 0 691,622,1096,11,411,590,964,899,893,909 0.146,0.206,0.268,0.285,0.342,0.344,0.365,0.381,0.39,0.406 ST2,ST2,EZB,EZB,EZB,ST2,BN2,ST2,N1,ST2 2.73785930734855,10.1567609628277,2.56105754181846,19.72841521345 0 0 35.184093025444696 19.72841521345 BN2 FALSE 1084 +Reddy_3924T BN2 0.5978300916102359 0 432,967,582,924,505,665,153,971,962,976 0.189,0.242,0.245,0.275,0.291,0.312,0.335,0.357,0.375,0.385 BN2,EZB,BN2,ST2,N1,BN2,BN2,EZB,BN2,BN2 20.8203887612936,6.93509245116913,3.44128952165248,3.62982808297435 0 0 34.82659881708953 20.820388761293568 EZB FALSE 1085 +Reddy_3925T ST2 0.9161014118567973 0 692,982,1053,1016,370,557,177,792,793,682 0.155,0.242,0.247,0.259,0.26,0.287,0.302,0.311,0.331,0.342 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2 3.21351013317245,35.0888046529532 0 0 38.302314786125606 35.08880465295315 ST2 TRUE 1086 +Reddy_3928T MCD 0.9066346452075212 0 511,722,536,721,817,869,937,340,690,415 0.164,0.175,0.183,0.194,0.209,0.209,0.215,0.226,0.228,0.234 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD 4.64357529628558,45.0919535474355 0 0 49.73552884372103 45.09195354743545 MCD TRUE 1087 +Reddy_3930T EZB 0.8734000316672588 0 203,644,83,152,84,599,418,1000,615,150 0.175,0.184,0.204,0.218,0.229,0.232,0.238,0.254,0.257,0.261 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.72160341799685,39.4727476813559 0 0 45.19435109935276 39.47274768135591 EZB TRUE 1088 +Reddy_3932T EZB 0.24738678313391135 0 935,1044,747,554,850,673,124,696,792,1105 0.226,0.252,0.29,0.306,0.338,0.378,0.387,0.444,0.462,0.465 EZB,MCD,ST2,ST2,EZB,BN2,N1,MCD,BN2,BN2 6.95859024169844,7.3891031623682,6.21582384501081,2.58355173939653,6.72155644398678 0 0 29.868625432460753 7.3891031623681975 MCD FALSE 1089 +Reddy_3933T MCD 1 0 994,380,660,439,966,106,932,280,991,188 0.118,0.165,0.192,0.197,0.205,0.21,0.234,0.259,0.259,0.275 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 50.0949226568363 0 0 50.094922656836346 50.094922656836346 MCD TRUE 1090 +Reddy_3937T ST2 0.5829603740983242 0 586,834,320,974,685,657,194,372,1024,824 0.149,0.207,0.214,0.229,0.292,0.302,0.323,0.34,0.366,0.376 ST2,ST2,ST2,EZB,ST2,BN2,EZB,ST2,EZB,EZB 3.31417396141651,12.851336007317,22.5970175339762 0 0 38.7625275027097 22.597017533976228 EZB FALSE 1091 +Reddy_3939T EZB 0.7126800155721473 0 111,45,6,334,764,882,120,303,718,883 0.145,0.21,0.22,0.249,0.261,0.274,0.279,0.302,0.335,0.336 ST2,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.75935823297489,28.9027167198686,6.89289514361024 0 0 40.55497009645376 28.902716719868632 ST2 FALSE 1092 +Reddy_3944T MCD 0.7291281616834906 0 574,616,502,1054,1047,475,1105,697,992,790 0.136,0.196,0.2,0.271,0.283,0.29,0.291,0.351,0.359,0.371 N1,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.43871848269539,29.1004936626594,7.3721438986485 0 0 39.911356044003256 29.100493662659364 MCD TRUE 1093 +Reddy_3948T EZB 1 0 1102,885,861,809,7,886,112,1095,680,1072 0.122,0.142,0.155,0.173,0.185,0.19,0.196,0.199,0.199,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1181985065071 0 0 58.11819850650713 58.11819850650713 ST2 FALSE 1094 +Reddy_3952T EZB 1 0 892,680,648,706,1072,71,885,112,711,861 0.116,0.126,0.138,0.153,0.158,0.159,0.161,0.168,0.171,0.174 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.7058779617133 0 0 66.70587796171327 66.70587796171327 EZB TRUE 1095 +Reddy_3967T ST2 0.6306730344038728 0 590,964,899,622,691,1084,909,893,11,544 0.186,0.238,0.253,0.254,0.266,0.268,0.309,0.335,0.448,0.459 ST2,BN2,ST2,ST2,ST2,BN2,ST2,N1,EZB,ST2 7.9244774446937,2.23452972631304,2.98851259950124,22.451071708689 0 0 35.598591479197 22.45107170868902 EZB FALSE 1096 +Reddy_3972T EZB 0.8002858030286724 0 115,812,426,284,874,862,1006,753,1075,424 0.138,0.165,0.186,0.262,0.296,0.302,0.31,0.314,0.344,0.345 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,ST2 33.1132775161832,8.26353735528695 0 0 41.37681487147015 33.1132775161832 EZB TRUE 1097 +Reddy_3977T BN2 1 0 553,258,187,109,562,10,352,503,463,355 0.158,0.165,0.203,0.231,0.282,0.287,0.322,0.338,0.343,0.343 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.5581717463513 0 0 40.55817174635129 40.55817174635129 BN2 TRUE 1098 +Reddy_3982T N1 0.7845227284663123 0 1104,496,41,516,325,137,486,523,854,438 0.134,0.137,0.223,0.257,0.335,0.338,0.339,0.351,0.362,0.365 N1,N1,N1,N1,ST2,N1,ST2,N1,BN2,N1 2.76551154450111,31.6600903990266,5.93025933396502 0 0 40.35586127749268 31.660090399026558 N1 TRUE 1099 +Reddy_3983T EZB 1 0 823,76,471,908,766,921,235,216,638,32 0.191,0.203,0.262,0.285,0.289,0.303,0.305,0.311,0.314,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.0035025056776 0 0 37.003502505677645 37.003502505677645 EZB TRUE 1100 +Reddy_3987T MCD 0.9242889161022892 0 367,452,978,878,961,514,295,39,465,621 0.12,0.152,0.177,0.196,0.197,0.213,0.218,0.222,0.226,0.249 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 48.99317568138,4.01316771174797 0 0 53.00634339312798 48.99317568138 MCD TRUE 1101 +Reddy_3991T EZB 0.8613140467834578 0 1094,861,885,7,809,680,1095,706,112,892 0.122,0.135,0.146,0.164,0.186,0.188,0.193,0.194,0.205,0.206 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1081868853007,8.2292720545159 0 0 59.33745893981662 51.10818688530072 EZB TRUE 1102 +Reddy_3993T MCD 0.8864873899404359 0 918,169,778,990,312,155,286,450,433,1046 0.119,0.119,0.128,0.139,0.149,0.162,0.163,0.168,0.168,0.17 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.78582108992427,60.8041010855836 0 0 68.58992217550782 60.804101085583554 ST2 FALSE 1103 +Reddy_3997T N1 0.7826066922433542 0 1099,496,41,516,137,325,523,486,438,854 0.134,0.167,0.189,0.286,0.311,0.315,0.319,0.343,0.369,0.386 N1,N1,N1,N1,N1,ST2,N1,ST2,N1,BN2 2.58902807289811,31.2566348411713,6.09347273473737 0 0 39.93913564880683 31.256634841171348 N1 TRUE 1104 +Reddy_684T MCD 0.7955657976784132 0 696,1093,574,935,616,223,475,502,985,697 0.219,0.291,0.324,0.345,0.384,0.385,0.388,0.39,0.395,0.412 MCD,MCD,N1,EZB,MCD,MCD,MCD,MCD,MCD,MCD 2.8995290364209,23.3063349182908,3.08943127580521 0 0 29.295295230516917 23.306334918290805 BN2 FALSE 1105 +Reddy_689T BN2 0.757851793282285 0 186,462,672,470,840,22,153,571,539,864 0.143,0.184,0.184,0.217,0.23,0.235,0.267,0.284,0.339,0.339 BN2,BN2,BN2,BN2,BN2,EZB,BN2,EZB,EZB,BN2 33.5451959014079,10.718334512794 0 0 44.263530414201966 33.54519590140792 BN2 TRUE 1106 +Reddy_695T EZB 0.45122075492302766 0 950,965,361,178,787,1025,770,1041,661,365 0.154,0.203,0.213,0.218,0.249,0.281,0.297,0.306,0.312,0.315 EZB,EZB,ST2,ST2,EZB,BN2,BN2,ST2,EZB,ST2 6.92573331550293,18.6345519518821,15.7377972090314 0 0 41.29808247641647 18.6345519518821 ST2 FALSE 1107 +Reddy_705T EZB 1 0 92,105,287,675,738,78,117,397,247,317 0.147,0.153,0.177,0.188,0.188,0.202,0.22,0.225,0.225,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.9916339799038 0 0 51.99163397990384 51.99163397990384 EZB TRUE 1108 +Reddy_759T MCD 1 0 712,879,500,762,350,417,1015,1048,767,246 0.125,0.132,0.134,0.167,0.168,0.17,0.171,0.173,0.182,0.183 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.4323873978751 0 0 63.43238739787513 63.43238739787513 MCD TRUE 1109 +Reddy_799T MCD 0.9054771589907213 0 299,690,469,447,721,937,722,283,550,511 0.12,0.155,0.181,0.188,0.196,0.203,0.233,0.238,0.248,0.25 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 4.93686078271522,47.2924282441624 0 0 52.22928902687758 47.292428244162366 MCD TRUE 1110 +Reddy_816T BN2 1 0 957,264,578,401,640,482,318,951,1062,871 0.148,0.166,0.171,0.179,0.19,0.199,0.237,0.253,0.274,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.8203492852907 0 0 49.82034928529071 49.82034928529071 BN2 TRUE 1111 +Reddy_823T BN2 0.4327246041431883 0 455,506,123,579,347,1061,521,624,799,173 0.167,0.308,0.313,0.359,0.398,0.401,0.412,0.424,0.442,0.444 MCD,BN2,N1,N1,BN2,BN2,MCD,EZB,BN2,BN2 12.7676575599056,2.36020958849165,8.39789450156501,5.97951130576916 0 0 29.50527295573142 12.767657559905599 BN2 TRUE 1112 +Reddy_830T BN2 1 0 695,681,652,674,65,666,1019,584,589,659 0.153,0.163,0.17,0.217,0.221,0.228,0.231,0.255,0.279,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.4528931889932 0 0 47.4528931889932 47.4528931889932 BN2 TRUE 1113 +SU-DHL-10 EZB 1 0 1006,874,688,753,601,407,1075,164,344,812 0.166,0.176,0.176,0.236,0.241,0.247,0.261,0.276,0.293,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5822920423909 0 0 43.58229204239091 43.58229204239091 EZB TRUE 1114 +SU-DHL-4 EZB 1 0 588,183,224,259,225,1051,655,268,410,737 0.176,0.184,0.191,0.193,0.2,0.23,0.246,0.251,0.271,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3224236865746 0 0 46.32242368657461 46.32242368657461 EZB TRUE 1115 +SU-DHL-5 EZB 0.6609816726970834 0 988,768,993,194,684,810,714,913,974,824 0.213,0.224,0.352,0.391,0.414,0.441,0.447,0.47,0.475,0.51 BN2,EZB,EZB,EZB,ST2,ST2,EZB,EZB,EZB,EZB 4.70259425868679,18.2976016160517,4.68226851400892 0 0 27.68246438874743 18.297601616051722 ST2 FALSE 1116 +SU-DHL-6 EZB 1 0 848,877,740,192,93,387,757,631,873,228 0.13,0.219,0.222,0.224,0.233,0.236,0.269,0.277,0.301,0.302 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7288759335614 0 0 43.728875933561355 43.728875933561355 EZB TRUE 1117 +SU-DHL-9 ST2 0.651392998953499 0 689,607,338,606,828,620,346,144,1052,545 0.183,0.195,0.233,0.291,0.333,0.343,0.387,0.449,0.453,0.456 ST2,ST2,BN2,ST2,ST2,EZB,ST2,EZB,EZB,ST2 4.29903231454278,7.35498670238244,21.7762304672803 0 0 33.43024948420552 21.776230467280296 ST2 TRUE 1118 From 704b1261d5385f4b421d29f8abe87b446c29d138 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 3 Jun 2025 12:57:23 -0700 Subject: [PATCH 25/79] fix name_overlap, and mssing feature statement --- R/umap.R | 81 +- ...ass_accuracy_0.870optimize_for_other=F.tsv | 6 - ...edictions_acc0.870optimize_for_other=F.tsv | 1119 --------- ...ass_accuracy_0.494optimize_for_other=T.tsv | 7 - ...edictions_acc0.494optimize_for_other=T.tsv | 2221 ----------------- 5 files changed, 43 insertions(+), 3391 deletions(-) delete mode 100644 temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv delete mode 100644 temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv delete mode 100644 temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv delete mode 100644 temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv diff --git a/R/umap.R b/R/umap.R index 7c61378..2924043 100644 --- a/R/umap.R +++ b/R/umap.R @@ -491,7 +491,7 @@ make_and_annotate_umap = function(df, keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] df= df[keep_rows,] metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) - message(paste("kept",nrow(metadata),"rows of the data that match the metadata provided")) + message(paste(nrow(metadata),"rows of the data have atleast 1 feature")) } if(missing(umap_out)){ @@ -1291,23 +1291,9 @@ predict_single_sample_DLBCLone <- function( title3 ="predicted_class_for_Other", seed = 12345 ){ - if(ignore_top){ - # Allow overlapping samples: rename test sample ID's to be: paste0(sample_id,"_test") - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - test_df <- test_df %>% - mutate(sample_id = paste0(sample_id, "_test")) - } - } else { - # Drop overlaps to prevent rowname collisions - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - warning(paste("Removing", length(dupes), - "overlapping samples from train_df to avoid duplicated rownames.\n", - "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) - train_df <- train_df %>% filter(!sample_id %in% dupes) - train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) - } + + if(nrow(test_df)>1){ + message("Warning: you have supplied more than one sample to test with. Will proceed with all") } trained_features = colnames(umap_out$features) @@ -1323,21 +1309,48 @@ predict_single_sample_DLBCLone <- function( select(all_of(trained_features)) %>% rownames_to_column("sample_id") test_id <- test_df$sample_id - - combined_df <- bind_rows(train_df, test_df) - # Make dummy metadata for test samples - test_metadata <- data.frame( - sample_id = test_df$sample_id, - cohort = "Test-Sample", - lymphgen = NA # or any placeholder? - ) + if(ignore_top){ + if(test_id %in% train_df$sample_id){ + combined_df = train_df + combined_metadata <- train_metadata + }else{ + combined_df = bind_rows(train_df,test_df) - # Ensure train_metadata has matching columns - train_metadata <- train_metadata %>% - select(sample_id, cohort, lymphgen) + # Make dummy metadata for test samples + test_metadata <- data.frame( + sample_id = test_df$sample_id, + cohort = "Test-Sample", + lymphgen = NA # or any placeholder? + ) + # Ensure train_metadata has matching columns + train_metadata <- train_metadata %>% + select(sample_id, cohort, lymphgen) + combined_metadata <- bind_rows(train_metadata, test_metadata) + } + } else { + # Drop overlaps to prevent rowname collisions + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + warning(paste("Removing", length(dupes), + "overlapping samples from train_df to avoid duplicated rownames.\n", + "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) + train_df <- train_df %>% filter(!sample_id %in% dupes) + train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) + } + combined_df <- bind_rows(train_df, test_df) - combined_metadata <- bind_rows(train_metadata, test_metadata) + # Make dummy metadata for test samples + test_metadata <- data.frame( + sample_id = test_df$sample_id, + cohort = "Test-Sample", + lymphgen = NA # or any placeholder? + ) + # Ensure train_metadata has matching columns + train_metadata <- train_metadata %>% + select(sample_id, cohort, lymphgen) + combined_metadata <- bind_rows(train_metadata, test_metadata) + } projection <- make_and_annotate_umap( df = combined_df, @@ -1415,10 +1428,6 @@ predict_single_sample_DLBCLone <- function( anno_out = left_join(test_pred,anno_umap,by="sample_id") - if(ignore_top && length(dupes) > 0){ # remove "_test" from plotted test labels - anno_out$sample_id <- sub("_test$", "", anno_out$sample_id) - } - anno_out = anno_out %>% mutate( label = as.character(paste( @@ -1552,10 +1561,6 @@ predict_single_sample_DLBCLone <- function( } } - if(ignore_top && length(dupes) > 0){ # remove "_test" for returned prediction - test_pred$sample_id <- sub("_test$", "", test_pred$sample_id) - } - return(list( prediction = test_pred, train_prediction = train_pred, diff --git a/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv b/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv deleted file mode 100644 index 77b63cd..0000000 --- a/temp_tsv_results/optimize_for_other_F/class_accuracy_0.870optimize_for_other=F.tsv +++ /dev/null @@ -1,6 +0,0 @@ -true_label acc n -BN2 0.8406374501992032 251 -EZB 0.9176954732510288 486 -MCD 0.9223744292237442 219 -N1 0.34782608695652173 23 -ST2 0.762589928057554 139 diff --git a/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv b/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv deleted file mode 100644 index 81f4145..0000000 --- a/temp_tsv_results/optimize_for_other_F/loocv_predictions_acc0.870optimize_for_other=F.tsv +++ /dev/null @@ -1,1119 +0,0 @@ -sample_id predicted_label confidence other_score neighbor distance label weighted_votes neighbors_other other_weighted_votes total_w pred_w true_label correct fold -00-14595_tumorC EZB 1 0 139,610,649,9,273,1072,664,648,112,374 0.119,0.145,0.148,0.17,0.172,0.204,0.218,0.22,0.221,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.6020551081224 0 0 56.60205510812236 56.60205510812236 EZB TRUE 1 -00-15201_tumorB BN2 0.6402838443240962 0 980,827,327,438,41,577,895,611,1104,523 0.275,0.338,0.374,0.396,0.467,0.471,0.481,0.483,0.489,0.494 BN2,BN2,BN2,N1,N1,BN2,BN2,BN2,N1,N1 15.5428504395784,8.73208727337596 0 0 24.27493771295438 15.542850439578416 N1 FALSE 2 -FL1015T2 EZB 0.694617059653598 0 478,55,230,771,54,782,800,40,806,733 0.164,0.179,0.203,0.224,0.282,0.294,0.371,0.38,0.381,0.399 EZB,MCD,EZB,EZB,MCD,EZB,EZB,EZB,N1,EZB 26.7276928671703,9.12881716631189,2.62180329861372 0 0 38.478313332095894 26.72769286717029 EZB TRUE 3 -00-23442_tumorB EZB 1 0 234,235,921,471,76,336,908,32,28,1100 0.133,0.174,0.175,0.224,0.238,0.291,0.292,0.326,0.337,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4449799067832 0 0 43.444979906783246 43.444979906783246 EZB TRUE 4 -00-23442_tumorA EZB 1 0 403,686,141,708,838,237,949,289,310,963 0.17,0.175,0.184,0.189,0.201,0.249,0.256,0.273,0.3,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3477487607725 0 0 45.347748760772475 45.347748760772475 EZB TRUE 5 -FL1002T2 EZB 0.8003116814545509 0 882,120,334,111,303,1092,942,883,182,156 0.157,0.171,0.172,0.188,0.193,0.22,0.233,0.234,0.259,0.262 EZB,EZB,EZB,ST2,EZB,ST2,EZB,EZB,EZB,EZB 39.462718712829,9.84646872912647 0 0 49.30918744195551 39.46271871282904 EZB TRUE 6 -FL1006T2 EZB 0.8977685904330628 0 861,1102,706,1094,680,885,71,1095,192,892 0.145,0.164,0.181,0.185,0.194,0.198,0.206,0.214,0.217,0.22 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 47.3655035636608,5.39364179785467 0 0 52.759145361515515 47.365503563660845 EZB TRUE 7 -01-12047_tumorB EZB 1 0 97,12,209,1000,98,977,27,171,129,208 0.158,0.159,0.173,0.185,0.212,0.218,0.247,0.248,0.261,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8311483267336 0 0 48.83114832673358 48.83114832673358 EZB TRUE 8 -01-12047_tumorC EZB 1 0 664,273,1,139,649,610,374,983,343,648 0.15,0.157,0.17,0.173,0.179,0.206,0.254,0.256,0.267,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.3974666306824 0 0 50.39746663068243 50.39746663068243 EZB TRUE 9 -01-14774_tumorB BN2 1 0 352,109,531,258,952,463,1098,611,553,187 0.197,0.205,0.227,0.241,0.269,0.281,0.287,0.293,0.308,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.1703465142347 0 0 39.17034651423474 39.17034651423474 BN2 TRUE 10 -01-16433_tumorB EZB 0.47541138489930945 0 1084,411,691,622,1096,858,90,608,899,909 0.285,0.301,0.313,0.372,0.448,0.455,0.469,0.484,0.526,0.527 BN2,EZB,ST2,ST2,EZB,EZB,EZB,EZB,ST2,ST2 3.50829938762364,11.9543221941359,9.68259490620444 0 0 25.145216487963992 11.954322194135912 EZB TRUE 11 -01-16433_tumorA EZB 1 0 8,209,97,98,977,129,1000,171,27,30 0.159,0.178,0.2,0.203,0.207,0.212,0.241,0.245,0.247,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.5991356431291 0 0 47.59913564312906 47.59913564312906 EZB TRUE 12 -01-20774T EZB 1 0 140,277,418,1037,84,83,102,222,107,1079 0.149,0.187,0.19,0.206,0.211,0.212,0.215,0.227,0.233,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0342577647599 0 0 49.03425776475989 49.03425776475989 EZB TRUE 13 -01-23117_tumorB BN2 0.40240358179816577 0 891,802,504,699,388,23,127,495,552,185 0.147,0.148,0.17,0.239,0.243,0.268,0.284,0.285,0.318,0.331 MCD,ST2,MCD,BN2,BN2,EZB,BN2,MCD,BN2,BN2 17.9711620261526,3.73048067587552,16.1852033773509,6.77270179503921 0 0 44.659547874418216 17.97116202615255 BN2 TRUE 14 -01-23942_tumorA BN2 1 0 175,341,563,282,1062,871,197,318,488,763 0.161,0.173,0.174,0.181,0.193,0.199,0.201,0.211,0.226,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.942930924542 0 0 51.942930924541976 51.942930924541976 BN2 TRUE 15 -01-28152_tumorA MCD 0.8867144864589598 0 900,1042,844,580,526,453,549,678,276,296 0.13,0.151,0.164,0.189,0.197,0.198,0.205,0.222,0.227,0.232 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.11402455442833,47.8560230122748 0 0 53.97004756670314 47.856023012274804 MCD TRUE 16 -02-11616_tumorA MCD 1 0 166,246,767,297,762,879,413,851,165,804 0.13,0.133,0.146,0.151,0.156,0.159,0.16,0.166,0.166,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.4037473314394 0 0 65.4037473314394 65.4037473314394 MCD TRUE 17 -02-13135T EZB 1 0 756,190,116,754,406,868,865,251,709,182 0.14,0.143,0.165,0.174,0.191,0.216,0.246,0.248,0.258,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.681808152646 0 0 51.68180815264596 51.68180815264596 EZB TRUE 18 -FL1008T2 EZB 0.9201372791303423 0 733,744,429,658,241,1050,643,645,454,254 0.167,0.173,0.22,0.276,0.285,0.296,0.307,0.32,0.326,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 36.0458408165768,3.12857547339804 0 0 39.17441628997488 36.04584081657684 EZB TRUE 19 -02-15745_tumorD EZB 1 0 239,852,25,266,746,72,603,1059,677,167 0.12,0.127,0.16,0.177,0.193,0.199,0.203,0.208,0.218,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1634002312649 0 0 57.16340023126486 57.16340023126486 EZB TRUE 20 -02-17305_tumorB EZB 1 0 250,597,816,709,938,251,284,868,1075,164 0.131,0.164,0.174,0.177,0.191,0.212,0.241,0.246,0.25,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2268447338511 0 0 51.22684473385111 51.22684473385111 EZB TRUE 21 -02-18356_tumorA BN2 0.8092793077730909 0 840,470,88,539,571,189,1106,462,186,570 0.147,0.166,0.208,0.214,0.229,0.235,0.235,0.236,0.24,0.264 BN2,BN2,BN2,EZB,EZB,BN2,BN2,BN2,BN2,BN2 38.3471093467462,9.03716080377411 0 0 47.38427015052035 38.347109346746244 EZB FALSE 22 -02-24492_tumorA BN2 0.6661187436097572 0 495,802,659,855,388,127,666,891,179,14 0.17,0.221,0.221,0.235,0.24,0.24,0.252,0.252,0.256,0.268 MCD,ST2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 28.7094151312575,9.85640196002366,4.53372845772802 0 0 43.09954554900917 28.70941513125749 EZB FALSE 23 -02-28397_tumorA ST2 0.8073244129140299 0 59,281,490,970,946,396,1032,759,422,412 0.231,0.233,0.328,0.347,0.36,0.368,0.375,0.421,0.436,0.498 ST2,ST2,ST2,BN2,BN2,ST2,ST2,ST2,ST2,ST2 5.66122957794121,23.7209545563426 0 0 29.382184134283833 23.72095455634262 ST2 TRUE 24 -03-11110T EZB 1 0 603,1059,239,20,384,605,852,149,61,833 0.143,0.148,0.148,0.16,0.178,0.179,0.187,0.19,0.219,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.580004420683 0 0 57.58000442068297 57.58000442068297 EZB TRUE 25 -03-19969_tumorB BN2 1 0 1022,569,566,498,482,353,401,951,957,399 0.142,0.146,0.192,0.241,0.264,0.273,0.278,0.288,0.316,0.325 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.0118273558491 0 0 44.011827355849086 44.011827355849086 BN2 TRUE 26 -03-23488_tumorA EZB 1 0 98,866,152,776,613,97,151,8,12,599 0.146,0.16,0.17,0.184,0.197,0.208,0.228,0.247,0.247,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.799401319636 0 0 50.799401319635976 50.799401319635976 EZB TRUE 27 -03-23488_tumorB EZB 1 0 32,85,336,306,921,402,76,79,4,1100 0.128,0.171,0.186,0.244,0.265,0.274,0.304,0.334,0.337,0.352 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6706165555349 0 0 42.67061655553495 42.67061655553495 EZB TRUE 28 -FL1001T2 EZB 1 0 80,805,876,46,1078,594,760,632,667,914 0.122,0.161,0.166,0.167,0.168,0.171,0.176,0.179,0.193,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.3896017451861 0 0 59.38960174518607 59.38960174518607 EZB TRUE 29 -03-33266_tumorA EZB 1 0 208,171,977,256,364,53,209,35,48,129 0.116,0.125,0.148,0.175,0.183,0.187,0.192,0.203,0.204,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.5924092330831 0 0 59.59240923308311 59.59240923308311 EZB TRUE 30 -04-11038_tumorB EZB 1 0 114,211,360,1034,939,57,51,233,927,210 0.225,0.292,0.345,0.346,0.351,0.357,0.362,0.366,0.369,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.1990376799793 0 0 30.199037679979284 30.199037679979284 EZB TRUE 31 -04-11038_tumorA EZB 1 0 28,85,336,921,306,402,76,79,1100,4 0.128,0.192,0.2,0.252,0.257,0.273,0.28,0.318,0.325,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4349279554029 0 0 42.434927955402856 42.434927955402856 EZB TRUE 32 -04-13783_tumorB MCD 1 0 451,902,1008,468,1021,276,465,568,1076,961 0.123,0.153,0.175,0.184,0.184,0.189,0.191,0.203,0.203,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5145679648155 0 0 56.51456796481549 56.51456796481549 MCD TRUE 33 -04-14093_tumorB BN2 0.5825659512681077 0 701,104,103,497,442,742,484,100,694,521 0.191,0.203,0.221,0.251,0.341,0.343,0.355,0.376,0.377,0.382 BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD,BN2,MCD 20.5444822041933,14.7210223442151 0 0 35.26550454840837 20.544482204193304 BN2 TRUE 34 -04-16803_tumorB EZB 1 0 48,125,635,364,931,256,30,53,208,820 0.133,0.135,0.151,0.153,0.162,0.185,0.203,0.204,0.212,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.773168789628 0 0 58.773168789627995 58.773168789627995 EZB TRUE 35 -04-21856_tumorA EZB 1 0 963,887,707,289,304,997,237,972,750,653 0.121,0.149,0.161,0.166,0.172,0.203,0.209,0.221,0.233,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5032651173225 0 0 55.50326511732253 55.50326511732253 EZB TRUE 36 -04-21856_tumorB EZB 1 0 761,89,880,430,159,113,1031,997,1063,931 0.134,0.147,0.168,0.174,0.19,0.203,0.213,0.228,0.233,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.6617502749463 0 0 53.661750274946264 53.661750274946264 EZB TRUE 37 -04-24061_tumorB BN2 1 0 342,763,654,501,518,563,333,63,871,488 0.158,0.178,0.217,0.217,0.227,0.228,0.237,0.241,0.241,0.254 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.4250740734587 0 0 46.42507407345866 46.42507407345866 BN2 TRUE 38 -04-24937T MCD 1 0 130,906,323,514,1027,332,311,367,1101,349 0.137,0.151,0.157,0.169,0.182,0.186,0.211,0.22,0.222,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.961429122224 0 0 54.96142912222402 54.96142912222402 MCD TRUE 39 -04-32952_tumorC EZB 0.4920784679186051 0 54,800,253,145,806,230,755,146,55,221 0.198,0.213,0.244,0.257,0.262,0.291,0.295,0.302,0.306,0.323 MCD,EZB,EZB,BN2,N1,EZB,EZB,BN2,MCD,EZB 7.19984280394886,18.7287284662599,8.32007821622435,3.8118016149506 0 0 38.06045110138368 18.728728466259867 EZB TRUE 40 -04-35039T N1 0.8150968867344255 0 1104,1099,523,137,325,496,486,516,174,438 0.189,0.223,0.231,0.235,0.256,0.257,0.349,0.356,0.42,0.422 N1,N1,N1,N1,ST2,N1,ST2,N1,N1,N1 29.8290251226189,6.76665522910569 0 0 36.59568035172454 29.829025122618855 N1 TRUE 41 -05-12963_tumorB BN2 0.6830045235143078 0 180,742,100,185,127,179,484,388,472,651 0.113,0.167,0.17,0.208,0.254,0.266,0.279,0.288,0.308,0.335 MCD,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.8005230478119,14.7592316141142 0 0 46.55975466192615 31.800523047811943 EZB FALSE 42 -05-18426T MCD 0.4042321096411385 0 525,803,124,716,779,747,1044,1045,205,554 0.213,0.239,0.252,0.254,0.272,0.343,0.369,0.386,0.39,0.398 MCD,BN2,N1,BN2,MCD,ST2,MCD,BN2,MCD,ST2 10.7132862983163,13.6455473791573,3.96961295295945,5.42826653931304 0 0 33.756713169746114 13.645547379157275 ST2 FALSE 43 -05-21634T MCD 1 0 856,1047,519,1054,475,198,697,790,449,616 0.161,0.217,0.229,0.229,0.253,0.271,0.274,0.287,0.316,0.332 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.515835968023 0 0 40.51583596802299 40.51583596802299 MCD TRUE 44 -05-22052T EZB 0.7047528478430924 0 1092,111,6,764,334,882,120,303,836,941 0.21,0.239,0.326,0.329,0.359,0.375,0.375,0.412,0.421,0.43 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 21.3516853798928,8.94501430034259 0 0 30.29669968023537 21.351685379892785 BN2 FALSE 45 -05-23110T EZB 1 0 760,876,914,632,80,667,29,984,637,594 0.123,0.138,0.144,0.144,0.148,0.154,0.167,0.181,0.188,0.194 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.4893246688809 0 0 64.48932466888091 64.48932466888091 EZB TRUE 46 -05-24006T EZB 1 0 870,727,593,726,725,784,344,219,839,220 0.19,0.21,0.242,0.252,0.264,0.289,0.322,0.335,0.408,0.409 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.3754073835793 0 0 36.37540738357926 36.37540738357926 EZB TRUE 47 -05-24401T EZB 1 0 35,256,931,125,635,53,364,30,208,880 0.133,0.162,0.162,0.167,0.17,0.178,0.181,0.204,0.209,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.079952722436 0 0 57.079952722436026 57.079952722436026 EZB TRUE 48 -05-24666T EZB 1 0 1012,101,392,313,302,604,655,986,183,1051 0.112,0.157,0.165,0.175,0.196,0.201,0.206,0.211,0.217,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.9846747775875 0 0 55.984674777587536 55.984674777587536 EZB TRUE 49 -05-24904T EZB 1 0 213,723,291,68,255,257,181,734,669,853 0.139,0.167,0.173,0.174,0.2,0.211,0.214,0.215,0.231,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.2537147120016 0 0 52.25371471200162 52.25371471200162 EZB TRUE 50 -05-25439T EZB 1 0 233,1034,211,939,719,360,31,960,537,948 0.108,0.12,0.257,0.266,0.291,0.357,0.362,0.372,0.413,0.439 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6334716727759 0 0 41.63347167277588 41.63347167277588 EZB TRUE 51 -05-25674T EZB 0.9191401313324873 0 1020,886,610,112,1072,649,885,1,139,1094 0.142,0.216,0.242,0.244,0.256,0.266,0.286,0.287,0.299,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.8746756283179,3.33196342182409 0 0 41.206639050142016 37.87467562831793 EZB TRUE 52 -05-27675_tumorB EZB 1 0 256,48,208,30,171,35,113,977,364,931 0.12,0.178,0.179,0.187,0.189,0.204,0.218,0.227,0.228,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6526118690831 0 0 52.652611869083145 52.652611869083145 EZB TRUE 53 -05-32150_tumorB EZB 0.7848002392431485 0 230,40,55,800,806,3,782,253,771,478 0.196,0.198,0.208,0.231,0.268,0.282,0.286,0.288,0.319,0.338 EZB,EZB,MCD,EZB,N1,EZB,EZB,EZB,EZB,EZB 31.0999107339748,4.80066756758947,3.72722655737303 0 0 39.62780485893725 31.099910733974756 MCD FALSE 54 -05-32150T EZB 0.8156829672885244 0 230,3,54,478,771,782,40,800,806,733 0.127,0.179,0.208,0.23,0.264,0.29,0.306,0.318,0.34,0.359 EZB,EZB,MCD,EZB,EZB,EZB,EZB,EZB,N1,EZB 34.2620925663313,4.80066756758947,2.94141788664598 0 0 42.004178020566776 34.262092566331326 MCD FALSE 55 -05-32762T EZB 1 0 247,826,294,236,718,156,1108,78,675,117 0.15,0.156,0.165,0.18,0.207,0.228,0.256,0.261,0.268,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0559485475516 0 0 49.05594854755164 49.05594854755164 EZB TRUE 56 -FL1018T2 EZB 1 0 1080,609,600,948,31,960,82,939,292,138 0.178,0.297,0.301,0.32,0.357,0.362,0.363,0.391,0.422,0.434 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.9738653586675 0 0 30.973865358667467 30.973865358667467 EZB TRUE 57 -06-11677_tumorA BN2 1 0 564,357,1028,393,158,172,589,64,695,337 0.207,0.226,0.267,0.288,0.318,0.365,0.433,0.459,0.466,0.467 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.1283257956809 0 0 31.128325795680873 31.128325795680873 BN2 TRUE 58 -06-11677_tumorB ST2 0.8609218875706557 0 281,24,396,490,412,759,970,946,830,529 0.104,0.231,0.24,0.314,0.368,0.377,0.387,0.402,0.422,0.43 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2 5.07077381823605,31.389124361732 0 0 36.459898179968036 31.389124361731994 ST2 TRUE 59 -06-14634T ST2 1 0 394,728,1081,822,508,656,911,339,904,483 0.222,0.238,0.249,0.257,0.272,0.319,0.319,0.336,0.366,0.378 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 34.9165957214667 0 0 34.91659572146672 34.91659572146672 ST2 TRUE 60 -06-15922T EZB 1 0 833,629,384,605,74,184,1059,603,25,163 0.105,0.136,0.147,0.147,0.153,0.186,0.188,0.198,0.219,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.9518296473584 0 0 61.951829647358394 61.951829647358394 EZB TRUE 61 -06-18449_tumorB EZB 1 0 912,307,220,818,316,219,676,252,585,593 0.247,0.267,0.28,0.298,0.314,0.345,0.354,0.359,0.37,0.402 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.6177078269457 0 0 31.617707826945725 31.617707826945725 EZB TRUE 62 -06-22057T BN2 1 0 518,333,542,488,559,342,654,763,1077,331 0.119,0.134,0.158,0.166,0.183,0.183,0.184,0.198,0.203,0.223 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.0854240068163 0 0 59.085424006816275 59.085424006816275 BN2 TRUE 63 -06-22314_tumorB BN2 1 0 589,65,584,681,695,1113,477,674,345,1060 0.137,0.182,0.218,0.262,0.264,0.301,0.32,0.336,0.34,0.347 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.2179813987399 0 0 40.217981398739866 40.217981398739866 BN2 TRUE 64 -06-22314_tumorA BN2 1 0 589,584,681,64,695,1113,674,652,1019,795 0.17,0.177,0.18,0.182,0.193,0.221,0.261,0.287,0.342,0.345 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.4222986658779 0 0 45.42229866587789 45.42229866587789 BN2 TRUE 65 -06-23907T MCD 1 0 460,888,730,1043,513,419,459,314,739,897 0.258,0.278,0.279,0.303,0.344,0.367,0.383,0.406,0.413,0.417 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.8901382810901 0 0 29.890138281090096 29.890138281090096 BN2 FALSE 66 -06-24255_tumorD EZB 0.9088784393223224 0 454,142,241,429,1050,645,581,658,669,643 0.135,0.212,0.235,0.242,0.244,0.251,0.257,0.261,0.265,0.278 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 39.7159925506065,3.9818121637616 0 0 43.697804714368125 39.71599255060652 EZB TRUE 67 -06-24255_tumorC EZB 1 0 291,669,255,734,50,181,213,257,723,136 0.145,0.164,0.172,0.172,0.174,0.187,0.201,0.216,0.228,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8797594575461 0 0 53.87975945754612 53.87975945754612 EZB TRUE 68 -06-24925T EZB 1 0 244,240,225,243,268,343,224,1115,1051,410 0.124,0.158,0.18,0.195,0.197,0.201,0.236,0.277,0.286,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8807276141989 0 0 49.88072761419892 49.88072761419892 EZB TRUE 69 -06-25674T BN2 1 0 515,998,815,945,591,354,796,75,973,331 0.116,0.205,0.208,0.232,0.249,0.259,0.265,0.266,0.276,0.277 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.2428135332005 0 0 45.24281353320046 45.24281353320046 BN2 TRUE 70 -06-29769_tumorB EZB 1 0 706,711,229,680,892,648,1095,374,861,7 0.126,0.133,0.134,0.139,0.147,0.151,0.159,0.185,0.187,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.4380497054113 0 0 65.43804970541133 65.43804970541133 EZB TRUE 71 -06-30025T EZB 1 0 746,266,852,677,20,167,239,191,96,25 0.125,0.126,0.175,0.19,0.199,0.199,0.217,0.228,0.254,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7803532782503 0 0 53.78035327825033 53.78035327825033 EZB TRUE 72 -06-30145T EZB 1 0 149,1059,603,74,231,605,384,735,25,61 0.169,0.189,0.189,0.193,0.197,0.202,0.203,0.223,0.23,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7446957577074 0 0 49.744695757707404 49.744695757707404 EZB TRUE 73 -06-33777T EZB 1 0 61,605,384,833,1059,629,73,603,231,163 0.153,0.155,0.156,0.157,0.185,0.188,0.193,0.194,0.215,0.229 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.8709872468122 0 0 55.87098724681222 55.87098724681222 EZB TRUE 74 -06-34043T BN2 1 0 305,354,559,945,998,542,702,331,796,1077 0.129,0.152,0.16,0.164,0.168,0.179,0.195,0.199,0.202,0.208 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 58.0998909883884 0 0 58.09989098838838 58.09989098838838 BN2 TRUE 75 -07-10483T EZB 1 0 471,921,1100,235,4,823,234,908,32,28 0.196,0.203,0.203,0.218,0.238,0.241,0.246,0.257,0.28,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.7005900875435 0 0 42.70059008754354 42.70059008754354 EZB TRUE 76 -FL1013T2 BN2 1 0 212,633,634,493,487,1069,390,898,399,636 0.139,0.155,0.158,0.175,0.185,0.186,0.244,0.248,0.251,0.257 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.4647122230314 0 0 52.46471222303142 52.46471222303142 EZB FALSE 77 -07-25012T EZB 1 0 117,675,287,1108,397,105,92,56,247,236 0.118,0.119,0.158,0.202,0.217,0.222,0.233,0.261,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9943005335278 0 0 52.994300533527785 52.994300533527785 EZB TRUE 78 -07-25994_tumorC EZB 1 0 402,271,1013,917,927,210,306,32,1100,28 0.23,0.235,0.253,0.267,0.268,0.273,0.311,0.318,0.326,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1317082790187 0 0 36.13170827901866 36.13170827901866 EZB TRUE 79 -07-30628T EZB 1 0 29,876,46,760,632,594,805,667,1078,914 0.122,0.144,0.148,0.154,0.158,0.163,0.166,0.171,0.186,0.191 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.3674507585309 0 0 63.367450758530936 63.367450758530936 EZB TRUE 80 -07-31833T EZB 1 0 270,819,168,908,269,235,234,471,4,766 0.23,0.261,0.329,0.33,0.33,0.335,0.341,0.353,0.374,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3292406338814 0 0 31.329240633881394 31.329240633881394 EZB TRUE 81 -07-32561_tumorB EZB 1 0 600,609,751,1080,138,292,726,57,725,727 0.162,0.226,0.237,0.305,0.328,0.329,0.352,0.363,0.408,0.432 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.5445969077382 0 0 34.5445969077382 34.5445969077382 EZB TRUE 82 -07-40648_tumorA EZB 0.9232595209116665 0 84,418,140,1088,13,644,1037,599,107,203 0.129,0.193,0.196,0.204,0.212,0.216,0.229,0.257,0.263,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.68127971311468,44.2892275973399 0 0 47.97050731045456 44.289227597339874 EZB TRUE 83 -07-40648_tumorB EZB 1 0 83,140,13,418,1088,107,644,1037,102,599 0.129,0.182,0.211,0.215,0.229,0.237,0.246,0.25,0.272,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.434779658514 0 0 46.434779658513975 46.434779658513975 EZB TRUE 84 -08-13706T EZB 1 0 28,306,32,336,402,921,79,360,76,927 0.171,0.181,0.192,0.233,0.239,0.335,0.336,0.359,0.372,0.393 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.051539178059 0 0 39.05153917805903 39.05153917805903 EZB TRUE 85 -08-15460T BN2 0.9118514132431504 0 670,355,321,363,187,558,553,258,262,531 0.182,0.297,0.358,0.364,0.374,0.394,0.423,0.434,0.435,0.445 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 26.2818559224175,2.54066443640032 0 0 28.822520358817844 26.281855922417527 BN2 TRUE 86 -08-17645_tumorA BN2 0.6883832189961813 0 189,539,88,576,926,561,470,636,95,358 0.315,0.347,0.372,0.373,0.409,0.411,0.412,0.418,0.435,0.438 BN2,EZB,BN2,EZB,ST2,BN2,BN2,BN2,BN2,BN2 17.6915734275757,5.56210908330486,2.44649863730897 0 0 25.7001811481895 17.691573427575662 BN2 TRUE 87 -08-17645_tumorB BN2 0.6514955328728309 0 539,189,22,358,570,470,840,571,487,462 0.129,0.161,0.208,0.218,0.233,0.237,0.24,0.295,0.309,0.317 EZB,BN2,EZB,BN2,BN2,BN2,BN2,EZB,BN2,BN2 29.8755685086253,15.9813361072608 0 0 45.856904615886144 29.87556850862532 BN2 TRUE 88 -08-24058_tumorB EZB 1 0 880,37,159,761,1063,931,430,820,1031,48 0.123,0.147,0.179,0.181,0.187,0.198,0.211,0.214,0.217,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.9413826993904 0 0 54.941382699390424 54.941382699390424 EZB TRUE 89 -08-24058_tumorA EZB 0.9259594706736657 0 858,608,411,143,786,884,252,409,614,954 0.204,0.224,0.298,0.362,0.387,0.398,0.412,0.433,0.442,0.453 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 27.5934156262546,2.20639365284861 0 0 29.7998092791032 27.59341562625459 EZB TRUE 90 -08-29440_tumorA EZB 1 0 1040,1026,317,1002,397,914,984,105,667,760 0.116,0.159,0.161,0.172,0.191,0.205,0.224,0.23,0.236,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3263666773265 0 0 54.32636667732646 54.32636667732646 EZB TRUE 91 -08-29440_tumorB EZB 1 0 105,738,1108,287,317,397,675,78,984,91 0.121,0.146,0.147,0.188,0.207,0.209,0.215,0.233,0.238,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.1182935495735 0 0 54.118293549573494 54.118293549573494 EZB TRUE 92 -08-33625T EZB 1 0 740,631,228,757,387,274,102,947,1117,1079 0.112,0.15,0.175,0.185,0.188,0.205,0.225,0.231,0.233,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2947773580224 0 0 54.2947773580224 54.2947773580224 EZB TRUE 93 -09-11467T MCD 0.4581242627616985 0 371,534,813,193,170,419,930,541,444,510 0.152,0.193,0.212,0.261,0.274,0.3,0.326,0.342,0.35,0.366 EZB,MCD,EZB,BN2,MCD,MCD,ST2,MCD,EZB,MCD 3.82702082951094,14.1714641594429,17.8122842316567,3.07013205337874 0 0 38.88090127398929 17.812284231656726 EZB FALSE 94 -09-12737T BN2 0.8373107169533547 0 926,575,561,636,399,1022,569,498,26,1069 0.182,0.192,0.281,0.293,0.303,0.366,0.391,0.392,0.398,0.417 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.222382872136,5.48360261294913 0 0 33.705985485085144 28.22238287213601 BN2 TRUE 95 -09-12864T EZB 1 0 226,191,199,1039,1030,677,986,167,604,72 0.116,0.129,0.141,0.169,0.2,0.226,0.227,0.231,0.237,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6207501192223 0 0 55.62075011922234 55.62075011922234 EZB TRUE 96 -09-15842_tumorB EZB 0.9020417044171873 0 1000,8,98,12,27,203,209,152,1088,866 0.153,0.158,0.189,0.2,0.208,0.21,0.231,0.25,0.265,0.267 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.75969043399267,43.8292565834541 0 0 48.58894701744674 43.82925658345407 EZB TRUE 97 -09-15842_tumorA EZB 0.923105987469731 0 27,97,866,12,8,776,152,613,1000,203 0.146,0.189,0.199,0.203,0.212,0.213,0.215,0.226,0.24,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.73076476392654,44.7875091713548 0 0 48.51827393528132 44.78750917135478 EZB TRUE 98 -09-16981T BN2 1 0 780,435,959,943,538,650,345,477,646,335 0.138,0.157,0.174,0.176,0.187,0.188,0.199,0.214,0.223,0.236 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.1696379635696 0 0 54.16963796356961 54.16963796356961 BN2 TRUE 99 -09-21480T BN2 0.6293379944502788 0 180,742,42,185,127,103,388,552,484,179 0.162,0.164,0.17,0.248,0.285,0.29,0.306,0.313,0.313,0.329 MCD,BN2,EZB,BN2,BN2,MCD,BN2,BN2,BN2,BN2 26.3210966504968,5.87524975825284,9.62712028578554 0 0 41.82346669453519 26.32109665049681 MCD FALSE 100 -09-27193T EZB 1 0 392,1012,49,604,313,183,986,981,302,655 0.128,0.145,0.157,0.167,0.178,0.206,0.207,0.214,0.223,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7729495502341 0 0 55.77294955023413 55.77294955023413 EZB TRUE 101 -09-27866_tumorB EZB 1 0 757,228,107,631,140,387,1079,13,277,93 0.164,0.164,0.17,0.181,0.197,0.202,0.207,0.215,0.221,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1235731005717 0 0 52.12357310057174 52.12357310057174 EZB TRUE 102 -09-31008_tumorB BN2 0.5571087481485455 0 104,34,497,742,100,701,180,42,484,552 0.146,0.221,0.253,0.285,0.29,0.309,0.342,0.345,0.368,0.429 MCD,BN2,BN2,BN2,MCD,BN2,MCD,EZB,BN2,BN2 20.279157188403,2.90201093464359,13.2195476363043 0 0 36.4007157593509 20.279157188403012 MCD FALSE 103 -09-31008_tumorA BN2 0.5868454522458296 0 103,34,497,701,742,100,180,42,442,484 0.146,0.203,0.207,0.294,0.328,0.336,0.388,0.39,0.391,0.398 MCD,BN2,BN2,BN2,BN2,MCD,MCD,EZB,BN2,BN2 21.2639118934461,2.56618310822947,12.4041670379639 0 0 36.23426203963946 21.263911893446117 MCD FALSE 104 -FL1020T2 EZB 1 0 92,1108,738,287,397,317,675,78,91,117 0.121,0.153,0.16,0.172,0.189,0.2,0.203,0.222,0.23,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.2111519711252 0 0 55.2111519711252 55.2111519711252 EZB TRUE 105 -09-31601_tumorA MCD 1 0 439,991,288,280,994,1090,966,897,729,660 0.143,0.165,0.177,0.202,0.203,0.21,0.213,0.218,0.23,0.231 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.2977997900922 0 0 51.297799790092164 51.297799790092164 MCD TRUE 106 -09-31895T EZB 1 0 102,757,140,387,873,228,13,84,631,83 0.17,0.185,0.191,0.206,0.208,0.227,0.233,0.237,0.237,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1094815474575 0 0 47.10948154745753 47.10948154745753 EZB TRUE 107 -09-32452_tumorA MCD 1 0 794,1035,413,290,297,528,165,446,17,798 0.149,0.158,0.163,0.168,0.177,0.191,0.204,0.217,0.22,0.235 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.3603233617405 0 0 54.360323361740484 54.360323361740484 MCD TRUE 108 -09-33003T BN2 1 0 352,10,463,258,1098,611,553,187,531,562 0.194,0.205,0.221,0.225,0.231,0.273,0.277,0.302,0.33,0.367 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.6547021982942 0 0 39.654702198294174 39.654702198294174 BN2 TRUE 109 -09-33003_tumorB BN2 0.8380287200511392 0 262,279,928,321,1049,773,355,196,936,704 0.184,0.206,0.222,0.275,0.284,0.304,0.325,0.384,0.392,0.441 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,ST2 29.941346592687,5.7869594620982 0 0 35.72830605478522 29.941346592687026 BN2 TRUE 110 -09-41082T EZB 0.7386410893571581 0 1092,6,882,334,120,45,303,764,883,182 0.145,0.188,0.236,0.237,0.238,0.239,0.279,0.29,0.317,0.317 ST2,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.1856560673677,31.3097154975455,6.89289514361024 0 0 42.38826670852343 31.309715497545504 ST2 FALSE 111 -09-41114T EZB 0.9096646246546373 0 1072,885,1095,892,886,680,648,1094,610,1102 0.121,0.159,0.168,0.181,0.191,0.192,0.193,0.196,0.202,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 51.4355021646982,5.10786642482584 0 0 56.54336858952401 51.435502164698164 EZB TRUE 112 -10-10826T EZB 1 0 761,37,53,89,256,430,880,48,931,159 0.198,0.203,0.218,0.23,0.234,0.248,0.252,0.26,0.286,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.9644858879228 0 0 41.96448588792279 41.96448588792279 EZB TRUE 113 -10-11584_tumorB EZB 1 0 31,210,927,271,360,211,917,1013,402,1034 0.225,0.262,0.271,0.301,0.311,0.317,0.332,0.382,0.42,0.431 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9908549888743 0 0 31.9908549888743 31.9908549888743 EZB TRUE 114 -10-11741_tumorB EZB 0.7971172766363178 0 1097,812,426,862,284,424,874,1006,753,407 0.138,0.203,0.212,0.27,0.298,0.319,0.324,0.339,0.35,0.364 EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB,EZB,EZB 30.8590417907428,7.85426012257421 0 0 38.71330191331705 30.85904179074284 EZB TRUE 115 -10-15025T EZB 1 0 406,754,18,182,756,190,1018,865,356,251 0.149,0.161,0.165,0.196,0.196,0.205,0.228,0.235,0.237,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1266283895568 0 0 51.126628389556835 51.126628389556835 EZB TRUE 116 -FL1016T2 EZB 1 0 78,675,287,1108,397,105,92,236,56,836 0.118,0.135,0.171,0.22,0.223,0.238,0.25,0.263,0.27,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1253711425695 0 0 50.12537114256953 50.12537114256953 EZB TRUE 117 -10-28165T EZB 1 0 831,596,379,269,732,687,979,369,136,592 0.252,0.28,0.292,0.295,0.324,0.325,0.355,0.364,0.389,0.398 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.164139250367 0 0 31.164139250367036 31.164139250367036 EZB TRUE 118 -10-31625T ST2 0.48908612704932675 0 1066,308,381,412,830,919,293,481,642,530 0.164,0.258,0.285,0.303,0.306,0.314,0.358,0.382,0.383,0.385 N1,ST2,ST2,ST2,ST2,BN2,MCD,BN2,ST2,N1 5.80373492317513,2.7949520204372,8.70494373831188,16.564368599424 0 0 33.86799928134819 16.564368599423968 BN2 FALSE 119 -10-32847T EZB 0.9156883588420189 0 882,6,182,303,942,1018,334,111,883,356 0.125,0.171,0.188,0.206,0.216,0.22,0.227,0.238,0.248,0.254 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 45.5768127602446,4.19646689341522 0 0 49.77327965365986 45.57681276024465 EZB TRUE 120 -10-36955_tumorB EZB 1 0 122,849,190,1014,601,756,868,18,164,639 0.116,0.122,0.25,0.26,0.272,0.274,0.279,0.291,0.318,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3010062961506 0 0 45.30100629615064 45.30100629615064 EZB TRUE 121 -10-36955_tumorA EZB 1 0 121,849,190,1014,756,868,18,601,639,754 0.116,0.135,0.241,0.244,0.269,0.281,0.283,0.287,0.305,0.323 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.9634903695642 0 0 44.96349036956424 44.96349036956424 EZB TRUE 122 -10-39294_tumorA BN2 0.6093653875735058 0 1061,506,455,1112,579,1045,293,530,799,919 0.271,0.272,0.301,0.313,0.337,0.337,0.341,0.37,0.385,0.405 BN2,BN2,MCD,BN2,N1,BN2,MCD,N1,BN2,BN2 18.5974942706987,6.25283521911367,5.66911688825311 0 0 30.519446378065467 18.59749427069869 N1 FALSE 123 -10-39294_tumorB MCD 0.5111309569432871 0 1044,43,747,779,525,205,935,554,696,803 0.235,0.252,0.276,0.288,0.293,0.336,0.338,0.353,0.368,0.369 MCD,ST2,ST2,MCD,MCD,MCD,EZB,ST2,MCD,BN2 2.71158531937857,2.96151348551784,16.8250491850829,10.4191483633889 0 0 32.91729635336824 16.825049185082886 N1 FALSE 124 -10-41170T EZB 1 0 35,635,364,48,931,820,256,30,1063,208 0.135,0.137,0.15,0.167,0.168,0.208,0.219,0.224,0.226,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.8364007862396 0 0 55.83640078623957 55.83640078623957 EZB TRUE 125 -1001-2027_FFPE BN2 0.5776108436900259 0 512,131,864,665,505,924,132,967,976,582 0.153,0.189,0.248,0.253,0.313,0.314,0.33,0.338,0.368,0.377 N1,BN2,BN2,BN2,N1,ST2,BN2,EZB,BN2,BN2 21.6884256419429,2.96183392196738,9.71437798489911,3.18387089571342 0 0 37.548508444522774 21.688425641942864 EZB FALSE 126 -1001-2035_FFPE BN2 0.5586507293263471 0 388,185,179,891,23,180,42,802,659,14 0.144,0.148,0.223,0.239,0.24,0.248,0.254,0.258,0.261,0.284 BN2,BN2,BN2,MCD,EZB,MCD,EZB,ST2,BN2,BN2 25.5374471165899,8.09446593546845,8.20742887118256,3.87338299772073 0 0 45.71272492096165 25.537447116589906 BN2 TRUE 127 -1001-2036_FFPE MCD 1 0 466,489,837,223,985,790,198,713,697,475 0.181,0.236,0.24,0.247,0.276,0.306,0.316,0.319,0.328,0.399 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.7280401446759 0 0 36.72804014467594 36.72804014467594 MCD TRUE 128 -1001-2038_FFPE EZB 1 0 977,364,30,12,209,171,208,125,8,35 0.196,0.21,0.212,0.212,0.218,0.224,0.225,0.257,0.261,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3304719863211 0 0 44.33047198632107 44.33047198632107 EZB TRUE 129 -1001-2039_FFPE MCD 1 0 39,1027,323,906,332,311,514,349,491,326 0.137,0.147,0.151,0.153,0.167,0.184,0.187,0.212,0.219,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.6891122685148 0 0 57.68911226851485 57.68911226851485 MCD TRUE 130 -1001-2040_FFPE BN2 0.4549356001892212 0 512,126,665,505,132,864,924,967,976,582 0.17,0.189,0.198,0.225,0.261,0.268,0.269,0.282,0.288,0.331 N1,EZB,BN2,N1,BN2,BN2,ST2,EZB,BN2,BN2 19.106740632711,8.84000637967936,10.3355857618702,3.71644820652874 0 0 41.998780980789306 19.10674063271103 BN2 TRUE 131 -1001-2040_plasma BN2 0.3642871626029762 0 976,971,505,131,665,126,512,782,806,967 0.159,0.221,0.241,0.261,0.325,0.33,0.33,0.333,0.368,0.382 BN2,EZB,N1,BN2,BN2,EZB,N1,EZB,N1,EZB 13.2144854006013,13.1706161074358,9.88981223357968 0 0 36.274913741616736 13.214485400601273 BN2 TRUE 132 -1003-2043_FFPE EZB 1 0 214,215,428,547,923,157,351,336,873,85 0.342,0.412,0.422,0.438,0.458,0.503,0.553,0.558,0.56,0.58 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 21.2906843974184 0 0 21.29068439741836 21.29068439741836 EZB TRUE 133 -1003-2043_plasma EZB 0.9115500096632847 0 389,398,615,1000,203,97,8,209,1088,171 0.14,0.232,0.245,0.292,0.314,0.342,0.345,0.357,0.377,0.379 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.18944559453203,32.869864105676 0 0 36.05930970020801 32.86986410567599 EZB TRUE 134 -1003-2046_FFPE BN2 0.5103307061540363 0 285,552,386,440,375,891,388,100,14,127 0.184,0.2,0.22,0.26,0.275,0.374,0.38,0.381,0.395,0.407 BN2,BN2,MCD,MCD,MCD,MCD,BN2,MCD,BN2,BN2 18.0472268251488,17.3165610236299 0 0 35.36378784877871 18.04722682514877 MCD FALSE 135 -1004-2033_FFPE EZB 1 0 181,257,732,255,979,291,853,272,68,596 0.153,0.158,0.166,0.168,0.176,0.198,0.219,0.231,0.24,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7439586219202 0 0 52.74395862192023 52.74395862192023 EZB TRUE 136 -1006-2010_FFPE N1 0.6823768850610115 0 325,523,41,174,1104,486,1099,496,516,854 0.157,0.167,0.235,0.299,0.311,0.326,0.338,0.376,0.426,0.477 ST2,N1,N1,N1,N1,ST2,N1,N1,N1,BN2 2.0967898783924,24.7625207510434,9.42931709691133 0 0 36.28862772634714 24.762520751043414 N1 TRUE 137 -1007-2013_FFPE EZB 1 0 292,427,219,220,82,600,593,307,1080,726 0.114,0.254,0.296,0.327,0.328,0.329,0.339,0.353,0.356,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5891867672658 0 0 36.5891867672658 36.5891867672658 EZB TRUE 138 -1011-2017_FFPE EZB 1 0 1,610,649,9,273,1072,648,374,664,112 0.119,0.159,0.167,0.173,0.187,0.2,0.206,0.214,0.218,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5057636819493 0 0 55.50576368194928 55.50576368194928 EZB TRUE 139 -1011-2019_FFPE EZB 1 0 13,84,107,83,102,418,277,1037,757,228 0.149,0.182,0.191,0.196,0.197,0.217,0.224,0.243,0.253,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.692749452467 0 0 48.692749452467005 48.692749452467005 EZB TRUE 140 -1011-2020_FFPE EZB 1 0 838,5,403,686,708,237,949,289,310,723 0.181,0.184,0.254,0.255,0.272,0.315,0.319,0.349,0.381,0.385 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.891187778204 0 0 36.89118777820397 36.89118777820397 EZB TRUE 141 -1012-2006_FFPE EZB 1 0 67,669,454,581,369,181,255,68,291,136 0.212,0.245,0.247,0.259,0.263,0.263,0.265,0.27,0.276,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.0639566692163 0 0 39.06395666921631 39.06395666921631 EZB TRUE 142 -1013-2006_FFPE EZB 0.9227801093225226 0 786,144,620,884,846,90,608,889,1058,545 0.197,0.211,0.308,0.334,0.349,0.362,0.38,0.399,0.407,0.408 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 29.2819365352409,2.4503648434037 0 0 31.732301378644593 29.28193653524089 EZB TRUE 143 -1013-2006_plasma EZB 0.7557408623342362 0 786,620,143,846,545,689,884,1118,1058,608 0.203,0.206,0.211,0.31,0.313,0.4,0.42,0.449,0.451,0.458 EZB,EZB,EZB,EZB,ST2,ST2,EZB,ST2,EZB,EZB 24.5101188333804,7.92179011180572 0 0 32.43190894518607 24.510118833380353 EZB TRUE 144 -1013-2010_FFPE EZB 0.7765758849314409 0 146,221,253,592,248,40,254,755,758,54 0.154,0.176,0.204,0.22,0.246,0.257,0.295,0.31,0.325,0.341 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,MCD 6.49315001006839,32.7735839112941,2.93594652137039 0 0 42.20268044273291 32.773583911294125 BN2 FALSE 145 -1013-2010_plasma EZB 0.8584052063199104 0 221,145,592,253,248,254,40,687,831,385 0.122,0.154,0.175,0.208,0.236,0.272,0.302,0.316,0.317,0.324 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 6.49315001006839,39.3641152276536 0 0 45.85726523772196 39.36411522765357 BN2 FALSE 146 -11-13204_tumorA MCD 1 0 520,739,314,202,513,729,459,897,288,1043 0.2,0.218,0.232,0.25,0.253,0.254,0.257,0.263,0.264,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.5458640316711 0 0 40.54586403167108 40.54586403167108 MCD TRUE 147 -11-14210T ST2 0.8303147028407138 0 1005,218,996,359,783,671,774,668,842,982 0.192,0.197,0.325,0.346,0.347,0.364,0.379,0.387,0.39,0.399 ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2,ST2,ST2 2.58279179762009,2.87801166052926,26.7211448282907 0 0 32.18194828644003 26.721144828290676 ST2 TRUE 148 -11-21727T EZB 1 0 603,73,1059,735,25,605,384,239,74,20 0.167,0.169,0.175,0.181,0.19,0.211,0.212,0.234,0.234,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.6213465191374 0 0 50.62134651913739 50.62134651913739 EZB TRUE 149 -11-31342_tumorA EZB 1 0 599,151,644,152,1037,866,418,613,776,27 0.132,0.158,0.181,0.189,0.197,0.205,0.216,0.221,0.223,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3283896857783 0 0 52.328389685778326 52.328389685778326 EZB TRUE 150 -11-31342_tumorB EZB 1 0 150,613,866,776,599,152,644,27,1037,98 0.158,0.168,0.17,0.173,0.184,0.191,0.227,0.228,0.254,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0921943200874 0 0 51.09219432008736 51.09219432008736 EZB TRUE 151 -11-31342_tumorC EZB 1 0 866,27,599,644,150,151,776,613,98,1088 0.16,0.17,0.181,0.183,0.189,0.191,0.196,0.205,0.215,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.8403648412573 0 0 52.840364841257305 52.840364841257305 EZB TRUE 152 -11-35935T BN2 0.7470874847535309 0 672,962,186,582,432,1106,967,924,571,840 0.189,0.226,0.233,0.253,0.26,0.267,0.306,0.309,0.315,0.328 BN2,BN2,BN2,BN2,BN2,BN2,EZB,ST2,EZB,BN2 28.5965893292218,6.44408338584206,3.23675785741589 0 0 38.27743057247976 28.59658932922181 BN2 TRUE 153 -12-17272_tumorA MCD 0.9033533116560285 0 457,383,445,1070,934,480,441,458,749,522 0.197,0.308,0.363,0.393,0.411,0.423,0.488,0.507,0.515,0.517 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 2.54329111049066,23.7720555823927 0 0 26.315346692883367 23.772055582392706 MCD TRUE 154 -12-17272_tumorB MCD 0.9027534489099935 0 450,350,312,176,778,990,918,417,500,1048 0.106,0.128,0.137,0.138,0.146,0.153,0.155,0.157,0.159,0.161 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.85539140481754,63.6395652592243 0 0 70.49495666404181 63.63956525922428 MCD TRUE 155 -13-26601T EZB 1 0 883,718,303,743,826,334,942,294,236,56 0.143,0.152,0.181,0.191,0.194,0.201,0.202,0.202,0.223,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.218712568929 0 0 53.21871256892903 53.21871256892903 EZB TRUE 156 -FL1017T2 EZB 1 0 719,537,265,351,377,206,133,647,233,51 0.33,0.355,0.399,0.422,0.47,0.497,0.503,0.508,0.515,0.518 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 22.6864684308848 0 0 22.686468430884805 22.686468430884805 EZB TRUE 157 -13-30451T BN2 1 0 357,564,393,968,58,651,1019,777,472,652 0.203,0.257,0.266,0.296,0.318,0.35,0.377,0.381,0.412,0.413 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 32.0969442966255 0 0 32.09694429662547 32.09694429662547 BN2 TRUE 158 -13-31210T EZB 1 0 1031,880,89,37,430,997,1063,761,820,972 0.143,0.174,0.179,0.19,0.195,0.201,0.206,0.207,0.236,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.7446138218305 0 0 51.744613821830505 51.744613821830505 EZB TRUE 159 -13-38657_tumorA ST2 0.8073254931380475 0 1029,161,366,602,583,842,668,671,359,425 0.131,0.161,0.193,0.194,0.218,0.218,0.22,0.235,0.24,0.243 ST2,ST2,BN2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 9.71636021554983,40.7125230539499 0 0 50.42888326949974 40.71252305394991 ST2 TRUE 160 -13-38657_tumorB ST2 0.7437683448936322 0 1029,160,602,842,671,359,668,366,996,785 0.141,0.161,0.161,0.166,0.177,0.179,0.181,0.211,0.231,0.255 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,EZB 10.2441118051117,3.91640138436852,41.1039824623232 0 0 55.264495651803436 41.103982462323174 ST2 TRUE 161 -13-42815T MCD 1 0 479,1046,260,769,916,956,920,467,169,918 0.266,0.274,0.286,0.297,0.311,0.312,0.325,0.326,0.331,0.337 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 32.8493693096532 0 0 32.84936930965318 32.84936930965318 MCD TRUE 162 -14-13959T EZB 1 0 184,833,61,629,74,1078,805,242,605,384 0.195,0.218,0.22,0.225,0.229,0.242,0.259,0.263,0.264,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4401721671438 0 0 42.4401721671438 42.4401721671438 EZB TRUE 163 -14-16281T EZB 1 0 1075,753,597,868,601,709,1006,21,874,250 0.15,0.184,0.203,0.226,0.233,0.235,0.25,0.255,0.261,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.446108644953 0 0 45.44610864495299 45.44610864495299 EZB TRUE 164 -14-20552_tumorA MCD 1 0 290,413,794,879,712,1015,17,297,1109,1035 0.148,0.151,0.16,0.16,0.161,0.162,0.166,0.18,0.186,0.186 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.5502334713295 0 0 60.55023347132949 60.55023347132949 MCD TRUE 165 -14-20552_tumorB MCD 1 0 246,767,762,17,851,879,434,804,712,297 0.103,0.116,0.129,0.13,0.136,0.16,0.169,0.173,0.175,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 70.3431710703099 0 0 70.34317107030985 70.34317107030985 MCD TRUE 166 -14-20962T EZB 1 0 677,266,852,72,191,20,746,239,96,226 0.11,0.184,0.197,0.199,0.203,0.22,0.221,0.226,0.231,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6689537617088 0 0 51.66895376170881 51.66895376170881 EZB TRUE 167 -14-24534_tumorB EZB 1 0 217,766,1064,216,908,823,471,249,819,235 0.169,0.184,0.204,0.217,0.218,0.285,0.292,0.3,0.324,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8637337256432 0 0 41.863733725643186 41.863733725643186 EZB TRUE 168 -14-25466T MCD 0.7646204231943748 0 1103,918,778,990,1046,433,312,286,260,479 0.119,0.131,0.143,0.153,0.158,0.164,0.164,0.167,0.175,0.177 ST2,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.99638221858212,49.9764376267813,8.38828758882389 0 0 65.36110743418733 49.97643762678132 MCD TRUE 169 -14-27873T MCD 0.7385450095896632 0 419,541,200,510,371,94,420,775,1082,567 0.154,0.169,0.234,0.25,0.263,0.274,0.275,0.285,0.295,0.303 MCD,MCD,MCD,MCD,EZB,EZB,MCD,EZB,MCD,MCD 10.9777653892699,31.0094438509937 0 0 41.98720924026358 31.009443850993662 MCD TRUE 170 -14-32442T EZB 1 0 208,30,977,209,256,53,364,48,129,35 0.114,0.125,0.139,0.175,0.182,0.189,0.209,0.223,0.224,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6563764071955 0 0 58.65637640719553 58.65637640719553 EZB TRUE 171 -14-33262T BN2 1 0 337,1028,58,999,646,404,261,517,548,543 0.203,0.237,0.365,0.383,0.425,0.428,0.43,0.434,0.438,0.441 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.344995503772 0 0 28.344995503772033 28.344995503772033 BN2 TRUE 172 -14-33436T BN2 0.7841564894834385 0 535,624,765,347,196,773,1049,301,506,1112 0.131,0.176,0.179,0.249,0.281,0.297,0.337,0.367,0.437,0.444 BN2,EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2 31.3879269174924,5.67635775308144,2.96334672500274 0 0 40.02763139557656 31.387926917492386 BN2 TRUE 173 -14-33798_tumorB N1 0.5301451158890936 0 523,137,325,346,41,828,486,1104,607,1099 0.292,0.299,0.327,0.415,0.42,0.483,0.487,0.505,0.516,0.534 N1,N1,ST2,ST2,N1,ST2,ST2,N1,ST2,N1 13.0055609845907,11.5265163557416 0 0 24.53207734033228 13.005560984590664 N1 TRUE 174 -14-35632T BN2 1 0 282,341,197,15,973,563,1077,488,331,315 0.119,0.123,0.144,0.161,0.184,0.188,0.188,0.192,0.199,0.211 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.6768950064588 0 0 60.67689500645882 60.67689500645882 BN2 TRUE 175 -14-36022T MCD 0.8178436665699491 0 450,155,990,350,500,312,778,1103,918,286 0.134,0.138,0.156,0.161,0.173,0.174,0.178,0.184,0.184,0.185 MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2,MCD,MCD 5.61934359233507,49.6816453789995,5.44612861333197 0 0 60.74711758466658 49.68164537899955 MCD TRUE 176 -14-37722T ST2 0.8254375915068647 0 682,1016,370,692,673,1053,1086,792,557,793 0.144,0.21,0.235,0.262,0.266,0.271,0.302,0.317,0.322,0.36 ST2,ST2,ST2,ST2,BN2,ST2,ST2,BN2,ST2,ST2 6.91120587582789,32.6803988430064 0 0 39.59160471883426 32.68039884300637 ST2 TRUE 177 -15-11617T ST2 0.463484873601641 0 787,361,770,965,1107,365,661,950,195,1009 0.143,0.163,0.195,0.201,0.218,0.236,0.239,0.243,0.267,0.311 EZB,ST2,BN2,EZB,ST2,ST2,EZB,EZB,ST2,ST2 5.13809841367045,20.2740357216887,21.9530432566587 0 0 47.36517739201783 21.953043256658685 ST2 TRUE 178 -15-13383T BN2 0.8262653653360833 0 659,472,185,127,666,651,23,1019,388,42 0.173,0.19,0.207,0.223,0.223,0.253,0.256,0.257,0.263,0.266 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB 36.4915954187678,7.67290299745813 0 0 44.16449841622588 36.49159541876775 BN2 TRUE 179 -15-13383_tumorB BN2 0.6776135139020748 0 42,100,742,185,127,179,388,484,472,552 0.113,0.162,0.17,0.203,0.248,0.269,0.28,0.29,0.317,0.339 EZB,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.6568681197668,8.88398185586137,6.17732708295643 0 0 46.71817705858462 31.656868119766823 MCD FALSE 180 -15-15757T EZB 1 0 255,291,257,136,68,213,50,732,723,853 0.115,0.146,0.148,0.153,0.187,0.209,0.214,0.216,0.222,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1699510405322 0 0 57.16995104053216 57.16995104053216 EZB TRUE 181 -15-21654T EZB 1 0 120,116,356,1018,882,406,941,754,942,251 0.188,0.196,0.2,0.201,0.206,0.21,0.231,0.241,0.248,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5647284792508 0 0 46.56472847925079 46.56472847925079 EZB TRUE 182 -15-21665_tumorA EZB 1 0 1051,655,1115,101,1012,410,49,392,981,588 0.174,0.183,0.184,0.206,0.211,0.215,0.217,0.234,0.242,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.8896251806646 0 0 47.88962518066464 47.88962518066464 EZB TRUE 183 -15-21665_tumorB EZB 1 0 629,833,61,163,384,605,74,1059,603,25 0.162,0.181,0.186,0.195,0.229,0.23,0.23,0.268,0.277,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.1122441930165 0 0 46.11224419301648 46.11224419301648 EZB TRUE 184 -15-24306T BN2 0.5382271924190616 0 127,388,180,179,42,100,659,742,23,891 0.148,0.188,0.203,0.207,0.208,0.248,0.264,0.273,0.276,0.286 BN2,BN2,MCD,BN2,EZB,MCD,BN2,BN2,EZB,MCD 24.3448333270245,8.43387140368231,12.4528139846644 0 0 45.231518715371166 24.344833327024464 BN2 TRUE 185 -15-26538T BN2 0.8226729299491123 0 1106,672,840,462,153,22,470,571,962,582 0.143,0.144,0.22,0.225,0.233,0.24,0.241,0.256,0.326,0.329 BN2,BN2,BN2,BN2,BN2,EZB,BN2,EZB,BN2,BN2 37.465409876498,8.07566545560936 0 0 45.5410753321074 37.465409876498036 BN2 TRUE 186 -15-31924T BN2 1 0 553,258,1098,355,109,10,562,531,86,352 0.15,0.178,0.203,0.254,0.302,0.308,0.354,0.356,0.374,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.6596818058629 0 0 38.6596818058629 38.6596818058629 BN2 TRUE 187 -15-34472T MCD 1 0 456,932,987,414,461,380,340,660,869,565 0.17,0.176,0.183,0.188,0.197,0.214,0.233,0.237,0.253,0.261 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 48.4184120023178 0 0 48.41841200231778 48.41841200231778 MCD TRUE 188 -15-38154T BN2 0.7218614567099477 0 539,88,470,22,358,840,570,462,87,487 0.133,0.161,0.235,0.235,0.265,0.278,0.293,0.307,0.315,0.343 EZB,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 30.6220221575306,11.7988909870742 0 0 42.420913144604796 30.622022157530587 BN2 TRUE 189 -POG721T EZB 1 0 18,756,754,116,868,406,122,849,1014,121 0.143,0.15,0.193,0.205,0.217,0.22,0.241,0.247,0.248,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.1034151532694 0 0 49.10341515326941 49.10341515326941 EZB TRUE 190 -15-43657T EZB 1 0 96,226,199,1039,677,167,1030,72,266,746 0.129,0.139,0.171,0.181,0.198,0.203,0.221,0.228,0.234,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.4595901602152 0 0 53.4595901602152 53.4595901602152 EZB TRUE 191 -15-43891T EZB 1 0 848,7,1117,706,71,877,740,229,861,93 0.201,0.217,0.224,0.225,0.23,0.231,0.239,0.239,0.247,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.638917555545 0 0 43.63891755554499 43.63891755554499 EZB TRUE 192 -16-12281_tumorB EZB 0.5435880830392247 0 813,534,444,94,371,510,420,170,775,567 0.173,0.201,0.212,0.261,0.309,0.312,0.324,0.325,0.356,0.363 EZB,MCD,EZB,EZB,EZB,MCD,MCD,MCD,EZB,MCD 20.3748240279078,17.107278070415 0 0 37.48210209832286 20.374824027907827 BN2 FALSE 193 -16-13732T ST2 0.5536546642246294 0 974,684,988,586,1091,810,1116,320,834,657 0.198,0.272,0.279,0.295,0.323,0.339,0.391,0.393,0.413,0.416 EZB,ST2,BN2,ST2,EZB,ST2,ST2,ST2,ST2,BN2 5.99082637655286,8.14334165140168,17.5322724948333 0 0 31.666440522787845 17.5322724948333 EZB FALSE 194 -16-16192T ST2 0.7872548063589758 0 1009,748,770,907,787,1065,875,1004,683,814 0.165,0.167,0.178,0.218,0.227,0.24,0.241,0.243,0.249,0.254 ST2,ST2,BN2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 5.60802482789287,4.40435392966649,37.0503942536785 0 0 47.06277301123787 37.050394253678505 ST2 TRUE 195 -16-16723T BN2 0.8067417558388709 0 773,535,173,765,279,1049,624,572,110,301 0.25,0.27,0.281,0.295,0.302,0.304,0.339,0.349,0.384,0.385 BN2,BN2,BN2,BN2,BN2,ST2,EZB,BN2,BN2,BN2 26.0296354687819,2.94767473474571,3.28782951483942 0 0 32.26513971836704 26.029635468781915 BN2 TRUE 196 -16-17861T BN2 1 0 341,282,175,1077,973,331,488,333,563,559 0.128,0.129,0.144,0.144,0.15,0.158,0.163,0.196,0.199,0.201 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.6527745673858 0 0 63.65277456738576 63.65277456738576 BN2 TRUE 197 -16-18029T MCD 1 0 837,790,489,697,44,475,128,466,223,856 0.195,0.235,0.238,0.246,0.271,0.296,0.316,0.324,0.33,0.331 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0314172605331 0 0 37.03141726053312 37.03141726053312 MCD TRUE 198 -16-23208T EZB 1 0 226,96,1039,191,1030,986,604,313,392,302 0.136,0.141,0.166,0.171,0.186,0.192,0.196,0.222,0.236,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7040259577729 0 0 54.70402595777287 54.70402595777287 EZB TRUE 199 -16-27074_tumorA MCD 0.9123866442886828 0 1082,541,201,419,170,775,510,420,567,532 0.164,0.166,0.181,0.228,0.234,0.252,0.254,0.268,0.268,0.277 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.9697600407878,41.3402273299507 0 0 45.309987370738526 41.34022732995072 MCD TRUE 200 -16-27074_tumorB MCD 0.9047126762047418 0 1082,200,532,541,775,567,510,420,448,859 0.167,0.181,0.196,0.238,0.242,0.251,0.269,0.27,0.282,0.292 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 4.12746397578924,39.1885177455343 0 0 43.31598172132354 39.1885177455343 MCD TRUE 201 -16-32248_tumorB MCD 1 0 147,729,324,520,280,739,288,376,314,897 0.25,0.262,0.279,0.313,0.329,0.346,0.347,0.353,0.358,0.375 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 31.7001005411927 0 0 31.700100541192725 31.700100541192725 MCD TRUE 202 -16-43741_tumorA EZB 1 0 1088,1000,97,615,152,644,27,8,98,83 0.175,0.184,0.21,0.224,0.243,0.248,0.256,0.262,0.268,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6581671066354 0 0 43.6581671066354 43.6581671066354 BN2 FALSE 203 -17-16667_tumorA MCD 1 0 467,881,920,956,494,678,260,526,580,433 0.151,0.158,0.174,0.183,0.184,0.196,0.208,0.213,0.222,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.0604630141775 0 0 53.06046301417747 53.06046301417747 MCD TRUE 204 -17-23504T MCD 0.7047096172690674 0 779,617,789,713,525,985,696,124,441,43 0.235,0.237,0.285,0.298,0.313,0.325,0.33,0.336,0.349,0.39 MCD,N1,MCD,MCD,MCD,MCD,MCD,N1,MCD,ST2 23.2918278052277,7.19773922450213,2.56210025911685 0 0 33.05166728884667 23.291827805227694 MCD TRUE 205 -17-33848_tumorB EZB 1 0 265,267,351,1083,877,720,619,983,848,374 0.206,0.212,0.248,0.292,0.314,0.315,0.348,0.395,0.408,0.412 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.6505128099552 0 0 33.65051280995519 33.65051280995519 EZB TRUE 206 -17-36275_tumorB ST2 0.9042774476401273 0 483,656,1057,728,362,807,822,263,60,797 0.129,0.211,0.213,0.253,0.274,0.344,0.366,0.387,0.391,0.396 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.64930984203628,34.4745152343841 0 0 38.12382507642033 34.47451523438406 ST2 TRUE 207 -17-40409_tumorB EZB 1 0 171,30,977,256,53,209,364,48,35,129 0.114,0.116,0.149,0.17,0.179,0.188,0.198,0.209,0.212,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.869713237662 0 0 59.86971323766204 59.86971323766204 EZB TRUE 208 -17-40409_tumorA EZB 1 0 977,8,171,12,208,30,129,97,1000,256 0.146,0.173,0.175,0.178,0.188,0.192,0.218,0.231,0.246,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3669279099246 0 0 51.366927909924634 51.366927909924634 EZB TRUE 209 -18-36878_tumorB EZB 1 0 927,271,917,1013,114,360,79,402,211,306 0.138,0.14,0.211,0.253,0.262,0.271,0.273,0.286,0.349,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4676364384655 0 0 43.46763643846554 43.46763643846554 EZB TRUE 210 -19-16466_tumorA EZB 1 0 360,233,1034,51,31,114,927,210,306,402 0.205,0.254,0.256,0.257,0.292,0.317,0.322,0.349,0.364,0.369 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.6265428777636 0 0 34.626542877763576 34.626542877763576 EZB TRUE 211 -19-25121_tumorA BN2 0.8577681528182167 0 77,487,634,633,390,493,1069,358,636,399 0.139,0.159,0.164,0.186,0.208,0.209,0.225,0.242,0.267,0.281 EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2628992219034,7.17368912631419 0 0 50.4365883482176 43.2628992219034 BN2 TRUE 212 -2001-2024_FFPE EZB 1 0 723,50,291,257,255,68,853,181,949,272 0.129,0.139,0.181,0.189,0.199,0.201,0.204,0.209,0.237,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8507952759654 0 0 53.85079527596538 53.85079527596538 EZB TRUE 213 -2001-2026_FFPE EZB 1 0 215,923,428,133,336,28,921,4,85,32 0.216,0.223,0.331,0.342,0.408,0.484,0.485,0.485,0.492,0.505 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.7110517439094 0 0 27.7110517439094 27.7110517439094 EZB TRUE 214 -2001-2026_plasma EZB 1 0 923,214,428,133,336,4,398,234,921,547 0.181,0.216,0.244,0.412,0.494,0.524,0.532,0.54,0.542,0.576 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.9322981051048 0 0 27.932298105104767 27.932298105104767 EZB TRUE 215 -2004-2030_FFPE EZB 1 0 217,766,1064,168,823,249,908,1100,471,847 0.148,0.171,0.2,0.217,0.222,0.225,0.252,0.311,0.313,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.498259145426 0 0 44.49825914542597 44.49825914542597 EZB TRUE 216 -2004-2030_plasma EZB 1 0 216,766,168,1064,908,823,249,471,1100,235 0.148,0.15,0.169,0.184,0.226,0.24,0.25,0.296,0.33,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6865835537842 0 0 46.68658355378417 46.68658355378417 EZB TRUE 217 -2004-2033_plasma ST2 1 0 1005,148,996,774,982,825,359,1068,671,940 0.185,0.197,0.281,0.288,0.322,0.323,0.327,0.327,0.331,0.345 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.733798343447 0 0 35.733798343447 35.733798343447 ST2 TRUE 218 -4002-2003_FFPE EZB 1 0 220,593,307,138,870,292,427,726,47,62 0.184,0.193,0.257,0.296,0.298,0.308,0.315,0.333,0.335,0.345 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5615203512308 0 0 36.561520351230754 36.561520351230754 EZB TRUE 219 -4002-2003_plasma EZB 1 0 307,219,818,593,62,427,585,138,292,870 0.175,0.184,0.268,0.272,0.28,0.284,0.306,0.327,0.337,0.356 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.7325524505238 0 0 37.73255245052384 37.73255245052384 EZB TRUE 220 -7004-2002_FFPE EZB 0.6969348321757893 0 146,592,145,253,248,254,831,687,385,975 0.122,0.159,0.176,0.219,0.235,0.269,0.295,0.296,0.311,0.322 BN2,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 13.8862463189714,31.9330948434083 0 0 45.819341162379715 31.93309484340834 EZB TRUE 221 -75-10141_tumorA EZB 0.9097921097897486 0 329,277,1037,418,13,245,368,227,587,150 0.171,0.198,0.201,0.227,0.227,0.238,0.243,0.249,0.265,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.01599212660049,40.503307872992 0 0 44.51929999959249 40.503307872992 EZB TRUE 222 -8005-2002_FFPE MCD 1 0 466,985,790,697,128,475,837,198,713,489 0.167,0.209,0.23,0.244,0.247,0.299,0.311,0.33,0.335,0.337 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.8020413714182 0 0 38.80204137141817 38.80204137141817 MCD TRUE 223 -8005-2003_FFPE EZB 1 0 225,268,588,1115,240,259,69,244,183,983 0.166,0.168,0.19,0.191,0.208,0.221,0.236,0.26,0.27,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1989553287302 0 0 47.19895532873021 47.19895532873021 EZB TRUE 224 -8005-2003_plasma EZB 1 0 224,268,240,69,1115,244,588,1051,183,343 0.166,0.173,0.179,0.18,0.2,0.203,0.24,0.253,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8493300148829 0 0 48.84933001488294 48.84933001488294 EZB TRUE 225 -8006-2002_plasma EZB 1 0 96,199,191,1039,1030,986,604,677,167,313 0.116,0.136,0.139,0.153,0.209,0.227,0.23,0.231,0.235,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7038613565271 0 0 55.703861356527064 55.703861356527064 EZB TRUE 226 -89-62169T EZB 0.9179515205692883 0 368,587,995,863,329,1074,618,1079,1056,867 0.114,0.143,0.15,0.171,0.179,0.185,0.19,0.199,0.2,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 54.522952713047,4.8733786740761 0 0 59.396331387123155 54.52295271304705 BN2 FALSE 227 -92-38267_tumorA EZB 1 0 631,102,757,1079,93,740,274,863,387,1074 0.126,0.164,0.17,0.174,0.175,0.186,0.199,0.201,0.203,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.2781176057449 0 0 56.2781176057449 56.2781176057449 EZB TRUE 228 -92-38267_tumorB EZB 1 0 711,71,374,648,706,892,680,1095,861,720 0.114,0.134,0.156,0.156,0.16,0.166,0.168,0.181,0.221,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.723218127388 0 0 61.72321812738798 61.72321812738798 EZB TRUE 229 -94-25764_tumorB EZB 0.6843097668846683 0 55,54,3,478,771,40,782,800,253,733 0.127,0.196,0.203,0.248,0.289,0.291,0.308,0.316,0.338,0.339 MCD,MCD,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.1615041614403,12.991648291207 0 0 41.153152452647355 28.161504161440327 EZB TRUE 230 -95-32814T EZB 1 0 242,953,73,74,805,605,384,594,149,61 0.154,0.174,0.197,0.215,0.249,0.26,0.261,0.266,0.266,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.9075740294213 0 0 44.90757402942135 44.90757402942135 EZB TRUE 231 -FL1007T2 EZB 0.610448973884648 0 1085,432,771,478,971,3,962,505,967,582 0.423,0.449,0.498,0.508,0.522,0.535,0.555,0.559,0.565,0.565 EZB,BN2,EZB,EZB,EZB,EZB,BN2,N1,EZB,BN2 5.79981450280603,11.8943822062147,1.79044937126293 0 0 19.484646080283643 11.89438220621468 BN2 FALSE 232 -96-31596T EZB 1 0 51,1034,211,939,719,360,31,960,537,114 0.108,0.127,0.254,0.274,0.289,0.352,0.366,0.38,0.415,0.443 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.0174418434535 0 0 41.017441843453454 41.017441843453454 EZB TRUE 233 -97-18502_tumorA EZB 1 0 4,235,921,471,76,908,336,81,823,1100 0.133,0.152,0.203,0.207,0.246,0.269,0.324,0.341,0.343,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.4216932431238 0 0 43.421693243123755 43.421693243123755 EZB TRUE 234 -97-18502_tumorB EZB 1 0 234,471,4,76,908,921,823,766,1100,168 0.152,0.156,0.174,0.218,0.218,0.219,0.293,0.298,0.305,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5463363660062 0 0 45.54633636600621 45.54633636600621 EZB TRUE 235 -FL1004T2 EZB 1 0 294,826,718,56,156,247,764,334,883,117 0.125,0.138,0.175,0.18,0.223,0.228,0.246,0.262,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8881942482066 0 0 50.88819424820656 50.88819424820656 EZB TRUE 236 -98-28290T EZB 1 0 289,707,686,963,36,708,887,972,403,304 0.154,0.183,0.187,0.188,0.209,0.214,0.221,0.222,0.223,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6868524365022 0 0 49.68685243650221 49.68685243650221 EZB TRUE 237 -99-13280T EZB 1 0 981,737,259,588,101,1115,1039,604,392,183 0.198,0.25,0.258,0.288,0.313,0.315,0.317,0.318,0.319,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.32264384324 0 0 35.32264384323998 35.32264384323998 EZB TRUE 238 -FL1010T2 EZB 1 0 20,852,25,603,266,1059,746,384,605,72 0.12,0.143,0.148,0.191,0.194,0.195,0.213,0.215,0.216,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1534162947477 0 0 56.15341629474765 56.15341629474765 EZB TRUE 239 -CABN-0003P EZB 1 0 268,69,244,343,225,224,243,664,1115,710 0.148,0.158,0.174,0.177,0.179,0.208,0.212,0.267,0.276,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.360512962291 0 0 50.360512962291004 50.360512962291004 EZB TRUE 240 -CABN0001_2015-08-11 EZB 0.8700911833247835 0 1050,645,643,975,581,385,454,429,369,67 0.116,0.139,0.149,0.182,0.19,0.201,0.234,0.234,0.235,0.235 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.1696389587045,7.19195978160128 0 0 55.36159874030575 48.169638958704475 EZB TRUE 241 -CABN0002_2015-08-19 EZB 1 0 953,231,805,594,74,73,989,29,80,163 0.145,0.154,0.195,0.214,0.231,0.245,0.247,0.254,0.261,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2153619850813 0 0 47.21536198508126 47.21536198508126 EZB TRUE 242 -CABN0003_2016-07-05 EZB 0.9187334630845931 0 343,244,69,710,240,268,225,693,273,664 0.169,0.176,0.195,0.206,0.212,0.26,0.272,0.281,0.313,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.55363676975035,40.174531116245 0 0 43.728167885995404 40.17453111624505 EZB TRUE 243 -CABN0003_2013-11-28 EZB 1 0 69,240,243,343,225,268,224,710,1051,1115 0.124,0.174,0.176,0.197,0.203,0.218,0.26,0.281,0.298,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.2177276231064 0 0 48.21772762310637 48.21772762310637 EZB TRUE 244 -CAGJ0001_Biopsy EZB 0.8932674607461879 0 587,329,227,857,368,1056,222,595,995,277 0.182,0.193,0.209,0.211,0.219,0.23,0.238,0.252,0.259,0.275 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.7772188626374,39.9815669400415 0 0 44.75878580267893 39.98156694004153 EZB TRUE 245 -CAHN0001_2016-07-13 MCD 1 0 166,767,762,851,17,879,434,804,712,297 0.103,0.113,0.127,0.133,0.133,0.161,0.168,0.174,0.176,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 70.5422188593448 0 0 70.54221885934476 70.54221885934476 MCD TRUE 246 -CALM0002_Biopsy EZB 1 0 56,826,294,1108,236,718,78,675,156,92 0.15,0.206,0.215,0.225,0.228,0.254,0.262,0.262,0.267,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.055148526797 0 0 44.05514852679705 44.05514852679705 EZB TRUE 247 -CAMP-0009P EZB 0.7597697294960183 0 592,221,146,145,758,253,831,755,269,40 0.202,0.235,0.236,0.246,0.319,0.34,0.345,0.35,0.372,0.392 EZB,EZB,BN2,BN2,EZB,EZB,EZB,EZB,EZB,EZB 8.30660958332184,26.2710877480566 0 0 34.577697331378424 26.27108774805658 EZB TRUE 248 -CAMP0009_2016-07-04 EZB 1 0 1064,216,217,509,766,168,847,823,638,736 0.206,0.225,0.25,0.277,0.292,0.3,0.318,0.337,0.361,0.363 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.3022090524888 0 0 35.302209052488806 35.302209052488806 EZB TRUE 249 -CAMP0010_2017-07-18 EZB 1 0 21,709,938,597,251,816,941,868,356,164 0.131,0.163,0.164,0.168,0.181,0.184,0.23,0.235,0.241,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.2337357041391 0 0 53.23373570413906 53.23373570413906 EZB TRUE 250 -CAMP0010-Biopsy EZB 1 0 938,356,709,941,250,21,597,868,756,116 0.159,0.172,0.18,0.18,0.181,0.212,0.216,0.226,0.234,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9736434219218 0 0 50.97364342192176 50.97364342192176 EZB TRUE 251 -CARM0001_2016-03-24 EZB 1 0 316,912,409,858,884,62,889,90,818,1058 0.172,0.231,0.235,0.316,0.342,0.359,0.38,0.412,0.426,0.46 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.8506409489035 0 0 32.850640948903504 32.850640948903504 EZB TRUE 252 -CASA0002_2016-08-24 EZB 0.6722953399272144 0 145,254,146,221,40,592,54,643,385,975 0.204,0.205,0.208,0.219,0.244,0.277,0.288,0.303,0.303,0.303 BN2,EZB,BN2,EZB,EZB,EZB,MCD,EZB,EZB,EZB 9.71069038502009,27.0548079108881,3.47694621340101 0 0 40.24244450930916 27.054807910888062 EZB TRUE 253 -CASA0002_2015-03-10 EZB 0.8988241572910893 0 643,253,975,385,645,1050,241,581,369,221 0.198,0.205,0.206,0.212,0.225,0.233,0.245,0.248,0.265,0.269 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 39.3973445347426,4.4347490011942 0 0 43.83209353593685 39.39734453474264 EZB TRUE 254 -CATC0001_2016-01-11 EZB 1 0 181,291,257,136,68,213,50,723,669,853 0.115,0.131,0.154,0.168,0.172,0.199,0.2,0.215,0.223,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1069313667673 0 0 58.10693136676735 58.10693136676735 EZB TRUE 255 -CATC0003_Biopsy EZB 1 0 53,48,208,30,171,35,364,125,977,931 0.12,0.162,0.17,0.175,0.182,0.185,0.207,0.219,0.219,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.4521067599403 0 0 55.45210675994035 55.45210675994035 EZB TRUE 256 -DFCIDL009-Tumor EZB 1 0 181,255,136,291,853,213,723,272,979,50 0.148,0.154,0.158,0.17,0.176,0.189,0.192,0.201,0.209,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1045516586617 0 0 56.10455165866166 56.10455165866166 EZB TRUE 257 -DLBCL-DFCI_DLBCL_Goe08-Tumor BN2 1 0 1098,553,187,109,10,352,531,355,463,562 0.165,0.167,0.178,0.225,0.241,0.302,0.319,0.332,0.346,0.347 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.4763304409581 0 0 41.476330440958066 41.476330440958066 BN2 TRUE 258 -DLBCL-DFCI_DLBCL_Goe16-Tumor EZB 1 0 588,737,1115,224,183,238,981,225,619,268 0.131,0.179,0.193,0.221,0.256,0.258,0.262,0.269,0.277,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3055156884276 0 0 45.30551568842758 45.30551568842758 EZB TRUE 259 -DLBCL-MC_F089_MLB-Tumor MCD 0.8272826232142025 0 479,956,1046,169,1103,433,918,204,467,778 0.124,0.129,0.164,0.175,0.194,0.198,0.201,0.208,0.209,0.213 MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD,MCD,BN2 4.70522253769687,47.2399907048561,5.15738915627027 0 0 57.10260239882328 47.23999070485614 MCD TRUE 260 -DLBCL-MC_F132_MLM-Tumor BN2 1 0 404,499,543,517,527,555,1001,546,815,929 0.12,0.157,0.164,0.169,0.225,0.23,0.241,0.249,0.265,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.184891421207 0 0 51.184891421207 51.184891421207 BN2 TRUE 261 -DLBCL-MC_F139_AD-Tumor BN2 0.8977977572737798 0 928,110,321,1049,279,773,936,355,196,765 0.146,0.184,0.203,0.249,0.272,0.289,0.312,0.34,0.408,0.423 BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2 35.299798754862,4.01840901394787 0 0 39.3182077688099 35.29979875486203 BN2 TRUE 262 -DLBCL-MC_F139_LJF-Tumor ST2 0.873778215570892 0 362,822,656,807,483,1081,728,207,703,60 0.272,0.289,0.301,0.311,0.359,0.372,0.375,0.387,0.41,0.44 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 3.6731736349939,25.4277747599872 0 0 29.100948394981128 25.427774759987223 ST2 TRUE 263 -DLBCL-MC_F262_WLC-Tumor BN2 1 0 640,1111,957,578,871,318,401,1062,482,15 0.128,0.166,0.211,0.215,0.219,0.22,0.245,0.252,0.264,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.8873683987927 0 0 47.88736839879268 47.88736839879268 BN2 TRUE 264 -DLBCL-MC_F300_TBF-Tumor EZB 1 0 351,206,267,877,1083,157,720,848,1117,619 0.176,0.206,0.311,0.338,0.341,0.399,0.409,0.42,0.434,0.453 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.4684879755381 0 0 31.46848797553813 31.46848797553813 EZB TRUE 265 -DLBCL-MC_F344_CJS-Tumor EZB 1 0 72,746,852,677,20,167,239,191,25,96 0.126,0.139,0.152,0.176,0.177,0.184,0.194,0.234,0.236,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.0128318878684 0 0 56.01283188786842 56.01283188786842 EZB TRUE 266 -DLBCL-MC_F349_BJJ-Tumor EZB 1 0 720,206,619,877,983,374,265,229,351,711 0.205,0.212,0.281,0.29,0.298,0.303,0.311,0.321,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.6739610532123 0 0 35.67396105321234 35.67396105321234 EZB TRUE 267 -DLBCL-MC_F358_WDS-Tumor EZB 1 0 240,224,225,69,343,244,1115,664,588,243 0.148,0.168,0.173,0.197,0.215,0.218,0.251,0.256,0.258,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.3797695733563 0 0 48.37976957335629 48.37976957335629 EZB TRUE 268 -DLBCL-MC_F362_MJB1-Tumor EZB 1 0 819,118,81,831,270,248,592,379,687,221 0.239,0.295,0.33,0.336,0.338,0.372,0.379,0.422,0.435,0.437 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.8097878401432 0 0 28.809787840143194 28.809787840143194 EZB TRUE 269 -DLBCL-MC_F502_RFK-Tumor EZB 1 0 81,269,819,118,234,235,4,908,471,168 0.23,0.338,0.344,0.403,0.405,0.419,0.432,0.448,0.453,0.459 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.475270925855 0 0 26.475270925854957 26.475270925854957 EZB TRUE 270 -DLBCL-MC_F606_DMJ-Tumor EZB 1 0 210,927,917,1013,79,402,360,114,306,211 0.14,0.156,0.191,0.225,0.235,0.269,0.29,0.301,0.348,0.378 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.499558525961 0 0 43.49955852596099 43.49955852596099 EZB TRUE 271 -DLBCL-MC_F739_AMA3-Tumor EZB 1 0 853,724,257,979,723,136,213,181,255,949 0.136,0.142,0.201,0.213,0.219,0.231,0.24,0.249,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0412381999253 0 0 49.04123819992527 49.04123819992527 EZB TRUE 272 -DLBCL-MC_F815_BAT-Tumor EZB 1 0 649,9,1,610,139,664,343,710,1020,1072 0.142,0.157,0.172,0.183,0.187,0.197,0.259,0.265,0.266,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.2088435321814 0 0 50.20884353218145 50.20884353218145 EZB TRUE 273 -DLBCL-RICOVER_1013-Tumor EZB 1 0 947,1074,618,863,995,1079,631,228,93,740 0.126,0.147,0.169,0.177,0.181,0.185,0.196,0.199,0.205,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.8959446621376 0 0 56.89594466213762 56.89594466213762 EZB TRUE 274 -DLBCL-RICOVER_1018-Tumor ST2 0.45447717859059106 0 850,300,792,476,903,370,1089,1086,692,177 0.307,0.33,0.366,0.392,0.443,0.452,0.539,0.544,0.577,0.58 EZB,ST2,BN2,EZB,EZB,ST2,MCD,ST2,ST2,ST2 2.73368651261012,8.05935146403868,1.85467131081335,10.5368556676494 0 0 23.184564955111522 10.536855667649379 ST2 TRUE 275 -DLBCL-RICOVER_1032-Tumor MCD 1 0 1008,468,443,1076,451,33,900,328,1021,568 0.12,0.12,0.165,0.173,0.181,0.189,0.198,0.202,0.207,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.7629243689948 0 0 58.76292436899479 58.76292436899479 MCD TRUE 276 -DLBCL-RICOVER_1046-Tumor EZB 0.9060871287436413 0 1079,329,13,863,222,368,227,995,102,140 0.179,0.183,0.187,0.192,0.198,0.2,0.213,0.213,0.221,0.224 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.70099192496062,45.3559583319225 0 0 50.05695025688311 45.355958331922494 EZB TRUE 277 -DLBCL-RICOVER_1061-Tumor ST2 0.8333432133734309 0 910,911,904,843,394,372,1081,925,60,339 0.161,0.191,0.25,0.257,0.27,0.281,0.349,0.367,0.392,0.398 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.20752719071787,31.0398439867139 0 0 37.24737117743179 31.039843986713915 ST2 TRUE 278 -DLBCL-RICOVER_1081-Tumor BN2 0.8066679375393261 0 110,262,773,1049,928,196,704,321,560,355 0.206,0.272,0.28,0.291,0.292,0.302,0.347,0.373,0.412,0.414 BN2,BN2,BN2,ST2,BN2,BN2,ST2,BN2,BN2,BN2 26.3587755027982,6.31734099589823 0 0 32.676116498696416 26.358775502798185 BN2 TRUE 279 -DLBCL-RICOVER_1106-Tumor MCD 1 0 324,439,966,729,106,660,288,565,679,991 0.159,0.171,0.175,0.18,0.202,0.207,0.231,0.245,0.253,0.256 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.4299484611692 0 0 49.42994846116923 49.42994846116923 MCD TRUE 280 -DLBCL-RICOVER_1138-Tumor ST2 0.8607259529336008 0 59,24,396,490,412,759,970,946,830,529 0.104,0.233,0.238,0.312,0.365,0.379,0.386,0.401,0.418,0.427 ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2 5.08685086914988,31.4371891533585 0 0 36.52404002250836 31.43718915335848 ST2 TRUE 281 -DLBCL-RICOVER_1150-Tumor BN2 1 0 175,341,197,973,1077,15,331,488,563,315 0.119,0.123,0.129,0.165,0.172,0.181,0.181,0.187,0.2,0.205 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 62.3723661255734 0 0 62.37236612557339 62.37236612557339 BN2 TRUE 282 -DLBCL-RICOVER_1192-Tumor MCD 1 0 447,550,1036,469,326,416,349,821,491,299 0.157,0.159,0.165,0.17,0.183,0.204,0.204,0.209,0.219,0.233 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.5772909016993 0 0 53.57729090169934 53.57729090169934 MCD TRUE 283 -DLBCL-RICOVER_1199-Tumor EZB 0.87665269547303 0 426,812,816,21,753,1097,1075,250,597,115 0.199,0.201,0.226,0.241,0.26,0.262,0.263,0.27,0.278,0.298 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.6619422926716,5.01772763457238 0 0 40.679669927243964 35.66194229267158 EZB TRUE 284 -DLBCL-RICOVER_120-Tumor MCD 0.713852345248543 0 386,135,375,440,552,330,100,891,845,388 0.144,0.184,0.218,0.222,0.283,0.396,0.441,0.457,0.461,0.464 MCD,MCD,MCD,MCD,BN2,BN2,MCD,MCD,BN2,BN2 10.3839271286763,25.9047754213957 0 0 36.288702550071974 25.904775421395662 BN2 FALSE 285 -DLBCL-RICOVER_1204-Tumor MCD 0.8040747505171887 0 433,990,1103,169,918,176,778,155,450,312 0.129,0.139,0.163,0.167,0.18,0.185,0.186,0.191,0.194,0.202 MCD,MCD,ST2,MCD,MCD,MCD,BN2,MCD,MCD,MCD 5.37139877917413,47.2178616938554,6.13396353504237 0 0 58.723224008071874 47.217861693855376 MCD TRUE 286 -DLBCL-RICOVER_1210-Tumor EZB 1 0 675,78,397,117,105,1108,92,738,317,91 0.14,0.158,0.165,0.171,0.172,0.177,0.188,0.231,0.247,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.5347508253772 0 0 54.53475082537723 54.53475082537723 EZB TRUE 287 -DLBCL-RICOVER_1219-Tumor MCD 1 0 991,897,520,106,314,739,459,729,439,280 0.152,0.155,0.17,0.177,0.18,0.184,0.192,0.207,0.208,0.231 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.7752429862631 0 0 54.775242986263116 54.775242986263116 MCD TRUE 288 -DLBCL-RICOVER_1235-Tumor EZB 1 0 963,237,36,887,707,304,686,708,403,750 0.149,0.154,0.166,0.168,0.173,0.191,0.199,0.209,0.225,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7756097354918 0 0 54.77560973549181 54.77560973549181 EZB TRUE 289 -DLBCL-RICOVER_1248-Tumor MCD 1 0 1035,794,165,413,108,297,1015,17,879,712 0.139,0.144,0.148,0.148,0.168,0.184,0.194,0.198,0.208,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.8695429461506 0 0 58.86954294615059 58.86954294615059 MCD TRUE 290 -DLBCL-RICOVER_126-Tumor EZB 1 0 255,68,181,257,50,213,136,723,669,734 0.131,0.145,0.146,0.17,0.173,0.181,0.198,0.203,0.205,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.9968801160118 0 0 57.996880116011795 57.996880116011795 EZB TRUE 291 -DLBCL-RICOVER_1263-Tumor EZB 1 0 138,427,219,600,82,220,1080,593,307,726 0.114,0.251,0.308,0.326,0.329,0.337,0.345,0.353,0.359,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.2853949408711 0 0 36.28539494087111 36.28539494087111 EZB TRUE 292 -DLBCL-RICOVER_1278-Tumor BN2 0.6215450220923513 0 919,530,1061,481,1066,123,799,119,506,698 0.192,0.219,0.252,0.263,0.338,0.341,0.35,0.358,0.363,0.388 BN2,N1,BN2,BN2,N1,N1,BN2,BN2,BN2,ST2 21.40069926386,10.4557300114065,2.57502541134223 0 0 34.43145468660875 21.40069926386003 MCD FALSE 293 -DLBCL-RICOVER_1283-Tumor EZB 1 0 826,236,718,56,156,247,883,334,764,303 0.114,0.125,0.159,0.165,0.202,0.215,0.243,0.254,0.262,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2689466252525 0 0 54.268946625252454 54.268946625252454 EZB TRUE 294 -DLBCL-RICOVER_1289-Tumor MCD 0.8661812929741975 0 621,878,452,978,961,1101,367,465,902,33 0.152,0.164,0.167,0.189,0.217,0.218,0.236,0.248,0.248,0.264 ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.6630267585941,6.59112719813996 0 0 49.254153956734015 42.66302675859406 MCD TRUE 295 -DLBCL-RICOVER_1293-Tumor MCD 1 0 549,453,494,298,920,526,916,580,1042,467 0.127,0.14,0.164,0.167,0.183,0.186,0.188,0.198,0.206,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.036788886276 0 0 58.03678888627597 58.03678888627597 MCD TRUE 296 -DLBCL-RICOVER_1299-Tumor MCD 1 0 413,794,528,17,804,108,166,246,165,290 0.136,0.143,0.145,0.151,0.166,0.177,0.177,0.18,0.18,0.184 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.7201160155384 0 0 61.72011601553839 61.72011601553839 MCD TRUE 297 -DLBCL-RICOVER_134-Tumor MCD 1 0 296,549,916,453,443,1076,494,328,900,526 0.167,0.171,0.18,0.19,0.2,0.229,0.229,0.234,0.24,0.241 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.0231623882177 0 0 49.02316238821772 49.02316238821772 MCD TRUE 298 -DLBCL-RICOVER_150-Tumor MCD 0.904411316246163 0 1110,690,447,721,469,937,722,283,511,550 0.12,0.144,0.179,0.182,0.185,0.193,0.219,0.233,0.235,0.251 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.1682365052103,48.8992148106766 0 0 54.06745131588692 48.899214810676625 MCD TRUE 299 -DLBCL-RICOVER_151-Tumor ST2 0.6760360713215876 0 792,275,370,1086,476,692,850,148,177,982 0.281,0.33,0.339,0.35,0.366,0.397,0.454,0.469,0.469,0.478 BN2,ST2,ST2,ST2,EZB,ST2,EZB,ST2,ST2,ST2 3.55482125265656,4.9366382750611,17.7196670083691 0 0 26.211126536086766 17.71966700836911 ST2 TRUE 300 -DLBCL-RICOVER_181-Tumor BN2 0.9134050190474167 0 777,535,572,694,173,196,624,765,393,968 0.276,0.336,0.343,0.362,0.367,0.385,0.436,0.444,0.454,0.464 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 24.2002948662234,2.29429883708438 0 0 26.494593703307824 24.200294866223448 BN2 TRUE 301 -DLBCL-RICOVER_208-Tumor EZB 1 0 313,986,1030,49,1012,392,604,101,199,752 0.152,0.161,0.194,0.196,0.201,0.205,0.207,0.223,0.251,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7704694544831 0 0 49.77046945448312 49.77046945448312 EZB TRUE 302 -DLBCL-RICOVER_267-Tumor EZB 1 0 883,942,334,156,882,6,743,120,718,1018 0.143,0.154,0.165,0.181,0.182,0.193,0.193,0.206,0.212,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.9464489506231 0 0 54.94644895062312 54.94644895062312 EZB TRUE 303 -DLBCL-RICOVER_269-Tumor EZB 1 0 887,750,36,963,289,310,653,707,237,708 0.126,0.161,0.172,0.183,0.191,0.205,0.224,0.229,0.245,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3960274822473 0 0 52.3960274822473 52.3960274822473 EZB TRUE 304 -DLBCL-RICOVER_290-Tumor BN2 1 0 75,702,542,559,354,998,796,945,1023,591 0.129,0.17,0.178,0.178,0.18,0.182,0.189,0.193,0.199,0.202 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.3395738819857 0 0 56.339573881985736 56.339573881985736 BN2 TRUE 305 -DLBCL-RICOVER_299-Tumor EZB 1 0 85,402,28,32,360,79,336,927,271,210 0.181,0.186,0.244,0.257,0.278,0.311,0.314,0.325,0.348,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.6222609151148 0 0 37.62226091511475 37.62226091511475 EZB TRUE 306 -DLBCL-RICOVER_320-Tumor EZB 1 0 220,818,585,219,427,62,912,593,138,292 0.175,0.193,0.238,0.257,0.259,0.267,0.337,0.347,0.353,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0360116843802 0 0 38.03601168438017 38.03601168438017 EZB TRUE 307 -DLBCL-RICOVER_336-Tumor ST2 0.7719313082927433 0 642,412,119,1066,381,830,396,915,793,557 0.24,0.251,0.258,0.296,0.332,0.339,0.346,0.366,0.397,0.43 ST2,ST2,BN2,N1,ST2,ST2,ST2,ST2,ST2,ST2 3.87872810019165,3.38192340836816,24.5747199061164 0 0 31.835371414676224 24.574719906116417 ST2 TRUE 308 -DLBCL-RICOVER_361-Tumor ST2 1 0 544,1067,1007,899,909,797,590,508,1057,339 0.126,0.283,0.348,0.371,0.388,0.422,0.446,0.448,0.478,0.482 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 30.6629817571299 0 0 30.66298175712993 30.66298175712993 ST2 TRUE 309 -DLBCL-RICOVER_401-Tumor EZB 1 0 750,304,949,708,887,403,289,686,36,724 0.152,0.205,0.211,0.216,0.22,0.231,0.248,0.249,0.268,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.2031433710589 0 0 45.2031433710589 45.2031433710589 EZB TRUE 310 -DLBCL-RICOVER_417-Tumor MCD 1 0 332,491,416,821,323,326,906,130,1027,1036 0.131,0.141,0.158,0.164,0.165,0.169,0.175,0.184,0.187,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.8226438039436 0 0 60.82264380394356 60.82264380394356 MCD TRUE 311 -DLBCL-RICOVER_431-Tumor MCD 0.7902525970156028 0 778,350,918,417,155,1048,450,1103,769,169 0.121,0.133,0.133,0.136,0.137,0.138,0.142,0.149,0.161,0.164 BN2,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 8.23838980603686,56.2739042308567,6.69772721881087 0 0 71.2100212557044 56.27390423085668 MCD TRUE 312 -DLBCL-RICOVER_440-Tumor EZB 1 0 986,302,604,392,1012,49,101,1030,199,226 0.136,0.152,0.156,0.156,0.174,0.175,0.178,0.198,0.222,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.28334776879 0 0 57.28334776878997 57.28334776878997 EZB TRUE 313 -DLBCL-RICOVER_449-Tumor MCD 1 0 739,459,897,520,513,288,991,147,106,729 0.115,0.127,0.139,0.147,0.175,0.18,0.2,0.232,0.252,0.26 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.9824032012004 0 0 58.98240320120037 58.98240320120037 MCD TRUE 314 -DLBCL-RICOVER_451-Tumor BN2 1 0 282,175,973,197,341,331,15,1062,1077,945 0.205,0.211,0.215,0.226,0.228,0.241,0.247,0.249,0.251,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.0203993926156 0 0 43.020399392615644 43.020399392615644 BN2 TRUE 315 -DLBCL-RICOVER_463-Tumor EZB 1 0 912,252,409,62,884,818,889,858,307,585 0.17,0.172,0.216,0.314,0.344,0.355,0.358,0.383,0.403,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.5461715307673 0 0 35.54617153076731 35.54617153076731 EZB TRUE 316 -DLBCL-RICOVER_469-Tumor EZB 1 0 91,984,914,1040,1026,738,105,1002,92,397 0.161,0.17,0.175,0.176,0.184,0.193,0.2,0.202,0.207,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7148444025887 0 0 53.714844402588696 53.714844402588696 EZB TRUE 317 -DLBCL-RICOVER_473-Tumor BN2 1 0 1062,15,264,1111,640,957,871,175,563,282 0.137,0.211,0.22,0.237,0.239,0.246,0.247,0.26,0.276,0.277 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.1775245841318 0 0 44.177524584131845 44.177524584131845 BN2 TRUE 318 -DLBCL-RICOVER_522-Tumor EZB 0.8493252373249097 0 322,1033,714,913,810,715,890,993,839,684 0.107,0.135,0.213,0.25,0.27,0.308,0.334,0.335,0.335,0.336 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 37.6854771452582,6.68560178788977 0 0 44.37107893314795 37.68547714525818 EZB TRUE 319 -DLBCL-RICOVER_554-Tumor ST2 0.6353633882204924 0 834,685,586,1091,1024,824,901,974,372,925 0.147,0.179,0.205,0.214,0.282,0.287,0.313,0.317,0.358,0.38 ST2,ST2,ST2,EZB,EZB,EZB,ST2,EZB,ST2,ST2 14.8723006752221,25.9143351007137 0 0 40.786635775935764 25.9143351007137 ST2 TRUE 320 -DLBCL-RICOVER_575-Tumor BN2 0.7413654855109897 0 262,928,110,936,1049,355,558,86,485,773 0.203,0.207,0.275,0.286,0.318,0.327,0.338,0.358,0.36,0.369 BN2,BN2,BN2,BN2,ST2,BN2,ST2,BN2,ST2,BN2 25.4568286132036,8.88092936275256 0 0 34.33775797595613 25.456828613203573 BN2 TRUE 321 -DLBCL-RICOVER_577-Tumor EZB 0.8523212170041123 0 319,1033,714,913,810,715,890,839,993,684 0.107,0.134,0.212,0.246,0.274,0.302,0.33,0.332,0.334,0.341 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 37.9639943751415,6.57789149810238 0 0 44.54188587324391 37.96399437514153 EZB TRUE 322 -DLBCL-RICOVER_585-Tumor MCD 1 0 906,332,130,39,311,1027,491,416,514,821 0.11,0.135,0.151,0.157,0.165,0.188,0.206,0.222,0.225,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.0842667712063 0 0 59.08426677120634 59.08426677120634 MCD TRUE 323 -DLBCL-RICOVER_597-Tumor MCD 1 0 280,729,966,439,679,565,1038,660,106,288 0.159,0.169,0.225,0.23,0.24,0.247,0.253,0.255,0.256,0.263 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.7992642406948 0 0 44.79926424069482 44.79926424069482 MCD TRUE 324 -DLBCL-RICOVER_623-Tumor N1 0.8299545044227933 0 137,523,41,486,1104,174,1099,496,516,854 0.157,0.224,0.256,0.273,0.315,0.327,0.335,0.372,0.395,0.433 N1,N1,N1,ST2,N1,N1,N1,N1,N1,BN2 2.30988619510898,29.1574794957368,3.66405336930486 0 0 35.13141906015064 29.1574794957368 ST2 FALSE 325 -DLBCL-RICOVER_685-Tumor MCD 1 0 416,1036,821,550,491,311,283,332,469,1027 0.123,0.128,0.131,0.135,0.137,0.169,0.183,0.198,0.202,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.5519000303537 0 0 64.55190003035372 64.55190003035372 MCD TRUE 326 -DLBCL-RICOVER_711-Tumor BN2 0.8435301812717451 0 827,611,463,980,895,352,438,391,348,2 0.146,0.216,0.272,0.293,0.307,0.312,0.321,0.322,0.328,0.374 BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2,N1 31.1950049308545,5.786487401552 0 0 36.98149233240654 31.195004930854537 BN2 TRUE 327 -DLBCL-RICOVER_720-Tumor MCD 1 0 1076,568,1021,443,468,1008,276,465,33,451 0.129,0.139,0.146,0.162,0.183,0.187,0.202,0.202,0.217,0.229 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.672500220392 0 0 57.67250022039203 57.67250022039203 MCD TRUE 328 -DLBCL-RICOVER_744-Tumor EZB 0.8896225824874139 0 222,368,227,277,245,587,995,863,1079,1074 0.171,0.176,0.179,0.183,0.193,0.195,0.216,0.217,0.227,0.252 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.57497569807806,44.9333241309368 0 0 50.508299829014845 44.93332413093679 EZB TRUE 329 -DLBCL-RICOVER_826-Tumor MCD 0.5754402612707918 0 845,436,444,400,386,193,285,420,375,567 0.257,0.304,0.307,0.318,0.378,0.392,0.396,0.396,0.403,0.403 BN2,MCD,EZB,MCD,MCD,BN2,BN2,MCD,MCD,MCD 8.96722324783725,3.25820290280215,16.5701119925499 0 0 28.795538143189304 16.570111992549904 BN2 FALSE 330 -DLBCL-RICOVER_829-Tumor BN2 1 0 1077,973,354,945,197,559,282,488,341,75 0.125,0.126,0.147,0.156,0.158,0.161,0.181,0.186,0.186,0.199 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 62.9982039096411 0 0 62.99820390964114 62.99820390964114 BN2 TRUE 331 -DLBCL-RICOVER_850-Tumor MCD 1 0 311,323,906,130,491,1027,39,416,821,326 0.131,0.135,0.145,0.167,0.172,0.186,0.186,0.188,0.194,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.0830581300369 0 0 60.083058130036854 60.083058130036854 MCD TRUE 332 -DLBCL-RICOVER_865-Tumor BN2 1 0 488,63,518,763,542,342,1077,559,197,563 0.133,0.134,0.144,0.177,0.179,0.182,0.184,0.184,0.196,0.196 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.6912050554779 0 0 59.69120505547791 59.69120505547791 BN2 TRUE 333 -DLBCL-RICOVER_866-Tumor EZB 0.8326174620262691 0 303,6,883,718,156,882,942,120,111,1092 0.165,0.172,0.189,0.2,0.201,0.203,0.22,0.227,0.237,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,ST2 40.9893515409563,8.2401607025223 0 0 49.22951224347857 40.989351540956264 EZB TRUE 334 -DLBCL-RICOVER_945-Tumor BN2 1 0 630,1060,943,477,650,99,780,345,538,435 0.131,0.158,0.173,0.179,0.185,0.236,0.249,0.259,0.271,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.1789731632918 0 0 50.178973163291815 50.178973163291815 BN2 TRUE 335 -DLBCL-RICOVER_948-Tumor EZB 1 0 28,32,85,921,4,76,306,234,235,402 0.186,0.2,0.233,0.233,0.291,0.313,0.314,0.324,0.351,0.358 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5167062029519 0 0 37.51670620295191 37.51670620295191 EZB TRUE 336 -DLBCL-RICOVER_977-Tumor BN2 1 0 172,1028,404,815,261,517,543,999,499,548 0.203,0.324,0.344,0.349,0.35,0.372,0.377,0.394,0.403,0.415 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.4003063997538 0 0 29.400306399753774 29.400306399753774 BN2 TRUE 337 -DLBCL-RICOVER_978-Tumor ST2 0.6846002394847925 0 606,689,1118,607,1052,736,620,346,545,781 0.184,0.219,0.233,0.285,0.346,0.355,0.413,0.419,0.446,0.45 ST2,ST2,ST2,ST2,EZB,EZB,EZB,ST2,ST2,EZB 10.344303512335,22.4531326538737 0 0 32.797436166208676 22.453132653873656 BN2 FALSE 338 -DLBCL-RICOVER_985-Tumor ST2 0.9169924459753512 0 508,909,911,394,60,899,910,278,728,1081 0.182,0.27,0.312,0.32,0.336,0.34,0.388,0.398,0.444,0.446 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2 2.57627636580456,28.460373202734 0 0 31.036649568538568 28.46037320273401 ST2 TRUE 339 -DLBCL-c_D_1104-Tumor MCD 1 0 869,414,536,461,456,987,415,511,1038,565 0.123,0.145,0.158,0.167,0.176,0.179,0.189,0.192,0.2,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.0330894758442 0 0 59.03308947584417 59.03308947584417 MCD TRUE 340 -DLBCL-c_D_1106-Tumor BN2 1 0 175,282,197,488,1077,15,973,563,331,333 0.123,0.123,0.128,0.169,0.171,0.173,0.177,0.178,0.186,0.202 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.2077382074819 0 0 63.207738207481874 63.207738207481874 BN2 TRUE 341 -DLBCL-c_D_1107-Tumor BN2 1 0 763,38,518,654,333,63,501,488,563,542 0.154,0.158,0.169,0.177,0.182,0.183,0.204,0.206,0.209,0.24 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.0468781270124 0 0 54.04687812701237 54.04687812701237 BN2 TRUE 342 -DLBCL-c_D_1110-Tumor EZB 1 0 243,240,244,69,268,710,664,225,273,9 0.169,0.177,0.197,0.201,0.215,0.219,0.248,0.255,0.259,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.4063919310712 0 0 46.406391931071234 46.406391931071234 EZB TRUE 343 -DLBCL-c_D_1113-Tumor EZB 1 0 725,727,688,726,1114,601,47,407,784,1006 0.179,0.222,0.228,0.285,0.293,0.321,0.322,0.323,0.332,0.359 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.495942338292 0 0 36.49594233829201 36.49594233829201 EZB TRUE 344 -DLBCL-c_D_1114-Tumor BN2 1 0 780,999,959,477,943,646,99,548,435,335 0.165,0.179,0.179,0.188,0.19,0.197,0.199,0.234,0.25,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.073183486102 0 0 50.073183486101975 50.073183486101975 BN2 TRUE 345 -DLBCL-c_D_1117-Tumor ST2 0.5687986415461891 0 607,606,1118,174,338,828,689,872,126,523 0.297,0.385,0.387,0.415,0.419,0.442,0.464,0.579,0.602,0.607 ST2,ST2,ST2,N1,BN2,ST2,ST2,BN2,EZB,N1 4.11080223821441,1.66169888074566,4.05478299760117,12.9631916644048 0 0 22.79047578096603 12.9631916644048 ST2 TRUE 346 -DLBCL-c_D_1118-Tumor BN2 0.6806375449766406 0 624,765,173,535,799,506,773,698,1049,936 0.174,0.219,0.249,0.277,0.306,0.31,0.356,0.357,0.367,0.396 EZB,BN2,BN2,BN2,BN2,BN2,BN2,ST2,ST2,BN2 24.0128077317185,5.7446948894366,5.52237270888214 0 0 35.279875330037214 24.01280773171848 BN2 TRUE 347 -DLBCL-c_D_1123-Tumor BN2 1 0 391,623,827,327,492,980,577,463,473,611 0.144,0.232,0.318,0.328,0.329,0.335,0.338,0.348,0.363,0.367 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.7766096175063 0 0 34.77660961750632 34.77660961750632 BN2 TRUE 348 -DLBCL-c_D_1124-Tumor MCD 1 0 1027,283,130,326,1036,550,416,311,491,821 0.166,0.204,0.212,0.212,0.222,0.224,0.232,0.232,0.235,0.242 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.3595459429224 0 0 46.35954594292242 46.35954594292242 MCD TRUE 349 -DLBCL-c_D_1125-Tumor MCD 0.9059114662666361 0 155,450,417,312,1048,500,778,176,918,1109 0.128,0.128,0.132,0.133,0.136,0.142,0.152,0.161,0.164,0.168 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 6.58003086862869,63.3544298731631 0 0 69.93446074179182 63.35442987316313 MCD TRUE 350 -DLBCL-c_D_1127-Tumor EZB 1 0 265,206,877,267,848,1117,720,1083,192,157 0.176,0.248,0.289,0.327,0.359,0.369,0.409,0.413,0.42,0.422 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3382526741385 0 0 31.338252674138477 31.338252674138477 EZB TRUE 351 -DLBCL-c_D_1128-Tumor BN2 1 0 463,109,611,10,895,258,531,952,327,1098 0.191,0.194,0.197,0.197,0.276,0.302,0.308,0.311,0.312,0.322 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.2217226292124 0 0 40.22172262921243 40.22172262921243 BN2 TRUE 352 -DLBCL-c_D_1129-Tumor BN2 1 0 951,566,482,578,401,26,957,569,1111,1022 0.142,0.187,0.214,0.232,0.249,0.273,0.282,0.289,0.293,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.7533396730781 0 0 42.75333967307813 42.75333967307813 BN2 TRUE 353 -DLBCL-c_D_1130-Tumor BN2 1 0 945,559,331,75,1077,973,305,542,998,197 0.127,0.142,0.147,0.152,0.161,0.17,0.18,0.188,0.188,0.203 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 61.449628232367 0 0 61.44962823236696 61.44962823236696 BN2 TRUE 354 -DLBCL-c_D_1132-Tumor BN2 1 0 187,553,86,110,321,258,262,1098,670,928 0.254,0.285,0.297,0.325,0.327,0.332,0.34,0.343,0.379,0.382 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.0601931558982 0 0 31.060193155898165 31.060193155898165 BN2 TRUE 355 -DLBCL-c_D_1133-Tumor EZB 1 0 941,251,938,182,116,250,709,120,21,18 0.135,0.172,0.187,0.2,0.237,0.241,0.252,0.254,0.271,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2008931484633 0 0 47.20089314846326 47.20089314846326 EZB TRUE 356 -DLBCL-c_D_1134-Tumor BN2 1 0 564,158,393,58,1028,968,572,777,651,1019 0.157,0.203,0.204,0.226,0.387,0.395,0.409,0.443,0.445,0.455 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.8819846449542 0 0 34.88198464495415 34.88198464495415 BN2 TRUE 357 -DLBCL-c_D_1135-Tumor BN2 0.7358929579167797 0 570,487,390,88,539,212,189,77,634,22 0.186,0.199,0.217,0.218,0.242,0.242,0.265,0.278,0.297,0.3 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,EZB 30.8570498589925,11.0743880315737 0 0 41.931437890566215 30.857049858992504 BN2 TRUE 358 -DLBCL-c_D_1139-Tumor ST2 0.8224306669178493 0 671,842,996,161,668,602,1029,160,1005,366 0.137,0.159,0.169,0.179,0.182,0.194,0.218,0.24,0.259,0.266 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,BN2 9.25577786142228,42.869089089225 0 0 52.12486695064726 42.869089089224985 ST2 TRUE 359 -DLBCL-c_D_1141-Tumor EZB 1 0 211,927,402,210,306,271,114,31,233,51 0.205,0.236,0.266,0.271,0.278,0.29,0.311,0.345,0.352,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.3795578652145 0 0 35.37955786521448 35.37955786521448 EZB TRUE 360 -DLBCL-c_D_1146-Tumor ST2 0.5500228445783244 0 787,178,770,950,1107,965,195,875,1065,1009 0.156,0.163,0.189,0.212,0.213,0.246,0.267,0.284,0.286,0.292 EZB,ST2,BN2,EZB,ST2,EZB,ST2,ST2,ST2,ST2 5.27744321538524,15.2000488325764,25.0303560755106 0 0 45.50784812347222 25.030356075510554 ST2 TRUE 361 -DLBCL-c_D_1148-Tumor ST2 1 0 807,483,656,263,207,1032,728,822,1057,1081 0.182,0.255,0.262,0.272,0.274,0.341,0.358,0.373,0.373,0.476 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 33.7216470100066 0 0 33.721647010006556 33.721647010006556 BN2 FALSE 362 -DLBCL-c_D_1150-Tumor BN2 0.5647264569925512 0 670,408,422,1007,86,808,946,797,970,531 0.287,0.301,0.312,0.337,0.364,0.408,0.442,0.45,0.455,0.482 BN2,N1,ST2,ST2,BN2,BN2,BN2,ST2,BN2,BN2 15.2074475613278,3.32180042932079,8.39962593233882 0 0 26.92887392298737 15.207447561327761 BN2 TRUE 363 -DLBCL-c_D_1157-Tumor EZB 1 0 125,35,48,30,635,208,256,171,129,931 0.15,0.153,0.181,0.183,0.187,0.198,0.207,0.209,0.21,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.6547909668106 0 0 53.65479096681058 53.65479096681058 EZB TRUE 364 -DLBCL-c_D_1158-Tumor EZB 0.5001846277972772 0 661,965,178,787,770,361,1107,195,950,1041 0.112,0.223,0.236,0.258,0.288,0.298,0.315,0.324,0.359,0.362 EZB,EZB,ST2,EZB,BN2,ST2,ST2,ST2,EZB,ST2 3.47113563866364,20.1117307917129,16.6257478972595 0 0 40.208614327636106 20.111730791712933 ST2 FALSE 365 -DLBCL-c_D_1159-Tumor ST2 0.7872797152835607 0 425,583,785,160,668,612,161,1029,359,602 0.158,0.187,0.188,0.193,0.197,0.21,0.211,0.215,0.266,0.27 ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.0749286103877,5.32513217890994,38.490719904989 0 0 48.89078069428662 38.49071990498897 BN2 FALSE 366 -DLBCL-c_D_1163-Tumor MCD 1 0 1101,452,978,961,514,878,39,465,295,906 0.12,0.169,0.184,0.197,0.2,0.207,0.22,0.223,0.236,0.255 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.8298518580067 0 0 51.829851858006734 51.829851858006734 MCD TRUE 367 -DLBCL-c_D_pair20-Tumor EZB 0.7757922606477001 0 227,995,587,863,329,1074,1079,618,277,867 0.114,0.144,0.157,0.159,0.176,0.18,0.186,0.191,0.2,0.213 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 8.73708271195448,46.5042978365312,4.70288571385791 0 0 59.94426626234357 46.50429783653118 EZB TRUE 368 -DLBCL-c_D_pair5-Tumor EZB 0.9062397238863048 0 581,385,687,975,379,645,643,1050,241,831 0.145,0.155,0.156,0.17,0.173,0.198,0.216,0.222,0.235,0.253 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 48.7434997238295,5.04304090010013 0 0 53.78654062392959 48.743499723829466 EZB TRUE 369 -DLBCL-c_D_pair9-Tumor ST2 0.7707104267350875 0 792,177,692,1086,682,1016,1053,300,673,557 0.186,0.235,0.258,0.26,0.276,0.292,0.331,0.339,0.353,0.386 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 8.20425652240359,27.5769454118191 0 0 35.7812019342227 27.576945411819114 ST2 TRUE 370 -DLBCL10452T MCD 0.4724939531329812 0 94,534,170,813,419,193,541,930,510,444 0.152,0.245,0.263,0.263,0.275,0.309,0.332,0.337,0.381,0.389 EZB,MCD,MCD,EZB,MCD,BN2,MCD,ST2,MCD,EZB 3.23649163374541,12.9604661939139,17.1685653869856,2.97052992031153 0 0 36.336053134956465 17.168565386985634 EZB FALSE 371 -DLBCL10456T ST2 0.6001594913715487 0 910,278,922,834,1024,843,657,901,1091,925 0.277,0.281,0.301,0.311,0.324,0.325,0.327,0.335,0.34,0.345 EZB,ST2,ST2,ST2,EZB,ST2,BN2,ST2,EZB,ST2 3.05762340988479,9.64407639562204,19.0652160807217 0 0 31.766915886228485 19.065216080721658 ST2 TRUE 372 -DLBCL10458T BN2 0.8933599346708686 0 905,395,958,540,702,841,929,1023,555,546 0.163,0.166,0.168,0.191,0.217,0.218,0.22,0.229,0.252,0.269 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 43.9156520317886,5.24219614053624 0 0 49.15784817232485 43.9156520317886 BN2 TRUE 373 -DLBCL10459T EZB 1 0 711,229,648,71,892,720,680,1095,706,139 0.152,0.156,0.17,0.185,0.192,0.199,0.207,0.208,0.21,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.5764531777289 0 0 53.57645317772892 53.57645317772892 EZB TRUE 374 -DLBCL10461T MCD 0.5269148827710218 0 440,386,285,135,552,330,193,813,444,534 0.14,0.178,0.218,0.275,0.369,0.403,0.418,0.438,0.442,0.456 MCD,MCD,BN2,MCD,BN2,BN2,BN2,EZB,EZB,MCD 12.1638732438502,4.54406320069403,18.6090410634534 0 0 35.31697750799766 18.609041063453404 MCD TRUE 375 -DLBCL10463T MCD 1 0 480,934,202,445,532,147,859,458,749,933 0.229,0.325,0.353,0.424,0.431,0.435,0.462,0.488,0.493,0.496 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 25.4998829050328 0 0 25.499882905032763 25.499882905032763 MCD TRUE 376 -DLBCL10464T EZB 1 0 378,626,745,537,647,1083,157,719,265,238 0.254,0.322,0.348,0.367,0.396,0.425,0.47,0.53,0.543,0.563 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 25.1435286500871 0 0 25.143528650087067 25.143528650087067 EZB TRUE 377 -DLBCL10465T EZB 1 0 745,377,626,647,537,1083,184,238,629,163 0.238,0.254,0.376,0.45,0.482,0.514,0.543,0.548,0.59,0.594 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.0793686458132 0 0 24.0793686458132 24.0793686458132 EZB TRUE 378 -DLBCL10466T EZB 0.91894137165257 0 687,369,831,581,385,975,645,142,643,118 0.141,0.173,0.191,0.218,0.221,0.239,0.271,0.283,0.287,0.292 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 41.8483496463054,3.69138872791514 0 0 45.53973837422051 41.84834964630537 EZB TRUE 379 -DLBCL10467T MCD 1 0 1090,994,660,932,188,966,987,439,461,106 0.165,0.181,0.19,0.195,0.214,0.218,0.244,0.245,0.259,0.269 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.0139349075279 0 0 47.01393490752792 47.01393490752792 MCD TRUE 380 -DLBCL10469T ST2 0.6552959114669504 0 830,412,1066,529,119,308,919,396,698,293 0.123,0.226,0.236,0.281,0.285,0.332,0.353,0.363,0.39,0.438 ST2,ST2,N1,ST2,BN2,ST2,BN2,ST2,ST2,MCD 6.33460240610028,2.28201227219954,4.23208093073474,24.4258712918528 0 0 37.274566900887386 24.425871291852822 ST2 TRUE 381 -DLBCL10470T EZB 0.5387402127784001 0 657,922,791,641,974,1091,372,586,194,910 0.242,0.245,0.278,0.386,0.437,0.438,0.439,0.476,0.498,0.512 BN2,ST2,EZB,EZB,EZB,EZB,ST2,ST2,EZB,EZB 4.13880981633157,14.7208699427755,8.46493717798169 0 0 27.32461693708872 14.720869942775453 EZB TRUE 382 -DLBCL10471T MCD 0.764460607077467 0 1070,457,445,154,749,934,933,772,859,533 0.207,0.246,0.284,0.308,0.375,0.381,0.397,0.431,0.433,0.44 BN2,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 4.83571753415705,2.31961667273875,23.2231690154893 0 0 30.378503222385092 23.223169015489294 MCD TRUE 383 -DLBCL10472T EZB 1 0 605,1059,61,603,833,74,629,25,73,149 0.102,0.141,0.147,0.151,0.151,0.156,0.17,0.178,0.203,0.212 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.5656649601506 0 0 64.56566496015056 64.56566496015056 EZB TRUE 384 -DLBCL10475T EZB 0.895258823244195 0 975,581,369,645,643,1050,687,241,254,379 0.12,0.141,0.155,0.162,0.168,0.186,0.189,0.201,0.212,0.221 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 52.7146338259534,6.16737041381348 0 0 58.882004239766935 52.71463382595345 EZB TRUE 385 -DLBCL10480T MCD 0.47608277860633613 0 285,375,440,135,552,330,845,193,444,100 0.144,0.178,0.192,0.22,0.32,0.378,0.462,0.464,0.465,0.485 BN2,MCD,MCD,MCD,BN2,BN2,BN2,BN2,EZB,MCD 17.025915281961,2.1484432954324,17.4237103434716 0 0 36.59806892086502 17.423710343471612 MCD TRUE 386 -DLBCL10481T EZB 1 0 757,93,740,631,102,228,107,873,1117,848 0.139,0.188,0.188,0.189,0.202,0.203,0.206,0.209,0.236,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.6849836277551 0 0 50.68498362775512 50.68498362775512 EZB TRUE 387 -DLBCL10482T BN2 0.5334332552248533 0 127,185,891,802,23,14,179,180,552,42 0.144,0.188,0.197,0.224,0.24,0.243,0.263,0.28,0.28,0.288 BN2,BN2,MCD,ST2,EZB,BN2,BN2,MCD,BN2,EZB 23.7223265780729,7.63951623004831,8.6406435794201,4.46854414105491 0 0 44.471030528596266 23.722326578072934 BN2 TRUE 388 -DLBCL10483T EZB 0.9112956873439079 0 134,615,398,1000,203,8,97,209,171,977 0.14,0.26,0.266,0.267,0.303,0.311,0.315,0.318,0.339,0.346 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.29623545639835,33.8635751288873 0 0 37.159810585285676 33.863575128887334 EZB TRUE 389 -DLBCL10487T BN2 0.9013695002652803 0 212,570,358,487,634,860,77,633,493,88 0.208,0.213,0.217,0.222,0.226,0.237,0.244,0.271,0.295,0.324 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 37.4273428529503,4.09540984939333 0 0 41.52275270234362 37.42734285295028 BN2 TRUE 390 -DLBCL10488T BN2 1 0 348,623,463,827,327,611,492,980,577,473 0.144,0.198,0.313,0.321,0.322,0.341,0.363,0.363,0.382,0.395 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.9994613839862 0 0 34.99946138398624 34.99946138398624 BN2 TRUE 391 -DLBCL10489T EZB 1 0 101,604,313,1012,49,986,302,981,183,199 0.128,0.141,0.156,0.156,0.165,0.18,0.205,0.222,0.234,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2476572819226 0 0 57.247657281922585 57.247657281922585 EZB TRUE 392 -DLBCL10490T BN2 0.9344932921556488 0 564,357,158,58,572,777,1028,968,301,704 0.182,0.204,0.266,0.288,0.306,0.405,0.416,0.42,0.454,0.47 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 30.363446523087,2.12843627368077 0 0 32.49188279676779 30.36344652308702 BN2 TRUE 393 -DLBCL10491T ST2 0.9131147031241165 0 911,60,1081,278,904,508,843,910,822,339 0.203,0.222,0.235,0.27,0.273,0.297,0.307,0.309,0.312,0.32 ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2 3.23564743671523,34.0048012129267 0 0 37.2404486496419 34.004801212926665 ST2 TRUE 394 -DLBCL10495T BN2 0.9194354170776942 0 958,702,905,373,1023,305,796,540,542,75 0.139,0.153,0.154,0.166,0.193,0.206,0.226,0.232,0.232,0.235 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 49.2689207820525,4.31713851795506 0 0 53.58605930000755 49.26892078205249 BN2 TRUE 395 -DLBCL10497T ST2 1 0 281,59,412,308,830,759,381,24,915,642 0.238,0.24,0.255,0.346,0.348,0.354,0.363,0.368,0.378,0.399 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 31.505575172095 0 0 31.50557517209504 31.50557517209504 ST2 TRUE 396 -DLBCL10498T EZB 1 0 287,105,91,1040,675,317,92,78,117,1108 0.165,0.189,0.191,0.198,0.201,0.209,0.209,0.217,0.223,0.225 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.719604706545 0 0 49.71960470654505 49.71960470654505 EZB TRUE 397 -DLBCL10501T EZB 0.9036932222899354 0 615,134,389,203,1000,1088,84,83,428,97 0.197,0.232,0.266,0.319,0.347,0.352,0.366,0.373,0.389,0.398 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 3.12998843627658,29.3702001350775 0 0 32.500188571354066 29.37020013507749 EZB TRUE 398 -DLBCL10506T BN2 0.8119780079193942 0 636,561,575,1069,498,926,77,493,898,633 0.196,0.199,0.212,0.214,0.227,0.239,0.251,0.26,0.266,0.266 BN2,BN2,BN2,BN2,BN2,ST2,EZB,BN2,BN2,BN2 35.262925123395,3.98781590721928,4.17768286691518 0 0 43.42842389752944 35.26292512339497 BN2 TRUE 399 -DLBCL10508T MCD 0.7254197197073472 0 436,567,420,775,448,533,772,510,444,933 0.15,0.202,0.218,0.22,0.222,0.233,0.242,0.243,0.272,0.28 MCD,MCD,MCD,EZB,MCD,MCD,EZB,MCD,EZB,MCD 12.3582643406458,32.6495720832742 0 0 45.00783642392006 32.64957208327422 MCD TRUE 400 -DLBCL10509T BN2 1 0 482,957,1111,578,951,264,353,566,640,26 0.134,0.14,0.179,0.186,0.218,0.245,0.249,0.261,0.269,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.3494627636994 0 0 49.349462763699414 49.349462763699414 BN2 TRUE 401 -DLBCL10512T EZB 1 0 306,79,85,927,360,271,32,28,210,917 0.186,0.23,0.239,0.257,0.266,0.269,0.273,0.274,0.286,0.347 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9877384252548 0 0 38.98773842525483 38.98773842525483 EZB TRUE 402 -DLBCL10514T EZB 1 0 708,686,5,949,237,289,310,141,838,750 0.12,0.137,0.17,0.214,0.223,0.225,0.231,0.254,0.26,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0927165113371 0 0 51.092716511337116 51.092716511337116 EZB TRUE 403 -DLBCL10515T BN2 1 0 261,499,543,517,527,555,815,1001,591,546 0.12,0.158,0.184,0.189,0.213,0.225,0.245,0.259,0.26,0.261 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.174425124833 0 0 50.17442512483296 50.17442512483296 BN2 TRUE 404 -DLBCL10516T EZB 1 0 1013,1058,917,638,889,847,271,210,79,846 0.289,0.309,0.317,0.354,0.365,0.378,0.407,0.426,0.427,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.4841857593101 0 0 27.48418575931005 27.48418575931005 EZB TRUE 405 -DLBCL10518T EZB 1 0 754,116,865,18,1018,182,190,756,1073,942 0.133,0.149,0.19,0.191,0.198,0.21,0.22,0.229,0.234,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3894651666958 0 0 51.389465166695835 51.389465166695835 EZB TRUE 406 -DLBCL10519T EZB 0.9145589582164062 0 890,1114,839,874,1006,715,688,344,424,784 0.242,0.247,0.25,0.257,0.265,0.281,0.284,0.323,0.325,0.339 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 32.9811266847614,3.08120301903589 0 0 36.06232970379732 32.981126684761435 EZB TRUE 407 -DLBCL10520T BN2 0.8248070994872702 0 808,363,531,952,1007,670,86,10,422,895 0.237,0.301,0.348,0.36,0.376,0.407,0.451,0.475,0.5,0.502 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,ST2,BN2 21.9502241379663,4.66233066619499 0 0 26.612554804161253 21.95022413796626 N1 FALSE 408 -DLBCL10522T EZB 1 0 316,884,252,889,912,1058,858,818,62,90 0.216,0.23,0.235,0.246,0.274,0.326,0.366,0.4,0.42,0.433 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.929207564884 0 0 33.92920756488404 33.92920756488404 EZB TRUE 409 -DLBCL10523T EZB 1 0 655,1051,752,183,49,1012,101,1115,225,392 0.133,0.142,0.16,0.215,0.226,0.231,0.263,0.271,0.278,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8255664904428 0 0 48.82556649044275 48.82556649044275 EZB TRUE 410 -DLBCL10524T EZB 0.5389361992022118 0 608,90,11,691,858,1084,622,954,614,893 0.284,0.298,0.301,0.329,0.332,0.342,0.353,0.43,0.459,0.488 EZB,EZB,EZB,ST2,EZB,BN2,ST2,ST2,EZB,N1 2.92049275456891,15.3957486386815,2.0509955199164,8.19968559356373 0 0 28.56692250673049 15.39574863868145 EZB TRUE 411 -DLBCL10527T ST2 0.8053814085684721 0 830,381,308,396,1066,119,529,281,59,642 0.219,0.226,0.251,0.255,0.292,0.303,0.361,0.365,0.368,0.369 ST2,ST2,ST2,ST2,N1,BN2,ST2,ST2,ST2,ST2 3.29531348994769,3.43018107896257,27.8318132372994 0 0 34.55730780620962 27.83181323729936 ST2 TRUE 412 -DLBCL10529T MCD 1 0 794,297,290,165,17,108,1035,528,166,246 0.115,0.136,0.148,0.151,0.16,0.163,0.176,0.178,0.19,0.193 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5550111912165 0 0 63.55501119121652 63.55501119121652 MCD TRUE 413 -DLBCL10530T MCD 1 0 456,340,461,987,869,188,932,536,565,1038 0.14,0.145,0.145,0.149,0.165,0.188,0.195,0.197,0.207,0.213 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.7456550628095 0 0 58.74565506280948 58.74565506280948 MCD TRUE 414 -DLBCL10532T MCD 1 0 1038,679,565,869,340,461,987,414,536,1087 0.13,0.136,0.148,0.171,0.189,0.19,0.205,0.213,0.22,0.234 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5806723289143 0 0 56.58067232891432 56.58067232891432 MCD TRUE 415 -DLBCL10533T MCD 1 0 821,491,326,1036,550,311,332,283,469,1027 0.111,0.118,0.123,0.142,0.15,0.158,0.188,0.204,0.213,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.2606306833547 0 0 65.26063068335466 65.26063068335466 MCD TRUE 416 -DLBCL10534T MCD 0.9068643553642043 0 1048,350,312,769,778,155,450,500,1015,918 0.105,0.132,0.136,0.153,0.156,0.157,0.159,0.159,0.16,0.167 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.39444310729711,62.2628699150414 0 0 68.65731302233851 62.262869915041406 MCD TRUE 417 -DLBCL10537T EZB 1 0 1037,644,13,599,83,84,150,140,222,1088 0.136,0.182,0.19,0.192,0.193,0.215,0.216,0.217,0.227,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0115245164913 0 0 51.011524516491264 51.011524516491264 EZB TRUE 418 -DLBCL10538T MCD 0.8258117612282001 0 170,541,200,371,1082,510,94,201,888,420 0.154,0.18,0.228,0.275,0.279,0.293,0.3,0.308,0.315,0.317 MCD,MCD,MCD,EZB,MCD,MCD,EZB,MCD,MCD,MCD 6.96564215460226,33.0235224624635 0 0 39.98916461706574 33.02352246246348 MCD TRUE 419 -DLBCL10540T MCD 0.7696136458940591 0 510,775,567,400,444,541,448,200,436,201 0.126,0.133,0.141,0.218,0.248,0.248,0.266,0.268,0.268,0.27 MCD,EZB,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 11.5508235754031,38.5859287519139 0 0 50.13675232731704 38.58592875191392 MCD TRUE 420 -DLBCL10542T MCD 0.39190983096977344 0 663,801,789,441,716,617,525,779,1045,579 0.254,0.275,0.31,0.317,0.379,0.387,0.399,0.4,0.402,0.422 ST2,ST2,MCD,MCD,BN2,N1,MCD,MCD,BN2,N1 5.12547887726848,11.3853118228385,4.95353361945763,7.5865208574133 0 0 29.050845176977873 11.385311822838457 MCD TRUE 421 -DLBCL10543T ST2 0.5437214228019601 0 946,970,363,797,1007,490,670,24,1067,558 0.291,0.299,0.312,0.33,0.369,0.408,0.416,0.436,0.456,0.479 BN2,BN2,BN2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 12.3924388071271,14.7673697537477 0 0 27.159808560874836 14.767369753747724 ST2 TRUE 422 -DLBCL10547T ST2 0.8308504416901662 0 875,1065,814,1004,1009,896,748,361,1025,770 0.266,0.268,0.277,0.317,0.355,0.369,0.383,0.391,0.393,0.395 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2 5.07943484679398,24.9498179490655 0 0 30.02925279585952 24.94981794906554 ST2 TRUE 423 -DLBCL10549T EZB 1 0 862,715,115,890,407,839,913,1097,322,1033 0.166,0.214,0.319,0.319,0.325,0.335,0.336,0.345,0.38,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.1166699365011 0 0 34.11666993650114 34.11666993650114 ST2 FALSE 424 -DLBCL10552T ST2 0.6585273568773167 0 366,612,583,785,160,668,161,1029,896,662 0.158,0.168,0.176,0.2,0.243,0.246,0.269,0.269,0.298,0.315 BN2,ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2,ST2 10.4012002201839,5.00018147750353,29.7014457406854 0 0 45.10282743837287 29.7014457406854 ST2 TRUE 425 -DLBCL10553T EZB 1 0 812,1097,284,115,753,816,874,1075,21,1006 0.163,0.186,0.199,0.212,0.315,0.316,0.332,0.333,0.34,0.343 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4426611915157 0 0 39.44266119151571 39.44266119151571 ST2 FALSE 426 -DLBCL10554T EZB 1 0 585,292,138,307,818,220,219,593,62,1080 0.233,0.251,0.254,0.259,0.273,0.284,0.315,0.399,0.427,0.448 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5194772473775 0 0 33.51947724737753 33.51947724737753 EZB TRUE 427 -DLBCL10555T EZB 1 0 215,923,214,398,133,134,547,615,389,873 0.244,0.321,0.331,0.389,0.422,0.45,0.47,0.486,0.488,0.57 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 25.3887809839328 0 0 25.388780983932783 25.388780983932783 EZB TRUE 428 -DLBCL10565T EZB 0.9178849934540938 0 744,658,454,19,241,733,67,1050,645,643 0.169,0.182,0.21,0.22,0.234,0.237,0.242,0.25,0.273,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 40.9469936081665,3.66316332890122 0 0 44.61015693706773 40.94699360816651 EZB TRUE 429 -DLBCL10780T EZB 1 0 761,997,37,1031,159,89,880,113,36,707 0.153,0.162,0.174,0.187,0.195,0.211,0.226,0.248,0.254,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8622173536659 0 0 49.86221735366593 49.86221735366593 EZB TRUE 430 -DLBCL10833T BN2 0.42424593363733454 0 442,801,894,497,521,34,663,701,104,421 0.231,0.289,0.333,0.358,0.367,0.438,0.443,0.447,0.457,0.463 BN2,ST2,MCD,BN2,MCD,BN2,ST2,BN2,MCD,MCD 11.6425321698843,10.0792654170336,5.72108694485672 0 0 27.442884531774602 11.642532169884282 BN2 TRUE 431 -DLBCL10834T BN2 0.5662880108352032 0 1085,582,153,967,962,924,672,665,505,186 0.189,0.236,0.26,0.264,0.286,0.291,0.347,0.349,0.363,0.387 EZB,BN2,BN2,EZB,BN2,ST2,BN2,BN2,N1,BN2 19.9216943986391,9.065529572408,2.75675641329894,3.43545837731364 0 0 35.179438761659696 19.92169439863911 BN2 TRUE 432 -DLBCL10835T MCD 0.804347365277495 0 286,990,169,1103,956,918,778,260,176,155 0.129,0.16,0.164,0.168,0.182,0.187,0.196,0.198,0.212,0.212 MCD,MCD,MCD,ST2,MCD,MCD,BN2,MCD,MCD,MCD 5.10673675613268,45.4255701144267,5.94275859913997 0 0 56.47506546969937 45.425570114426726 MCD TRUE 433 -DLBCL10836T MCD 1 0 804,851,246,166,767,551,17,798,528,762 0.133,0.161,0.168,0.169,0.169,0.177,0.181,0.182,0.185,0.187 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.9301251813661 0 0 58.93012518136605 58.93012518136605 MCD TRUE 434 -DLBCL10839T BN2 1 0 538,99,780,700,959,650,955,943,731,548 0.149,0.157,0.185,0.186,0.197,0.207,0.219,0.23,0.238,0.238 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.0771729854921 0 0 51.07717298549206 51.07717298549206 BN2 TRUE 435 -DLBCL10842T MCD 0.809343589910559 0 400,448,533,772,567,775,933,420,749,510 0.15,0.228,0.23,0.236,0.248,0.267,0.267,0.268,0.278,0.294 MCD,MCD,MCD,EZB,MCD,EZB,MCD,MCD,MCD,MCD 7.98472379387782,33.8954510721653 0 0 41.88017486604312 33.8954510721653 MCD TRUE 436 -DLBCL10843T N1 0.5354434012369208 0 854,516,486,496,964,1099,1104,893,590,41 0.155,0.222,0.331,0.372,0.372,0.379,0.407,0.409,0.409,0.472 BN2,N1,ST2,N1,BN2,N1,N1,N1,ST2,N1 9.15263717218876,16.8529403580155,5.46915970455062 0 0 31.47473723475491 16.852940358015527 BN2 FALSE 437 -DLBCL10844T BN2 0.6374514047226192 0 895,327,827,611,496,952,1099,1104,352,2 0.216,0.321,0.339,0.34,0.34,0.342,0.365,0.369,0.381,0.396 BN2,BN2,BN2,BN2,N1,BN2,N1,N1,BN2,N1 19.1834103387537,10.9105077177939 0 0 30.093918056547615 19.18341033875367 N1 FALSE 438 -DLBCL10848T MCD 1 0 106,966,280,660,1090,994,991,288,729,324 0.143,0.17,0.171,0.192,0.197,0.198,0.208,0.208,0.224,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.463178916591 0 0 52.46317891659099 52.46317891659099 MCD TRUE 439 -DLBCL10850T MCD 0.5322297728229195 0 375,386,285,135,552,330,193,813,891,444 0.14,0.192,0.222,0.26,0.347,0.44,0.451,0.465,0.477,0.48 MCD,MCD,BN2,MCD,BN2,BN2,BN2,EZB,MCD,EZB 11.8734202689629,4.23441786310853,18.327526062182 0 0 34.43536419425343 18.327526062182 MCD TRUE 440 -DLBCL10852T MCD 0.7878634875507154 0 789,617,713,421,205,779,985,525,457,801 0.173,0.219,0.27,0.317,0.349,0.387,0.406,0.439,0.442,0.456 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 25.1124522790484,4.56837815885319,2.19328566959463 0 0 31.87411610749622 25.11245227904841 MCD TRUE 441 -DLBCL10853T BN2 0.4565105087379326 0 431,521,497,701,34,801,104,894,103,579 0.231,0.251,0.321,0.327,0.341,0.389,0.391,0.401,0.433,0.476 BN2,MCD,BN2,BN2,BN2,ST2,MCD,MCD,MCD,N1 13.4455378742692,11.332844198898,2.10252023687844,2.57195280919506 0 0 29.452855119240734 13.44553787426921 BN2 TRUE 442 -DLBCL10854T MCD 1 0 1076,468,328,1008,276,1021,568,298,900,33 0.139,0.152,0.162,0.162,0.165,0.194,0.196,0.2,0.211,0.227 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.5675773752687 0 0 56.567577375268684 56.567577375268684 MCD TRUE 443 -DLBCL10856T MCD 0.5911056699035582 0 193,420,510,400,567,775,813,330,436,534 0.212,0.248,0.248,0.272,0.28,0.28,0.284,0.307,0.308,0.311 BN2,MCD,MCD,MCD,MCD,EZB,EZB,BN2,MCD,MCD 7.96943819678449,7.09856159587112,21.782596274797 0 0 36.85059606745263 21.78259627479701 EZB FALSE 444 -DLBCL10857T MCD 0.9074464762991163 0 934,749,933,383,859,772,533,448,154,457 0.202,0.258,0.277,0.284,0.29,0.319,0.328,0.351,0.363,0.369 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.13743262668942,30.7611430432012 0 0 33.8985756698906 30.76114304320118 MCD TRUE 445 -DLBCL10858T MCD 1 0 1035,108,519,290,794,413,297,165,528,573 0.209,0.217,0.233,0.248,0.258,0.273,0.293,0.295,0.304,0.323 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.4374463516816 0 0 38.43744635168162 38.43744635168162 MCD TRUE 446 -DLBCL10860T MCD 1 0 469,283,299,1110,550,1036,690,326,817,721 0.153,0.157,0.179,0.188,0.195,0.203,0.215,0.228,0.232,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4418315752756 0 0 51.44183157527556 51.44183157527556 MCD TRUE 447 -DLBCL10863T MCD 0.7829501981955937 0 533,772,933,859,749,400,567,436,775,532 0.126,0.135,0.174,0.177,0.196,0.222,0.225,0.228,0.241,0.249 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 11.5851032559213,41.7902196405317 0 0 53.375322896453056 41.79021964053173 MCD TRUE 448 -DLBCL10864T MCD 1 0 829,573,856,798,519,44,528,1047,804,1054 0.15,0.223,0.255,0.284,0.304,0.316,0.319,0.327,0.34,0.351 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0579401773711 0 0 37.05794017737112 37.05794017737112 MCD TRUE 449 -DLBCL10866T MCD 0.9056753929140929 0 155,350,176,312,778,500,990,417,918,1048 0.106,0.128,0.134,0.142,0.152,0.155,0.157,0.159,0.161,0.163 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.57613586088433,63.1420009440223 0 0 69.718136804906635 63.1420009440223 MCD TRUE 450 -DLBCL10867T MCD 1 0 33,902,1008,276,468,1021,1076,465,568,961 0.123,0.145,0.17,0.181,0.181,0.202,0.211,0.214,0.221,0.227 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.2916670356899 0 0 55.291667035689876 55.291667035689876 MCD TRUE 451 -DLBCL10870T MCD 0.9074182587248952 0 978,1101,878,295,367,961,621,465,39,514 0.151,0.152,0.154,0.167,0.169,0.181,0.199,0.216,0.264,0.264 MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD 49.349544355056,5.03501742839545 0 0 54.384561783451474 49.34954435505602 MCD TRUE 452 -DLBCL10876T MCD 1 0 549,296,526,580,1042,494,298,678,920,16 0.119,0.14,0.151,0.161,0.166,0.17,0.19,0.19,0.194,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.0201743590276 0 0 61.020174359027635 61.020174359027635 MCD TRUE 453 -DLBCL10879T EZB 0.9111859995944036 0 67,429,658,241,1050,142,645,581,744,643 0.135,0.21,0.227,0.234,0.245,0.247,0.258,0.275,0.278,0.281 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 39.7911259847754,3.87847166322142 0 0 43.66959764799681 39.79112598477539 EZB TRUE 454 -DLBCL10880T BN2 0.6082094632070897 0 1112,579,123,506,521,1045,1061,663,347,799 0.167,0.293,0.301,0.347,0.381,0.404,0.424,0.459,0.463,0.486 BN2,N1,N1,BN2,MCD,BN2,BN2,ST2,BN2,BN2 17.9056664141258,2.62149760548025,6.73619542230644,2.17660737430436 0 0 29.439966816216796 17.90566641412575 MCD FALSE 455 -DLBCL10883T MCD 1 0 414,188,340,987,461,869,932,536,511,565 0.14,0.17,0.176,0.177,0.18,0.199,0.209,0.214,0.245,0.246 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.5691879907226 0 0 52.56918799072262 52.56918799072262 MCD TRUE 456 -DLBCL10887T MCD 0.8820616000241736 0 154,383,1070,445,441,934,480,749,789,713 0.197,0.246,0.309,0.369,0.442,0.443,0.5,0.501,0.514,0.52 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 3.24139883929494,24.2424303457654 0 0 27.483829185060337 24.2424303457654 MCD TRUE 457 -DLBCL10889T MCD 1 0 522,717,480,489,376,154,837,128,198,934 0.276,0.305,0.399,0.476,0.488,0.507,0.516,0.57,0.574,0.579 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 22.6979994369171 0 0 22.69799943691709 22.69799943691709 MCD TRUE 458 -DLBCL10890T MCD 1 0 314,897,739,513,520,288,991,147,106,730 0.127,0.14,0.14,0.166,0.174,0.192,0.198,0.257,0.257,0.265 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.6992742708396 0 0 55.69927427083961 55.69927427083961 MCD TRUE 459 -DLBCL10891T MCD 0.9183422431558526 0 730,888,1043,513,66,459,314,739,897,520 0.123,0.149,0.151,0.206,0.258,0.266,0.28,0.281,0.305,0.316 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 3.8807603297329,43.6439388502819 0 0 47.5246991800148 43.6439388502819 MCD TRUE 460 -DLBCL10893T MCD 1 0 987,414,932,340,565,869,1038,456,415,188 0.116,0.145,0.165,0.167,0.168,0.176,0.179,0.18,0.19,0.197 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.6736549086766 0 0 60.67365490867657 60.67365490867657 MCD TRUE 461 -DLBCL10894T BN2 0.7375877595772992 0 470,1106,186,22,840,672,539,189,88,571 0.181,0.184,0.225,0.236,0.258,0.267,0.307,0.307,0.317,0.335 BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2,EZB 29.4562359002625,10.4796707329271 0 0 39.93590663318955 29.45623590026248 BN2 TRUE 462 -DLBCL10896T BN2 1 0 611,352,109,327,10,827,391,623,895,1098 0.177,0.191,0.221,0.272,0.281,0.312,0.313,0.331,0.332,0.343 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 37.9976998467448 0 0 37.9976998467448 37.9976998467448 BN2 TRUE 463 -DLBCL10897T BN2 1 0 315,70,1062,515,973,282,175,318,197,331 0.329,0.407,0.411,0.42,0.432,0.434,0.438,0.439,0.454,0.456 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.8862017124927 0 0 23.886201712492745 23.886201712492745 BN2 TRUE 464 -DLBCL10899T MCD 1 0 961,1021,568,978,878,33,328,1076,451,452 0.134,0.159,0.165,0.165,0.184,0.191,0.202,0.213,0.214,0.216 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.5001686107268 0 0 55.50016861072684 55.50016861072684 MCD TRUE 465 -DLBCL10900T MCD 1 0 223,128,985,790,837,697,489,713,198,475 0.167,0.181,0.213,0.262,0.277,0.282,0.292,0.304,0.324,0.347 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.8497418387377 0 0 39.84974183873769 39.84974183873769 MCD TRUE 466 -DLBCL10901T MCD 1 0 920,494,204,881,956,526,296,678,260,453 0.127,0.145,0.151,0.18,0.196,0.207,0.208,0.208,0.209,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.9517818054681 0 0 55.95178180546813 55.95178180546813 MCD TRUE 467 -DLBCL10902T MCD 1 0 1008,276,443,1076,451,328,33,1021,568,900 0.111,0.12,0.152,0.154,0.181,0.183,0.184,0.189,0.201,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.9435661871563 0 0 61.94356618715625 61.94356618715625 MCD TRUE 468 -DLBCL10904T MCD 1 0 447,550,283,1036,1110,299,326,821,416,690 0.153,0.167,0.17,0.175,0.181,0.185,0.202,0.211,0.213,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8227545013049 0 0 53.82275450130492 53.82275450130492 MCD TRUE 469 -DLBCL10905T BN2 0.6922706337929736 0 22,462,840,1106,539,189,88,186,672,571 0.166,0.181,0.203,0.217,0.229,0.235,0.237,0.241,0.284,0.288 EZB,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,EZB 31.1994785728352,13.8688473821345 0 0 45.06832595496964 31.199478572835154 BN2 TRUE 470 -DLBCL10910T EZB 1 0 235,908,76,234,4,823,921,766,1100,168 0.156,0.175,0.196,0.207,0.224,0.238,0.244,0.248,0.262,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0906961946751 0 0 46.090696194675125 46.090696194675125 EZB TRUE 471 -DLBCL10912T BN2 0.9250408364804547 0 651,1019,179,659,666,652,185,1113,42,127 0.167,0.186,0.19,0.21,0.223,0.238,0.288,0.307,0.308,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2 40.0367255800668,3.2443102414396 0 0 43.281035821506435 40.036725580066836 BN2 TRUE 472 -DLBCL10913T BN2 1 0 492,577,348,391,623,464,95,980,507,827 0.138,0.293,0.363,0.395,0.416,0.47,0.482,0.485,0.506,0.551 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.3939058689247 0 0 28.393905868924712 28.393905868924712 BN2 TRUE 473 -DLBCL10915T BN2 1 0 795,1060,630,335,584,650,477,943,674,855 0.218,0.283,0.305,0.329,0.38,0.389,0.391,0.4,0.414,0.441 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3896963228852 0 0 29.389696322885243 29.389696322885243 N1 FALSE 474 -DLBCL10916T MCD 1 0 1054,697,1047,790,44,616,856,1093,502,198 0.169,0.172,0.19,0.195,0.253,0.271,0.288,0.29,0.29,0.296 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.4922070128011 0 0 43.49220701280112 43.49220701280112 MCD TRUE 475 -DLBCL10917T ST2 0.7086517158582633 0 300,275,783,792,148,850,1086,370,218,692 0.366,0.392,0.504,0.534,0.569,0.597,0.601,0.602,0.641,0.653 ST2,ST2,EZB,BN2,ST2,EZB,ST2,ST2,ST2,ST2 1.87347388784947,3.65846440866614,13.4554338543511 0 0 18.987372150866726 13.455433854351108 EZB FALSE 476 -DLBCL10918T BN2 1 0 943,335,345,630,780,1060,99,650,959,999 0.142,0.179,0.188,0.189,0.205,0.208,0.214,0.222,0.246,0.265 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.9465014986444 0 0 49.946501498644416 49.946501498644416 BN2 TRUE 477 -DLBCL10919T EZB 0.7830642452219093 0 3,55,230,771,54,782,733,19,800,40 0.164,0.23,0.248,0.268,0.338,0.353,0.39,0.391,0.434,0.435 EZB,MCD,EZB,EZB,MCD,EZB,EZB,EZB,EZB,EZB 26.4162342267493,7.31820632258688 0 0 33.73444054933623 26.416234226749346 EZB TRUE 478 -DLBCL10924T MCD 0.8220199636454484 0 260,1046,956,169,1103,918,778,433,769,467 0.124,0.147,0.153,0.177,0.195,0.198,0.208,0.214,0.216,0.225 MCD,MCD,MCD,MCD,ST2,MCD,BN2,MCD,MCD,MCD 4.79702005110951,45.79459401268,5.11821785999088 0 0 55.70983192378038 45.79459401267999 MCD TRUE 479 -DLBCL10925T MCD 1 0 376,934,445,458,154,859,202,532,749,933 0.229,0.297,0.373,0.399,0.423,0.478,0.48,0.489,0.489,0.498 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 25.5555629100752 0 0 25.555562910075214 25.555562910075214 MCD TRUE 480 -DLBCL10927T N1 0.3688937641437906 0 530,293,803,919,119,1066,1061,123,43,554 0.15,0.263,0.295,0.335,0.382,0.4,0.4,0.414,0.431,0.448 N1,MCD,BN2,BN2,BN2,N1,BN2,N1,ST2,ST2 11.4908343917032,3.80435216503759,11.6000768656191,4.55031249444391 0 0 31.44557591680384 11.600076865619098 BN2 FALSE 481 -DLBCL10928T BN2 1 0 401,957,578,951,1111,353,566,26,264,640 0.134,0.171,0.179,0.186,0.199,0.214,0.232,0.264,0.264,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.3720312093218 0 0 49.37203120932184 49.37203120932184 BN2 TRUE 482 -DLBCL10929T ST2 0.9000448207511254 0 207,656,728,1057,362,807,822,263,60,797 0.129,0.184,0.24,0.241,0.255,0.33,0.341,0.359,0.378,0.425 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.9158584869313,35.2602854243447 0 0 39.176143911275986 35.26028542434469 ST2 TRUE 483 -DLBCL10931T BN2 0.6901524035344244 0 742,968,42,180,651,100,694,472,701,34 0.25,0.262,0.279,0.29,0.311,0.313,0.324,0.338,0.349,0.355 BN2,BN2,EZB,MCD,BN2,MCD,BN2,BN2,BN2,BN2 22.7578783349603,3.58410158548734,6.6331690733284 0 0 32.97514899377603 22.757878334960285 BN2 TRUE 484 -DLBCL10932T ST2 0.5166418404777333 0 936,558,529,698,321,928,490,1049,262,946 0.241,0.246,0.297,0.357,0.36,0.391,0.41,0.424,0.425,0.448 BN2,ST2,ST2,ST2,BN2,BN2,ST2,ST2,BN2,BN2 14.0589104551165,15.0269965025974 0 0 29.085906957713938 15.026996502597438 ST2 TRUE 485 -DLBCL10934T N1 0.6678914274316028 0 325,854,516,137,437,1099,1104,41,496,893 0.273,0.277,0.291,0.326,0.331,0.339,0.343,0.349,0.364,0.368 ST2,BN2,N1,N1,BN2,N1,N1,N1,N1,N1 6.62764372014819,20.6972563418333,3.66405336930486 0 0 30.988953431286358 20.69725634183331 ST2 FALSE 486 -DLBCL10935T BN2 0.8817658472542491 0 212,77,358,390,634,636,633,561,493,1069 0.159,0.185,0.199,0.222,0.223,0.237,0.239,0.249,0.26,0.263 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.3724164083687,5.41345354121632 0 0 45.78586994958502 40.3724164083687 BN2 TRUE 487 -DLBCL10937T BN2 1 0 333,1077,197,63,341,518,563,763,559,331 0.133,0.161,0.163,0.166,0.169,0.178,0.18,0.182,0.184,0.186 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.3048899955353 0 0 59.30488999553529 59.30488999553529 BN2 TRUE 488 -DLBCL10938T MCD 1 0 837,128,198,466,522,790,697,223,717,985 0.147,0.236,0.238,0.292,0.305,0.316,0.336,0.337,0.34,0.403 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.473744099333 0 0 36.47374409933305 36.47374409933305 MCD TRUE 489 -DLBCL10939T ST2 0.7120159392322972 0 970,946,281,59,529,24,558,396,422,485 0.212,0.224,0.312,0.314,0.32,0.328,0.356,0.405,0.408,0.41 BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 9.18959640325409,22.720490490988 0 0 31.910086894242077 22.72049049098799 ST2 TRUE 490 -DLBCL10940T MCD 1 0 416,821,326,311,1036,550,332,323,1027,906 0.118,0.122,0.137,0.141,0.159,0.168,0.172,0.206,0.208,0.216 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.4261471207709 0 0 63.426147120770906 63.426147120770906 MCD TRUE 491 -DLBCL10942T BN2 1 0 473,577,348,391,623,980,95,464,827,507 0.138,0.264,0.329,0.363,0.392,0.448,0.498,0.505,0.513,0.514 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.4866807557847 0 0 29.486680755784736 29.486680755784736 BN2 TRUE 492 -DLBCL10947T BN2 0.8896151582630442 0 633,1069,634,77,898,212,498,487,399,390 0.125,0.149,0.17,0.175,0.179,0.209,0.254,0.26,0.26,0.295 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 46.0126335421799,5.70931961340554 0 0 51.72195315558544 46.0126335421799 BN2 TRUE 493 -DLBCL10949T MCD 1 0 920,467,296,453,549,526,881,204,580,678 0.124,0.145,0.164,0.17,0.176,0.177,0.181,0.184,0.192,0.194 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.6205911596787 0 0 59.62059115967871 59.62059115967871 MCD TRUE 494 -DLBCL10950T BN2 0.6646012771584993 0 23,855,802,659,666,699,14,891,674,388 0.17,0.178,0.241,0.266,0.277,0.284,0.285,0.287,0.288,0.302 EZB,BN2,ST2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 26.8111889771956,5.89548181743369,3.48243241611564,4.15266244433592 0 0 40.341765655080884 26.811188977195638 MCD FALSE 495 -DLBCL10952T N1 0.7106529536696489 0 1099,1104,516,41,438,854,486,437,325,137 0.137,0.167,0.252,0.257,0.34,0.362,0.364,0.372,0.372,0.376 N1,N1,N1,N1,N1,BN2,ST2,BN2,ST2,N1 5.44891373137968,26.7268608685611,5.43310429171755 0 0 37.608878891658364 26.726860868561126 N1 TRUE 496 -DLBCL10953T BN2 0.5541341867049107 0 104,34,103,894,442,701,431,845,521,742 0.207,0.251,0.253,0.309,0.321,0.324,0.358,0.396,0.434,0.436 MCD,BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD,BN2 17.7995768941426,14.3218430095553 0 0 32.12141990369792 17.799576894142575 BN2 TRUE 497 -DLBCL10957T BN2 0.9218291529946979 0 569,898,1022,1069,399,26,493,566,633,77 0.196,0.198,0.209,0.216,0.227,0.241,0.254,0.272,0.276,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 39.3622899343735,3.33791086368035 0 0 42.70020079805389 39.36228993437354 BN2 TRUE 498 -DLBCL10960T BN2 1 0 261,404,555,527,543,517,546,929,1001,1023 0.157,0.158,0.175,0.185,0.191,0.2,0.212,0.219,0.23,0.231 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.0052855476226 0 0 52.005285547622556 52.005285547622556 BN2 TRUE 499 -DLBCL10961T MCD 1 0 1109,350,450,712,417,155,1048,879,176,312 0.134,0.142,0.155,0.158,0.159,0.159,0.163,0.166,0.173,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5474141786596 0 0 63.54741417865959 63.54741417865959 MCD TRUE 500 -DLBCL10966T BN2 1 0 654,342,38,518,63,763,333,542,958,702 0.157,0.204,0.217,0.222,0.24,0.258,0.262,0.275,0.287,0.293 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.7666483388759 0 0 42.76664833887594 42.76664833887594 BN2 TRUE 501 -DLBCL10967T MCD 0.8654238359032304 0 616,574,1093,1047,1054,475,992,856,44,449 0.119,0.172,0.2,0.237,0.238,0.29,0.295,0.328,0.346,0.352 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.4243445385006,5.81960482584914 0 0 43.24394936434978 37.42434453850065 MCD TRUE 502 -DLBCL10968T BN2 1 0 562,705,560,524,1098,623,553,507,109,258 0.185,0.222,0.278,0.325,0.338,0.343,0.376,0.386,0.389,0.403 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 32.7860788910709 0 0 32.78607889107087 32.78607889107087 BN2 TRUE 503 -DLBCL10972T BN2 0.5850536612677033 0 14,699,802,891,495,23,388,127,552,855 0.17,0.187,0.2,0.217,0.307,0.312,0.313,0.352,0.37,0.373 BN2,BN2,ST2,MCD,MCD,EZB,BN2,BN2,BN2,BN2 22.6599068780435,3.20921246066632,7.86859671996845,4.99361472035434 0 0 38.731330779032646 22.659906878043536 MCD FALSE 504 -DLBCL10973T BN2 0.5235868345588223 0 665,976,131,971,132,967,924,512,1085,582 0.215,0.224,0.225,0.237,0.241,0.245,0.257,0.28,0.291,0.298 BN2,BN2,BN2,EZB,BN2,EZB,ST2,N1,EZB,BN2 21.0848994317616,11.7315311022638,3.56826310864624,3.88541637552205 0 0 40.27011001819369 21.084899431761553 N1 FALSE 505 -DLBCL10974T BN2 0.5476785882515381 0 1061,799,123,1112,347,698,455,293,919,624 0.212,0.239,0.272,0.308,0.31,0.343,0.347,0.363,0.374,0.376 BN2,BN2,N1,BN2,BN2,ST2,MCD,MCD,BN2,EZB 18.0344360763527,2.6600149608685,5.63689664923125,3.68263971595758,2.91488084845143 0 0 32.928868250861434 18.034436076352687 BN2 TRUE 506 -DLBCL10975T BN2 1 0 705,503,524,623,560,562,464,473,391,492 0.268,0.386,0.411,0.422,0.457,0.463,0.481,0.506,0.508,0.514 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.4285876607882 0 0 23.42858766078821 23.42858766078821 BN2 TRUE 507 -DLBCL10978T ST2 0.9251715480722383 0 339,60,394,909,911,728,899,1081,278,910 0.182,0.272,0.297,0.306,0.327,0.365,0.372,0.402,0.418,0.424 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 2.35919809821575,29.1688909833717 0 0 31.528089081587446 29.168890983371693 ST2 TRUE 508 -DLBCL10981T EZB 1 0 781,736,1052,1064,249,217,168,216,766,819 0.228,0.242,0.249,0.271,0.277,0.355,0.36,0.362,0.405,0.423 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0126352302327 0 0 33.01263523023268 33.01263523023268 EZB TRUE 509 -DLBCL10983T MCD 0.7780961693764602 0 420,775,567,541,400,444,170,200,201,448 0.126,0.148,0.162,0.227,0.243,0.248,0.25,0.254,0.269,0.288 MCD,EZB,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 10.7767846044287,37.7883283733258 0 0 48.56511297775457 37.78832837332585 MCD TRUE 510 -DLBCL10984T MCD 0.8952489312790529 0 722,536,721,937,1087,869,340,690,414,299 0.118,0.135,0.154,0.162,0.164,0.187,0.192,0.196,0.231,0.235 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.16378120860865,52.678398483407 0 0 58.84217969201565 52.678398483407 MCD TRUE 511 -DLBCL10985T BN2 0.5847639886806556 0 126,131,665,864,924,505,967,582,132,976 0.153,0.17,0.203,0.208,0.261,0.28,0.285,0.323,0.33,0.358 EZB,BN2,BN2,BN2,ST2,N1,EZB,BN2,BN2,BN2 24.5415521380212,10.0232586558481,3.56826310864624,3.83522888813999 0 0 41.96830279065556 24.54155213802123 N1 FALSE 512 -DLBCL10987T MCD 1 0 459,314,739,730,1043,897,460,520,888,147 0.166,0.175,0.175,0.201,0.205,0.205,0.206,0.21,0.237,0.253 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.9481428110507 0 0 49.948142811050694 49.948142811050694 MCD TRUE 513 -DLBCL10992T MCD 1 0 39,130,367,1027,1101,906,323,332,349,452 0.169,0.187,0.2,0.209,0.213,0.22,0.225,0.251,0.252,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.4853263116606 0 0 46.48532631166059 46.48532631166059 MCD TRUE 514 -DLBCL10993T BN2 1 0 70,815,998,945,591,796,354,527,75,305 0.116,0.192,0.203,0.239,0.239,0.256,0.264,0.265,0.267,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.8536118989926 0 0 45.85361189899258 45.85361189899258 BN2 TRUE 515 -DLBCL10995T N1 0.551849683347814 0 854,437,496,1099,1104,486,41,325,137,523 0.213,0.222,0.252,0.257,0.286,0.291,0.356,0.395,0.426,0.464 BN2,BN2,N1,N1,N1,ST2,N1,ST2,N1,N1 9.20485235171345,18.6828226444649,5.96723732546138 0 0 33.854912321639716 18.682822644464885 N1 TRUE 516 -DLBCL10996T BN2 1 0 543,261,404,1001,499,1071,548,546,646,555 0.11,0.169,0.189,0.195,0.2,0.229,0.233,0.235,0.267,0.272 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.7090996434564 0 0 50.709099643456376 50.709099643456376 BN2 TRUE 517 -DLBCL10998T BN2 1 0 63,333,654,342,542,488,763,559,1077,501 0.119,0.144,0.167,0.169,0.171,0.178,0.193,0.201,0.221,0.222 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 57.9419994487296 0 0 57.941999448729646 57.941999448729646 BN2 TRUE 518 -DLBCL10999T MCD 1 0 856,44,446,573,108,1035,528,449,794,798 0.219,0.229,0.233,0.253,0.258,0.296,0.298,0.304,0.305,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.5908891991375 0 0 37.59088919913755 37.59088919913755 MCD TRUE 519 -DLBCL11000T MCD 1 0 739,314,897,288,459,147,513,991,729,106 0.138,0.147,0.166,0.17,0.174,0.2,0.21,0.211,0.219,0.246 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.8784903783682 0 0 54.87849037836819 54.87849037836819 MCD TRUE 520 -DLBCL11001T BN2 0.7345594177764221 0 442,701,431,455,34,694,1112,579,497,104 0.251,0.318,0.367,0.381,0.382,0.398,0.412,0.423,0.434,0.469 BN2,BN2,BN2,MCD,BN2,BN2,BN2,N1,BN2,MCD 19.7088205614284,4.75578535650314,2.36619916668474 0 0 26.830805084616312 19.708820561428425 MCD FALSE 521 -DLBCL11004T MCD 1 0 717,458,489,837,198,128,466,446,790,154 0.189,0.276,0.305,0.343,0.398,0.417,0.489,0.492,0.513,0.517 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 27.9832236373133 0 0 27.98322363731327 27.98322363731327 MCD TRUE 522 -DLBCL11005T N1 0.7901927423032509 0 137,325,41,174,1104,1099,496,486,516,2 0.167,0.224,0.231,0.292,0.319,0.351,0.386,0.389,0.464,0.494 N1,ST2,N1,N1,N1,N1,N1,ST2,N1,N1 26.4921479813844,7.03403691391746 0 0 33.5261848953019 26.492147981384438 N1 TRUE 523 -DLBCL11181T BN2 0.8832873175972394 0 560,704,705,562,503,507,279,1098,553,110 0.195,0.299,0.3,0.322,0.325,0.411,0.45,0.495,0.504,0.505 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 25.2704586143783,3.33909810734642 0 0 28.609556721724747 25.270458614378324 BN2 TRUE 524 -DLBCL11182T MCD 0.4002364211795327 0 779,43,716,124,205,803,1045,789,617,421 0.179,0.213,0.22,0.293,0.313,0.332,0.368,0.369,0.384,0.399 MCD,ST2,BN2,N1,MCD,BN2,BN2,MCD,N1,MCD 10.2668369720563,13.997325508784,6.01254846251665,4.69593214477731 0 0 34.9726430881343 13.997325508783991 MCD TRUE 525 -DLBCL11187T MCD 0.9145816672953094 0 580,678,1042,453,881,549,494,296,844,16 0.115,0.139,0.148,0.151,0.165,0.17,0.177,0.186,0.187,0.197 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 5.35257853160209,57.3105332632288 0 0 62.66311179483094 57.31053326322885 MCD TRUE 526 -DLBCL11191T BN2 1 0 591,555,796,1023,499,404,998,815,261,905 0.15,0.152,0.158,0.173,0.185,0.213,0.221,0.225,0.225,0.233 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2185865310043 0 0 53.218586531004306 53.218586531004306 BN2 TRUE 527 -DLBCL11195T MCD 1 0 297,798,804,413,794,434,17,108,573,166 0.145,0.148,0.152,0.178,0.18,0.185,0.186,0.191,0.198,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.4270805330322 0 0 57.4270805330322 57.4270805330322 MCD TRUE 528 -DLBCL11196T ST2 0.7531326878151179 0 830,381,485,490,412,698,558,1066,970,946 0.266,0.281,0.297,0.32,0.361,0.361,0.365,0.408,0.415,0.421 ST2,ST2,ST2,ST2,ST2,ST2,ST2,N1,BN2,BN2 4.78700278570589,2.45308933110362,22.0877765780435 0 0 29.327868694853 22.087776578043492 ST2 TRUE 529 -DLBCL11203T BN2 0.7015701618727962 0 481,293,919,803,1061,123,119,1066,1045,506 0.15,0.219,0.299,0.315,0.351,0.37,0.385,0.392,0.45,0.452 BN2,MCD,BN2,BN2,BN2,N1,BN2,N1,BN2,BN2 23.0902119669446,4.56806360908603,5.25391656353637 0 0 32.912192139566955 23.09021196694456 N1 FALSE 530 -DLBCL11205T BN2 0.9176420618033492 0 952,808,10,352,258,895,109,408,187,553 0.196,0.22,0.227,0.308,0.319,0.323,0.33,0.348,0.356,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 31.987977957949,2.87090579361559 0 0 34.85888375156461 31.987977957949017 BN2 TRUE 531 -DLBCL11242T MCD 0.9035380869000501 0 201,859,1082,448,533,772,933,200,749,567 0.196,0.221,0.245,0.249,0.261,0.262,0.27,0.277,0.287,0.291 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 3.81974693188095,35.7787516788929 0 0 39.59849861077381 35.77875167889286 MCD TRUE 532 -DLBCL11256T MCD 0.7689056783881708 0 772,448,933,859,749,436,400,567,532,775 0.11,0.126,0.151,0.167,0.171,0.23,0.233,0.25,0.261,0.266 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 12.8839302653778,42.8678951170693 0 0 55.75182538244707 42.86789511706931 MCD TRUE 533 -DLBCL11257T EZB 0.5167621771356284 0 813,94,193,371,444,930,170,419,510,541 0.129,0.193,0.201,0.245,0.311,0.321,0.334,0.374,0.379,0.396 EZB,EZB,BN2,EZB,EZB,ST2,MCD,MCD,MCD,MCD 4.98437973770489,20.2428215580019,10.8329701648694,3.11224179576207 0 0 39.17241325633826 20.24282155800191 MCD FALSE 534 -DLBCL11258T BN2 0.7987519745556503 0 173,624,765,196,347,773,301,1049,572,1112 0.131,0.207,0.208,0.27,0.277,0.311,0.336,0.355,0.446,0.448 BN2,EZB,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2 30.3858705022205,4.8385874993476,2.81722636374981 0 0 38.041684365317884 30.38587050222047 BN2 TRUE 535 -DLBCL11353T MCD 0.90564133910778 0 511,722,869,340,1087,937,721,414,456,415 0.135,0.149,0.156,0.158,0.183,0.184,0.185,0.197,0.214,0.22 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.42655843220515,52.0833551347494 0 0 57.50991356695458 52.08335513474943 MCD TRUE 536 -DLBCL11428T EZB 1 0 647,719,157,377,51,939,233,1034,960,378 0.253,0.276,0.355,0.367,0.413,0.413,0.415,0.419,0.432,0.482 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.145267090822 0 0 27.145267090821957 27.145267090821957 EZB TRUE 537 -DLBCL11429T BN2 1 0 435,99,650,700,955,1010,780,731,943,959 0.149,0.187,0.187,0.198,0.213,0.214,0.222,0.223,0.242,0.244 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.045762267134 0 0 49.04576226713399 49.04576226713399 BN2 TRUE 538 -DLBCL11430T BN2 0.8288073907137542 0 88,189,22,470,358,840,570,462,571,487 0.129,0.133,0.214,0.229,0.242,0.253,0.261,0.307,0.316,0.326 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2 37.9506723509583,7.83882322566278 0 0 45.78949557662111 37.95067235095833 EZB FALSE 539 -DLBCL11432T BN2 1 0 929,841,546,905,373,555,1023,395,1001,499 0.133,0.137,0.179,0.184,0.191,0.215,0.231,0.232,0.248,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.469331659313 0 0 52.469331659313006 52.469331659313006 MCD FALSE 540 -DLBCL11433T MCD 0.84385197082676 0 200,170,419,510,1082,201,775,420,567,371 0.166,0.169,0.18,0.227,0.229,0.238,0.246,0.248,0.265,0.332 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,EZB 7.08546693957048,38.291134847119 0 0 45.37660178668948 38.291134847119 MCD TRUE 541 -DLBCL11434T BN2 1 0 559,63,518,305,702,75,333,354,488,1077 0.15,0.158,0.171,0.178,0.179,0.179,0.179,0.188,0.197,0.2 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.6440807790698 0 0 56.64408077906984 56.64408077906984 BN2 TRUE 542 -DLBCL11435T BN2 1 0 517,261,404,499,1001,546,1071,548,555,929 0.11,0.164,0.184,0.191,0.191,0.227,0.23,0.242,0.262,0.263 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.611235388721 0 0 51.611235388721006 51.611235388721006 BN2 TRUE 543 -DLBCL11437T ST2 0.9294686914386189 0 309,1067,899,909,1007,590,508,797,1096,339 0.126,0.302,0.345,0.363,0.373,0.424,0.432,0.444,0.459,0.463 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2 2.17678777842418,28.6859286920889 0 0 30.86271647051308 28.685928692088904 ST2 TRUE 544 -DLBCL11439T EZB 0.9059349678616722 0 846,620,144,736,1052,689,249,847,143,786 0.263,0.308,0.313,0.358,0.374,0.375,0.387,0.402,0.408,0.413 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 25.6639245612075,2.66473640414133 0 0 28.328660965348824 25.663924561207494 ST2 FALSE 545 -DLBCL11443T BN2 0.8890255889917776 0 929,841,1001,540,499,555,543,1071,517,905 0.151,0.166,0.169,0.179,0.212,0.224,0.227,0.227,0.235,0.248 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 44.8197316095137,5.59471333390202 0 0 50.414444943415766 44.819731609513745 BN2 TRUE 546 -DLBCL11444T EZB 1 0 873,387,107,757,1117,102,848,84,740,140 0.224,0.324,0.327,0.349,0.365,0.385,0.394,0.402,0.405,0.405 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.7777581046657 0 0 28.777758104665708 28.777758104665708 EZB TRUE 547 -DLBCL11445T BN2 1 0 646,959,999,780,1071,517,345,99,435,543 0.14,0.168,0.207,0.214,0.219,0.233,0.234,0.237,0.238,0.242 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.3216782531329 0 0 48.321678253132866 48.321678253132866 BN2 TRUE 548 -DLBCL11447T MCD 1 0 453,296,526,298,494,580,1042,920,16,678 0.119,0.127,0.17,0.171,0.176,0.18,0.181,0.198,0.205,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.4717947188702 0 0 59.47179471887016 59.47179471887016 MCD TRUE 549 -DLBCL11450T MCD 1 0 1036,326,416,821,283,469,491,447,311,349 0.108,0.135,0.15,0.152,0.159,0.167,0.168,0.195,0.204,0.224 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.6422637989661 0 0 62.64226379896612 62.64226379896612 MCD TRUE 550 -DLBCL11451T MCD 1 0 434,851,767,804,246,762,166,17,798,528 0.177,0.186,0.207,0.21,0.216,0.216,0.218,0.243,0.247,0.261 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.4263092771587 0 0 46.42630927715871 46.42630927715871 MCD TRUE 551 -DLBCL11452T MCD 0.5162070018973152 0 135,388,285,891,127,100,14,386,185,180 0.2,0.28,0.283,0.288,0.307,0.313,0.318,0.32,0.321,0.339 MCD,BN2,BN2,MCD,BN2,MCD,BN2,MCD,BN2,MCD 16.6194736455328,17.7329740143345 0 0 34.352447659867316 17.73297401433455 BN2 FALSE 552 -DLBCL11454T BN2 1 0 187,1098,258,109,355,562,10,352,503,531 0.15,0.158,0.167,0.277,0.285,0.307,0.308,0.363,0.376,0.379 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.631520643389 0 0 40.63152064338896 40.63152064338896 BN2 TRUE 553 -DLBCL11455T ST2 0.41283510615494007 0 747,673,1044,1089,124,935,682,803,43,177 0.178,0.247,0.292,0.306,0.353,0.381,0.383,0.397,0.398,0.41 ST2,BN2,MCD,MCD,N1,EZB,ST2,BN2,ST2,ST2 6.57172407355645,2.6235013567236,6.69713132456042,2.83079789870513,13.1642331140314 0 0 31.887387767576985 13.164233114031383 ST2 TRUE 554 -DLBCL11457T BN2 0.9131142736375694 0 527,1023,499,796,591,905,929,540,546,404 0.152,0.157,0.175,0.177,0.183,0.193,0.198,0.215,0.224,0.225 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 48.9428824767028,4.6570708804266 0 0 53.59995335712942 48.94288247670283 BN2 TRUE 555 -DLBCL11458T EZB 1 0 811,724,272,979,596,628,853,732,310,750 0.167,0.233,0.272,0.285,0.293,0.303,0.307,0.338,0.344,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1886409944813 0 0 36.18864099448135 36.18864099448135 EZB TRUE 556 -DLBCL11459T ST2 1 0 793,1053,982,1016,692,598,1086,642,177,940 0.147,0.156,0.205,0.212,0.244,0.276,0.287,0.299,0.322,0.33 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.5327255005947 0 0 43.53272550059469 43.53272550059469 ST2 TRUE 557 -DLBCL11460T BN2 0.6671704129248003 0 485,936,321,946,970,490,529,670,86,928 0.246,0.333,0.338,0.342,0.35,0.356,0.365,0.387,0.394,0.417 ST2,BN2,BN2,BN2,BN2,ST2,ST2,BN2,BN2,BN2 19.2581229216921,9.6072502252774 0 0 28.86537314696946 19.258122921692056 ST2 FALSE 558 -DLBCL11461T BN2 1 0 354,542,1077,75,331,945,305,63,488,333 0.142,0.15,0.157,0.16,0.161,0.169,0.178,0.183,0.184,0.184 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.4139160516377 0 0 60.4139160516377 60.4139160516377 BN2 TRUE 559 -DLBCL11464T BN2 0.9042039892467344 0 524,562,503,705,704,1098,553,279,110,187 0.195,0.244,0.278,0.31,0.339,0.405,0.41,0.412,0.444,0.448 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 27.8720719464608,2.95291033400912 0 0 30.82498228046997 27.87207194646085 BN2 TRUE 560 -DLBCL11473T BN2 0.8057727181268259 0 636,926,399,575,487,77,212,95,1069,633 0.113,0.199,0.199,0.221,0.249,0.27,0.281,0.281,0.283,0.311 BN2,ST2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 36.2517012809773,3.70270878555701,5.03557341820238 0 0 44.98998348473665 36.25170128097726 BN2 TRUE 561 -DLBCL11474T BN2 1 0 503,560,1098,705,553,524,258,187,109,623 0.185,0.244,0.282,0.294,0.307,0.322,0.347,0.354,0.367,0.408 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 33.7080507377908 0 0 33.708050737790785 33.708050737790785 BN2 TRUE 562 -DLBCL11477T BN2 1 0 763,15,341,488,871,175,333,197,282,342 0.156,0.174,0.178,0.18,0.184,0.188,0.196,0.199,0.2,0.209 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.980016215704 0 0 53.98001621570405 53.98001621570405 BN2 TRUE 563 -DLBCL11482T BN2 1 0 357,393,58,158,1028,572,968,172,777,651 0.157,0.182,0.207,0.257,0.35,0.381,0.444,0.466,0.468,0.502 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.5850922931558 0 0 34.585092293155796 34.585092293155796 BN2 TRUE 564 -DLBCL11489T MCD 1 0 1038,679,415,461,987,869,340,932,414,966 0.12,0.14,0.148,0.168,0.178,0.194,0.203,0.204,0.207,0.218 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.164803451732 0 0 58.16480345173203 58.16480345173203 MCD TRUE 565 -DLBCL11490T BN2 1 0 353,26,569,951,1022,482,401,498,578,957 0.187,0.192,0.203,0.213,0.223,0.232,0.261,0.272,0.288,0.301 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2706569829355 0 0 43.27065698293551 43.27065698293551 BN2 TRUE 566 -DLBCL11491T MCD 0.7582851704462675 0 775,420,510,400,448,436,533,201,772,541 0.119,0.141,0.162,0.202,0.225,0.248,0.25,0.251,0.259,0.265 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 12.2497723417704,38.4288408173255 0 0 50.678613159095846 38.42884081732545 MCD TRUE 567 -DLBCL11493T MCD 1 0 1021,328,1076,465,443,961,468,1008,33,276 0.119,0.139,0.158,0.165,0.196,0.199,0.201,0.201,0.203,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.5638731044218 0 0 57.56387310442182 57.56387310442182 MCD TRUE 568 -DLBCL11494T BN2 1 0 1022,26,498,566,898,399,353,482,1069,951 0.128,0.146,0.196,0.203,0.287,0.289,0.289,0.302,0.311,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8423845360634 0 0 44.84238453606339 44.84238453606339 BN2 TRUE 569 -DLBCL11496T BN2 0.7103487880525121 0 358,390,88,571,840,539,22,487,189,212 0.186,0.213,0.233,0.243,0.256,0.261,0.264,0.271,0.293,0.293 BN2,BN2,BN2,EZB,BN2,EZB,EZB,BN2,BN2,BN2 28.8046105565793,11.7453432703989 0 0 40.549953826978275 28.80461055657934 BN2 TRUE 570 -DLBCL11497T BN2 0.8039191730537639 0 840,22,570,186,672,1106,470,88,153,539 0.185,0.229,0.243,0.256,0.259,0.284,0.288,0.295,0.315,0.316 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 30.8586028278709,7.5266028771399 0 0 38.38520570501078 30.85860282787089 EZB FALSE 571 -DLBCL11498T BN2 0.8869845087558504 0 393,704,301,196,564,357,777,535,158,279 0.306,0.339,0.343,0.349,0.381,0.409,0.41,0.446,0.446,0.467 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 23.1766007247592,2.95305598961611 0 0 26.129656714375265 23.176600724759155 BN2 TRUE 572 -DLBCL11500T MCD 1 0 798,528,449,804,297,829,108,519,434,794 0.175,0.198,0.223,0.23,0.241,0.241,0.246,0.253,0.257,0.263 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.5923986199939 0 0 43.592398619993894 43.592398619993894 MCD TRUE 573 -DLBCL11503T MCD 0.9246001479721179 0 1093,502,616,1054,1047,475,1105,992,697,856 0.136,0.172,0.174,0.272,0.279,0.303,0.324,0.324,0.369,0.388 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.08943127580521,37.8845387349419 0 0 40.97397001074708 37.88453873494187 N1 FALSE 574 -DLBCL11506T BN2 0.8464013966349968 0 926,95,399,561,636,1022,498,569,1069,26 0.159,0.192,0.212,0.221,0.23,0.299,0.303,0.318,0.326,0.336 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 34.6106728250469,6.28088638391368 0 0 40.89155920896058 34.6106728250469 BN2 TRUE 575 -DLBCL11507T BN2 0.8111462473575551 0 462,87,470,189,539,1106,864,22,88,186 0.371,0.373,0.403,0.426,0.452,0.452,0.458,0.467,0.478,0.495 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2 18.7141863543878,4.35709877496143 0 0 23.071285129349206 18.714186354387774 EZB FALSE 576 -DLBCL11508T BN2 0.920412083072746 0 492,473,980,348,391,827,623,2,327,95 0.264,0.293,0.322,0.338,0.382,0.434,0.455,0.471,0.471,0.484 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 24.5651907620178,2.12414895200272 0 0 26.689339714020512 24.56519076201779 BN2 TRUE 577 -DLBCL11511T BN2 1 0 1111,482,401,957,951,264,640,353,566,318 0.171,0.179,0.186,0.188,0.19,0.215,0.227,0.232,0.288,0.308 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.4620665464375 0 0 47.46206654643749 47.46206654643749 BN2 TRUE 578 -DLBCL11513T BN2 0.40059562807807286 0 1045,663,455,123,1112,716,801,421,521,442 0.246,0.269,0.293,0.337,0.359,0.385,0.408,0.422,0.423,0.476 BN2,ST2,MCD,N1,BN2,BN2,ST2,MCD,MCD,BN2 11.5509938026914,8.14837695161104,2.96925821045842,6.16591887618615 0 0 28.83454784094702 11.550993802691412 N1 FALSE 579 -DLBCL11514T MCD 0.9069658329607718 0 526,678,1042,453,881,844,549,16,494,296 0.115,0.137,0.138,0.161,0.169,0.173,0.18,0.189,0.192,0.198 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.79682945971136,56.5117787021385 0 0 62.30860816184983 56.51177870213848 MCD TRUE 580 -DLBCL11516T EZB 0.8880059732313779 0 385,975,369,645,1050,643,241,687,379,254 0.141,0.142,0.145,0.155,0.177,0.178,0.19,0.199,0.218,0.248 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 51.2022343484644,6.45755161237081 0 0 57.65978596083525 51.202234348464444 EZB TRUE 581 -DLBCL11518T BN2 0.45528573827442087 0 967,924,665,432,1085,153,864,505,672,512 0.154,0.163,0.233,0.236,0.245,0.253,0.279,0.298,0.304,0.323 EZB,ST2,BN2,BN2,EZB,BN2,BN2,N1,BN2,N1 19.3674167229409,10.5952458263947,6.45202512543017,6.12434901095472 0 0 42.53903668572047 19.367416722940916 BN2 TRUE 582 -DLBCL11519T ST2 0.7148586036625073 0 612,425,366,160,1029,896,785,161,668,662 0.151,0.176,0.187,0.218,0.249,0.264,0.266,0.267,0.281,0.3 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2,ST2 8.91903782277529,3.7633127236905,31.7950585893686 0 0 44.47740913583443 31.795058589368644 ST2 TRUE 583 -DLBCL11520T BN2 1 0 65,681,64,589,674,695,1113,795,652,1060 0.177,0.192,0.218,0.229,0.231,0.25,0.255,0.268,0.324,0.326 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.9246079486202 0 0 41.92460794862025 41.92460794862025 BN2 TRUE 584 -DLBCL11526T EZB 1 0 818,427,307,220,912,62,219,292,138,316 0.173,0.233,0.238,0.306,0.366,0.37,0.375,0.382,0.384,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.2912011501147 0 0 33.29120115011468 33.29120115011468 EZB TRUE 585 -DLBCL11527T EZB 0.5242189729618344 0 1091,320,974,834,685,194,824,657,1024,372 0.149,0.205,0.212,0.218,0.282,0.295,0.339,0.344,0.379,0.384 EZB,ST2,EZB,ST2,ST2,EZB,EZB,BN2,EZB,ST2 2.90873502968123,20.4043614612649,15.6102590411522 0 0 38.92335553209832 20.404361461264916 ST2 FALSE 586 -DLBCL11530T EZB 0.784827254364737 0 227,368,1056,245,595,995,329,857,867,618 0.143,0.157,0.17,0.182,0.184,0.187,0.195,0.197,0.202,0.207 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 7.00202315498083,43.5743910116191,4.94458203897852 0 0 55.52099620557848 43.574391011619134 EZB TRUE 587 -DLBCL11532T EZB 1 0 259,1115,224,737,225,183,268,619,981,238 0.131,0.176,0.19,0.2,0.24,0.251,0.258,0.271,0.283,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.29302205437 0 0 46.293022054370006 46.293022054370006 EZB TRUE 588 -DLBCL11537T BN2 1 0 64,65,584,695,681,1113,674,652,477,345 0.137,0.17,0.229,0.236,0.249,0.279,0.331,0.336,0.357,0.373 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.8658326598655 0 0 40.86583265986549 40.86583265986549 BN2 TRUE 589 -DLBCL11538T BN2 0.38637406855241607 0 964,1096,899,622,893,691,1084,909,854,437 0.173,0.186,0.278,0.298,0.299,0.331,0.344,0.347,0.408,0.409 BN2,EZB,ST2,ST2,N1,ST2,BN2,ST2,BN2,BN2 13.5882476393233,5.38140869280194,3.34321936211639,12.8557568442536 0 0 35.16863253849516 13.58824763932326 ST2 FALSE 590 -DLBCL11539T BN2 1 0 796,527,1023,998,555,305,75,815,905,499 0.12,0.15,0.162,0.174,0.183,0.202,0.21,0.23,0.23,0.234 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.0425512582718 0 0 55.04255125827184 55.04255125827184 BN2 TRUE 591 -DLBCL11541T EZB 0.7536409408794107 0 221,146,248,145,831,253,687,254,379,385 0.159,0.175,0.202,0.22,0.263,0.277,0.293,0.315,0.32,0.332 EZB,BN2,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 10.2613796458082,31.3907507139908 0 0 41.652130359799 31.390750713990787 EZB TRUE 592 -DLBCL11542T EZB 1 0 219,870,47,726,220,727,138,725,307,292 0.193,0.223,0.242,0.261,0.272,0.301,0.339,0.344,0.347,0.353 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.1787877353356 0 0 36.178787735335646 36.178787735335646 EZB TRUE 593 -DLBCL11558T EZB 1 0 805,876,80,632,29,667,760,46,953,989 0.138,0.16,0.163,0.166,0.171,0.172,0.182,0.194,0.203,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7798200970318 0 0 57.77982009703184 57.77982009703184 EZB TRUE 594 -DLBCL11559T EZB 0.7871826743970243 0 1056,867,587,857,618,809,227,995,368,1074 0.124,0.148,0.184,0.185,0.19,0.199,0.207,0.217,0.219,0.222 EZB,ST2,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.83826868343364,42.855089468438,6.7477402898143 0 0 54.441098441685966 42.85508946843803 EZB TRUE 595 -DLBCL11562T EZB 1 0 979,732,136,118,257,724,272,556,181,853 0.182,0.182,0.242,0.28,0.287,0.287,0.288,0.293,0.294,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.2412499646302 0 0 39.24124996463016 39.24124996463016 EZB TRUE 596 -DLBCL11563T EZB 1 0 709,21,250,868,164,251,1075,938,816,753 0.14,0.164,0.168,0.189,0.203,0.216,0.217,0.227,0.238,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1695079139727 0 0 51.16950791397265 51.16950791397265 EZB TRUE 597 -DLBCL11564T ST2 0.922048511983224 0 940,1068,825,1003,774,793,982,915,557,835 0.154,0.197,0.219,0.234,0.24,0.25,0.258,0.259,0.276,0.296 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 3.37767179500539,39.9527620548075 0 0 43.330433849812856 39.952762054807465 ST2 TRUE 598 -DLBCL11566T EZB 1 0 150,644,152,1037,151,418,866,1088,776,613 0.132,0.149,0.181,0.182,0.184,0.192,0.216,0.232,0.24,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.163480843763 0 0 53.16348084376302 53.16348084376302 EZB TRUE 599 -DLBCL11567T EZB 1 0 82,609,1080,751,57,292,138,726,427,725 0.162,0.196,0.244,0.279,0.301,0.326,0.329,0.407,0.469,0.469 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.1074343987878 0 0 35.107434398787845 35.107434398787845 EZB TRUE 600 -DLBCL11568T EZB 1 0 688,164,1114,849,1075,1006,753,121,874,122 0.217,0.233,0.241,0.254,0.262,0.269,0.269,0.272,0.285,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9257055848102 0 0 38.92570558481024 38.92570558481024 EZB TRUE 601 -DLBCL11571T ST2 0.777775812313285 0 842,161,1029,671,359,160,996,668,366,835 0.14,0.161,0.162,0.166,0.194,0.194,0.21,0.236,0.27,0.273 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2,EZB 7.95008592918014,3.65963849473544,40.6335734131435 0 0 52.24329783705911 40.63357341314353 ST2 TRUE 602 -DLBCL11572T EZB 1 0 1059,25,384,605,149,73,239,74,61,833 0.11,0.143,0.151,0.151,0.167,0.189,0.191,0.194,0.198,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.0415565245514 0 0 61.04155652455141 61.04155652455141 EZB TRUE 603 -DLBCL11573T EZB 1 0 392,313,986,101,1012,199,49,302,1030,981 0.141,0.156,0.159,0.167,0.194,0.196,0.201,0.207,0.216,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8957603835297 0 0 54.89576038352974 54.89576038352974 EZB TRUE 604 -DLBCL11575T EZB 1 0 384,1059,61,833,603,74,629,25,73,149 0.102,0.141,0.147,0.151,0.151,0.155,0.171,0.179,0.202,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.5943218951677 0 0 64.59432189516767 64.59432189516767 EZB TRUE 605 -DLBCL11576T ST2 0.42550319020191063 0 338,1118,689,607,1052,346,736,781,872,620 0.184,0.291,0.3,0.307,0.376,0.385,0.389,0.451,0.471,0.496 BN2,ST2,ST2,ST2,EZB,ST2,EZB,EZB,BN2,EZB 7.57127708784354,9.46231772607042,12.6159950939926 0 0 29.649589907906588 12.615995093992627 ST2 TRUE 606 -DLBCL11579T ST2 0.6143157339109457 0 1118,689,338,346,606,828,620,174,614,1052 0.195,0.278,0.285,0.297,0.307,0.313,0.424,0.516,0.518,0.527 ST2,ST2,BN2,ST2,ST2,ST2,EZB,N1,EZB,EZB 3.51223589577449,6.1845404543512,1.93847919356281,18.532569713155 0 0 30.167825256843464 18.53256971315496 ST2 TRUE 607 -DLBCL11581T EZB 0.8230437310454322 0 90,411,614,858,954,786,143,944,144,622 0.224,0.284,0.323,0.325,0.329,0.36,0.38,0.427,0.458,0.467 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 24.0986572760463,5.18126597351923 0 0 29.27992324956557 24.098657276046342 EZB TRUE 608 -DLBCL11584T EZB 1 0 600,82,751,1080,57,292,138,948,639,122 0.196,0.226,0.261,0.276,0.297,0.421,0.425,0.427,0.43,0.443 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9961145215271 0 0 31.99611452152709 31.99611452152709 EZB TRUE 609 -DLBCL11585T EZB 1 0 649,1,139,273,1072,1020,112,9,648,1095 0.142,0.145,0.159,0.183,0.191,0.201,0.202,0.206,0.235,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.140700473744 0 0 54.140700473744005 54.140700473744005 EZB TRUE 610 -DLBCL11586T BN2 0.9239278035451852 0 463,352,327,827,895,109,10,438,391,952 0.177,0.197,0.216,0.261,0.267,0.273,0.293,0.34,0.341,0.364 BN2,BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 35.7318539240647,2.94200542615586 0 0 38.67385935022053 35.73185392406468 BN2 TRUE 611 -DLBCL11587T ST2 0.7259186020031683 0 583,425,366,896,662,160,785,1029,668,161 0.151,0.168,0.21,0.232,0.258,0.265,0.268,0.296,0.306,0.309 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2,ST2 8.03845231305224,3.72631402607679,31.1595854232078 0 0 42.9243517623368 31.159585423207762 ST2 TRUE 612 -DLBCL11589T EZB 1 0 776,866,151,27,152,150,98,599,644,97 0.114,0.146,0.168,0.197,0.205,0.221,0.226,0.241,0.272,0.305 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6236067562591 0 0 51.62360675625911 51.62360675625911 EZB TRUE 613 -DLBCL11590T EZB 0.6205452692239313 0 954,944,828,608,786,90,620,411,143,893 0.171,0.264,0.31,0.323,0.379,0.442,0.454,0.459,0.459,0.464 ST2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,N1 18.3435429043736,2.1562808703383,9.06053840049969 0 0 29.560362175211573 18.34354290437358 EZB TRUE 614 -DLBCL11591T EZB 0.8823838198698618 0 398,203,134,1088,389,1000,83,84,97,644 0.197,0.224,0.245,0.257,0.26,0.267,0.297,0.298,0.314,0.34 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.46183498772074,33.473719310879 0 0 37.935554298599726 33.473719310878984 EZB TRUE 615 -DLBCL11646T MCD 0.8700873603391058 0 502,574,1093,1047,1054,475,992,856,44,697 0.119,0.174,0.196,0.22,0.22,0.271,0.314,0.317,0.332,0.343 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.542539287304,5.754781929134 0 0 44.297321216438036 38.54253928730404 MCD TRUE 616 -DLBCL11648T MCD 1 0 789,713,441,205,985,779,525,421,466,223 0.177,0.206,0.219,0.237,0.315,0.315,0.384,0.387,0.403,0.422 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.6899057138429 0 0 35.68990571384294 35.68990571384294 N1 FALSE 617 -DLBCL11651T EZB 0.798543626490755 0 1074,947,867,995,274,863,227,595,368,1056 0.137,0.143,0.149,0.156,0.169,0.18,0.19,0.19,0.191,0.205 EZB,EZB,ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB 5.27139314594622,47.5186212498303,6.71659197293695 0 0 59.50660636871351 47.518621249830346 EZB TRUE 618 -DLBCL11656T EZB 1 0 983,737,588,259,267,224,720,268,664,1083 0.197,0.246,0.271,0.277,0.281,0.283,0.29,0.305,0.311,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.4413430704149 0 0 36.4413430704149 36.4413430704149 EZB TRUE 619 -DLBCL11657T EZB 0.467016131205936 0 144,786,689,143,545,1118,846,828,338,607 0.206,0.25,0.297,0.308,0.308,0.343,0.377,0.398,0.413,0.424 EZB,EZB,ST2,EZB,ST2,ST2,EZB,ST2,BN2,ST2 2.42001322409297,14.7409029855385,14.4030962527953 0 0 31.564012462426692 14.740902985538463 EZB TRUE 620 -DLBCL11658T MCD 1 0 295,878,978,902,452,961,33,451,465,1101 0.152,0.162,0.193,0.198,0.199,0.21,0.222,0.231,0.233,0.249 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.9481863193142 0 0 49.948186319314225 49.948186319314225 ST2 FALSE 621 -DLBCL11661T ST2 0.337066436803223 0 691,1084,1096,893,964,590,411,11,899,944 0.163,0.206,0.254,0.287,0.291,0.298,0.353,0.372,0.402,0.433 ST2,BN2,EZB,N1,BN2,ST2,EZB,EZB,ST2,EZB 8.2987135204085,11.7748525985251,3.48270885831474,11.9771122051052 0 0 35.533387182353565 11.977112205105232 ST2 TRUE 622 -DLBCL11662T BN2 1 0 391,348,463,503,705,611,492,327,109,827 0.198,0.232,0.331,0.343,0.363,0.384,0.392,0.398,0.399,0.407 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.7001169422988 0 0 30.700116942298823 30.700116942298823 BN2 TRUE 623 -DLBCL11664T BN2 0.9235670722022774 0 765,347,173,535,773,1049,196,506,799,936 0.159,0.174,0.176,0.207,0.306,0.331,0.339,0.376,0.38,0.406 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 36.5130793294019,3.02176381126488 0 0 39.534843140666815 36.51307932940194 EZB FALSE 624 -DLBCL11665T ST2 0.7098139324637762 0 969,703,901,1025,925,1024,950,1107,1041,685 0.243,0.274,0.313,0.314,0.322,0.328,0.35,0.36,0.366,0.375 ST2,ST2,ST2,BN2,ST2,EZB,EZB,ST2,ST2,ST2 3.18548379763693,5.90339542938218,22.2319877745799 0 0 31.32086700159905 22.231987774579945 ST2 TRUE 625 -DLBCL11666T EZB 1 0 1083,377,745,238,737,378,206,259,619,981 0.245,0.322,0.344,0.36,0.369,0.376,0.434,0.44,0.445,0.459 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.2391912473473 0 0 27.239191247347275 27.239191247347275 EZB TRUE 626 -DLBCL11667T EZB 1 0 1073,743,865,942,1018,883,156,639,247,406 0.265,0.281,0.294,0.332,0.333,0.342,0.344,0.358,0.359,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.8601591683131 0 0 30.86015916831308 30.86015916831308 EZB TRUE 627 -DLBCL11669T EZB 1 0 653,811,556,750,304,887,310,36,724,430 0.231,0.236,0.303,0.323,0.334,0.357,0.362,0.381,0.382,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3608486352754 0 0 31.360848635275403 31.360848635275403 EZB TRUE 628 -DLBCL11670T EZB 1 0 833,61,184,384,605,74,1059,603,163,25 0.132,0.136,0.162,0.17,0.171,0.188,0.207,0.216,0.225,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5271600777563 0 0 56.527160077756285 56.527160077756285 EZB TRUE 629 -DLBCL11671T BN2 1 0 1060,335,477,943,650,99,780,345,538,795 0.127,0.131,0.189,0.196,0.215,0.264,0.274,0.275,0.301,0.303 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.2014540047202 0 0 48.20145400472025 48.20145400472025 BN2 TRUE 630 -DLBCL11672T EZB 1 0 228,93,740,757,102,387,1079,274,863,947 0.126,0.15,0.161,0.164,0.181,0.189,0.194,0.196,0.217,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1322440849661 0 0 57.13224408496606 57.13224408496606 EZB TRUE 631 -DLBCL11675T EZB 1 0 667,876,760,46,80,594,914,29,1002,1026 0.114,0.114,0.122,0.144,0.158,0.166,0.169,0.179,0.186,0.189 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1978715070976 0 0 67.1978715070976 67.1978715070976 EZB TRUE 632 -DLBCL11676T BN2 0.878411001149196 0 493,634,77,1069,212,898,487,399,390,498 0.125,0.147,0.155,0.163,0.186,0.203,0.239,0.266,0.271,0.276 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.5025407045576,6.43684717164143 0 0 52.939387876199035 46.5025407045576 BN2 TRUE 633 -DLBCL11677T BN2 0.871988312287511 0 633,77,212,493,1069,487,390,898,358,399 0.147,0.158,0.164,0.17,0.209,0.223,0.226,0.247,0.297,0.301 BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.2420224649849,6.34811751240415 0 0 49.59013997738908 43.24202246498493 BN2 TRUE 634 -DLBCL11678T EZB 1 0 125,931,35,48,820,364,1063,880,256,89 0.137,0.138,0.151,0.17,0.172,0.187,0.19,0.224,0.231,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5288362380621 0 0 56.52883623806206 56.52883623806206 EZB TRUE 635 -DLBCL11679T BN2 0.8095534640463232 0 561,399,926,575,487,77,212,1069,95,633 0.113,0.196,0.211,0.23,0.237,0.257,0.267,0.272,0.293,0.299 BN2,BN2,ST2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 36.6726005603819,3.89256824823629,4.73461951800364 0 0 45.2997883266218 36.67260056038187 BN2 TRUE 636 -DLBCL11680T EZB 1 0 984,914,46,760,1078,317,80,29,738,876 0.143,0.181,0.188,0.21,0.21,0.213,0.215,0.221,0.222,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1933706394934 0 0 50.19337063949339 50.19337063949339 EZB TRUE 637 -DLBCL11684T EZB 1 0 847,1100,823,216,405,1013,249,79,766,217 0.176,0.314,0.316,0.342,0.354,0.361,0.361,0.378,0.383,0.389 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.1474438504728 0 0 31.147443850472847 31.147443850472847 EZB TRUE 638 -DLBCL11685T EZB 1 0 1014,865,122,121,190,754,849,18,406,627 0.175,0.279,0.305,0.32,0.322,0.323,0.34,0.351,0.352,0.358 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.3439198997024 0 0 33.343919899702364 33.343919899702364 EZB TRUE 639 -DLBCL_P04-Tumor BN2 1 0 264,1111,871,578,957,318,1062,401,15,482 0.128,0.19,0.204,0.227,0.236,0.239,0.268,0.269,0.27,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.5103437265782 0 0 45.51034372657823 45.51034372657823 BN2 TRUE 640 -SP124957 EZB 0.7677939265927072 0 810,684,382,319,322,791,1033,676,194,714 0.336,0.341,0.386,0.393,0.399,0.406,0.408,0.415,0.434,0.442 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 19.5299070358298,5.90648463048493 0 0 25.43639166631473 19.5299070358298 EZB TRUE 641 -SP124969 ST2 0.9174542849756441 0 308,793,915,557,1053,598,1016,412,119,940 0.24,0.261,0.265,0.299,0.339,0.343,0.361,0.369,0.383,0.385 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.61434925878239,29.0571827887746 0 0 31.671532047556948 29.057182788774558 ST2 TRUE 642 -SP124971 EZB 0.867294206357812 0 645,1050,975,241,385,581,254,369,733,687 0.131,0.135,0.148,0.149,0.168,0.178,0.198,0.216,0.254,0.257 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.9383390249784,7.64112900201748 0 0 57.579468026995876 49.9383390249784 EZB TRUE 643 -SP124973 EZB 1 0 599,150,418,152,1088,1037,83,151,866,84 0.149,0.181,0.182,0.183,0.184,0.192,0.216,0.227,0.236,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2041833543833 0 0 51.20418335438326 51.20418335438326 EZB TRUE 644 -SP192765 EZB 1 0 1050,643,241,975,581,385,369,254,687,67 0.124,0.131,0.139,0.144,0.155,0.162,0.198,0.225,0.247,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.9610721666017 0 0 59.96107216660167 59.96107216660167 ST2 FALSE 645 -SP192767 BN2 1 0 548,959,999,780,345,99,435,1071,943,517 0.14,0.149,0.17,0.192,0.197,0.223,0.241,0.255,0.265,0.267 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.1101002016708 0 0 50.110100201670804 50.110100201670804 BN2 TRUE 646 -SP193976 EZB 1 0 537,960,939,948,719,377,1034,51,378,233 0.253,0.339,0.371,0.385,0.39,0.396,0.447,0.45,0.45,0.455 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.1681554975212 0 0 26.16815549752124 26.16815549752124 EZB TRUE 647 -SP192997 EZB 1 0 892,1095,711,680,71,229,706,374,1072,112 0.123,0.138,0.143,0.146,0.151,0.156,0.163,0.17,0.176,0.193 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.1058976126808 0 0 65.10589761268079 65.10589761268079 EZB TRUE 648 -SP192993 EZB 1 0 610,273,1,139,9,664,1020,1072,112,648 0.142,0.142,0.148,0.167,0.179,0.227,0.227,0.231,0.243,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.436897691134 0 0 53.43689769113404 53.43689769113404 EZB TRUE 649 -SP192970 BN2 1 0 943,335,538,99,435,630,780,477,1060,959 0.184,0.185,0.187,0.188,0.207,0.215,0.219,0.222,0.242,0.262 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.0217976138198 0 0 48.02179761381985 48.02179761381985 BN2 TRUE 650 -SP193005 BN2 0.9231821696216669 0 472,1019,179,652,659,666,968,484,1113,42 0.167,0.198,0.253,0.261,0.275,0.277,0.279,0.311,0.329,0.335 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 35.902994579756,2.98748203600182 0 0 38.8904766157578 35.90299457975598 BN2 TRUE 651 -SP193025 BN2 1 0 1019,1113,666,695,681,659,472,651,674,179 0.163,0.17,0.186,0.2,0.232,0.237,0.238,0.261,0.263,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2830759089026 0 0 46.28307590890263 46.28307590890263 BN2 TRUE 652 -SP193794 EZB 1 0 304,628,887,750,36,430,997,963,761,310 0.224,0.231,0.24,0.245,0.254,0.268,0.273,0.274,0.295,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.7840938931038 0 0 38.78409389310379 38.78409389310379 EZB TRUE 653 -SP192856 BN2 1 0 501,518,342,63,333,38,542,763,488,702 0.157,0.167,0.177,0.184,0.21,0.217,0.219,0.227,0.243,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.9716407108667 0 0 49.97164071086669 49.97164071086669 BN2 TRUE 654 -SP192850 EZB 1 0 1051,410,183,752,49,1012,101,1115,392,225 0.117,0.133,0.183,0.183,0.206,0.209,0.235,0.246,0.258,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5183991879829 0 0 52.51839918798294 52.51839918798294 EZB TRUE 655 -SP192833 ST2 0.9004890409902653 0 483,728,207,822,362,263,1057,60,807,1081 0.184,0.196,0.211,0.257,0.262,0.301,0.316,0.319,0.345,0.354 ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 3.81263033588574,34.5010425884478 0 0 38.313672924333574 34.50104258844783 ST2 TRUE 656 -SP192815 ST2 0.5124147544078913 0 922,382,1091,372,974,586,834,320,194,791 0.204,0.242,0.302,0.327,0.333,0.344,0.374,0.404,0.416,0.419 ST2,EZB,EZB,ST2,EZB,ST2,ST2,ST2,EZB,EZB 15.2416638458944,16.0178215131947 0 0 31.259485359089037 16.017821513194683 BN2 FALSE 657 -SP193512 EZB 0.922656082539285 0 429,744,454,67,19,241,733,1050,645,643 0.182,0.204,0.227,0.261,0.276,0.308,0.312,0.323,0.345,0.355 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 34.6213094185951,2.90221649076885 0 0 37.52352590936392 34.621309418595075 EZB TRUE 658 -SP193337 BN2 0.8158234903840249 0 666,179,472,23,1019,652,127,185,495,651 0.157,0.173,0.21,0.221,0.234,0.237,0.261,0.264,0.266,0.275 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,MCD,BN2 36.6803888551595,4.52416494326226,3.75662872145554 0 0 44.9611825198773 36.68038885515951 BN2 TRUE 659 -SP192798 MCD 1 0 966,932,380,439,1090,280,987,994,461,565 0.131,0.166,0.19,0.192,0.192,0.207,0.207,0.208,0.216,0.219 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.9900263309803 0 0 52.99002633098027 52.99002633098027 MCD TRUE 660 -SP193546 ST2 0.6363818148929522 0 365,965,178,787,770,361,1107,195,1041,950 0.112,0.217,0.239,0.264,0.296,0.301,0.312,0.334,0.352,0.358 ST2,EZB,ST2,EZB,BN2,ST2,ST2,ST2,ST2,EZB 3.3787031745656,11.2000175880589,25.5147656462926 0 0 40.09348640891715 25.51476564629261 EZB FALSE 661 -SP193375 ST2 1 0 896,683,907,1004,748,612,814,1009,583,1065 0.157,0.176,0.203,0.22,0.246,0.258,0.26,0.266,0.3,0.304 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.7017733329819 0 0 43.70177333298188 43.70177333298188 ST2 TRUE 662 -SP193420 MCD 0.4489214942919664 0 421,579,1045,801,716,525,789,779,431,455 0.254,0.269,0.274,0.293,0.326,0.405,0.441,0.443,0.443,0.459 MCD,N1,BN2,ST2,BN2,MCD,MCD,MCD,BN2,MCD 8.97554460892989,13.1172283354386,3.71661011596333,3.41004283534057 0 0 29.219425895672433 13.117228335438648 ST2 FALSE 663 -SP194216 EZB 1 0 9,273,983,139,1,649,343,610,268,240 0.15,0.197,0.216,0.218,0.218,0.227,0.248,0.256,0.256,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5721640677076 0 0 45.57216406770764 45.57216406770764 EZB TRUE 664 -SP194228 BN2 0.37586977370337316 0 924,967,131,512,505,864,582,126,1085,132 0.171,0.186,0.198,0.203,0.215,0.227,0.233,0.253,0.312,0.325 ST2,EZB,BN2,N1,N1,BN2,BN2,EZB,EZB,BN2 16.8328243657202,12.5166861227402,9.59137509587787,5.8427749900931 0 0 44.78366057443131 16.83282436572017 BN2 TRUE 665 -SP193725 BN2 0.9121745972362711 0 659,652,1019,472,179,1113,23,674,855,681 0.157,0.186,0.205,0.223,0.223,0.228,0.252,0.258,0.272,0.274 BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 41.1592080013435,3.96286416121032 0 0 45.1220721625538 41.15920800134347 BN2 TRUE 666 -SP193957 EZB 1 0 632,876,760,46,80,914,594,1002,1026,29 0.114,0.127,0.131,0.154,0.171,0.172,0.172,0.174,0.179,0.193 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.7479112259367 0 0 64.74791122593668 64.74791122593668 EZB TRUE 667 -SP193967 ST2 0.7840389071461353 0 161,359,785,366,671,1029,842,160,602,425 0.181,0.182,0.188,0.197,0.211,0.217,0.22,0.22,0.236,0.246 ST2,ST2,EZB,BN2,ST2,ST2,ST2,ST2,ST2,ST2 5.0749286103877,5.32159933925559,37.7442172756013 0 0 48.14074522524461 37.744217275601315 BN2 FALSE 668 -SP194035 EZB 1 0 734,68,291,255,50,181,142,213,67,257 0.147,0.164,0.205,0.223,0.231,0.235,0.245,0.263,0.265,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.2118064403822 0 0 46.21180644038216 46.21180644038216 EZB TRUE 669 -SP193934 BN2 0.736389145642204 0 86,363,355,558,408,321,422,187,946,808 0.182,0.287,0.379,0.387,0.407,0.415,0.416,0.442,0.442,0.446 BN2,BN2,BN2,ST2,N1,BN2,ST2,BN2,BN2,BN2 20.7893388689415,2.45524950324123,4.98686912820064 0 0 28.231457500383357 20.789338868941492 BN2 TRUE 670 -SP59304 ST2 0.8465934104819731 0 842,359,996,602,161,1029,668,160,1005,835 0.127,0.137,0.154,0.166,0.177,0.207,0.211,0.235,0.273,0.276 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,EZB 4.73516337722098,3.61963004742697,46.1069702511958 0 0 54.46176367584376 46.10697025119581 ST2 TRUE 671 -SP59312 BN2 0.8267306461517281 0 186,1106,153,840,571,462,22,470,962,582 0.144,0.184,0.189,0.246,0.259,0.267,0.275,0.284,0.285,0.304 BN2,BN2,BN2,BN2,EZB,BN2,EZB,BN2,BN2,BN2 35.7746406263335,7.4977852754491 0 0 43.27242590178265 35.77464062633354 BN2 TRUE 672 -SP59368 ST2 0.7559338889478932 0 682,554,177,747,370,1016,1089,792,1053,1044 0.237,0.247,0.266,0.324,0.353,0.362,0.378,0.408,0.424,0.425 ST2,ST2,ST2,ST2,ST2,ST2,MCD,BN2,ST2,MCD 2.4526252799782,4.99810471002978,23.0767773230302 0 0 30.52750731303818 23.0767773230302 BN2 FALSE 673 -SP59452 BN2 0.9152623867850839 0 681,1113,855,584,695,666,65,652,495,795 0.189,0.217,0.22,0.231,0.257,0.258,0.261,0.263,0.288,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 37.5002373762991,3.47187938250382 0 0 40.9721167588029 37.500237376299076 BN2 TRUE 674 -SP59460 EZB 1 0 78,117,287,1108,397,105,92,738,247,56 0.119,0.135,0.14,0.188,0.201,0.203,0.215,0.26,0.262,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2366634097355 0 0 54.23666340973549 54.23666340973549 EZB TRUE 675 -SP116618 EZB 1 0 870,62,791,641,593,47,220,219,912,307 0.349,0.354,0.376,0.415,0.43,0.434,0.441,0.445,0.472,0.479 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.1164265066532 0 0 24.11642650665322 24.11642650665322 EZB TRUE 676 -SP116620 EZB 1 0 167,266,72,852,191,746,20,239,96,226 0.11,0.176,0.19,0.194,0.198,0.213,0.218,0.226,0.226,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7235237385383 0 0 52.72352373853833 52.72352373853833 EZB TRUE 677 -SP116726 MCD 0.9087327459133048 0 580,881,526,1042,844,453,494,204,467,920 0.137,0.138,0.139,0.172,0.189,0.19,0.194,0.196,0.208,0.209 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 5.2874519859008,52.6462728622054 0 0 57.93372484810617 52.64627286220538 MCD TRUE 678 -SP116657 MCD 1 0 1038,415,565,461,869,987,340,414,324,932 0.125,0.136,0.14,0.204,0.206,0.216,0.221,0.237,0.24,0.244 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8327904179286 0 0 53.83279041792861 53.83279041792861 MCD TRUE 679 -SP116659 EZB 1 0 892,1095,706,71,648,861,711,229,885,1072 0.125,0.126,0.127,0.139,0.146,0.161,0.161,0.168,0.169,0.184 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.5479992992813 0 0 67.54799929928133 67.54799929928133 EZB TRUE 680 -SP116663 BN2 1 0 1113,695,65,674,584,652,589,64,666,1019 0.163,0.177,0.18,0.189,0.192,0.232,0.249,0.262,0.274,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.0288321644228 0 0 47.028832164422795 47.028832164422795 BN2 TRUE 681 -SP116668 ST2 0.8128109977730037 0 177,1016,673,370,1053,692,557,1086,792,793 0.144,0.225,0.237,0.276,0.288,0.297,0.333,0.342,0.356,0.365 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 7.03031211218044,30.5269804025533 0 0 37.55729251473371 30.52698040255327 ST2 TRUE 682 -SP116676 ST2 1 0 907,662,748,1004,896,1009,195,814,1065,875 0.132,0.176,0.192,0.217,0.219,0.222,0.249,0.258,0.289,0.292 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 46.9287240121868 0 0 46.928724012186805 46.928724012186805 ST2 TRUE 683 -SP116688 EZB 0.7336730592080931 0 810,194,714,1033,988,319,322,993,641,974 0.166,0.272,0.291,0.321,0.332,0.336,0.341,0.341,0.341,0.351 ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 3.01079924948865,24.849861274214,6.0098230143412 0 0 33.8704835380438 24.849861274213954 ST2 FALSE 684 -SP116690 ST2 0.693819752259605 0 320,834,1024,824,901,586,1091,925,625,372 0.179,0.2,0.239,0.256,0.267,0.282,0.292,0.337,0.375,0.387 ST2,ST2,EZB,EZB,ST2,ST2,EZB,ST2,ST2,ST2 11.5133084916083,26.0897327776321 0 0 37.60304126924042 26.089732777632094 ST2 TRUE 685 -SP116701 EZB 1 0 708,403,5,237,289,838,887,310,963,949 0.137,0.137,0.175,0.187,0.199,0.242,0.248,0.249,0.249,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8363385710439 0 0 50.836338571043854 50.836338571043854 EZB TRUE 686 -SP116709 EZB 0.9170639893512733 0 379,369,385,831,581,975,645,643,1050,254 0.141,0.156,0.189,0.199,0.199,0.209,0.247,0.257,0.271,0.276 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 44.7821296590744,4.0499367823885 0 0 48.83206644146293 44.78212965907443 EZB TRUE 687 -DOHH-2 EZB 1 0 1114,601,344,1006,874,407,753,725,164,1075 0.176,0.217,0.228,0.24,0.252,0.284,0.295,0.304,0.308,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4654531628054 0 0 39.4654531628054 39.4654531628054 EZB TRUE 688 -Farage ST2 0.530020852564546 0 1118,338,607,620,606,545,1052,736,828,144 0.183,0.219,0.278,0.297,0.3,0.375,0.386,0.387,0.39,0.4 ST2,BN2,ST2,EZB,ST2,ST2,EZB,EZB,ST2,EZB 4.56919284032616,11.0504547950865,17.6151197380862 0 0 33.23476737349884 17.615119738086214 ST2 TRUE 689 -HBL-1 MCD 0.8800839725431598 0 721,299,937,1110,722,511,447,536,1087,469 0.142,0.144,0.15,0.155,0.179,0.196,0.215,0.226,0.228,0.228 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.67963463142747,49.0229663726926 0 0 55.70260100412009 49.022966372692615 MCD TRUE 690 -HT ST2 0.3816649110847545 0 1084,622,1096,11,411,590,964,893,899,909 0.146,0.163,0.266,0.313,0.329,0.331,0.34,0.35,0.397,0.431 BN2,ST2,EZB,EZB,EZB,ST2,BN2,N1,ST2,ST2 9.81415588872676,10.002258964436,2.85685803884429,13.9949888619605 0 0 36.66826175396758 13.994988861960538 ST2 TRUE 691 -HTMCP-01-02-00013-01A-01D ST2 0.9280996478821996 0 1086,1053,1016,982,557,370,177,793,682,792 0.155,0.196,0.204,0.23,0.244,0.258,0.262,0.29,0.297,0.327 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 3.05355501343529,39.4157086757381 0 0 42.46926368917342 39.41570867573813 ST2 TRUE 692 -HTMCP-01-06-00105-01A-01D EZB 1 0 710,243,244,343,69,240,273,268,225,649 0.259,0.281,0.34,0.34,0.364,0.393,0.423,0.441,0.443,0.447 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.7054304246825 0 0 27.705430424682508 27.705430424682508 BN2 FALSE 693 -HTMCP-01-06-00136-01A-01D BN2 0.7576899720471828 0 777,701,484,968,301,34,521,742,103,104 0.247,0.3,0.324,0.33,0.362,0.377,0.398,0.451,0.469,0.472 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD,MCD 21.1468456047588,6.76278285135326 0 0 27.909628456112102 21.146845604758838 BN2 TRUE 694 -HTMCP-01-06-00175-01A-01D BN2 1 0 1113,681,65,652,589,1019,584,674,64,666 0.153,0.177,0.193,0.2,0.236,0.25,0.25,0.257,0.264,0.275 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.9331573098885 0 0 45.933157309888486 45.933157309888486 BN2 TRUE 695 -HTMCP-01-06-00185-01A-01D MCD 0.6395086923622119 0 1105,935,205,985,1044,124,223,1093,779,466 0.219,0.319,0.33,0.345,0.361,0.368,0.381,0.404,0.423,0.432 BN2,EZB,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD 4.5617337979395,3.13254765557608,18.4740954809057,2.71957361503291 0 0 28.887950549454196 18.474095480905692 MCD TRUE 696 -HTMCP-01-06-00242-01A-01D MCD 1 0 790,475,1054,223,198,1047,44,466,837,856 0.123,0.172,0.237,0.244,0.246,0.255,0.274,0.282,0.291,0.325 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.9706636501566 0 0 43.97066365015657 43.97066365015657 MCD TRUE 697 -HTMCP-01-06-00253-01A-01D BN2 0.6462952339838987 0 799,1061,919,506,485,347,529,936,293,381 0.204,0.315,0.32,0.343,0.357,0.357,0.361,0.377,0.388,0.39 BN2,BN2,BN2,BN2,ST2,BN2,ST2,BN2,MCD,ST2 19.5696951817581,2.57502541134223,8.13508676541353 0 0 30.279807358513825 19.569695181758068 ST2 FALSE 698 -HTMCP-01-06-00299-01A-01D BN2 0.42768483982800737 0 504,14,802,891,495,23,855,388,127,659 0.187,0.239,0.244,0.283,0.284,0.317,0.331,0.367,0.4,0.437 MCD,BN2,ST2,MCD,MCD,EZB,BN2,BN2,BN2,BN2 14.7027014893163,3.15414229212775,12.4166205061905,4.10395599976442 0 0 34.377420287398984 14.702701489316325 BN2 TRUE 699 -HTMCP-01-06-00443-01A-01D BN2 1 0 955,731,435,1071,538,1010,99,1001,959,548 0.146,0.172,0.186,0.19,0.198,0.206,0.241,0.25,0.253,0.257 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.2357136621826 0 0 49.23571366218262 49.23571366218262 BN2 TRUE 700 -HTMCP-01-06-00497-01A-01D BN2 0.6250262081277284 0 34,104,694,103,521,497,442,484,742,100 0.191,0.294,0.3,0.309,0.318,0.324,0.327,0.349,0.389,0.434 BN2,MCD,BN2,MCD,MCD,BN2,BN2,BN2,BN2,MCD 20.1582942966309,12.0936241581412 0 0 32.25191845477204 20.15829429663087 BN2 TRUE 701 -HTMCP-01-06-00634-01A-01D BN2 1 0 395,958,305,542,75,905,1023,559,373,63 0.153,0.161,0.17,0.179,0.195,0.201,0.211,0.214,0.217,0.226 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.7166083385059 0 0 52.71660833850587 52.71660833850587 BN2 TRUE 702 -HTMCP-01-10-00160-01A-01D ST2 0.7516381072818805 0 969,625,925,1025,901,843,904,1024,1081,950 0.132,0.274,0.29,0.296,0.335,0.346,0.357,0.364,0.386,0.408 ST2,ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,EZB 3.37360786759798,5.19451275253069,25.9304110441196 0 0 34.49853166424824 25.93041104411957 ST2 TRUE 703 -HTMCP-01-10-00778-01A-01D BN2 1 0 524,572,560,279,196,110,393,562,773,705 0.299,0.339,0.339,0.347,0.39,0.441,0.47,0.481,0.482,0.5 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 25.2415547107689 0 0 25.241554710768945 25.241554710768945 ST2 FALSE 704 -HTMCP-01-15-00366-01A-01E BN2 1 0 503,507,562,524,560,623,1098,391,348,553 0.222,0.268,0.294,0.3,0.31,0.363,0.46,0.461,0.492,0.497 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3261679409219 0 0 29.326167940921916 29.326167940921916 BN2 TRUE 705 -Karpas422 EZB 1 0 71,680,892,1095,711,229,861,648,7,885 0.126,0.127,0.149,0.153,0.159,0.16,0.161,0.163,0.181,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.7302291724027 0 0 64.73022917240269 64.73022917240269 EZB TRUE 706 -LY_RELY_028_tumorB EZB 1 0 963,36,972,289,237,997,887,304,1031,686 0.146,0.161,0.165,0.173,0.183,0.201,0.204,0.229,0.234,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7059188051094 0 0 52.705918805109434 52.705918805109434 EZB TRUE 707 -LY_RELY_109_tumorB EZB 1 0 403,686,5,289,237,310,949,887,750,304 0.12,0.137,0.189,0.209,0.214,0.216,0.216,0.241,0.242,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.986592076091 0 0 51.98659207609099 51.98659207609099 EZB TRUE 708 -LY_RELY_116_tumorA EZB 1 0 597,250,868,21,251,938,756,164,816,356 0.14,0.163,0.172,0.177,0.18,0.205,0.225,0.235,0.246,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.9076795597249 0 0 51.90767955972493 51.90767955972493 EZB TRUE 709 -LY_RELY_128_tumorA EZB 0.8963348963737848 0 243,343,693,273,244,649,240,69,9,664 0.206,0.219,0.259,0.265,0.281,0.292,0.294,0.297,0.307,0.316 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 3.86090935620508,33.3831508062807 0 0 37.24406016248581 33.383150806280725 EZB TRUE 710 -LY_RELY_128_tumorB EZB 1 0 229,71,648,374,892,706,680,1095,861,1072 0.114,0.133,0.143,0.152,0.155,0.159,0.161,0.171,0.218,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.8076917742234 0 0 63.80769177422335 63.80769177422335 EZB TRUE 711 -OCI-Ly10 MCD 1 0 879,1109,1015,500,165,762,17,166,246,417 0.115,0.125,0.156,0.158,0.161,0.169,0.173,0.175,0.176,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.3391042834941 0 0 64.33910428349405 64.33910428349405 MCD TRUE 712 -OCI-Ly3 MCD 0.8570465647048454 0 617,985,789,441,205,466,128,223,779,696 0.206,0.238,0.269,0.27,0.298,0.304,0.319,0.335,0.408,0.434 N1,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.1534576701244,4.8627310303862 0 0 34.016188700510554 29.15345767012435 MCD TRUE 713 -OZM073-003_LowGradeDiagnosis EZB 0.688675212295522 0 1033,913,322,319,993,810,684,715,424,988 0.178,0.183,0.212,0.213,0.225,0.235,0.291,0.365,0.406,0.424 EZB,EZB,EZB,EZB,EZB,ST2,ST2,EZB,ST2,BN2 2.35654548113571,27.6744480351661,10.1540567673006 0 0 40.18505028360234 27.674448035166066 EZB TRUE 714 -OZM073-003_Transformation EZB 0.8722752912269633 0 890,424,839,862,407,322,319,1033,913,784 0.214,0.214,0.229,0.279,0.281,0.302,0.308,0.317,0.321,0.353 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.8625571831999,4.66554065891742 0 0 36.5280978421173 31.862557183199876 EZB TRUE 715 -OZM073-007_Relapse MCD 0.39711281989472336 0 525,1045,43,803,779,663,421,579,124,205 0.22,0.248,0.254,0.292,0.294,0.326,0.379,0.385,0.39,0.427 MCD,BN2,ST2,BN2,MCD,ST2,MCD,N1,N1,MCD 7.44451078122729,12.9224590848372,5.15852144738232,7.01553591469203 0 0 32.541027228138816 12.922459084837179 BN2 FALSE 716 -OZM073-008_Diagnosis MCD 1 0 522,458,489,837,198,446,128,519,162,1035 0.189,0.305,0.34,0.366,0.394,0.42,0.469,0.491,0.503,0.51 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 27.2637066845663 0 0 27.263706684566277 27.263706684566277 MCD TRUE 717 -OZM073-011_LowGradeDiagnosis EZB 1 0 156,826,294,236,883,334,56,303,743,942 0.152,0.157,0.159,0.175,0.188,0.2,0.207,0.212,0.243,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9874049799795 0 0 52.987404979979466 52.987404979979466 EZB TRUE 718 -OZM073-012_Relapse EZB 1 0 537,233,51,1034,157,939,647,211,960,360 0.276,0.289,0.291,0.306,0.33,0.383,0.39,0.428,0.455,0.509 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.4955444541931 0 0 28.495544454193094 28.495544454193094 EZB TRUE 719 -OZM073-012_LowGradeDiagnosis EZB 1 0 374,267,229,711,983,71,648,892,706,619 0.199,0.205,0.228,0.233,0.252,0.261,0.267,0.286,0.287,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.545391041529 0 0 40.54539104152898 40.54539104152898 EZB TRUE 720 -OZM073-013_Relapse MCD 0.8655088455074126 0 937,722,690,511,299,536,1087,1110,447,869 0.128,0.137,0.142,0.154,0.182,0.185,0.194,0.196,0.24,0.24 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.81676994956836,50.3043011280034 0 0 58.12107107757179 50.30430112800343 MCD TRUE 721 -OZM073-013_Diagnosis MCD 0.8837104885889657 0 511,721,937,536,1087,690,869,340,299,1110 0.118,0.137,0.145,0.149,0.175,0.179,0.203,0.206,0.219,0.233 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.90293933182481,52.457008509265 0 0 59.35994784108978 52.457008509264966 MCD TRUE 722 -OZM073-014_LowGradeDiagnosis2 EZB 1 0 213,50,853,257,291,949,255,272,181,68 0.129,0.167,0.184,0.192,0.203,0.212,0.215,0.219,0.222,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1435331728248 0 0 52.14353317282475 52.14353317282475 EZB TRUE 723 -OZM073-014_LowGradeDiagnosis EZB 1 0 272,853,979,556,257,723,136,310,949,811 0.142,0.177,0.225,0.233,0.24,0.257,0.262,0.269,0.272,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3881759076937 0 0 44.388175907693686 44.388175907693686 EZB TRUE 724 -OZM073-015_PlasmaEx-T3 EZB 1 0 727,344,726,47,688,784,593,870,1114,601 0.154,0.179,0.206,0.264,0.304,0.338,0.344,0.352,0.372,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.994726340876 0 0 37.99472634087599 37.99472634087599 EZB TRUE 725 -OZM073-015_Relapse EZB 1 0 727,725,47,593,344,870,219,82,138,292 0.194,0.206,0.252,0.261,0.285,0.317,0.333,0.352,0.359,0.371 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.7857254189458 0 0 35.78572541894579 35.78572541894579 EZB TRUE 726 -OZM073-015_Diagnosis EZB 1 0 725,726,47,344,870,593,784,688,219,839 0.154,0.194,0.21,0.222,0.298,0.301,0.31,0.35,0.389,0.397 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.7539874987359 0 0 38.753987498735945 38.753987498735945 EZB TRUE 727 -OZM073-018_Diagnosis ST2 0.9221337629813409 0 656,60,483,207,822,1057,1081,394,362,508 0.196,0.238,0.24,0.253,0.254,0.322,0.323,0.356,0.358,0.365 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.79562178214264,33.1072533172759 0 0 35.90287509941855 33.10725331727591 ST2 TRUE 728 -OZM073-021_Diagnosis MCD 1 0 324,280,288,520,439,106,991,147,966,897 0.169,0.18,0.207,0.219,0.224,0.23,0.252,0.254,0.254,0.254 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 45.4871959288667 0 0 45.4871959288667 45.4871959288667 MCD TRUE 729 -OZM073-021_Relapse MCD 0.9273351644727906 0 460,1043,888,513,459,739,314,66,897,520 0.123,0.127,0.137,0.201,0.265,0.275,0.276,0.279,0.305,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 3.58597420487631,45.763538236166 0 0 49.349512441042286 45.76353823616598 MCD TRUE 730 -OZM073-022_Diagnosis BN2 1 0 955,1010,700,538,435,1071,1001,99,650,959 0.126,0.151,0.172,0.223,0.238,0.245,0.293,0.295,0.309,0.321 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2973173441344 0 0 46.29731734413443 46.29731734413443 BN2 TRUE 731 -OZM073-023_Relapse EZB 1 0 979,136,596,181,257,255,272,291,853,724 0.157,0.166,0.182,0.216,0.221,0.232,0.262,0.262,0.264,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3487347913874 0 0 46.34873479138737 46.34873479138737 EZB TRUE 732 -OZM073-026_Transformation EZB 0.9093600574375451 0 19,744,429,241,1050,643,645,254,975,658 0.167,0.222,0.237,0.244,0.252,0.254,0.273,0.281,0.302,0.312 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 36.8012725246639,3.66814574774731 0 0 40.469418272411176 36.801272524663865 EZB TRUE 733 -OZM073-029_Transformation EZB 1 0 669,68,50,291,255,213,181,723,257,142 0.147,0.172,0.215,0.217,0.243,0.253,0.258,0.281,0.287,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2959472854073 0 0 44.29594728540729 44.29594728540729 EZB TRUE 734 -OZM073-036_Transformation EZB 1 0 149,73,603,1059,25,605,384,20,239,231 0.181,0.223,0.248,0.256,0.262,0.292,0.293,0.294,0.297,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.6528070187518 0 0 38.652807018751794 38.652807018751794 EZB TRUE 735 -QC2-12T EZB 0.6271519507411953 0 1052,509,781,338,545,249,689,606,1064,1118 0.119,0.242,0.25,0.355,0.358,0.363,0.387,0.389,0.4,0.457 EZB,EZB,EZB,BN2,ST2,EZB,ST2,ST2,EZB,ST2 2.8149169760286,21.7855635046376,10.1368164039093 0 0 34.7372968845755 21.785563504637572 EZB TRUE 736 -QC2-15T EZB 1 0 259,588,619,238,1115,224,981,1083,983,183 0.179,0.2,0.246,0.25,0.272,0.28,0.299,0.324,0.327,0.331 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4183464162955 0 0 38.4183464162955 38.4183464162955 EZB TRUE 737 -QC2-17T EZB 1 0 92,105,1108,317,984,637,287,397,914,91 0.146,0.16,0.188,0.193,0.201,0.222,0.231,0.238,0.244,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.8022868437089 0 0 49.802286843708885 49.802286843708885 EZB TRUE 738 -QC2-19T MCD 1 0 314,520,459,897,513,288,991,147,729,106 0.115,0.138,0.14,0.152,0.175,0.184,0.21,0.218,0.255,0.259 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.9623623402534 0 0 57.96236234025342 57.96236234025342 MCD TRUE 739 -QC2-20T EZB 1 0 93,631,228,387,757,274,1117,848,102,947 0.112,0.161,0.186,0.188,0.19,0.213,0.222,0.232,0.234,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.1303151734321 0 0 53.130315173432145 53.130315173432145 EZB TRUE 740 -QC2-22T MCD 1 0 817,349,283,1087,447,469,299,550,721,511 0.178,0.278,0.28,0.281,0.283,0.333,0.337,0.338,0.339,0.339 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.7171241116813 0 0 34.717124111681294 34.717124111681294 MCD TRUE 741 -QC2-25T MCD 0.44875970938574744 0 100,42,180,484,185,103,127,179,104,34 0.164,0.167,0.17,0.25,0.273,0.285,0.318,0.328,0.328,0.343 MCD,EZB,MCD,BN2,BN2,MCD,BN2,BN2,MCD,BN2 16.7629433226456,5.99640059539324,18.5281749835238 0 0 41.287518901562656 18.52817498352381 BN2 FALSE 742 -QC2-3T EZB 1 0 942,1073,883,156,303,1018,718,334,882,120 0.156,0.168,0.168,0.191,0.193,0.202,0.243,0.253,0.253,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3836668427075 0 0 49.38366684270749 49.38366684270749 EZB TRUE 743 -QC2-30T EZB 0.9245075338275891 0 429,19,658,733,454,241,1050,67,643,645 0.169,0.173,0.204,0.222,0.278,0.284,0.299,0.31,0.32,0.323 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.9300777979269,3.09725449528993 0 0 41.02733229321684 37.930077797926906 EZB TRUE 744 -QC2-32T EZB 1 0 378,626,377,238,184,1039,167,677,1083,629 0.238,0.344,0.348,0.436,0.454,0.469,0.474,0.483,0.489,0.491 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.863239297758 0 0 24.86323929775796 24.86323929775796 EZB TRUE 745 -QC2-34T EZB 1 0 72,266,852,20,677,239,167,25,191,96 0.125,0.139,0.175,0.193,0.213,0.213,0.221,0.246,0.252,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6703743694063 0 0 51.670374369406275 51.670374369406275 EZB TRUE 746 -QC2-36T MCD 0.3728785076813135 0 554,1044,124,1089,673,935,43,803,525,779 0.178,0.223,0.276,0.29,0.324,0.33,0.343,0.377,0.436,0.456 ST2,MCD,N1,MCD,BN2,EZB,ST2,BN2,MCD,MCD 5.73819121662709,3.03002751322344,12.4291642051222,3.61873751172437,8.51689271732521 0 0 33.33301316402235 12.42916420512223 ST2 FALSE 747 -QC2-39T ST2 0.9187649249365673 0 1009,907,195,1004,683,814,1065,875,770,662 0.133,0.164,0.167,0.186,0.192,0.21,0.219,0.221,0.235,0.246 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 4.25273119343869,48.0981922237912 0 0 52.35092341722986 48.09819222379117 ST2 TRUE 748 -QC2-40T MCD 0.8741541580054929 0 933,772,859,533,448,445,436,532,934,400 0.122,0.162,0.165,0.171,0.196,0.258,0.278,0.287,0.292,0.295 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.18518843220844,42.9637411961412 0 0 49.14892962834968 42.963741196141235 MCD TRUE 749 -QC2-42T EZB 1 0 310,304,887,289,36,963,708,653,403,949 0.152,0.161,0.183,0.232,0.233,0.24,0.242,0.245,0.26,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.850796624181 0 0 46.85079662418098 46.85079662418098 EZB TRUE 750 -QC2-7T EZB 1 0 82,609,600,121,122,849,601,726,725,1014 0.237,0.261,0.279,0.32,0.325,0.332,0.372,0.385,0.392,0.4 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.196742044702 0 0 31.196742044701967 31.196742044701967 EZB TRUE 751 -Reddy_1008T EZB 1 0 410,655,1051,49,1012,183,101,302,313,392 0.16,0.183,0.198,0.228,0.237,0.263,0.279,0.28,0.291,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1688026280634 0 0 43.16880262806339 43.16880262806339 EZB TRUE 752 -Reddy_1016T EZB 1 0 1075,1006,164,874,1114,597,284,812,601,21 0.139,0.184,0.184,0.19,0.236,0.253,0.26,0.264,0.269,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5099289689163 0 0 46.509928968916256 46.509928968916256 EZB TRUE 753 -Reddy_2043T EZB 1 0 406,116,18,865,190,756,1018,182,1073,1014 0.133,0.161,0.174,0.178,0.193,0.214,0.228,0.241,0.253,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2074677988847 0 0 51.20746779888472 51.20746779888472 EZB TRUE 754 -Reddy_2044T EZB 0.5413090163488783 0 758,872,800,40,806,145,248,146,221,54 0.171,0.283,0.291,0.295,0.31,0.31,0.35,0.361,0.381,0.383 EZB,BN2,EZB,EZB,N1,BN2,EZB,BN2,EZB,MCD 9.53276105660172,18.1466378211492,2.61406962273872,3.23015160018411 0 0 33.523620100673746 18.146637821149188 EZB TRUE 755 -Reddy_2045T EZB 1 0 18,190,868,116,754,709,406,251,597,849 0.14,0.15,0.176,0.196,0.214,0.225,0.229,0.234,0.256,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0022329568881 0 0 50.002232956888065 50.002232956888065 EZB TRUE 756 -Reddy_2047T EZB 1 0 387,102,631,228,93,107,740,873,1079,140 0.139,0.164,0.164,0.17,0.185,0.185,0.19,0.227,0.241,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8388337902169 0 0 53.83883379021688 53.83883379021688 EZB TRUE 757 -Reddy_2060T EZB 0.6307055086241361 0 755,248,872,145,40,800,146,806,221,781 0.171,0.319,0.32,0.325,0.353,0.362,0.367,0.381,0.384,0.392 EZB,EZB,BN2,BN2,EZB,EZB,BN2,N1,EZB,EZB 8.92152606748121,19.721094200426,2.62568788980486 0 0 31.268308157712113 19.72109420042604 EZB TRUE 758 -Reddy_2073T ST2 0.9132389659665567 0 832,915,396,1032,1003,59,281,940,1068,835 0.266,0.32,0.354,0.363,0.372,0.377,0.379,0.411,0.416,0.417 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 2.40060161773928,25.2685201773506 0 0 27.66912179508993 25.268520177350645 ST2 TRUE 759 -Reddy_2075T EZB 1 0 876,632,46,667,914,80,29,594,1026,1002 0.122,0.122,0.123,0.131,0.149,0.154,0.176,0.182,0.187,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1441114257294 0 0 67.14411142572938 67.14411142572938 EZB TRUE 760 -Reddy_2076T EZB 1 0 37,430,89,113,880,159,997,1031,1063,931 0.134,0.153,0.181,0.198,0.201,0.207,0.214,0.218,0.266,0.276 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0620716864679 0 0 51.062071686467895 51.062071686467895 EZB TRUE 761 -Reddy_2081T MCD 1 0 767,246,166,851,879,17,1109,712,434,500 0.118,0.127,0.129,0.131,0.155,0.156,0.167,0.169,0.187,0.191 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.0615193320854 0 0 67.06151933208537 67.06151933208537 MCD TRUE 762 -Reddy_2091T BN2 1 0 342,563,333,38,488,518,63,871,341,654 0.154,0.156,0.177,0.178,0.182,0.193,0.198,0.21,0.218,0.227 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.673181542264 0 0 53.67318154226399 53.67318154226399 BN2 TRUE 763 -Reddy_2092T EZB 0.8077429742852865 0 836,236,334,718,1092,294,826,111,6,156 0.217,0.246,0.252,0.26,0.261,0.262,0.275,0.29,0.296,0.303 EZB,EZB,EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB 30.6234333872313,7.28891541942687 0 0 37.91234880665817 30.623433387231305 EZB TRUE 764 -Reddy_2095T BN2 0.7516269168389094 0 624,173,535,347,773,1049,196,936,928,799 0.159,0.179,0.208,0.219,0.247,0.275,0.295,0.373,0.387,0.415 EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 30.0483965254431,6.28646975751859,3.64294165906435 0 0 39.97780794202602 30.04839652544308 BN2 TRUE 765 -Reddy_2100T EZB 1 0 217,216,908,168,823,1064,471,1100,249,235 0.15,0.171,0.182,0.184,0.201,0.234,0.248,0.289,0.292,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.9817050675576 0 0 46.981705067557584 46.981705067557584 EZB TRUE 766 -Reddy_2103T MCD 1 0 246,166,762,851,17,879,434,712,804,1109 0.113,0.116,0.118,0.122,0.146,0.165,0.169,0.179,0.181,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.5176999603356 0 0 69.51769996033563 69.51769996033563 MCD TRUE 767 -Reddy_2111T EZB 0.5051701949052829 0 1116,988,993,194,824,684,714,810,974,913 0.224,0.321,0.453,0.495,0.518,0.537,0.56,0.565,0.568,0.571 ST2,BN2,EZB,EZB,EZB,ST2,EZB,ST2,EZB,EZB 3.11556956991156,11.4533468571882,8.10333734538511 0 0 22.672253772484908 11.453346857188237 EZB TRUE 768 -Reddy_2112T MCD 0.8097531378645632 0 1048,417,312,778,1046,1015,918,350,1103,169 0.148,0.153,0.161,0.168,0.169,0.172,0.172,0.18,0.19,0.193 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,ST2,MCD 5.95939564874569,47.779371081998,5.26609394568315 0 0 59.00486067642683 47.779371081997994 MCD TRUE 769 -Reddy_2127T ST2 0.8586924734109942 0 787,195,361,178,1009,748,875,1065,814,365 0.152,0.178,0.189,0.195,0.216,0.235,0.246,0.247,0.282,0.288 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.58792373312298,40.0332569792393 0 0 46.62118071236233 40.03325697923935 BN2 FALSE 770 -Reddy_2146T EZB 0.6393122685550168 0 782,3,55,478,230,971,54,806,800,976 0.207,0.224,0.264,0.268,0.289,0.305,0.319,0.332,0.345,0.35 EZB,EZB,MCD,EZB,EZB,EZB,MCD,N1,EZB,BN2 2.85750713291883,22.6751152712665,6.9263015121815,3.00905689138011 0 0 35.46798080774691 22.675115271266474 EZB TRUE 771 -Reddy_2148T MCD 0.9347050891534401 0 533,448,933,859,749,436,400,567,532,775 0.11,0.135,0.141,0.161,0.162,0.236,0.242,0.259,0.262,0.275 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 3.6366387033469,52.0589531306451 0 0 55.69559183399205 52.05895313064514 EZB FALSE 772 -Reddy_2152T BN2 0.747411700856546 0 1049,765,196,928,279,262,173,110,624,535 0.155,0.247,0.25,0.262,0.28,0.289,0.297,0.304,0.306,0.311 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,BN2 28.7560286399064,3.26448279887244,6.45363688797539 0 0 38.47414832675427 28.75602863990644 BN2 TRUE 773 -Reddy_2153T ST2 0.884073778516856 0 825,1068,835,1003,940,598,996,218,671,842 0.139,0.154,0.18,0.184,0.195,0.24,0.257,0.288,0.299,0.303 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.56037682624387,42.4044128055166 0 0 47.964789631760425 42.40441280551656 ST2 TRUE 774 -Reddy_2156T MCD 1 0 567,420,510,400,448,201,541,200,533,436 0.119,0.133,0.148,0.22,0.241,0.242,0.246,0.252,0.266,0.267 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.036413997916 0 0 51.03641399791599 51.03641399791599 EZB FALSE 775 -Reddy_2163T EZB 1 0 613,866,151,27,152,98,150,599,644,97 0.114,0.136,0.173,0.184,0.196,0.213,0.223,0.24,0.268,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9881269585051 0 0 52.98812695850515 52.98812695850515 EZB TRUE 776 -Reddy_2166T BN2 1 0 694,301,968,158,484,393,572,357,701,564 0.247,0.276,0.29,0.381,0.382,0.405,0.41,0.443,0.445,0.468 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 27.9087682695643 0 0 27.908768269564312 27.908768269564312 BN2 TRUE 777 -Reddy_2190T MCD 0.8907398976392183 0 918,312,1103,169,155,350,450,990,417,1048 0.112,0.121,0.128,0.143,0.146,0.152,0.152,0.153,0.156,0.158 MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.473686467694,7.78582108992427 0 0 71.25950755761829 63.47368646769402 BN2 FALSE 778 -Reddy_2191T MCD 0.5277687645580372 0 525,205,43,124,716,789,617,441,421,803 0.179,0.235,0.272,0.288,0.294,0.314,0.315,0.387,0.4,0.404 MCD,MCD,ST2,N1,BN2,MCD,N1,MCD,MCD,BN2 5.8832033720566,18.1209745207479,6.65067394074517,3.68021256898794 0 0 34.335064402537654 18.12097452074794 MCD TRUE 779 -Reddy_2195T BN2 1 0 99,959,345,943,435,646,477,548,650,999 0.138,0.147,0.165,0.179,0.185,0.192,0.205,0.214,0.219,0.219 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.9917987141525 0 0 54.991798714152516 54.991798714152516 BN2 TRUE 780 -Reddy_2199T EZB 0.847821324044056 0 509,1052,736,1064,758,249,248,819,338,606 0.228,0.241,0.25,0.387,0.392,0.405,0.429,0.444,0.45,0.451 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,ST2 2.2199958246875,24.7220703280361,2.21746266180695 0 0 29.159528814530503 24.722070328036054 EZB TRUE 781 -Reddy_2203T EZB 0.5187512166729139 0 771,806,800,971,54,55,3,976,230,132 0.207,0.232,0.256,0.284,0.286,0.29,0.294,0.304,0.308,0.333 EZB,N1,EZB,EZB,MCD,MCD,EZB,BN2,EZB,BN2 6.29598101757248,18.9142290967691,6.9386192509794,4.31224964299107 0 0 36.461079008312076 18.91422909676913 EZB TRUE 782 -Reddy_2204T ST2 0.5909576657905032 0 1017,148,1005,218,785,668,359,996,476,671 0.325,0.347,0.396,0.443,0.443,0.459,0.476,0.499,0.504,0.51 BN2,ST2,ST2,ST2,EZB,BN2,ST2,ST2,EZB,ST2 5.26220625652496,4.24077939823208,13.7292934029161 0 0 23.232279057673153 13.72929340291612 EZB FALSE 783 -Reddy_2209T EZB 1 0 839,890,47,727,344,870,725,407,715,322 0.228,0.244,0.289,0.31,0.332,0.336,0.338,0.339,0.353,0.384 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.5068314133115 0 0 32.50683141331149 32.50683141331149 EZB TRUE 784 -Reddy_2211T ST2 0.6483063658808581 0 366,668,425,1017,161,583,160,612,359,1029 0.188,0.188,0.2,0.236,0.255,0.266,0.267,0.268,0.269,0.279 BN2,BN2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2 14.8919043054871,27.4514959177943 0 0 42.34340022328141 27.451495917794286 EZB FALSE 785 -Reddy_2212T EZB 0.8485774942057832 0 143,144,620,608,614,90,846,545,828,884 0.197,0.203,0.25,0.36,0.379,0.387,0.404,0.413,0.423,0.429 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,ST2,EZB 26.8195036647217,4.78574611841463 0 0 31.605249783136316 26.819503664721687 EZB TRUE 786 -Reddy_2213T ST2 0.6166190952998081 0 178,770,361,195,965,1107,365,950,661,1009 0.143,0.152,0.156,0.227,0.244,0.249,0.258,0.262,0.264,0.268 ST2,BN2,ST2,ST2,EZB,ST2,ST2,EZB,EZB,ST2 6.58792373312298,11.7113337143352,29.4320124804701 0 0 47.73126992792832 29.432012480470096 EZB FALSE 787 -Reddy_2214T MCD 0.9121545052588331 0 992,829,449,502,616,574,573,1093,1047,856 0.232,0.327,0.361,0.396,0.413,0.443,0.467,0.478,0.483,0.484 MCD,MCD,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD 23.4531851922099,2.25867070171546 0 0 25.711855893925335 23.453185192209876 EZB FALSE 788 -Reddy_2215T MCD 0.7073911410177665 0 441,617,713,205,421,779,525,985,716,663 0.173,0.177,0.269,0.285,0.31,0.314,0.369,0.39,0.433,0.441 MCD,N1,MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2 2.30761283779795,24.6949510049741,5.63788887294008,2.26944305912608 0 0 34.90989577483826 24.694951004974143 MCD TRUE 789 -Reddy_2216T MCD 1 0 697,475,223,198,1054,466,837,1047,44,128 0.123,0.195,0.23,0.235,0.26,0.262,0.272,0.277,0.287,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.5989773124325 0 0 43.59897731243248 43.59897731243248 MCD TRUE 790 -Reddy_2217T EZB 0.7062032181433154 0 382,676,922,641,657,11,62,252,974,372 0.278,0.376,0.4,0.406,0.419,0.545,0.553,0.589,0.595,0.601 EZB,EZB,ST2,EZB,BN2,EZB,EZB,EZB,EZB,ST2 2.38584903834364,15.7434952076932,4.16380707814509 0 0 22.293151324181935 15.743495207693197 EZB TRUE 791 -Reddy_2219T ST2 0.8357323496193599 0 370,300,1086,177,692,682,850,275,1016,673 0.186,0.281,0.311,0.317,0.327,0.356,0.362,0.366,0.377,0.408 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2 2.4526252799782,2.76328442622912,26.5365972184431 0 0 31.752506924650373 26.536597218443053 BN2 FALSE 792 -Reddy_2222T ST2 1 0 557,1053,982,598,1016,642,692,940,915,1086 0.147,0.201,0.23,0.25,0.252,0.261,0.29,0.304,0.304,0.331 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 40.9865509007107 0 0 40.9865509007107 40.9865509007107 ST2 TRUE 793 -Reddy_2225T MCD 1 0 413,297,290,108,165,1035,17,528,166,246 0.115,0.143,0.144,0.149,0.16,0.165,0.174,0.18,0.204,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.7308385829719 0 0 62.73083858297188 62.73083858297188 MCD TRUE 794 -Reddy_2226T BN2 0.8610806312826945 0 474,584,1060,674,630,335,681,65,855,64 0.218,0.268,0.275,0.3,0.303,0.333,0.334,0.345,0.348,0.367 N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 28.4819782642465,4.59503825372905 0 0 33.077016517975544 28.481978264246496 BN2 TRUE 795 -Reddy_2228T BN2 1 0 591,1023,527,555,998,305,75,905,395,702 0.12,0.144,0.158,0.177,0.179,0.189,0.202,0.211,0.226,0.228 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.5502829510305 0 0 56.550282951030496 56.550282951030496 BN2 TRUE 796 -Reddy_2231T ST2 0.9196534911012629 0 1067,1057,1007,422,207,309,483,544,363,728 0.247,0.284,0.303,0.33,0.396,0.422,0.425,0.444,0.45,0.491 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 2.22247831609962,25.4386901224777 0 0 27.661168438577345 25.438690122477727 ST2 TRUE 797 -Reddy_2232T MCD 1 0 528,804,573,434,297,17,413,794,166,246 0.148,0.157,0.175,0.182,0.191,0.221,0.226,0.228,0.229,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.6490406876131 0 0 51.6490406876131 51.6490406876131 MCD TRUE 798 -Reddy_2234T BN2 0.6020700739159555 0 698,1061,506,347,919,293,624,123,765,936 0.204,0.232,0.239,0.306,0.313,0.35,0.38,0.385,0.415,0.437 ST2,BN2,BN2,BN2,BN2,MCD,EZB,N1,BN2,BN2 19.6612911942718,2.63230646526343,2.85985865731566,2.59906930052404,4.90362529607568 0 0 32.65615091345062 19.661291194271808 BN2 TRUE 799 -Reddy_2236T EZB 0.5486131980490389 0 806,40,54,782,755,230,55,872,771,253 0.15,0.213,0.231,0.256,0.291,0.316,0.318,0.329,0.345,0.356 N1,EZB,MCD,EZB,EZB,EZB,MCD,BN2,EZB,EZB 3.03868826174577,20.906892853978,7.47868838437516,6.68435225440215 0 0 38.1086217545011 20.90689285397802 EZB TRUE 800 -Reddy_2237T BN2 0.3934290334558426 0 421,431,663,442,579,441,1070,1045,894,789 0.275,0.289,0.293,0.389,0.408,0.456,0.465,0.465,0.47,0.472 MCD,BN2,ST2,BN2,N1,MCD,BN2,BN2,MCD,MCD 10.3390613243195,10.0809423008088,2.44930876022281,3.41004283534057 0 0 26.27935522069162 10.339061324319456 ST2 FALSE 801 -Reddy_2238T BN2 0.5586989161326144 0 14,891,504,23,388,495,699,127,185,855 0.148,0.152,0.2,0.221,0.224,0.241,0.244,0.258,0.306,0.317 BN2,MCD,MCD,EZB,BN2,MCD,BN2,BN2,BN2,BN2 25.6353294138779,4.53372845772802,15.7149212693041 0 0 45.88397914091004 25.635329413877926 ST2 FALSE 802 -Reddy_2241T BN2 0.31706831880259156 0 43,716,481,530,525,1045,124,747,554,779 0.239,0.292,0.295,0.315,0.332,0.355,0.369,0.377,0.397,0.404 ST2,BN2,BN2,N1,MCD,BN2,N1,ST2,ST2,MCD 9.62363730632558,5.49134118311939,5.88959801729201,9.3473594503755 0 0 30.351935957112488 9.623637306325584 BN2 TRUE 803 -Reddy_2243T MCD 1 0 434,528,798,297,17,166,246,767,851,762 0.133,0.152,0.157,0.166,0.172,0.173,0.174,0.181,0.181,0.199 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.9000389936201 0 0 59.900038993620086 59.900038993620086 MCD TRUE 804 -Reddy_2244T EZB 1 0 594,29,80,876,632,242,953,667,760,46 0.138,0.161,0.166,0.185,0.194,0.195,0.2,0.204,0.205,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.7477134183057 0 0 54.74771341830569 54.74771341830569 EZB TRUE 805 -Reddy_2247T EZB 0.6540346895876159 0 800,782,40,54,872,755,771,55,230,976 0.15,0.232,0.262,0.268,0.305,0.31,0.332,0.34,0.343,0.367 EZB,EZB,EZB,MCD,BN2,EZB,EZB,MCD,EZB,BN2 6.00509617172903,23.9592403054928,6.66864444401901 0 0 36.63298092124089 23.959240305492845 N1 FALSE 806 -Reddy_2249T ST2 0.8195605852956611 0 362,1032,263,483,207,656,832,1057,728,822 0.182,0.279,0.311,0.33,0.344,0.345,0.411,0.43,0.44,0.447 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.48497928666127,24.9129207267518 0 0 30.397900013413064 24.912920726751796 ST2 TRUE 807 -Reddy_2260T BN2 0.861374235134684 0 531,952,408,10,895,363,352,258,670,109 0.22,0.225,0.237,0.345,0.372,0.408,0.416,0.432,0.446,0.45 BN2,BN2,N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.1837624879937,4.21389908578162 0 0 30.397661573775295 26.183762487993672 BN2 TRUE 808 -Reddy_2262T EZB 0.771373549606777 0 1094,867,1102,886,595,885,618,861,1056,7 0.173,0.18,0.186,0.193,0.199,0.21,0.216,0.221,0.222,0.234 ST2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.2595612701123,11.3397039492044 0 0 49.59926521931668 38.25956127011226 EZB TRUE 809 -Reddy_2263T EZB 0.7623932160607518 0 684,714,1033,319,322,993,913,641,194,988 0.166,0.235,0.255,0.27,0.274,0.312,0.318,0.336,0.339,0.376 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 2.6584167982463,27.8132093652433,6.0098230143412 0 0 36.48144917783078 27.81320936524328 ST2 FALSE 810 -Reddy_2269T EZB 1 0 556,628,724,272,653,750,310,979,853,596 0.167,0.236,0.272,0.314,0.319,0.322,0.335,0.349,0.349,0.36 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8248826336618 0 0 34.82488263366183 34.82488263366183 EZB TRUE 811 -Reddy_2271T EZB 0.8563186429647514 0 426,1097,284,115,753,874,1006,1075,816,21 0.163,0.165,0.201,0.203,0.264,0.27,0.281,0.288,0.328,0.332 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5724740124581,6.13648054893819 0 0 42.708954561396276 36.57247401245809 EZB TRUE 812 -Reddy_2274T MCD 0.4764872313343525 0 534,193,94,371,444,170,930,510,419,420 0.129,0.173,0.212,0.263,0.284,0.332,0.348,0.362,0.376,0.378 MCD,BN2,EZB,EZB,EZB,MCD,ST2,MCD,MCD,MCD 5.79403607007985,12.0495242503798,18.856680595703,2.87412615112855 0 0 39.574367067291206 18.856680595702965 EZB FALSE 813 -Reddy_2278T ST2 1 0 1004,1065,875,1009,748,896,907,195,683,662 0.142,0.15,0.153,0.19,0.21,0.233,0.248,0.254,0.258,0.26 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 50.2682962395257 0 0 50.26829623952566 50.26829623952566 ST2 TRUE 814 -Reddy_2281T BN2 1 0 515,70,527,591,998,404,796,261,499,555 0.192,0.208,0.225,0.23,0.245,0.245,0.25,0.265,0.269,0.275 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.1068108921169 0 0 42.10681089211686 42.10681089211686 BN2 TRUE 815 -Reddy_2290T EZB 1 0 21,250,938,284,597,709,251,941,356,1075 0.174,0.184,0.198,0.226,0.238,0.246,0.249,0.257,0.283,0.307 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6201429364529 0 0 43.62014293645291 43.62014293645291 EZB TRUE 816 -Reddy_2297T MCD 1 0 741,1087,447,283,721,511,722,299,690,469 0.178,0.209,0.232,0.253,0.261,0.263,0.264,0.268,0.271,0.285 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.0415689531844 0 0 41.04156895318436 41.04156895318436 MCD TRUE 817 -Reddy_2298T EZB 1 0 585,307,220,427,62,912,219,316,409,138 0.173,0.193,0.268,0.273,0.298,0.302,0.348,0.355,0.4,0.407 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.658887064425 0 0 35.65888706442495 35.65888706442495 EZB TRUE 818 -Reddy_2303T EZB 1 0 269,81,168,270,1064,217,908,766,509,248 0.239,0.261,0.324,0.344,0.376,0.39,0.397,0.405,0.423,0.425 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.9217027281102 0 0 28.921702728110233 28.921702728110233 EZB TRUE 819 -Reddy_2450T EZB 1 0 1063,931,635,880,125,89,35,48,159,364 0.131,0.161,0.172,0.193,0.208,0.214,0.218,0.223,0.236,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.4796080519993 0 0 51.479608051999314 51.479608051999314 EZB TRUE 820 -Reddy_2453T MCD 1 0 416,491,326,1036,550,311,332,283,469,1027 0.111,0.122,0.131,0.145,0.152,0.164,0.194,0.209,0.211,0.225 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.5734444656084 0 0 63.57344446560836 63.57344446560836 MCD TRUE 821 -Reddy_2459T ST2 0.9224169782822506 0 1081,728,60,656,263,394,483,904,207,362 0.203,0.254,0.257,0.257,0.289,0.312,0.341,0.35,0.366,0.373 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 2.67782981597975,31.8378381316005 0 0 34.51566794758026 31.837838131600513 ST2 TRUE 822 -Reddy_2468T EZB 1 0 1100,766,908,216,471,217,76,168,235,638 0.191,0.201,0.222,0.222,0.238,0.24,0.241,0.285,0.293,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8156108452647 0 0 41.815610845264686 41.815610845264686 EZB TRUE 823 -Reddy_2471T ST2 0.6474643504714818 0 685,320,834,586,1091,1024,901,974,625,194 0.256,0.287,0.331,0.339,0.376,0.385,0.408,0.417,0.426,0.446 ST2,ST2,ST2,ST2,EZB,EZB,ST2,EZB,ST2,EZB 9.89509265567754,18.1732535354417 0 0 28.06834619111925 18.173253535441713 EZB FALSE 824 -Reddy_2473T ST2 0.8896770161361672 0 1068,774,1003,940,835,598,996,218,915,671 0.123,0.139,0.148,0.168,0.178,0.219,0.291,0.323,0.325,0.328 ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 5.61481382040532,45.2795114032078 0 0 50.89432522361311 45.279511403207785 ST2 TRUE 825 -Reddy_2475T EZB 1 0 294,236,56,718,156,247,883,334,303,764 0.114,0.138,0.156,0.157,0.194,0.206,0.236,0.255,0.267,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3089970703821 0 0 54.30899707038213 54.30899707038213 EZB TRUE 826 -Reddy_2478T BN2 0.8351288885485473 0 327,980,611,463,348,391,2,438,895,352 0.146,0.248,0.261,0.312,0.318,0.321,0.338,0.339,0.344,0.357 BN2,BN2,BN2,BN2,BN2,BN2,N1,N1,BN2,BN2 29.9159626983247,5.90600814777857 0 0 35.82197084610323 29.915962698324652 BN2 TRUE 827 -Reddy_2480T ST2 0.5187177414276689 0 614,607,1118,954,689,944,620,786,346,338 0.31,0.313,0.333,0.363,0.39,0.395,0.398,0.423,0.442,0.465 EZB,ST2,ST2,ST2,ST2,EZB,EZB,EZB,ST2,BN2 2.14892598045675,10.6373327180634,13.7808097333167 0 0 26.567068431836844 13.78080973331673 ST2 TRUE 828 -Reddy_2481T MCD 0.9128836378695838 0 449,573,798,856,788,528,804,519,434,44 0.15,0.241,0.286,0.304,0.327,0.328,0.338,0.348,0.351,0.365 MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 3.06184441178203,32.0847610812061 0 0 35.146605492988115 32.08476108120609 MCD TRUE 829 -Reddy_2483T ST2 0.7346769304299484 0 381,412,1066,529,119,308,396,919,698,281 0.123,0.219,0.259,0.266,0.306,0.339,0.348,0.375,0.398,0.418 ST2,ST2,N1,ST2,BN2,ST2,ST2,BN2,ST2,ST2 5.9407852295596,3.86463276679789,27.1511045263424 0 0 36.95652252269993 27.151104526342436 ST2 TRUE 830 -Reddy_2485T EZB 0.9184006380671825 0 379,687,118,369,592,385,221,581,975,146 0.191,0.199,0.252,0.253,0.263,0.285,0.295,0.297,0.305,0.317 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.15455895093763,35.5045540153482 0 0 38.65911296628584 35.504554015348205 EZB TRUE 831 -Reddy_2487T ST2 0.9010850514943948 0 759,1032,1003,835,807,825,915,1068,940,774 0.266,0.313,0.392,0.396,0.411,0.437,0.446,0.446,0.462,0.464 ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2 2.52842439928896,23.0331761220486 0 0 25.56160052133754 23.033176122048584 ST2 TRUE 832 -Reddy_2488T EZB 1 0 61,629,384,605,74,184,1059,603,163,25 0.105,0.132,0.151,0.151,0.157,0.181,0.192,0.201,0.218,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.5859293777597 0 0 61.58592937775968 61.58592937775968 EZB TRUE 833 -Reddy_2522T ST2 0.6368602490686615 0 320,685,1091,586,1024,901,372,974,824,925 0.147,0.2,0.207,0.218,0.262,0.292,0.311,0.328,0.331,0.354 ST2,ST2,EZB,ST2,EZB,ST2,ST2,EZB,EZB,ST2 14.7414306766258,25.8529428085647 0 0 40.59437348519055 25.852942808564723 ST2 TRUE 834 -Reddy_2523T ST2 1 0 1003,825,774,1068,940,996,842,602,671,598 0.177,0.178,0.18,0.2,0.243,0.254,0.269,0.273,0.276,0.296 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 44.2656634167762 0 0 44.26566341677617 44.26566341677617 EZB FALSE 835 -Reddy_2524T EZB 1 0 764,236,117,78,294,826,675,718,56,287 0.217,0.267,0.271,0.287,0.291,0.305,0.306,0.322,0.336,0.342 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.5068183278884 0 0 34.50681832788842 34.50681832788842 EZB TRUE 836 -Reddy_2529T MCD 1 0 489,198,128,790,466,697,223,522,475,44 0.147,0.195,0.24,0.272,0.277,0.291,0.311,0.343,0.358,0.364 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.506445348769 0 0 38.50644534876899 38.50644534876899 MCD TRUE 837 -Reddy_2530T EZB 1 0 141,5,686,403,237,708,289,707,963,949 0.181,0.201,0.242,0.26,0.268,0.273,0.314,0.346,0.356,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5166405660904 0 0 37.51664056609036 37.51664056609036 EZB TRUE 838 -Reddy_2533T EZB 0.9224521785311877 0 890,784,715,407,322,424,319,1033,344,1114 0.117,0.228,0.229,0.25,0.332,0.335,0.335,0.361,0.364,0.384 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 35.4579432082057,2.9808442253771 0 0 38.438787433582796 35.45794320820569 EZB TRUE 839 -Reddy_2534T BN2 0.6483344890235474 0 22,571,470,186,1106,88,672,539,570,462 0.147,0.185,0.203,0.22,0.23,0.24,0.246,0.253,0.256,0.258 EZB,EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2 29.8262942794532,16.1781907270045 0 0 46.00448500645764 29.826294279453165 BN2 TRUE 840 -Reddy_2539T BN2 0.8510224905322906 0 540,929,546,373,905,1001,555,499,1023,395 0.137,0.146,0.166,0.218,0.221,0.229,0.243,0.262,0.266,0.266 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.8087545245208,7.31891835092883 0 0 49.12767287544967 41.808754524520836 BN2 TRUE 841 -Reddy_2540T ST2 0.7837785855672981 0 671,602,359,161,996,1029,160,668,835,366 0.127,0.14,0.159,0.166,0.173,0.188,0.218,0.22,0.269,0.276 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,BN2 8.16039468535616,3.71579165089888,43.049854950703 0 0 54.926041286958004 43.04985495070296 ST2 TRUE 842 -Reddy_2543T ST2 0.8340745785992753 0 904,925,278,1081,901,911,394,1024,910,372 0.137,0.232,0.257,0.284,0.29,0.306,0.307,0.311,0.316,0.325 ST2,ST2,ST2,ST2,ST2,ST2,ST2,EZB,EZB,ST2 6.38581296247114,32.100230397026 0 0 38.48604335949718 32.100230397026046 ST2 TRUE 843 -Reddy_2544T MCD 1 0 1042,16,580,526,678,900,453,881,549,296 0.152,0.164,0.173,0.187,0.189,0.191,0.218,0.227,0.234,0.258 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4824109514507 0 0 51.48241095145073 51.48241095145073 EZB FALSE 844 -Reddy_2547T MCD 0.5070185471913775 0 330,894,436,497,400,1070,444,104,285,386 0.257,0.265,0.364,0.396,0.403,0.454,0.455,0.458,0.461,0.462 BN2,MCD,MCD,BN2,MCD,BN2,EZB,MCD,BN2,MCD 10.7816048761234,2.19732586706452,13.3484912505689 0 0 26.32742199375689 13.348491250568939 BN2 FALSE 845 -Reddy_2552T EZB 0.8667784802130977 0 545,144,1058,847,143,620,889,884,786,638 0.263,0.31,0.328,0.341,0.349,0.377,0.385,0.397,0.404,0.407 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 24.7746730492698,3.80780057557023 0 0 28.582473624839984 24.77467304926975 EZB TRUE 846 -Reddy_2553T EZB 1 0 638,249,216,846,823,1100,405,217,766,1064 0.176,0.318,0.336,0.341,0.349,0.37,0.378,0.384,0.391,0.4 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.613744400946 0 0 30.613744400946008 30.613744400946008 EZB TRUE 847 -Reddy_2554T EZB 1 0 1117,877,192,740,93,387,631,757,7,228 0.13,0.194,0.201,0.232,0.243,0.261,0.291,0.291,0.308,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.3590367455545 0 0 43.35903674555452 43.35903674555452 EZB TRUE 848 -Reddy_2555T EZB 1 0 121,122,190,601,868,756,1014,18,164,751 0.122,0.135,0.247,0.254,0.262,0.265,0.277,0.287,0.296,0.332 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.6254444773833 0 0 44.62544447738334 44.62544447738334 EZB TRUE 849 -Reddy_2561T ST2 0.40063721238544353 0 275,1089,792,935,903,370,300,1044,673,554 0.307,0.338,0.362,0.41,0.415,0.427,0.454,0.487,0.501,0.507 ST2,MCD,BN2,EZB,EZB,ST2,ST2,MCD,BN2,ST2 4.75815281846706,4.84836289237955,5.0100980306325,9.77030857589187 0 0 24.386922317370992 9.770308575891875 EZB FALSE 850 -Reddy_2562T MCD 1 0 767,762,246,166,434,17,804,879,551,1109 0.122,0.131,0.133,0.136,0.161,0.166,0.181,0.184,0.186,0.198 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.3243748759028 0 0 64.32437487590278 64.32437487590278 MCD TRUE 851 -Reddy_2566T EZB 1 0 20,239,266,746,72,25,677,167,603,1059 0.127,0.143,0.152,0.175,0.175,0.187,0.194,0.197,0.23,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1006688713383 0 0 57.100668871338264 57.100668871338264 EZB TRUE 852 -Reddy_2567T EZB 1 0 272,257,724,723,213,136,979,181,255,291 0.136,0.176,0.177,0.184,0.204,0.219,0.224,0.225,0.228,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1324434887607 0 0 51.13244348876067 51.13244348876067 EZB TRUE 853 -Reddy_2575T N1 0.46802969757875285 0 437,516,486,964,1099,496,893,1104,590,325 0.155,0.213,0.277,0.36,0.362,0.362,0.374,0.386,0.408,0.433 BN2,N1,ST2,BN2,N1,N1,N1,N1,ST2,ST2 9.24429738709351,15.4938387240178,8.36625620452957 0 0 33.10439231564085 15.493838724017778 BN2 FALSE 854 -Reddy_2580T BN2 0.6501877074218705 0 495,674,23,666,659,681,1113,802,652,699 0.178,0.22,0.235,0.272,0.287,0.306,0.313,0.317,0.328,0.331 MCD,BN2,EZB,BN2,BN2,BN2,BN2,ST2,BN2,BN2 24.2465625183621,4.26140508823571,5.63284654365583,3.15082062165673 0 0 37.29163477191041 24.246562518362136 BN2 TRUE 855 -Reddy_2581T MCD 1 0 44,519,1047,1054,449,573,475,829,616,697 0.161,0.219,0.222,0.243,0.255,0.287,0.288,0.304,0.317,0.325 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.792178893401 0 0 39.792178893401044 39.792178893401044 MCD TRUE 856 -Reddy_2582T EZB 0.8106919674970949 0 1056,595,587,245,867,227,368,618,809,995 0.163,0.185,0.197,0.211,0.232,0.239,0.253,0.267,0.272,0.273 EZB,EZB,EZB,EZB,ST2,BN2,EZB,EZB,EZB,EZB 4.19130400117245,36.3933642156332,4.30706091675532 0 0 44.891729133561014 36.393364215633234 EZB TRUE 857 -Reddy_2585T EZB 1 0 90,252,608,411,409,884,316,143,912,11 0.204,0.316,0.325,0.332,0.366,0.372,0.383,0.419,0.447,0.455 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.0165273152415 0 0 29.016527315241483 29.016527315241483 EZB TRUE 858 -Reddy_2594T MCD 0.8729648769256337 0 933,772,749,533,448,532,934,445,567,201 0.15,0.161,0.165,0.167,0.177,0.221,0.29,0.29,0.292,0.292 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.2061062691189,42.6473613304351 0 0 48.85346759955398 42.647361330435075 MCD TRUE 859 -Reddy_2596T BN2 0.8189316178693105 0 390,570,634,1055,212,358,633,77,571,487 0.237,0.298,0.307,0.32,0.326,0.345,0.351,0.352,0.356,0.356 BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB,EZB,BN2 25.5696000147738,5.6535197877081 0 0 31.22311980248194 25.56960001477384 BN2 TRUE 860 -Reddy_2597T EZB 0.8941734470795837 0 1102,7,885,1094,706,680,1095,892,71,648 0.135,0.145,0.154,0.155,0.161,0.161,0.174,0.183,0.187,0.206 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 54.567184889444,6.45809501308857 0 0 61.02527990253253 54.56718488944396 EZB TRUE 861 -Reddy_2600T EZB 0.73523282507832 0 424,115,715,1097,407,812,890,426,913,839 0.166,0.27,0.279,0.302,0.355,0.364,0.377,0.382,0.382,0.393 ST2,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 23.9784705995392,8.63496800337535 0 0 32.61343860291456 23.97847059953921 EZB TRUE 862 -Reddy_2603T EZB 0.904158615223084 0 995,1079,1074,368,227,274,947,618,277,228 0.132,0.133,0.147,0.159,0.171,0.177,0.178,0.18,0.192,0.201 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.84877126803628,55.1767583781609 0 0 61.0255296461972 55.17675837816092 EZB TRUE 863 -Reddy_2604T BN2 0.46998668646468716 0 512,665,924,126,967,131,582,1106,505,462 0.208,0.227,0.233,0.248,0.267,0.268,0.279,0.339,0.34,0.345 N1,BN2,ST2,EZB,EZB,BN2,BN2,BN2,N1,BN2 17.5758045115414,7.7839591589554,7.75123954709197,4.28538484554838 0 0 37.39638806313717 17.575804511541417 BN2 TRUE 864 -Reddy_2605T EZB 1 0 754,406,1073,1018,116,1014,18,190,639,756 0.178,0.19,0.219,0.233,0.235,0.245,0.246,0.251,0.279,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1949898403662 0 0 43.19498984036621 43.19498984036621 EZB TRUE 865 -Reddy_2606T EZB 1 0 776,613,27,152,151,98,150,599,644,97 0.136,0.146,0.16,0.16,0.17,0.199,0.205,0.216,0.236,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0742482341444 0 0 55.07424823414436 55.07424823414436 EZB TRUE 866 -Reddy_2608T EZB 0.9119038570895251 0 595,618,1056,809,1074,947,995,587,227,368 0.148,0.149,0.169,0.18,0.185,0.188,0.193,0.202,0.205,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.8733786740761,50.4454867503561 0 0 55.31886542443223 50.445486750356125 ST2 FALSE 867 -Reddy_2609T EZB 1 0 709,756,597,18,190,251,164,250,21,116 0.172,0.176,0.189,0.216,0.217,0.226,0.226,0.235,0.246,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0260880576312 0 0 47.026088057631156 47.026088057631156 EZB TRUE 868 -Reddy_2611T MCD 1 0 340,536,414,415,461,511,1038,987,565,456 0.123,0.156,0.165,0.171,0.176,0.187,0.187,0.19,0.194,0.199 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.2432903007032 0 0 58.24329030070322 58.24329030070322 MCD TRUE 869 -Reddy_2613T EZB 1 0 47,593,727,219,726,784,676,725,220,344 0.19,0.223,0.298,0.298,0.317,0.336,0.349,0.352,0.356,0.412 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5356420200717 0 0 33.5356420200717 33.5356420200717 EZB TRUE 870 -Reddy_2619T BN2 1 0 563,15,640,763,264,38,318,341,175,1062 0.184,0.199,0.204,0.21,0.219,0.241,0.247,0.25,0.25,0.255 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8215245392046 0 0 44.82152453920463 44.82152453920463 BN2 TRUE 871 -Reddy_2620T EZB 0.5392855872578031 0 755,806,758,800,132,40,782,976,54,606 0.283,0.305,0.32,0.329,0.394,0.409,0.413,0.431,0.459,0.471 EZB,N1,EZB,EZB,BN2,EZB,EZB,BN2,MCD,ST2 4.85996247795436,14.5664483436482,2.17810978426866,3.2817229883936,2.12439545825776 0 0 27.010639052522556 14.566448343648176 BN2 FALSE 872 -Reddy_2621T EZB 1 0 107,387,547,757,102,631,140,228,740,93 0.208,0.209,0.224,0.227,0.261,0.291,0.295,0.296,0.296,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.2269526064206 0 0 39.226952606420554 39.226952606420554 EZB TRUE 873 -Reddy_2626T EZB 1 0 1006,1114,753,1075,688,407,164,812,601,1097 0.116,0.176,0.19,0.226,0.252,0.257,0.261,0.27,0.285,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.2615212396805 0 0 46.261521239680526 46.261521239680526 EZB TRUE 874 -Reddy_2628T ST2 0.9195982438954846 0 1065,814,1004,1009,748,195,770,423,907,361 0.103,0.153,0.187,0.19,0.221,0.241,0.246,0.266,0.275,0.284 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.06251457188003,46.4651700050478 0 0 50.52768457692778 46.46517000504775 ST2 TRUE 875 -Reddy_2636T EZB 1 0 632,760,667,46,80,594,29,914,805,1002 0.114,0.122,0.127,0.138,0.144,0.16,0.166,0.171,0.185,0.199 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.5851344234991 0 0 67.58513442349906 67.58513442349906 EZB TRUE 876 -Reddy_2640T EZB 1 0 848,1117,192,351,267,720,229,206,740,71 0.194,0.219,0.231,0.289,0.29,0.302,0.312,0.314,0.321,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.8719077418726 0 0 36.871907741872576 36.871907741872576 EZB TRUE 877 -Reddy_2643T MCD 0.892659767296298 0 978,961,452,621,295,465,1101,367,33,902 0.131,0.154,0.154,0.162,0.164,0.184,0.196,0.207,0.217,0.221 MCD,MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD 51.3011691366063,6.16884465374083 0 0 57.470013790347146 51.30116913660632 MCD TRUE 878 -Reddy_2646T MCD 1 0 712,1109,762,17,166,165,246,767,500,1015 0.115,0.132,0.155,0.159,0.16,0.16,0.161,0.165,0.166,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 65.6982344768305 0 0 65.69823447683052 65.69823447683052 MCD TRUE 879 -Reddy_2650T EZB 1 0 89,1063,37,159,931,820,761,1031,48,635 0.123,0.165,0.168,0.174,0.186,0.193,0.201,0.215,0.221,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.058652235354 0 0 55.05865223535396 55.05865223535396 EZB TRUE 880 -Reddy_2652T MCD 1 0 678,204,526,580,467,494,920,453,1042,549 0.138,0.158,0.165,0.169,0.18,0.181,0.189,0.206,0.207,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.0295425845926 0 0 56.02954258459262 56.02954258459262 MCD TRUE 881 -Reddy_2654T EZB 0.9184391777962239 0 120,6,303,942,334,182,1018,883,111,743 0.125,0.157,0.182,0.198,0.203,0.206,0.217,0.224,0.236,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 47.6905613329553,4.23509959909229 0 0 51.92566093204755 47.690561332955255 EZB TRUE 882 -Reddy_2662T EZB 1 0 156,303,942,743,718,334,882,1073,6,1018 0.143,0.143,0.16,0.168,0.188,0.189,0.224,0.229,0.234,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.167041210681 0 0 54.16704121068098 54.16704121068098 EZB TRUE 883 -Reddy_2664T EZB 1 0 889,409,1058,143,252,316,858,846,90,912 0.192,0.23,0.253,0.334,0.342,0.344,0.372,0.397,0.398,0.404 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.5411193424041 0 0 32.54111934240414 32.54111934240414 EZB TRUE 884 -Reddy_2675T EZB 0.8840075754099747 0 1094,1102,861,112,1095,680,1072,892,706,886 0.142,0.146,0.154,0.159,0.161,0.169,0.17,0.176,0.188,0.193 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.821369528088,7.06201091480851 0 0 60.88338044289654 53.821369528088034 EZB TRUE 885 -Reddy_2677T EZB 0.8902971896098313 0 1094,112,809,885,1020,1102,1072,52,861,1095 0.19,0.191,0.193,0.193,0.211,0.211,0.213,0.216,0.24,0.245 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6397736037353,5.25409161493622 0 0 47.893865218671536 42.639773603735314 EZB TRUE 886 -Reddy_2678T EZB 1 0 304,36,963,289,750,707,310,237,653,708 0.126,0.149,0.158,0.168,0.183,0.204,0.22,0.221,0.24,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.647067520312 0 0 54.64706752031199 54.64706752031199 EZB TRUE 887 -Reddy_2690T MCD 0.9208264338187231 0 1043,730,460,513,66,1082,459,739,314,200 0.137,0.137,0.149,0.237,0.278,0.292,0.301,0.309,0.311,0.314 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 3.601271668708,41.8845115592759 0 0 45.485783227983866 41.88451155927587 MCD TRUE 888 -Reddy_2692T EZB 1 0 1058,884,409,316,405,252,846,143,912,858 0.18,0.192,0.246,0.358,0.365,0.38,0.385,0.399,0.403,0.457 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.780719918071 0 0 32.78071991807098 32.78071991807098 EZB TRUE 889 -Reddy_2696T EZB 0.9191759466075397 0 839,715,407,784,424,322,319,1033,344,862 0.117,0.214,0.242,0.244,0.319,0.33,0.334,0.358,0.372,0.377 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 35.6424270006966,3.13407398613225 0 0 38.7765009868289 35.64242700069665 EZB TRUE 890 -Reddy_2698T BN2 0.5878751604114065 0 14,802,388,504,127,23,699,185,495,552 0.147,0.152,0.197,0.217,0.239,0.252,0.283,0.286,0.287,0.288 BN2,ST2,BN2,MCD,BN2,EZB,BN2,BN2,MCD,BN2 26.5595451473892,3.96092014258998,8.08977710369399,6.56864410461383 0 0 45.17888649828698 26.559545147389183 MCD FALSE 891 -Reddy_2704T EZB 1 0 1095,648,680,71,706,711,229,1072,885,112 0.116,0.123,0.125,0.147,0.149,0.155,0.166,0.168,0.176,0.181 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.803165381908 0 0 67.80316538190803 67.80316538190803 EZB TRUE 892 -Reddy_2706T ST2 0.488781713557018 0 964,622,590,944,1096,691,486,854,1084,954 0.231,0.287,0.299,0.324,0.335,0.35,0.368,0.374,0.39,0.393 BN2,ST2,ST2,EZB,EZB,ST2,ST2,BN2,BN2,ST2 9.55774467896439,6.07459093954444,14.9462568009397 0 0 30.57859241944852 14.946256800939688 N1 FALSE 893 -Reddy_2707T BN2 0.7410876919765866 0 845,497,431,442,1070,104,330,103,34,801 0.265,0.309,0.333,0.401,0.402,0.406,0.422,0.446,0.458,0.47 BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD,BN2,ST2 19.5509014603763,4.70465241775135,2.12580648184712 0 0 26.38136035997481 19.550901460376345 MCD FALSE 894 -Reddy_2716T BN2 0.8647254888326197 0 438,952,611,352,327,10,531,463,827,109 0.216,0.249,0.267,0.276,0.307,0.31,0.323,0.332,0.344,0.367 N1,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.6051743594382,4.63132582677143 0 0 34.23650018620958 29.605174359438156 BN2 TRUE 895 -Reddy_2764T ST2 1 0 662,1004,683,612,814,907,748,583,1009,1065 0.157,0.202,0.219,0.232,0.233,0.238,0.26,0.264,0.27,0.282 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 43.5822963038685 0 0 43.58229630386845 43.58229630386845 ST2 TRUE 896 -Reddy_2772T MCD 1 0 314,459,739,288,991,520,513,106,729,439 0.139,0.14,0.152,0.155,0.161,0.166,0.205,0.218,0.254,0.256 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.9570572162648 0 0 56.95705721626479 56.95705721626479 MCD TRUE 897 -Reddy_2773T BN2 0.9073217939320305 0 1069,493,498,633,634,77,399,212,569,1022 0.172,0.179,0.198,0.203,0.247,0.248,0.266,0.285,0.287,0.305 BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 39.4217903436911,4.02673101591406 0 0 43.44852135960514 39.42179034369108 BN2 TRUE 898 -Reddy_2781T ST2 0.710957923549591 0 909,1096,590,339,544,964,309,508,1084,691 0.171,0.253,0.278,0.34,0.345,0.351,0.371,0.372,0.381,0.397 ST2,EZB,ST2,ST2,ST2,BN2,ST2,ST2,BN2,ST2 5.47834364111992,3.95362148816287,23.1998414405854 0 0 32.631806569868154 23.199841440585363 ST2 TRUE 899 -Reddy_2784T MCD 0.8990359593925075 0 16,1042,844,276,468,443,453,580,549,1008 0.13,0.178,0.191,0.198,0.21,0.211,0.212,0.215,0.216,0.216 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 5.22332438274205,46.5111778352133 0 0 51.7345022179553 46.51117783521325 MCD TRUE 900 -Reddy_2789T ST2 0.8081425029516552 0 1024,925,685,843,834,320,625,969,904,703 0.131,0.17,0.267,0.29,0.292,0.313,0.313,0.314,0.326,0.335 EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 7.63068187137834,32.1419722535503 0 0 39.77265412492869 32.14197225355035 ST2 TRUE 901 -Reddy_2792T MCD 0.8980333199954622 0 451,33,621,1008,878,276,468,465,961,1021 0.145,0.153,0.198,0.215,0.221,0.223,0.225,0.23,0.232,0.236 MCD,MCD,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.4586808247204,5.04803550173511 0 0 49.50671632645552 44.4586808247204 MCD TRUE 902 -Reddy_2794T MCD 0.39568149905334316 0 850,992,275,574,1093,788,502,616,1105,935 0.415,0.434,0.443,0.502,0.517,0.521,0.536,0.552,0.567,0.573 EZB,MCD,ST2,N1,MCD,EZB,MCD,MCD,BN2,EZB 1.76396462016698,6.0770685856743,7.91485085710135,1.99072593760113,2.25647550140012 0 0 20.003085501943882 7.91485085710135 EZB FALSE 903 -Reddy_2809T ST2 0.8408944753425914 0 843,278,1081,925,394,911,910,901,1024,372 0.137,0.25,0.251,0.265,0.273,0.283,0.311,0.326,0.347,0.348 ST2,ST2,ST2,ST2,ST2,ST2,EZB,ST2,EZB,ST2 6.0990088044219,32.2340963316474 0 0 38.33310513606929 32.234096331647386 ST2 TRUE 904 -Reddy_2815T BN2 0.8992649756790846 0 395,373,1023,540,958,555,929,702,796,841 0.154,0.163,0.168,0.184,0.187,0.193,0.198,0.201,0.211,0.221 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 48.418255634082,5.42377863120083 0 0 53.84203426528286 48.41825563408203 BN2 TRUE 905 -Reddy_2819T MCD 1 0 323,332,39,130,311,1027,491,514,416,821 0.11,0.145,0.151,0.153,0.175,0.193,0.216,0.22,0.232,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.7330158489373 0 0 57.73301584893731 57.73301584893731 MCD TRUE 906 -Reddy_2820T ST2 1 0 683,748,1009,662,1004,195,896,814,1065,875 0.132,0.164,0.196,0.203,0.21,0.218,0.238,0.248,0.272,0.275 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 48.5969695481761 0 0 48.596969548176084 48.596969548176084 ST2 TRUE 907 -Reddy_2823T EZB 1 0 471,766,168,235,823,217,216,76,234,1100 0.175,0.182,0.218,0.218,0.222,0.226,0.252,0.257,0.269,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3785273220184 0 0 44.37852732201845 44.37852732201845 EZB TRUE 908 -Reddy_2824T ST2 0.7431425696535922 0 899,339,508,1096,590,544,309,1084,964,691 0.171,0.27,0.306,0.309,0.347,0.363,0.388,0.406,0.419,0.431 ST2,ST2,ST2,EZB,ST2,ST2,ST2,BN2,BN2,ST2 4.84579313648566,3.23388585757011,23.3762106921379 0 0 31.455889686193654 23.376210692137878 ST2 TRUE 909 -Reddy_2827T ST2 1 0 278,911,372,394,904,843,922,339,1081,925 0.161,0.21,0.277,0.309,0.311,0.316,0.372,0.388,0.405,0.416 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 34.332109210429 0 0 34.332109210429024 34.332109210429024 EZB FALSE 910 -Reddy_2828T ST2 0.8712991113742121 0 278,394,910,904,843,339,60,1081,508,372 0.191,0.203,0.21,0.283,0.306,0.312,0.319,0.321,0.327,0.37 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 4.75060977277442,32.1614102101438 0 0 36.912019982918245 32.16141021014383 ST2 TRUE 911 -Reddy_2829T EZB 1 0 316,252,62,409,818,307,585,220,889,884 0.17,0.231,0.247,0.274,0.302,0.337,0.366,0.387,0.403,0.404 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.4435053296903 0 0 34.443505329690254 34.443505329690254 EZB TRUE 912 -Reddy_2832T EZB 0.7632623981595662 0 714,993,1033,322,319,810,715,424,684,862 0.183,0.218,0.22,0.246,0.25,0.318,0.321,0.336,0.372,0.382 EZB,EZB,EZB,EZB,EZB,ST2,EZB,ST2,ST2,EZB 28.3871970417288,8.80472687615066 0 0 37.191923917879485 28.387197041728825 EZB TRUE 913 -Reddy_2834T EZB 1 0 46,760,984,632,876,667,1026,317,637,1002 0.144,0.149,0.153,0.169,0.171,0.172,0.174,0.175,0.181,0.185 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.1613822929174 0 0 60.16138229291742 60.16138229291742 EZB TRUE 914 -Reddy_2874T ST2 1 0 598,642,940,1003,793,1068,759,825,557,774 0.259,0.265,0.272,0.3,0.304,0.307,0.32,0.325,0.35,0.361 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 33.0083270707261 0 0 33.008327070726075 33.008327070726075 ST2 TRUE 915 -Reddy_2879T MCD 1 0 298,296,549,494,920,453,467,526,443,580 0.18,0.188,0.212,0.222,0.225,0.228,0.25,0.273,0.276,0.286 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.729984725873 0 0 43.72998472587304 43.72998472587304 MCD TRUE 916 -Reddy_2882T EZB 1 0 1013,271,210,927,79,405,114,402,360,638 0.15,0.191,0.211,0.243,0.267,0.317,0.332,0.347,0.379,0.411 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.6107869790581 0 0 38.610786979058105 38.610786979058105 EZB TRUE 917 -Reddy_2888T MCD 0.7516878724908354 0 778,1103,169,312,990,155,450,350,1046,417 0.112,0.119,0.131,0.133,0.151,0.155,0.161,0.164,0.165,0.167 BN2,ST2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.94758568490785,52.5900839998871,8.42499157994203 0 0 69.962661264737 52.59008399988713 MCD TRUE 918 -Reddy_2894T BN2 0.46533870502583385 0 293,1061,1066,530,799,119,698,481,381,506 0.192,0.267,0.275,0.299,0.313,0.314,0.32,0.335,0.353,0.374 MCD,BN2,N1,N1,BN2,BN2,ST2,BN2,ST2,BN2 15.7936657049429,5.2164926646182,6.97512604238055,5.9548661372505 0 0 33.94015054919217 15.793665704942928 BN2 TRUE 919 -Reddy_2897T MCD 1 0 494,467,204,296,881,453,549,526,678,580 0.124,0.127,0.174,0.183,0.189,0.194,0.198,0.198,0.209,0.212 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 57.2369247997978 0 0 57.23692479979776 57.23692479979776 MCD TRUE 920 -Reddy_2899T EZB 1 0 4,76,234,235,336,471,32,28,1100,908 0.175,0.203,0.203,0.219,0.233,0.244,0.252,0.265,0.303,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6970051430655 0 0 42.69700514306547 42.69700514306547 EZB TRUE 921 -Reddy_2901T EZB 0.4810768984124146 0 657,382,372,910,1091,791,278,834,586,974 0.204,0.245,0.301,0.372,0.38,0.4,0.416,0.426,0.427,0.434 BN2,EZB,ST2,EZB,EZB,EZB,ST2,ST2,ST2,EZB 4.90302645642044,14.2071039888414,10.4217469650353 0 0 29.531877410297085 14.207103988841371 ST2 FALSE 922 -Reddy_2903T EZB 1 0 215,214,428,336,4,133,234,921,235,28 0.181,0.223,0.321,0.434,0.446,0.458,0.461,0.469,0.513,0.52 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.0410038997816 0 0 28.041003899781597 28.041003899781597 EZB TRUE 923 -Reddy_2910T BN2 0.585855223315257 0 967,582,665,864,505,512,131,1085,432,153 0.134,0.163,0.171,0.233,0.257,0.261,0.269,0.275,0.291,0.309 EZB,BN2,BN2,BN2,N1,N1,BN2,EZB,BN2,BN2 26.6411732878545,11.1121692659796,7.72064526366205 0 0 45.47398781749612 26.641173287854468 ST2 FALSE 924 -Reddy_2917T ST2 0.8673758662805816 0 901,1024,843,904,969,703,625,685,372,834 0.17,0.199,0.232,0.265,0.276,0.29,0.322,0.337,0.345,0.354 ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.02161621708375,32.8419013513518 0 0 37.86351756843557 32.84190135135182 ST2 TRUE 925 -Reddy_2918T BN2 0.930469217628049 0 575,95,561,636,399,487,498,1069,1022,77 0.159,0.182,0.199,0.211,0.239,0.348,0.35,0.351,0.357,0.361 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 37.093093830193,2.77184004129033 0 0 39.86493387148335 37.09309383019303 ST2 FALSE 926 -Reddy_2925T EZB 1 0 210,271,360,917,402,79,114,1013,211,306 0.138,0.156,0.236,0.243,0.257,0.268,0.271,0.28,0.322,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.0893160899486 0 0 43.089316089948625 43.089316089948625 EZB TRUE 927 -Reddy_2933T BN2 0.8175055815553613 0 262,321,1049,110,773,936,279,355,765,485 0.146,0.207,0.215,0.222,0.262,0.269,0.292,0.382,0.387,0.391 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 32.3028463343704,7.21106899928639 0 0 39.51391533365677 32.302846334370386 BN2 TRUE 928 -Reddy_2935T BN2 0.8585480099683959 0 540,841,546,555,905,499,373,1001,1023,527 0.133,0.146,0.151,0.198,0.198,0.219,0.22,0.22,0.228,0.249 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.6420750299092,7.51986175169178 0 0 53.16193678160095 45.642075029909165 BN2 TRUE 929 -Reddy_2936T EZB 0.4539506949065433 0 534,94,371,813,193,170,419,444,541,375 0.321,0.326,0.337,0.348,0.42,0.496,0.512,0.532,0.565,0.57 MCD,EZB,EZB,EZB,BN2,MCD,MCD,EZB,MCD,MCD 2.37994833219075,10.7952901402122,10.6055162688904 0 0 23.7807547412934 10.795290140212213 ST2 FALSE 930 -Reddy_2939T EZB 1 0 635,820,48,35,1063,125,880,89,364,256 0.138,0.161,0.162,0.162,0.166,0.168,0.186,0.198,0.212,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.4036767302892 0 0 57.40367673028922 57.40367673028922 EZB TRUE 931 -Reddy_2945T MCD 1 0 987,461,660,188,966,380,414,565,456,1038 0.151,0.165,0.166,0.176,0.192,0.195,0.195,0.204,0.209,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.0093604592706 0 0 54.00936045927056 54.00936045927056 MCD TRUE 932 -Reddy_2948T MCD 0.8646521237265254 0 749,772,859,533,448,436,532,445,400,567 0.122,0.141,0.15,0.151,0.174,0.267,0.27,0.277,0.28,0.3 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.07204533268048,45.1788323862369 0 0 52.25087771891736 45.178832386236884 MCD TRUE 933 -Reddy_2949T MCD 0.9111065314251034 0 445,859,749,480,933,376,532,772,533,448 0.202,0.29,0.292,0.297,0.303,0.325,0.339,0.34,0.349,0.365 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 2.94016703916405,30.1350080698692 0 0 33.07517510903328 30.13500806986923 MCD TRUE 934 -Reddy_2950T MCD 0.4692982156896029 0 1044,1089,696,747,124,1105,554,850,205,43 0.222,0.226,0.319,0.33,0.338,0.345,0.381,0.41,0.476,0.484 MCD,MCD,MCD,ST2,N1,BN2,ST2,EZB,MCD,ST2 2.8995290364209,2.43716092556622,14.1629274429509,2.96151348551784,7.71781923007554 0 0 30.178950120531425 14.162927442950924 EZB FALSE 935 -Reddy_2952T BN2 0.5832237443722503 0 485,928,1049,321,262,773,558,765,698,110 0.241,0.269,0.283,0.286,0.312,0.333,0.333,0.373,0.377,0.392 ST2,BN2,ST2,BN2,BN2,BN2,ST2,BN2,ST2,BN2 18.6566283416339,13.3321727345549 0 0 31.98880107618874 18.65662834163387 BN2 TRUE 936 -Reddy_2953T MCD 1 0 721,722,690,511,536,299,1110,1087,340,869 0.128,0.145,0.15,0.162,0.184,0.193,0.203,0.215,0.237,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 56.1361260886133 0 0 56.13612608861333 56.13612608861333 EZB FALSE 937 -Reddy_2955T EZB 1 0 251,250,941,356,21,816,709,597,868,182 0.159,0.164,0.168,0.187,0.191,0.198,0.205,0.227,0.27,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4953534057211 0 0 50.49535340572106 50.49535340572106 EZB TRUE 938 -Reddy_3382T EZB 1 0 960,1034,51,948,233,31,211,647,719,57 0.208,0.249,0.266,0.273,0.274,0.351,0.371,0.371,0.383,0.391 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.296239237016 0 0 33.29623923701597 33.29623923701597 EZB TRUE 939 -Reddy_3385T ST2 0.9132889607945325 0 1068,598,825,1003,774,835,915,982,793,557 0.145,0.154,0.168,0.181,0.195,0.243,0.272,0.299,0.304,0.33 ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2 4.11787905717574,43.3717957859333 0 0 47.48967484310905 43.371795785933315 ST2 TRUE 940 -Reddy_3388T EZB 1 0 356,938,251,250,182,709,816,21,116,120 0.135,0.168,0.18,0.23,0.231,0.255,0.257,0.258,0.271,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6593373273434 0 0 46.65933732734341 46.65933732734341 EZB TRUE 941 -Reddy_3390T EZB 1 0 303,743,883,1018,1073,882,156,120,334,6 0.154,0.156,0.16,0.174,0.185,0.198,0.202,0.216,0.22,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.7660168966717 0 0 53.766016896671744 53.766016896671744 EZB TRUE 942 -Reddy_3401T BN2 1 0 477,335,99,780,650,345,630,1060,959,435 0.142,0.173,0.176,0.179,0.184,0.19,0.196,0.221,0.224,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2432703274432 0 0 53.243270327443234 53.243270327443234 BN2 TRUE 943 -Reddy_3407T ST2 0.5904200655911392 0 954,614,893,486,828,608,622,964,325,691 0.207,0.264,0.324,0.395,0.395,0.427,0.433,0.456,0.46,0.485 ST2,EZB,N1,ST2,ST2,EZB,ST2,BN2,ST2,ST2 2.19529788468264,6.12577317291429,3.0860783400432,16.4437008011223 0 0 27.85085019876246 16.443700801122326 EZB FALSE 944 -Reddy_3409T BN2 1 0 354,331,75,559,973,998,1077,305,197,542 0.127,0.156,0.164,0.169,0.17,0.174,0.177,0.193,0.214,0.215 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 58.1013648048504 0 0 58.10136480485038 58.10136480485038 BN2 TRUE 945 -Reddy_3432T ST2 0.6130181182059324 0 970,490,422,558,24,281,59,529,363,670 0.115,0.224,0.291,0.342,0.36,0.401,0.402,0.421,0.442,0.442 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,BN2 13.2341823898224,20.9642724020869 0 0 34.19845479190937 20.964272402086934 BN2 FALSE 946 -Reddy_3438T EZB 0.8292691958464723 0 274,1074,618,995,863,867,1079,368,227,631 0.126,0.134,0.143,0.171,0.178,0.188,0.195,0.214,0.218,0.222 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,BN2,EZB 4.5972440269371,48.1272709520412,5.31124855003537 0 0 58.035763529013636 48.127270952041165 EZB TRUE 947 -Reddy_3441T EZB 1 0 960,939,57,647,1080,1034,609,51,31,233 0.174,0.273,0.32,0.385,0.396,0.422,0.427,0.439,0.44,0.447 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1334407731161 0 0 29.133440773116092 29.133440773116092 EZB TRUE 948 -Reddy_3450T EZB 1 0 310,723,403,708,213,853,686,272,5,750 0.211,0.212,0.214,0.216,0.237,0.238,0.25,0.255,0.256,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8176214127012 0 0 42.817621412701165 42.817621412701165 EZB TRUE 949 -Reddy_3451T ST2 0.5362670501593513 0 1107,361,1025,178,965,787,770,625,1041,661 0.154,0.212,0.231,0.243,0.255,0.262,0.301,0.35,0.351,0.358 ST2,ST2,BN2,ST2,EZB,EZB,BN2,ST2,ST2,EZB 7.64980266158617,10.5388586911943,21.0336137929269 0 0 39.22227514570734 21.033613792926918 EZB FALSE 950 -Reddy_3454T BN2 1 0 353,482,578,566,401,957,1111,26,264,569 0.142,0.186,0.19,0.213,0.218,0.247,0.253,0.288,0.305,0.312 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.9571232151621 0 0 44.95712321516207 44.95712321516207 BN2 TRUE 951 -Reddy_3456T BN2 0.8332009649901869 0 531,808,895,10,352,438,408,611,109,258 0.196,0.225,0.249,0.269,0.311,0.342,0.36,0.364,0.369,0.396 BN2,BN2,BN2,BN2,BN2,N1,N1,BN2,BN2,BN2 28.4581911695735,5.69706352328284 0 0 34.15525469285638 28.458191169573546 BN2 TRUE 952 -Reddy_3459T EZB 1 0 242,231,805,594,989,29,80,876,632,667 0.145,0.174,0.2,0.203,0.205,0.26,0.261,0.262,0.265,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.483084709872 0 0 46.48308470987196 46.48308470987196 EZB TRUE 953 -Reddy_3460T EZB 0.6808494113715429 0 614,944,608,828,893,411,786,622,90,691 0.171,0.207,0.329,0.363,0.393,0.43,0.441,0.445,0.453,0.485 EZB,EZB,EZB,ST2,N1,EZB,EZB,ST2,EZB,ST2 20.5014058187869,2.5472759182153,7.06283114142957 0 0 30.111512878431764 20.5014058187869 ST2 FALSE 954 -Reddy_3462T BN2 1 0 731,700,1010,538,435,1071,1001,99,959,650 0.126,0.146,0.17,0.213,0.219,0.221,0.273,0.276,0.297,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.4652601126088 0 0 48.46526011260879 48.46526011260879 BN2 TRUE 955 -Reddy_3465T MCD 0.9096944730633607 0 260,479,169,433,204,1046,467,1103,286,918 0.129,0.153,0.18,0.182,0.183,0.188,0.196,0.198,0.208,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 50.7854123096467,5.041476622226 0 0 55.82688893187272 50.78541230964672 MCD TRUE 956 -Reddy_3467T BN2 1 0 401,1111,482,578,264,640,318,951,353,1062 0.14,0.148,0.171,0.188,0.211,0.236,0.246,0.247,0.282,0.282 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.2208622266044 0 0 49.22086222660436 49.22086222660436 BN2 TRUE 957 -Reddy_3471T BN2 0.91990875403960215 0 395,702,373,905,305,1023,542,540,75,654 0.139,0.161,0.168,0.187,0.228,0.232,0.232,0.252,0.255,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 45.5589997557648,3.96656411750114 0 0 49.5255638732659 45.558999755764766 BN2 TRUE 958 -Reddy_3472T BN2 1 0 780,646,548,99,345,999,435,943,538,477 0.147,0.149,0.168,0.174,0.179,0.197,0.197,0.224,0.244,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.6115876029108 0 0 53.611587602910824 53.611587602910824 BN2 TRUE 959 -Reddy_3474T EZB 1 0 948,939,647,1034,57,51,233,31,537,1080 0.174,0.208,0.339,0.356,0.362,0.372,0.38,0.419,0.432,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.3550327166385 0 0 31.35503271663849 31.35503271663849 EZB TRUE 960 -Reddy_3476T MCD 0.9166536190396003 0 978,465,878,452,1021,1101,367,568,33,621 0.131,0.134,0.154,0.181,0.193,0.197,0.197,0.199,0.205,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 52.3445957549339,4.7594124196878 0 0 57.104008174621754 52.34459575493395 MCD TRUE 961 -Reddy_3479T BN2 0.8167812535340672 0 153,672,432,571,1055,186,582,1106,840,1085 0.226,0.285,0.286,0.321,0.323,0.326,0.354,0.368,0.375,0.375 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,EZB 25.7965604828608,5.78663314608657 0 0 31.583193628947363 25.79656048286079 BN2 TRUE 962 -Reddy_3480T EZB 1 0 36,707,289,887,304,237,972,997,750,686 0.121,0.146,0.149,0.158,0.183,0.188,0.21,0.212,0.24,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5810047636073 0 0 56.58100476360732 56.58100476360732 EZB TRUE 963 -Reddy_3482T ST2 0.5099301876748444 0 590,893,1096,622,691,899,854,1084,437,909 0.173,0.231,0.238,0.291,0.34,0.351,0.36,0.365,0.372,0.419 ST2,N1,EZB,ST2,ST2,ST2,BN2,BN2,BN2,ST2 8.20512434377919,4.19650862405857,4.31967840112604,17.3989526187911 0 0 34.120263987754896 17.39895261879109 BN2 FALSE 964 -Reddy_3488T ST2 0.6080515553998823 0 178,1107,661,365,787,361,950,1041,770,195 0.201,0.203,0.217,0.223,0.244,0.246,0.255,0.274,0.295,0.362 ST2,ST2,EZB,ST2,EZB,ST2,EZB,ST2,BN2,ST2 3.3942542562872,12.6328867726065,24.8638007510776 0 0 40.890941779971264 24.86380075107756 EZB FALSE 965 -Reddy_3496T MCD 1 0 660,439,280,932,1090,106,994,380,565,324 0.131,0.17,0.175,0.192,0.205,0.213,0.216,0.218,0.218,0.225 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.2025503188142 0 0 52.2025503188142 52.2025503188142 MCD TRUE 966 -Reddy_3508T BN2 0.5773112831300916 0 924,582,665,1085,505,432,864,131,512,153 0.134,0.154,0.186,0.242,0.245,0.264,0.267,0.282,0.285,0.306 ST2,BN2,BN2,EZB,N1,BN2,BN2,BN2,N1,BN2 26.2160006656731,4.13066871399604,7.58150094427909,7.48234118300526 0 0 45.41051150695352 26.21600066567313 EZB FALSE 967 -Reddy_3515T BN2 0.9193034952396054 0 484,651,777,158,694,472,1019,357,742,42 0.262,0.279,0.29,0.296,0.33,0.341,0.367,0.395,0.401,0.408 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 27.9473388507928,2.45321874037427 0 0 30.400557591167114 27.947338850792846 BN2 TRUE 968 -Reddy_3523T ST2 0.7505479543801752 0 703,625,925,1025,901,1024,843,904,950,1081 0.132,0.243,0.276,0.296,0.314,0.342,0.35,0.365,0.398,0.409 ST2,ST2,ST2,BN2,ST2,EZB,ST2,ST2,EZB,ST2 3.38062557992967,5.43430361524854,26.5222402125745 0 0 35.3371694077527 26.52224021257449 ST2 TRUE 969 -Reddy_3525T ST2 0.6840178633704107 0 946,490,422,24,558,281,59,529,485,363 0.115,0.212,0.299,0.347,0.35,0.386,0.387,0.415,0.451,0.455 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 10.906351724118,23.6093707165558 0 0 34.5157224406738 23.60937071655583 BN2 FALSE 970 -Reddy_3528T BN2 0.46268213157599014 0 976,132,505,782,771,131,665,1085,967,806 0.163,0.221,0.237,0.284,0.305,0.333,0.352,0.357,0.373,0.377 BN2,BN2,N1,EZB,EZB,BN2,BN2,EZB,EZB,N1 16.4996992363554,12.2893757026023,6.87190721341988 0 0 35.66098215237753 16.499699236355376 EZB FALSE 971 -Reddy_3532T EZB 1 0 707,1031,997,963,36,237,289,159,430,887 0.165,0.203,0.207,0.21,0.221,0.222,0.233,0.242,0.265,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5510399219586 0 0 45.55103992195856 45.55103992195856 EZB TRUE 972 -Reddy_3535T BN2 1 0 331,1077,197,282,354,945,341,175,559,488 0.126,0.14,0.15,0.165,0.17,0.17,0.177,0.184,0.187,0.197 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 61.0382754881278 0 0 61.03827548812781 61.03827548812781 BN2 TRUE 973 -Reddy_3548T ST2 0.552476753660187 0 194,586,1091,320,834,657,684,988,685,810 0.198,0.212,0.229,0.317,0.328,0.333,0.351,0.363,0.392,0.416 EZB,ST2,EZB,ST2,ST2,BN2,ST2,BN2,ST2,ST2 5.75171271782127,9.41483179758239,18.7234145860447 0 0 33.88995910144834 18.723414586044687 EZB FALSE 974 -Reddy_3549T EZB 0.8845872746814908 0 385,581,645,643,1050,369,241,254,687,379 0.12,0.142,0.144,0.148,0.167,0.17,0.182,0.206,0.209,0.239 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.2767223749045,6.9510515257404 0 0 60.227773900644856 53.27672237490445 EZB TRUE 975 -Reddy_3551T EZB 0.3967442876917855 0 132,971,505,131,782,665,771,512,806,126 0.159,0.163,0.224,0.288,0.304,0.328,0.35,0.358,0.367,0.368 BN2,EZB,N1,BN2,EZB,BN2,EZB,N1,N1,EZB 12.824315824578,15.005938777292,9.99244188758654 0 0 37.82269648945653 15.005938777292029 BN2 FALSE 976 -Reddy_3555T EZB 1 0 171,209,30,208,129,12,364,8,256,53 0.139,0.146,0.148,0.149,0.196,0.207,0.217,0.218,0.219,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.647831624429 0 0 55.64783162442902 55.64783162442902 EZB TRUE 977 -Reddy_3559T MCD 0.9112489803826599 0 878,961,452,465,1101,367,295,621,33,1021 0.131,0.131,0.151,0.165,0.177,0.184,0.189,0.193,0.223,0.223 MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2,MCD,MCD 53.2943471387362,5.19059856332248 0 0 58.48494570205865 53.29434713873617 MCD TRUE 978 -Reddy_3561T EZB 1 0 732,136,596,257,272,853,724,181,255,291 0.157,0.176,0.182,0.209,0.213,0.224,0.225,0.227,0.242,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.1779452346527 0 0 48.17794523465269 48.17794523465269 EZB TRUE 979 -Reddy_3565T BN2 0.7975365505539574 0 827,2,327,577,348,391,611,438,463,492 0.248,0.275,0.293,0.322,0.335,0.363,0.406,0.444,0.446,0.448 BN2,N1,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2 23.2132700420771,5.89294462100448 0 0 29.106214663081566 23.213270042077088 BN2 TRUE 980 -Reddy_3586T EZB 1 0 238,101,392,604,183,1012,259,49,1115,313 0.198,0.214,0.222,0.227,0.242,0.258,0.262,0.27,0.272,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.4418737186389 0 0 41.44187371863885 41.44187371863885 EZB TRUE 981 -Reddy_3593T ST2 1 0 557,1053,793,692,1086,598,1016,940,218,1068 0.205,0.215,0.23,0.23,0.242,0.258,0.274,0.299,0.322,0.325 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 39.4332448870612 0 0 39.43324488706124 39.43324488706124 ST2 TRUE 982 -Reddy_3595T EZB 1 0 619,664,720,9,268,224,374,267,139,240 0.197,0.216,0.252,0.256,0.265,0.278,0.284,0.298,0.3,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4675500488925 0 0 38.46755004889251 38.46755004889251 EZB TRUE 983 -Reddy_3600T EZB 1 0 637,914,317,46,760,738,1026,876,632,80 0.143,0.153,0.17,0.181,0.196,0.201,0.216,0.217,0.218,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3382489257165 0 0 53.33824892571647 53.33824892571647 EZB TRUE 984 -Reddy_3601T MCD 0.9087090360684633 0 223,466,713,128,617,205,790,696,697,837 0.209,0.213,0.238,0.276,0.315,0.325,0.339,0.345,0.352,0.39 MCD,MCD,MCD,MCD,N1,MCD,MCD,MCD,MCD,MCD 31.6454101544821,3.17916943965973 0 0 34.82457959414182 31.645410154482086 MCD TRUE 985 -Reddy_3609T EZB 1 0 313,604,302,1030,392,199,101,1012,49,96 0.136,0.159,0.161,0.164,0.18,0.192,0.207,0.209,0.211,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.4437893513866 0 0 55.443789351386606 55.443789351386606 EZB TRUE 986 -Reddy_3610T MCD 1 0 461,414,932,456,565,340,188,869,1038,415 0.116,0.149,0.151,0.177,0.178,0.179,0.183,0.19,0.192,0.205 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.6181157884448 0 0 59.6181157884448 59.6181157884448 MCD TRUE 987 -Reddy_3616T EZB 0.5679561935225336 0 1116,194,768,684,974,993,810,586,714,824 0.213,0.279,0.321,0.332,0.363,0.367,0.376,0.423,0.424,0.454 ST2,EZB,EZB,ST2,EZB,EZB,ST2,ST2,EZB,EZB 16.7422666281874,12.7358283712699 0 0 29.478094999457298 16.742266628187398 BN2 FALSE 988 -Reddy_3618T EZB 1 0 1011,953,594,667,632,805,876,242,760,1002 0.189,0.205,0.206,0.224,0.23,0.235,0.237,0.247,0.253,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2882510352007 0 0 44.28825103520068 44.28825103520068 EZB TRUE 989 -Reddy_3620T MCD 0.7909445214862874 0 286,1103,918,778,155,169,176,450,433,312 0.139,0.139,0.151,0.153,0.153,0.153,0.156,0.157,0.16,0.165 MCD,ST2,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.54663536289882,52.0286401112864,7.20511581484959 0 0 65.78039128903477 52.02864011128635 MCD TRUE 990 -Reddy_3622T MCD 1 0 288,897,106,459,314,439,739,520,994,729 0.152,0.161,0.165,0.198,0.2,0.208,0.21,0.211,0.247,0.252 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.2505009887633 0 0 51.250500988763335 51.250500988763335 MCD TRUE 991 -Reddy_3624T MCD 0.6679912043408849 0 788,502,616,574,1093,829,449,1047,1054,903 0.232,0.295,0.314,0.324,0.359,0.367,0.38,0.412,0.422,0.434 EZB,MCD,MCD,N1,MCD,MCD,MCD,MCD,MCD,EZB 6.61415875611843,19.5099687418123,3.08279710680716 0 0 29.206924604737846 19.50996874181226 MCD TRUE 992 -Reddy_3627T EZB 0.6380202880537215 0 913,714,1033,810,322,319,684,1116,988,715 0.218,0.225,0.3,0.312,0.334,0.335,0.341,0.352,0.367,0.439 EZB,EZB,EZB,ST2,EZB,EZB,ST2,ST2,BN2,EZB 2.7260236476154,20.6254046575764,8.97576417405384 0 0 32.32719247924565 20.62540465757641 EZB TRUE 993 -Reddy_3629T MCD 1 0 1090,380,439,106,660,966,991,932,280,288 0.118,0.181,0.198,0.203,0.208,0.216,0.247,0.251,0.264,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 48.844405321937 0 0 48.84440532193701 48.84440532193701 MCD TRUE 994 -Reddy_3632T EZB 0.8119241375885005 0 863,1074,368,227,618,1079,947,274,587,867 0.132,0.137,0.144,0.15,0.156,0.165,0.171,0.181,0.187,0.193 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,ST2 6.65888522925204,51.0642429078415,5.16974638626728 0 0 62.892874523360845 51.064242907841525 EZB TRUE 995 -Reddy_3636T ST2 0.8323738882460746 0 671,359,842,602,161,1005,668,835,774,1029 0.154,0.169,0.173,0.21,0.231,0.233,0.251,0.254,0.257,0.26 ST2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 3.98431521315886,3.93586781073616,39.3289176145653 0 0 47.249100638460334 39.32891761456531 ST2 TRUE 996 -Reddy_3647T EZB 1 0 430,1031,159,707,36,972,963,761,37,887 0.162,0.166,0.201,0.201,0.203,0.207,0.212,0.214,0.228,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.730074517588 0 0 49.73007451758803 49.73007451758803 EZB TRUE 997 -Reddy_3648T BN2 1 0 75,591,945,796,305,354,515,70,1023,559 0.168,0.174,0.174,0.179,0.182,0.188,0.203,0.205,0.219,0.219 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.7674522884134 0 0 52.767452288413374 52.767452288413374 BN2 TRUE 998 -Reddy_3651T BN2 1 0 646,345,959,548,780,99,477,943,435,517 0.17,0.179,0.197,0.207,0.219,0.257,0.265,0.268,0.293,0.316 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9388375985121 0 0 43.93883759851213 43.93883759851213 BN2 TRUE 999 -Reddy_3655T EZB 0.879693679482106 0 97,203,8,98,12,209,27,1088,615,389 0.153,0.184,0.185,0.24,0.241,0.246,0.252,0.254,0.267,0.267 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.4371610133822,39.7571478972082 0 0 45.19430891059041 39.757147897208206 EZB TRUE 1000 -Reddy_3659T BN2 0.9154925626789516 0 1071,546,543,517,929,841,499,261,540,700 0.164,0.169,0.191,0.195,0.22,0.229,0.23,0.241,0.248,0.25 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 43.7637210242575,4.03974872343145 0 0 47.80346974768892 43.76372102425747 BN2 TRUE 1001 -Reddy_3661T EZB 1 0 1026,1040,91,667,914,632,760,876,317,46 0.118,0.166,0.172,0.174,0.185,0.186,0.188,0.199,0.202,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.0198684444714 0 0 57.019868444471356 57.019868444471356 EZB TRUE 1002 -Reddy_3664T ST2 0.8780135241128382 0 825,1068,835,940,774,598,915,996,842,671 0.148,0.153,0.177,0.181,0.184,0.234,0.3,0.32,0.345,0.349 ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2,ST2,ST2 5.66121413600272,40.7473249649988 0 0 46.40853910100149 40.74732496499876 ST2 TRUE 1003 -Reddy_3667T ST2 1 0 814,1009,1065,748,875,896,907,683,662,195 0.142,0.179,0.184,0.186,0.187,0.202,0.21,0.217,0.22,0.243 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 51.7670552745133 0 0 51.767055274513275 51.767055274513275 ST2 TRUE 1004 -Reddy_3669T ST2 0.9158998581840754 0 218,148,996,359,671,842,668,774,161,602 0.185,0.192,0.233,0.259,0.273,0.299,0.312,0.312,0.338,0.338 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 3.20532629828837,34.9078828958658 0 0 38.11320919415417 34.907882895865804 ST2 TRUE 1005 -Reddy_3671T EZB 1 0 874,1114,753,1075,688,164,407,601,812,1097 0.116,0.166,0.184,0.218,0.24,0.25,0.265,0.269,0.281,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.1039648020711 0 0 47.10396480207114 47.10396480207114 EZB TRUE 1006 -Reddy_3673T ST2 0.6435633466181373 0 1067,797,363,309,422,544,408,1057,808,670 0.291,0.303,0.337,0.348,0.369,0.373,0.376,0.467,0.512,0.524 ST2,ST2,BN2,ST2,ST2,ST2,N1,ST2,BN2,BN2 6.83106728889076,2.66150390618913,17.139289207083 0 0 26.631860402162907 17.139289207083014 ST2 TRUE 1007 -Reddy_3674T MCD 1 0 468,276,1076,443,451,33,328,1021,568,902 0.111,0.12,0.16,0.162,0.17,0.175,0.187,0.188,0.201,0.215 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.7184536242275 0 0 61.718453624227465 61.718453624227465 MCD TRUE 1008 -Reddy_3684T ST2 0.9126011322915903 0 748,195,1004,1065,875,814,907,770,683,662 0.133,0.165,0.179,0.189,0.19,0.19,0.196,0.216,0.222,0.266 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2 4.62914443457599,48.336581048508 0 0 52.96572548308396 48.33658104850796 ST2 TRUE 1009 -Reddy_3686T BN2 1 0 731,955,700,538,435,650,1071,99,780,959 0.151,0.17,0.206,0.214,0.245,0.289,0.29,0.297,0.33,0.339 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.4067799947274 0 0 42.406779994727366 42.406779994727366 BN2 TRUE 1010 -Reddy_3688T EZB 1 0 989,1002,667,1026,632,594,876,1040,760,953 0.189,0.241,0.258,0.259,0.27,0.277,0.281,0.286,0.288,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.4179588620152 0 0 38.41795886201519 38.41795886201519 EZB TRUE 1011 -Reddy_3692T EZB 1 0 49,101,392,313,604,302,655,986,183,1051 0.112,0.145,0.156,0.174,0.194,0.201,0.209,0.209,0.211,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.0235743544542 0 0 57.0235743544542 57.0235743544542 EZB TRUE 1012 -Reddy_3694T EZB 1 0 917,271,210,79,927,405,402,638,114,360 0.15,0.225,0.253,0.253,0.28,0.289,0.357,0.361,0.382,0.414 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.64950505175 0 0 36.64950505175 36.64950505175 EZB TRUE 1013 -Reddy_3698T EZB 1 0 639,122,865,190,121,754,849,18,406,756 0.175,0.244,0.245,0.248,0.26,0.266,0.277,0.281,0.298,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.4064560582144 0 0 39.40645605821436 39.40645605821436 EZB TRUE 1014 -Reddy_3699T MCD 1 0 712,1048,417,165,879,1109,769,350,500,290 0.156,0.157,0.16,0.162,0.17,0.171,0.172,0.186,0.187,0.194 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.6648490130582 0 0 58.66484901305817 58.66484901305817 MCD TRUE 1015 -Reddy_3701T ST2 1 0 1053,692,177,557,682,793,1086,982,370,642 0.163,0.204,0.21,0.212,0.225,0.252,0.259,0.274,0.292,0.361 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 42.6572076984569 0 0 42.65720769845686 42.65720769845686 ST2 TRUE 1016 -Reddy_3703T ST2 0.5421503562397456 0 785,668,425,366,783,359,161,1005,612,671 0.236,0.291,0.323,0.323,0.325,0.352,0.372,0.382,0.387,0.388 EZB,BN2,ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2 6.52637115707369,7.32647223656603,16.4034723694747 0 0 30.256315763114443 16.403472369474727 BN2 FALSE 1017 -Reddy_3704T EZB 1 0 1073,942,406,182,743,882,120,303,754,116 0.169,0.174,0.198,0.201,0.202,0.217,0.22,0.222,0.228,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0560004892979 0 0 49.056000489297944 49.056000489297944 EZB TRUE 1018 -Reddy_3706T BN2 1 0 652,472,651,666,1113,659,695,179,681,674 0.163,0.186,0.198,0.205,0.231,0.234,0.25,0.257,0.294,0.323 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.4259064392884 0 0 44.42590643928836 44.42590643928836 BN2 TRUE 1019 -Reddy_3708T EZB 1 0 52,610,112,886,1072,649,1,139,885,273 0.142,0.201,0.211,0.211,0.22,0.227,0.245,0.257,0.26,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.9978503087693 0 0 45.9978503087693 45.9978503087693 EZB TRUE 1020 -Reddy_3711T MCD 1 0 568,328,1076,465,33,1008,468,961,443,451 0.119,0.146,0.155,0.159,0.184,0.188,0.189,0.193,0.194,0.202 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.3653356559226 0 0 59.36533565592265 59.36533565592265 MCD TRUE 1021 -Reddy_3715T BN2 1 0 569,26,498,566,399,575,898,482,353,401 0.128,0.142,0.209,0.223,0.283,0.299,0.305,0.306,0.308,0.32 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9117679681085 0 0 43.911767968108535 43.911767968108535 BN2 TRUE 1022 -Reddy_3717T BN2 1 0 796,555,591,905,527,395,305,702,998,75 0.144,0.157,0.162,0.168,0.173,0.193,0.199,0.211,0.219,0.221 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.2264212946487 0 0 55.2264212946487 55.2264212946487 BN2 TRUE 1023 -Reddy_3718T ST2 1 0 901,925,685,834,320,843,372,625,969,904 0.131,0.199,0.239,0.262,0.282,0.311,0.324,0.328,0.342,0.347 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 39.3637089883367 0 0 39.36370898833669 39.36370898833669 EZB FALSE 1024 -Reddy_3725T ST2 0.6952590063018006 0 950,1107,969,703,625,361,178,787,965,423 0.231,0.281,0.296,0.296,0.314,0.32,0.367,0.375,0.384,0.393 EZB,ST2,ST2,ST2,ST2,ST2,ST2,EZB,EZB,ST2 9.59806421609289,21.897745059893 0 0 31.49580927598584 21.89774505989295 BN2 FALSE 1025 -Reddy_3729T EZB 1 0 1002,1040,91,914,667,317,760,632,46,876 0.118,0.155,0.159,0.174,0.179,0.184,0.187,0.189,0.201,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.592535764587 0 0 58.59253576458696 58.59253576458696 EZB TRUE 1026 -Reddy_3732T MCD 1 0 130,349,39,332,311,323,906,326,491,514 0.147,0.166,0.182,0.186,0.187,0.188,0.193,0.205,0.208,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.0598289693713 0 0 54.059828969371296 54.059828969371296 MCD TRUE 1027 -Reddy_3734T BN2 1 0 172,58,337,564,357,393,158,999,589,64 0.237,0.267,0.324,0.35,0.387,0.416,0.484,0.497,0.512,0.526 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.8345543624395 0 0 26.83455436243952 26.83455436243952 BN2 TRUE 1028 -Reddy_3737T ST2 0.8244213715521092 0 160,161,602,842,671,366,668,359,583,996 0.131,0.141,0.162,0.188,0.207,0.215,0.217,0.218,0.249,0.26 ST2,ST2,ST2,ST2,ST2,BN2,BN2,ST2,ST2,ST2 9.25748369679233,43.4680887639802 0 0 52.72557246077255 43.46808876398022 ST2 TRUE 1029 -Reddy_3741T EZB 1 0 986,199,302,313,96,226,604,191,392,1039 0.164,0.186,0.194,0.198,0.2,0.209,0.216,0.221,0.244,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.6968502214526 0 0 48.69685022145262 48.69685022145262 EZB TRUE 1030 -Reddy_3743T EZB 1 0 159,997,430,972,37,880,89,761,707,1063 0.143,0.166,0.187,0.203,0.213,0.215,0.217,0.218,0.234,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1331889762957 0 0 50.13318897629574 50.13318897629574 EZB TRUE 1031 -Reddy_3745T ST2 0.8886043974135162 0 807,832,362,759,24,207,483,59,281,1057 0.279,0.313,0.341,0.363,0.375,0.435,0.436,0.443,0.447,0.473 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 2.93477398943019,23.4107362577246 0 0 26.34551024715481 23.410736257724615 ST2 TRUE 1032 -Reddy_3746T EZB 0.8402588327841515 0 322,319,714,913,810,993,715,684,890,839 0.134,0.135,0.178,0.22,0.255,0.3,0.317,0.321,0.358,0.361 EZB,EZB,EZB,EZB,ST2,EZB,EZB,ST2,EZB,EZB 37.0689243386599,7.0471538176792 0 0 44.11607815633907 37.06892433865987 EZB TRUE 1033 -Reddy_3747T EZB 1 0 51,233,939,211,719,31,960,360,537,948 0.12,0.127,0.249,0.256,0.306,0.346,0.356,0.358,0.419,0.422 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6445452622684 0 0 40.64454526226835 40.64454526226835 EZB TRUE 1034 -Reddy_3749T MCD 1 0 290,108,794,413,165,297,446,1015,17,528 0.139,0.158,0.165,0.176,0.186,0.208,0.209,0.226,0.232,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.2211884976349 0 0 53.221188497634884 53.221188497634884 MCD TRUE 1035 -Reddy_3753T MCD 1 0 550,326,416,821,491,283,469,311,447,349 0.108,0.128,0.142,0.145,0.159,0.165,0.175,0.196,0.203,0.222 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.6462295241017 0 0 63.64622952410171 63.64622952410171 MCD TRUE 1036 -Reddy_3756T EZB 1 0 418,599,644,150,222,13,83,140,277,84 0.136,0.182,0.192,0.197,0.201,0.206,0.229,0.243,0.249,0.25 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4782058861783 0 0 49.47820588617825 49.47820588617825 EZB TRUE 1037 -Reddy_3757T MCD 1 0 565,679,415,461,869,987,340,414,932,966 0.12,0.125,0.13,0.179,0.187,0.192,0.2,0.213,0.223,0.236 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.5901578064597 0 0 58.590157806459715 58.590157806459715 MCD TRUE 1038 -Reddy_3758T EZB 1 0 226,199,96,191,604,677,167,986,1030,392 0.153,0.166,0.169,0.181,0.232,0.247,0.247,0.251,0.252,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.0699102562458 0 0 48.06991025624585 48.06991025624585 EZB TRUE 1039 -Reddy_3759T EZB 1 0 91,1026,1002,317,397,914,667,984,760,632 0.116,0.155,0.166,0.176,0.198,0.212,0.234,0.236,0.238,0.244 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3572688271898 0 0 53.35726882718977 53.35726882718977 EZB TRUE 1040 -Reddy_3760T ST2 0.4982685254872068 0 965,1107,950,661,365,625,178,361,787,1025 0.274,0.306,0.351,0.352,0.362,0.366,0.372,0.404,0.415,0.441 EZB,ST2,EZB,EZB,ST2,ST2,ST2,ST2,EZB,BN2 2.26975866931073,11.7452743960266,13.918301352135 0 0 27.933334417472356 13.918301352134995 ST2 TRUE 1041 -Reddy_3761T MCD 0.8904327969103821 0 580,526,16,844,453,678,900,549,296,881 0.138,0.148,0.151,0.152,0.166,0.172,0.178,0.181,0.206,0.207 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.56105394452313,53.3204960039288 0 0 59.88154994845188 53.32049600392875 MCD TRUE 1042 -Reddy_3776T MCD 0.9305406130218533 0 730,888,460,513,459,739,314,1082,66,520 0.127,0.137,0.151,0.205,0.271,0.275,0.278,0.298,0.303,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 3.29905046137707,44.1970563271124 0 0 47.49610678848949 44.19705632711242 MCD TRUE 1043 -Reddy_3778T MCD 0.34519807942599007 0 935,747,124,1089,554,696,43,779,673,525 0.222,0.223,0.235,0.252,0.292,0.361,0.369,0.419,0.425,0.428 EZB,ST2,N1,MCD,ST2,MCD,ST2,MCD,BN2,MCD 2.35195967063854,4.49582982647557,11.4515654949311,4.2488321811637,10.6257097209258 0 0 33.17389689413471 11.451565494931117 MCD TRUE 1044 -Reddy_3780T MCD 0.3314626245953946 0 579,716,663,123,803,525,43,421,455,779 0.246,0.248,0.274,0.337,0.355,0.368,0.386,0.402,0.404,0.437 N1,BN2,ST2,N1,BN2,MCD,ST2,MCD,MCD,MCD 6.83870761093021,9.97042591859341,7.02935423711851,6.24160325648297 0 0 30.0800910231251 9.970425918593413 BN2 FALSE 1045 -Reddy_3782T MCD 0.8003461252493761 0 479,169,260,918,769,1103,778,312,956,1048 0.147,0.158,0.164,0.165,0.169,0.17,0.172,0.185,0.188,0.202 MCD,MCD,MCD,MCD,MCD,ST2,BN2,MCD,MCD,MCD 5.82086970655386,46.9030911935053,5.87954789920065 0 0 58.60350879925981 46.903091193505304 MCD TRUE 1046 -Reddy_3784T MCD 0.9219836610423915 0 1054,475,44,616,856,502,697,790,574,1093 0.124,0.19,0.217,0.22,0.222,0.237,0.255,0.277,0.279,0.283 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,N1,MCD 42.3098912859479,3.58017496328745 0 0 45.89006624923535 42.3098912859479 MCD TRUE 1047 -Reddy_3787T MCD 0.9067495283408784 0 417,350,312,769,1015,778,155,500,450,918 0.105,0.136,0.138,0.148,0.157,0.158,0.161,0.163,0.163,0.168 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 6.33869215364071,61.636215006206 0 0 67.97490715984668 61.63621500620597 MCD TRUE 1048 -Reddy_3812T BN2 0.9219372469649395 0 773,928,262,765,936,110,279,196,321,624 0.155,0.215,0.249,0.275,0.283,0.284,0.291,0.304,0.318,0.331 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,EZB 35.6876551341278,3.02176381126488 0 0 38.70941894539263 35.68765513412775 ST2 FALSE 1049 -Reddy_3814T EZB 0.8615156188660725 0 241,645,643,975,581,385,369,254,67,454 0.116,0.124,0.135,0.167,0.177,0.186,0.222,0.233,0.244,0.245 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0324607723073,8.04247098361379 0 0 58.07493175592106 50.03246077230726 EZB TRUE 1050 -Reddy_3820T EZB 1 0 655,410,183,752,49,1012,1115,101,225,392 0.117,0.142,0.174,0.198,0.218,0.219,0.23,0.241,0.253,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.7326303348028 0 0 51.732630334802764 51.732630334802764 EZB TRUE 1051 -Reddy_3823T EZB 0.6239461608870523 0 736,781,509,338,545,606,249,689,1064,1118 0.119,0.241,0.249,0.346,0.374,0.376,0.38,0.386,0.412,0.453 EZB,EZB,EZB,BN2,ST2,ST2,EZB,ST2,EZB,ST2 2.88937748752595,21.6191786657259,10.1405530503216 0 0 34.6491092035735 21.61917866572592 EZB TRUE 1052 -Reddy_3825T ST2 1 0 557,1016,692,793,982,1086,177,682,598,370 0.156,0.163,0.196,0.201,0.215,0.247,0.271,0.288,0.325,0.331 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 44.5819353198369 0 0 44.58193531983688 44.58193531983688 ST2 TRUE 1053 -Reddy_3826T MCD 0.9210811176075658 0 1047,475,616,44,697,502,856,790,1093,574 0.124,0.169,0.22,0.229,0.237,0.238,0.243,0.26,0.271,0.272 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,N1 42.9745795264285,3.68209240497697 0 0 46.65667193140544 42.97457952642847 MCD TRUE 1054 -Reddy_3832T BN2 0.803698127049192 0 860,962,571,570,390,153,840,672,186,22 0.32,0.323,0.357,0.418,0.429,0.431,0.442,0.446,0.472,0.486 BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2,EZB 19.8817586108756,4.85608504178068 0 0 24.737843652656263 19.88175861087558 BN2 TRUE 1055 -Reddy_3833T EZB 0.797792399026438 0 595,857,867,587,227,618,368,995,809,245 0.124,0.163,0.169,0.17,0.2,0.205,0.214,0.221,0.222,0.23 EZB,EZB,ST2,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.99415479190506,42.9953221119483,5.90339317665423 0 0 53.89287008050754 42.995322111948255 EZB TRUE 1056 -Reddy_3834T ST2 0.9143274548143769 0 207,483,797,1067,656,728,362,807,60,822 0.213,0.241,0.284,0.313,0.316,0.322,0.373,0.43,0.446,0.461 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 2.6774941046211,28.5751563077121 0 0 31.25265041233322 28.57515630771212 ST2 TRUE 1057 -Reddy_3835T EZB 1 0 889,884,405,409,846,143,847,316,144,638 0.18,0.253,0.309,0.326,0.328,0.407,0.433,0.434,0.451,0.458 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.3457380592031 0 0 30.345738059203075 30.345738059203075 EZB TRUE 1058 -Reddy_3838T EZB 1 0 603,384,605,25,149,74,61,73,833,239 0.11,0.141,0.141,0.148,0.175,0.185,0.188,0.189,0.192,0.195 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.1133766460426 0 0 62.11337664604259 62.11337664604259 EZB TRUE 1059 -Reddy_3839T BN2 0.9212995778879212 0 630,335,477,943,650,795,474,99,345,780 0.127,0.158,0.208,0.221,0.242,0.275,0.283,0.291,0.296,0.3 BN2,BN2,BN2,BN2,BN2,BN2,N1,BN2,BN2,BN2 41.4122657334192,3.53757113544901 0 0 44.94983686886824 41.412265733419225 BN2 TRUE 1060 -Reddy_3842T BN2 0.5973463502966281 0 506,799,293,919,123,698,530,347,481,1112 0.212,0.232,0.252,0.267,0.271,0.315,0.351,0.396,0.4,0.401 BN2,BN2,MCD,BN2,N1,ST2,N1,BN2,BN2,BN2 20.2963579863355,3.96817423671017,6.54103044345662,3.17197478100502 0 0 33.97753744750731 20.2963579863355 BN2 TRUE 1061 -Reddy_3846T BN2 1 0 318,15,175,282,315,264,341,871,563,640 0.137,0.193,0.232,0.248,0.249,0.252,0.253,0.255,0.265,0.268 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.1737298809272 0 0 44.173729880927155 44.173729880927155 BN2 TRUE 1062 -Reddy_3849T EZB 1 0 820,880,931,89,635,159,48,125,35,37 0.131,0.165,0.166,0.187,0.19,0.206,0.225,0.226,0.228,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7644420370538 0 0 52.76444203705383 52.76444203705383 EZB TRUE 1063 -Reddy_3851T EZB 1 0 217,216,168,249,766,509,908,823,819,471 0.184,0.2,0.204,0.206,0.234,0.271,0.304,0.318,0.376,0.377 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.9096548051495 0 0 39.90965480514946 39.90965480514946 EZB TRUE 1064 -Reddy_3856T ST2 0.920424252407817 0 875,814,1004,1009,748,195,770,423,907,896 0.103,0.15,0.184,0.189,0.219,0.24,0.247,0.268,0.272,0.282 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.04777459471645,46.8191630991756 0 0 50.86693769389203 46.819163099175576 ST2 TRUE 1065 -Reddy_3859T ST2 0.4944574306438026 0 119,381,830,919,412,308,293,530,481,529 0.164,0.236,0.259,0.275,0.292,0.296,0.338,0.392,0.4,0.408 BN2,ST2,ST2,BN2,ST2,ST2,MCD,N1,BN2,ST2 12.2404632967172,2.95661934164728,2.55405788574168,17.361907515967 0 0 35.11304804007315 17.36190751596698 N1 FALSE 1066 -Reddy_3867T ST2 1 0 797,309,1007,544,1057,207,483,728,422,508 0.247,0.283,0.291,0.302,0.313,0.412,0.437,0.446,0.456,0.481 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 28.7643297644871 0 0 28.764329764487137 28.764329764487137 ST2 TRUE 1067 -Reddy_3872T ST2 0.9014214305480986 0 825,940,1003,774,598,835,915,996,982,218 0.123,0.145,0.153,0.154,0.197,0.2,0.307,0.311,0.325,0.327 ST2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,ST2,ST2 5.00277977814569,45.7463820930524 0 0 50.74916187119812 45.74638209305243 ST2 TRUE 1068 -Reddy_3873T BN2 0.8924627961415962 0 493,633,898,77,634,399,498,212,487,636 0.149,0.163,0.172,0.186,0.209,0.214,0.216,0.225,0.263,0.272 BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2,BN2,BN2 44.6555066938467,5.38076023728682 0 0 50.03626693113356 44.65550669384674 BN2 TRUE 1069 -Reddy_3875T MCD 0.7587891738237165 0 383,457,445,154,894,749,933,845,801,772 0.207,0.309,0.375,0.393,0.402,0.424,0.444,0.454,0.465,0.47 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,ST2,EZB 2.20165819578298,2.12738658498657,20.3849416045069,2.15110645966016 0 0 26.865092844936566 20.384941604506853 BN2 FALSE 1070 -Reddy_3876T BN2 1 0 1001,700,548,955,546,517,543,435,731,959 0.164,0.19,0.219,0.221,0.227,0.229,0.23,0.243,0.245,0.254 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.7067555615328 0 0 45.706755561532816 45.706755561532816 BN2 TRUE 1071 -Reddy_3880T EZB 0.9169177288653769 0 112,1095,892,885,648,680,610,139,1,1094 0.121,0.158,0.168,0.17,0.176,0.184,0.191,0.2,0.204,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 52.6202266422725,4.76793915029695 0 0 57.38816579256941 52.620226642272456 EZB TRUE 1072 -Reddy_3882T EZB 1 0 743,1018,942,865,883,406,303,754,156,627 0.168,0.169,0.185,0.219,0.229,0.234,0.239,0.253,0.259,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.243775450377 0 0 46.243775450377036 46.243775450377036 EZB TRUE 1073 -Reddy_3885T EZB 0.8267969864659138 0 947,995,618,274,863,1079,368,227,867,587 0.134,0.137,0.137,0.147,0.147,0.173,0.18,0.185,0.185,0.215 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,ST2,EZB 5.41572978647013,51.6445271841165,5.4031143642164 0 0 62.463371334802986 51.644527184116455 EZB TRUE 1074 -Reddy_3887T EZB 1 0 753,164,597,1006,874,21,709,1114,601,284 0.139,0.15,0.217,0.218,0.226,0.25,0.255,0.261,0.262,0.263 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.852592821777 0 0 46.85259282177695 46.85259282177695 EZB TRUE 1075 -Reddy_3888T MCD 1 0 328,443,468,1021,568,1008,276,33,451,465 0.129,0.139,0.154,0.155,0.158,0.16,0.173,0.203,0.211,0.213 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.5849141496637 0 0 60.584914149663724 60.584914149663724 MCD TRUE 1076 -Reddy_3892T BN2 1 0 331,973,197,559,488,354,341,282,945,333 0.125,0.14,0.144,0.157,0.161,0.161,0.171,0.172,0.177,0.184 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 63.6125782734105 0 0 63.612578273410534 63.612578273410534 BN2 TRUE 1077 -Reddy_3893T EZB 1 0 29,80,637,46,805,876,760,594,632,163 0.168,0.186,0.21,0.211,0.218,0.227,0.23,0.238,0.241,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.6325327152737 0 0 46.63253271527374 46.63253271527374 EZB TRUE 1078 -Reddy_3896T EZB 0.911666526062844 0 863,995,1074,228,277,274,368,631,947,227 0.133,0.165,0.173,0.174,0.179,0.185,0.186,0.194,0.195,0.199 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 5.01993692642395,51.8094472546549 0 0 56.82938418107883 51.80944725465488 EZB TRUE 1079 -Reddy_3901T EZB 1 0 57,600,609,82,292,138,31,948,751,960 0.178,0.244,0.276,0.305,0.345,0.356,0.385,0.396,0.416,0.44 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.1030809145896 0 0 32.103080914589555 32.103080914589555 EZB TRUE 1080 -Reddy_3904T ST2 1 0 822,394,60,904,843,911,728,278,656,263 0.203,0.235,0.249,0.251,0.284,0.321,0.323,0.349,0.354,0.372 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.2899176567983 0 0 35.2899176567983 35.2899176567983 ST2 TRUE 1081 -Reddy_3905T MCD 0.9167051277176349 0 200,201,541,532,419,888,775,170,1043,567 0.164,0.167,0.229,0.245,0.279,0.292,0.293,0.295,0.298,0.306 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD 3.41355990517298,37.5680733171122 0 0 40.98163322228518 37.56807331711221 MCD TRUE 1082 -Reddy_3911T EZB 1 0 626,206,737,619,265,267,238,259,351,588 0.245,0.292,0.324,0.336,0.341,0.346,0.396,0.402,0.413,0.421 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1878389352069 0 0 29.187838935206912 29.187838935206912 EZB TRUE 1083 -Reddy_3918T ST2 0.5607197320443262 0 691,622,1096,11,411,590,964,899,893,909 0.146,0.206,0.268,0.285,0.342,0.344,0.365,0.381,0.39,0.406 ST2,ST2,EZB,EZB,EZB,ST2,BN2,ST2,N1,ST2 2.73785930734855,10.1567609628277,2.56105754181846,19.72841521345 0 0 35.184093025444696 19.72841521345 BN2 FALSE 1084 -Reddy_3924T BN2 0.5978300916102359 0 432,967,582,924,505,665,153,971,962,976 0.189,0.242,0.245,0.275,0.291,0.312,0.335,0.357,0.375,0.385 BN2,EZB,BN2,ST2,N1,BN2,BN2,EZB,BN2,BN2 20.8203887612936,6.93509245116913,3.44128952165248,3.62982808297435 0 0 34.82659881708953 20.820388761293568 EZB FALSE 1085 -Reddy_3925T ST2 0.9161014118567973 0 692,982,1053,1016,370,557,177,792,793,682 0.155,0.242,0.247,0.259,0.26,0.287,0.302,0.311,0.331,0.342 ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2 3.21351013317245,35.0888046529532 0 0 38.302314786125606 35.08880465295315 ST2 TRUE 1086 -Reddy_3928T MCD 0.9066346452075212 0 511,722,536,721,817,869,937,340,690,415 0.164,0.175,0.183,0.194,0.209,0.209,0.215,0.226,0.228,0.234 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD 4.64357529628558,45.0919535474355 0 0 49.73552884372103 45.09195354743545 MCD TRUE 1087 -Reddy_3930T EZB 0.8734000316672588 0 203,644,83,152,84,599,418,1000,615,150 0.175,0.184,0.204,0.218,0.229,0.232,0.238,0.254,0.257,0.261 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.72160341799685,39.4727476813559 0 0 45.19435109935276 39.47274768135591 EZB TRUE 1088 -Reddy_3932T EZB 0.24738678313391135 0 935,1044,747,554,850,673,124,696,792,1105 0.226,0.252,0.29,0.306,0.338,0.378,0.387,0.444,0.462,0.465 EZB,MCD,ST2,ST2,EZB,BN2,N1,MCD,BN2,BN2 6.95859024169844,7.3891031623682,6.21582384501081,2.58355173939653,6.72155644398678 0 0 29.868625432460753 7.3891031623681975 MCD FALSE 1089 -Reddy_3933T MCD 1 0 994,380,660,439,966,106,932,280,991,188 0.118,0.165,0.192,0.197,0.205,0.21,0.234,0.259,0.259,0.275 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 50.0949226568363 0 0 50.094922656836346 50.094922656836346 MCD TRUE 1090 -Reddy_3937T ST2 0.5829603740983242 0 586,834,320,974,685,657,194,372,1024,824 0.149,0.207,0.214,0.229,0.292,0.302,0.323,0.34,0.366,0.376 ST2,ST2,ST2,EZB,ST2,BN2,EZB,ST2,EZB,EZB 3.31417396141651,12.851336007317,22.5970175339762 0 0 38.7625275027097 22.597017533976228 EZB FALSE 1091 -Reddy_3939T EZB 0.7126800155721473 0 111,45,6,334,764,882,120,303,718,883 0.145,0.21,0.22,0.249,0.261,0.274,0.279,0.302,0.335,0.336 ST2,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.75935823297489,28.9027167198686,6.89289514361024 0 0 40.55497009645376 28.902716719868632 ST2 FALSE 1092 -Reddy_3944T MCD 0.7291281616834906 0 574,616,502,1054,1047,475,1105,697,992,790 0.136,0.196,0.2,0.271,0.283,0.29,0.291,0.351,0.359,0.371 N1,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.43871848269539,29.1004936626594,7.3721438986485 0 0 39.911356044003256 29.100493662659364 MCD TRUE 1093 -Reddy_3948T EZB 1 0 1102,885,861,809,7,886,112,1095,680,1072 0.122,0.142,0.155,0.173,0.185,0.19,0.196,0.199,0.199,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1181985065071 0 0 58.11819850650713 58.11819850650713 ST2 FALSE 1094 -Reddy_3952T EZB 1 0 892,680,648,706,1072,71,885,112,711,861 0.116,0.126,0.138,0.153,0.158,0.159,0.161,0.168,0.171,0.174 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.7058779617133 0 0 66.70587796171327 66.70587796171327 EZB TRUE 1095 -Reddy_3967T ST2 0.6306730344038728 0 590,964,899,622,691,1084,909,893,11,544 0.186,0.238,0.253,0.254,0.266,0.268,0.309,0.335,0.448,0.459 ST2,BN2,ST2,ST2,ST2,BN2,ST2,N1,EZB,ST2 7.9244774446937,2.23452972631304,2.98851259950124,22.451071708689 0 0 35.598591479197 22.45107170868902 EZB FALSE 1096 -Reddy_3972T EZB 0.8002858030286724 0 115,812,426,284,874,862,1006,753,1075,424 0.138,0.165,0.186,0.262,0.296,0.302,0.31,0.314,0.344,0.345 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,ST2 33.1132775161832,8.26353735528695 0 0 41.37681487147015 33.1132775161832 EZB TRUE 1097 -Reddy_3977T BN2 1 0 553,258,187,109,562,10,352,503,463,355 0.158,0.165,0.203,0.231,0.282,0.287,0.322,0.338,0.343,0.343 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.5581717463513 0 0 40.55817174635129 40.55817174635129 BN2 TRUE 1098 -Reddy_3982T N1 0.7845227284663123 0 1104,496,41,516,325,137,486,523,854,438 0.134,0.137,0.223,0.257,0.335,0.338,0.339,0.351,0.362,0.365 N1,N1,N1,N1,ST2,N1,ST2,N1,BN2,N1 2.76551154450111,31.6600903990266,5.93025933396502 0 0 40.35586127749268 31.660090399026558 N1 TRUE 1099 -Reddy_3983T EZB 1 0 823,76,471,908,766,921,235,216,638,32 0.191,0.203,0.262,0.285,0.289,0.303,0.305,0.311,0.314,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.0035025056776 0 0 37.003502505677645 37.003502505677645 EZB TRUE 1100 -Reddy_3987T MCD 0.9242889161022892 0 367,452,978,878,961,514,295,39,465,621 0.12,0.152,0.177,0.196,0.197,0.213,0.218,0.222,0.226,0.249 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,ST2 48.99317568138,4.01316771174797 0 0 53.00634339312798 48.99317568138 MCD TRUE 1101 -Reddy_3991T EZB 0.8613140467834578 0 1094,861,885,7,809,680,1095,706,112,892 0.122,0.135,0.146,0.164,0.186,0.188,0.193,0.194,0.205,0.206 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1081868853007,8.2292720545159 0 0 59.33745893981662 51.10818688530072 EZB TRUE 1102 -Reddy_3993T MCD 0.8864873899404359 0 918,169,778,990,312,155,286,450,433,1046 0.119,0.119,0.128,0.139,0.149,0.162,0.163,0.168,0.168,0.17 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.78582108992427,60.8041010855836 0 0 68.58992217550782 60.804101085583554 ST2 FALSE 1103 -Reddy_3997T N1 0.7826066922433542 0 1099,496,41,516,137,325,523,486,438,854 0.134,0.167,0.189,0.286,0.311,0.315,0.319,0.343,0.369,0.386 N1,N1,N1,N1,N1,ST2,N1,ST2,N1,BN2 2.58902807289811,31.2566348411713,6.09347273473737 0 0 39.93913564880683 31.256634841171348 N1 TRUE 1104 -Reddy_684T MCD 0.7955657976784132 0 696,1093,574,935,616,223,475,502,985,697 0.219,0.291,0.324,0.345,0.384,0.385,0.388,0.39,0.395,0.412 MCD,MCD,N1,EZB,MCD,MCD,MCD,MCD,MCD,MCD 2.8995290364209,23.3063349182908,3.08943127580521 0 0 29.295295230516917 23.306334918290805 BN2 FALSE 1105 -Reddy_689T BN2 0.757851793282285 0 186,462,672,470,840,22,153,571,539,864 0.143,0.184,0.184,0.217,0.23,0.235,0.267,0.284,0.339,0.339 BN2,BN2,BN2,BN2,BN2,EZB,BN2,EZB,EZB,BN2 33.5451959014079,10.718334512794 0 0 44.263530414201966 33.54519590140792 BN2 TRUE 1106 -Reddy_695T EZB 0.45122075492302766 0 950,965,361,178,787,1025,770,1041,661,365 0.154,0.203,0.213,0.218,0.249,0.281,0.297,0.306,0.312,0.315 EZB,EZB,ST2,ST2,EZB,BN2,BN2,ST2,EZB,ST2 6.92573331550293,18.6345519518821,15.7377972090314 0 0 41.29808247641647 18.6345519518821 ST2 FALSE 1107 -Reddy_705T EZB 1 0 92,105,287,675,738,78,117,397,247,317 0.147,0.153,0.177,0.188,0.188,0.202,0.22,0.225,0.225,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.9916339799038 0 0 51.99163397990384 51.99163397990384 EZB TRUE 1108 -Reddy_759T MCD 1 0 712,879,500,762,350,417,1015,1048,767,246 0.125,0.132,0.134,0.167,0.168,0.17,0.171,0.173,0.182,0.183 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.4323873978751 0 0 63.43238739787513 63.43238739787513 MCD TRUE 1109 -Reddy_799T MCD 0.9054771589907213 0 299,690,469,447,721,937,722,283,550,511 0.12,0.155,0.181,0.188,0.196,0.203,0.233,0.238,0.248,0.25 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 4.93686078271522,47.2924282441624 0 0 52.22928902687758 47.292428244162366 MCD TRUE 1110 -Reddy_816T BN2 1 0 957,264,578,401,640,482,318,951,1062,871 0.148,0.166,0.171,0.179,0.19,0.199,0.237,0.253,0.274,0.284 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 49.8203492852907 0 0 49.82034928529071 49.82034928529071 BN2 TRUE 1111 -Reddy_823T BN2 0.4327246041431883 0 455,506,123,579,347,1061,521,624,799,173 0.167,0.308,0.313,0.359,0.398,0.401,0.412,0.424,0.442,0.444 MCD,BN2,N1,N1,BN2,BN2,MCD,EZB,BN2,BN2 12.7676575599056,2.36020958849165,8.39789450156501,5.97951130576916 0 0 29.50527295573142 12.767657559905599 BN2 TRUE 1112 -Reddy_830T BN2 1 0 695,681,652,674,65,666,1019,584,589,659 0.153,0.163,0.17,0.217,0.221,0.228,0.231,0.255,0.279,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.4528931889932 0 0 47.4528931889932 47.4528931889932 BN2 TRUE 1113 -SU-DHL-10 EZB 1 0 1006,874,688,753,601,407,1075,164,344,812 0.166,0.176,0.176,0.236,0.241,0.247,0.261,0.276,0.293,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5822920423909 0 0 43.58229204239091 43.58229204239091 EZB TRUE 1114 -SU-DHL-4 EZB 1 0 588,183,224,259,225,1051,655,268,410,737 0.176,0.184,0.191,0.193,0.2,0.23,0.246,0.251,0.271,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3224236865746 0 0 46.32242368657461 46.32242368657461 EZB TRUE 1115 -SU-DHL-5 EZB 0.6609816726970834 0 988,768,993,194,684,810,714,913,974,824 0.213,0.224,0.352,0.391,0.414,0.441,0.447,0.47,0.475,0.51 BN2,EZB,EZB,EZB,ST2,ST2,EZB,EZB,EZB,EZB 4.70259425868679,18.2976016160517,4.68226851400892 0 0 27.68246438874743 18.297601616051722 ST2 FALSE 1116 -SU-DHL-6 EZB 1 0 848,877,740,192,93,387,757,631,873,228 0.13,0.219,0.222,0.224,0.233,0.236,0.269,0.277,0.301,0.302 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7288759335614 0 0 43.728875933561355 43.728875933561355 EZB TRUE 1117 -SU-DHL-9 ST2 0.651392998953499 0 689,607,338,606,828,620,346,144,1052,545 0.183,0.195,0.233,0.291,0.333,0.343,0.387,0.449,0.453,0.456 ST2,ST2,BN2,ST2,ST2,EZB,ST2,EZB,EZB,ST2 4.29903231454278,7.35498670238244,21.7762304672803 0 0 33.43024948420552 21.776230467280296 ST2 TRUE 1118 diff --git a/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv b/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv deleted file mode 100644 index 32c13e6..0000000 --- a/temp_tsv_results/optimize_for_other_T/class_accuracy_0.494optimize_for_other=T.tsv +++ /dev/null @@ -1,7 +0,0 @@ -true_label acc n -BN2 0.7370517928286853 251 -EZB 0.9238683127572016 486 -MCD 0.9406392694063926 219 -N1 0.6521739130434783 23 -Other 0.1297640653357532 1102 -ST2 0.7122302158273381 139 diff --git a/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv b/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv deleted file mode 100644 index 3e98a2e..0000000 --- a/temp_tsv_results/optimize_for_other_T/loocv_predictions_acc0.494optimize_for_other=T.tsv +++ /dev/null @@ -1,2221 +0,0 @@ -sample_id predicted_label confidence other_score neighbor distance label weighted_votes neighbors_other other_weighted_votes total_w pred_w true_label correct fold -00-14595_tumorC EZB 1 0 1,1279,369,1055,1120,316,1937,1563,428,429,1022 0.12,0.135,0.142,0.174,0.174,0.178,0.179,0.187,0.2,0.201,0.202 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.7949626152367 0 0 65.79496261523674 65.79496261523674 EZB TRUE 1 -00-15201_tumorA EZB 0.6130037775316853 1.1286077267377064 1400,781,2134,816,2138 0.13,0.179,0.26,0.266,0.284 EZB,MCD,MCD,EZB,EZB 14.9604944506319,9.44472946311243 4 16.884529632799723 24.40522391374431 14.960494450631884 Other FALSE 2 -00-15201_tumorB N1 1 1.9687166381307977 697,3,2077 0.205,0.218,0.257 N1,N1,N1 13.3344064177855 5 26.251667774292322 13.334406417785459 13.334406417785459 N1 TRUE 3 -FL1015T2 EZB 0.8499435544092154 0.09667623902957859 365,96,399,166,1244,578,341,340,1042,105 0.139,0.198,0.206,0.211,0.212,0.216,0.219,0.222,0.255,0.261 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 7.198612170868,40.7740833206617 1 3.9418850253202393 47.97269549152965 40.77408332066165 EZB TRUE 4 -00-23442_tumorB EZB 1 0 5,368,250,102,780,118,170,448,433,385,1468 0.107,0.179,0.18,0.191,0.196,0.206,0.22,0.25,0.25,0.264,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6005982431752 0 0 55.60059824317517 55.60059824317517 EZB TRUE 5 -00-23442_tumorA EZB 1 0.24214940990432238 1991,6,79,1142,1118,367,1872,1293,1788 0.129,0.185,0.194,0.211,0.221,0.221,0.249,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.905731658932 2 10.631747012627901 43.90573165893197 43.90573165893197 EZB TRUE 6 -00-26427_tumorA N1 1 11.893018813930084 697 0.275 N1 3.63020689217569 8 43.1741188671041 3.6302068921756865 3.6302068921756865 Other FALSE 7 -00-26427_tumorC MCD 0.7834380758276059 0.5815062648348855 952,1890,1398,285,2030,1194 0.13,0.15,0.158,0.188,0.193,0.227 BN2,MCD,MCD,MCD,MCD,MCD 7.71031054808198,27.8929497089898 3 16.219925000501938 35.60326025707173 27.892949708989754 Other FALSE 8 -FL1002T2 EZB 1 0 364,9,1017,728,1295,168,1511,227,86,1041,137 0.116,0.134,0.202,0.224,0.23,0.244,0.273,0.281,0.285,0.291,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.5486925905079 0 0 51.54869259050787 51.54869259050787 EZB TRUE 9 -FL1006T2 EZB 1 0 277,128,116,275,602,10,503,731,712,691,28 0.123,0.208,0.218,0.227,0.229,0.241,0.25,0.262,0.273,0.28,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8497107554635 0 0 48.84971075546353 48.84971075546353 EZB TRUE 10 -01-12047_tumorB EZB 0.9004251219844818 0.1071014953973078 343,11,143,575,16,308,139,1533,1058,218 0.171,0.171,0.179,0.202,0.204,0.204,0.222,0.232,0.238,0.244 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 4.89482439827688,44.2623977427741 1 4.740568988121527 49.15722214105098 44.2623977427741 EZB TRUE 11 -01-12047_tumorC EZB 1 0 1111,1055,429,428,1110,369,1563,372,1022,12,299 0.114,0.12,0.132,0.161,0.168,0.178,0.18,0.181,0.191,0.192,0.197 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 69.0500951119995 0 0 69.05009511199954 69.05009511199954 EZB TRUE 12 -01-14774_tumorB BN2 0.7012543932969935 0 1086,590,13,1076,932,157,960,412,1722,1806 0.137,0.162,0.167,0.175,0.191,0.205,0.229,0.247,0.248,0.28 BN2,BN2,BN2,EZB,EZB,BN2,ST2,BN2,BN2,BN2 35.9673410195553,10.9547179951214,4.367945530539 0 0 51.290004545215695 35.96734101955527 BN2 TRUE 13 -01-14774_tumorA BN2 0.7442303180346492 0 1806,590,1086,157,932,13,865,1076,945,1444,960 0.188,0.259,0.26,0.264,0.265,0.285,0.286,0.288,0.291,0.298,0.304 BN2,BN2,BN2,BN2,EZB,BN2,BN2,EZB,BN2,BN2,ST2 30.6348967960438,7.23849877950875,3.28979820153505 0 0 41.163193777087606 30.634896796043805 Other FALSE 14 -01-16433_tumorB ST2 0.3065584318286099 0.9775683190820227 476,15,1185,2121,636,1190,826,1183 0.115,0.173,0.218,0.285,0.311,0.33,0.356,0.366 MCD,EZB,ST2,BN2,ST2,BN2,BN2,ST2 9.34233901528769,5.781362521291,8.72487760599991,10.5430412003833 3 10.306543064271242 34.391620342961936 10.54304120038333 EZB FALSE 15 -01-16433_tumorA EZB 0.8459254254835458 0.23920025472044393 308,16,343,1533,11,42,575,143,155 0.121,0.129,0.141,0.149,0.15,0.21,0.225,0.245,0.256 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 8.27602292408043,45.4383744711667 2 10.868870747585984 53.714397395247104 45.43837447116667 EZB TRUE 16 -01-20774T EZB 1 0.1758428832202545 1705,333,149,404,121,80,17,187,319 0.103,0.164,0.165,0.181,0.185,0.206,0.22,0.233,0.246 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5640446071349 2 8.891327390996157 50.564044607134875 50.564044607134875 EZB TRUE 17 -01-23117_tumorA MCD 0.8785022388414534 0.4887091678374886 1194,285,2030,1398,650,1063,952 0.168,0.177,0.183,0.207,0.224,0.226,0.235 MCD,MCD,MCD,MCD,MCD,MCD,BN2 4.25797476506004,30.7877308056266 3 15.046246301622402 35.045705570686664 30.78773080562663 Other FALSE 18 -01-23117_tumorB BN2 1 4.092056531047736 19,871 0.202,0.297 BN2,BN2 8.3129492264896 8 34.01705817452499 8.3129492264896 8.3129492264896 BN2 TRUE 19 -01-23942_tumorA ST2 1 5.097541961794698 90,1946 0.309,0.33 ST2,ST2 6.2648120233629 8 31.935142171848344 6.264812023362904 6.264812023362904 BN2 FALSE 20 -01-28152_tumorA MCD 1 0 947,483,587,1572,888,540,839,516,707,515,387 0.104,0.124,0.13,0.133,0.146,0.152,0.156,0.164,0.173,0.181,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.5979028431696 0 0 75.59790284316958 75.59790284316958 MCD TRUE 21 -01-28152_tumorB ST2 0.3851327447443543 5.837128788182001 1416,1441,879 0.184,0.192,0.289 ST2,EZB,MCD 5.21272629939222,3.45667146862293,5.4302273037585 6 31.696936121140673 14.099625071773652 5.4302273037585005 Other FALSE 22 -02-11616_tumorA MCD 0.9080774374790431 0 759,1449,1009,1376,764,858,1904,479,305,1406,310 0.117,0.122,0.132,0.144,0.146,0.15,0.152,0.156,0.167,0.168,0.177 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.93120231727777,68.471420577374 0 0 75.40262289465178 68.47142057737402 MCD TRUE 23 -02-13135T EZB 1 1.0395842639746993 1289,1964,199,1542,477,72 0.262,0.289,0.31,0.347,0.381,0.388 EZB,EZB,EZB,EZB,EZB,EZB 18.5876473216996 4 19.323425659950416 18.587647321699645 18.587647321699645 EZB TRUE 24 -FL1008T2 EZB 1 0.22042471035895692 1539,654,1160,72,1450,699,25,2073,477,1542 0.206,0.218,0.22,0.261,0.303,0.305,0.309,0.325,0.339,0.346 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.5301618555884 1 8.052150346383893 36.53016185558842 36.53016185558842 EZB TRUE 25 -02-15630_tumorA BN2 1 1.2285651665837694 1148,1841,70,804,1980 0.127,0.164,0.172,0.175,0.215 BN2,BN2,BN2,BN2,BN2 30.1684185018812 6 37.06386810233252 30.168418501881177 30.168418501881177 Other FALSE 26 -02-15630_tumorB EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 27 -02-15745_tumorD EZB 1 0 366,1020,427,689,161,78,645,48,128,1090,326 0.186,0.191,0.199,0.237,0.275,0.279,0.292,0.32,0.324,0.329,0.357 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.5573763503526 0 0 42.55737635035261 42.55737635035261 EZB TRUE 28 -02-17305_tumorB EZB 1 0.16170079865681677 1032,1579,474,227,1079,1545,1054,1236,1809,1017 0.132,0.141,0.169,0.249,0.258,0.268,0.269,0.291,0.301,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.8661025770108 1 7.416585417978125 45.866102577010786 45.866102577010786 EZB TRUE 29 -02-18356_tumorB EZB 0.4929941044662162 2.9365986993334645 1052,1266,1608,2042 0.161,0.167,0.203,0.207 ST2,EZB,N1,EZB 10.8114813235141,4.92447992753476,6.19428340212019 5 31.748981992499573 21.930244653169062 10.811481323514109 Other FALSE 30 -02-18356_tumorA EZB 0.5473084996468225 7.442276665611066 31,1538 0.13,0.157 EZB,BN2 6.3754385268867,7.70796821238885 7 57.36483196633338 14.08340673927555 7.707968212388849 EZB TRUE 31 -02-20170T EZB 1 9.962609464985501 140 0.295 EZB 3.38870743575862 7 33.760368773555555 3.3887074357586178 3.3887074357586178 Other FALSE 32 -02-22991T ST2 1 7.852686904832889 828 0.184 ST2 5.43965339062664 6 42.71589494740364 5.43965339062664 5.43965339062664 Other FALSE 33 -02-24492_tumorA BN2 0.6340237843221346 0.5978572442932314 1112,1407,34,273,1191,274,897,1204 0.135,0.158,0.16,0.175,0.189,0.193,0.195,0.25 BN2,BN2,EZB,BN2,BN2,MCD,MCD,BN2 28.7107172236264,6.26545214876067,10.3071747758493 3 17.164910280999518 45.28334414823642 28.71071722362644 EZB FALSE 34 -02-28397_tumorA ST2 0.6584685576946493 3.5072928561053938 1793,566,35 0.257,0.26,0.274 BN2,ST2,ST2 3.89490028529359,7.50932141389422 6 26.33738934915046 11.404221699187813 7.509321413894222 ST2 TRUE 35 -02-28397_tumorB MCD 0.3904014966866076 8.264562306722606 1631,175,2211 0.254,0.31,0.342 MCD,N1,BN2 2.92293221347562,3.93962518136516,3.22865694424684 7 32.55927777652574 10.091214339087621 3.9396251813651633 Other FALSE 36 -03-10440_tumorA BN2 1 1.2659983892194624 987,754,1113,1381,1204 0.188,0.199,0.224,0.292,0.309 BN2,BN2,BN2,BN2,BN2 21.4801136923469 6 27.193789334762048 21.480113692346862 21.480113692346862 Other FALSE 37 -03-10440_tumorB BN2 1 1.7322193168310942 1381,987,52,754 0.176,0.215,0.249,0.292 BN2,BN2,BN2,BN2 17.7630938022294 7 30.76957421090452 17.763093802229438 17.763093802229438 Other FALSE 38 -03-11110T EZB 1 0 39,174,1889,93,2016,240,1081,1485,558,1560,1138 0.135,0.176,0.2,0.202,0.204,0.205,0.213,0.215,0.219,0.238,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0741921961292 0 0 55.07419219612916 55.07419219612916 EZB TRUE 39 -03-19103_tumorB EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 40 -03-19969_tumorB BN2 1 0.3629502098026451 687,1957,1973,99,41,1765,519,2069 0.147,0.148,0.171,0.187,0.202,0.226,0.244,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.2908844073868 3 15.349485368400453 42.290884407386805 42.290884407386805 BN2 TRUE 41 -03-23488_tumorA EZB 0.9050189809221849 0.3071656426190534 42,155,1933,639,16,1533,308,107,233 0.135,0.152,0.172,0.178,0.195,0.204,0.214,0.216,0.217 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.68191422828926,44.6112421701856 2 13.703040869239285 49.2931563984749 44.61124217018563 EZB TRUE 42 -03-23488_tumorB EZB 1 0.08724175462583297 1796,43,326,48,83,164,503,47,602,10 0.185,0.213,0.231,0.238,0.251,0.256,0.259,0.275,0.275,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.2279751960237 1 3.596800895771427 41.22797519602369 41.22797519602369 EZB TRUE 43 -FL1001T2 EZB 1 0 1809,168,227,86,1041,78,645,1094,9,329,138 0.158,0.207,0.21,0.231,0.251,0.258,0.276,0.286,0.287,0.303,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.3129081965523 0 0 45.31290819655225 45.31290819655225 EZB TRUE 44 -03-33266_tumorA EZB 1 0.22543415944697467 662,1060,75,259,319,320,205,1551,178 0.133,0.178,0.185,0.192,0.197,0.205,0.206,0.213,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.4719364629401 2 10.701796093843093 47.471936462940114 47.471936462940114 EZB TRUE 45 -03-33266_tumorB EZB 1 0.3419754149434457 1933,107,155,233,639,1606,1533,42 0.135,0.136,0.15,0.167,0.196,0.205,0.232,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.9631557298334 3 15.718269252819997 45.96315572983342 45.96315572983342 Other FALSE 46 -04-11038_tumorB EZB 1 0 47,1220,2061,43,1691,1796,448,83,10,503,48 0.21,0.224,0.229,0.239,0.253,0.261,0.267,0.285,0.286,0.299,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.964945692687 0 0 42.964945692686996 42.964945692686996 EZB TRUE 47 -04-11038_tumorA EZB 1 0 503,602,48,10,128,1796,116,277,326,691,366 0.108,0.133,0.176,0.19,0.234,0.264,0.269,0.28,0.297,0.325,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5511385951136 0 0 52.5511385951136 52.5511385951136 EZB TRUE 48 -04-13783_tumorB MCD 1 0.09401827849300655 214,883,1732,1127,1728,154,749,997,790,485 0.149,0.206,0.208,0.213,0.249,0.254,0.267,0.294,0.303,0.311 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.6668420897313 1 4.011463042009493 42.66684208973132 42.66684208973132 MCD TRUE 49 -04-14066_tumorA N1 0.8291542008177949 2.024480111062496 697,2077,3,1697 0.12,0.18,0.239,0.269 N1,N1,N1,BN2 3.71858892067717,18.0471725933729 7 36.536141976195644 21.76576151405009 18.04717259337292 Other FALSE 50 -04-14066_tumorB N1 0.81915070360382 1.542698283801635 2077,697,1697,3 0.123,0.146,0.236,0.238 N1,N1,BN2,N1 4.23316632038931,19.173982088504 5 29.579669261578356 23.407148408893278 19.173982088503966 Other FALSE 51 -04-14093_tumorB BN2 0.6725097636153107 4.013031873103741 52,1131,1381 0.167,0.216,0.285 BN2,ST2,BN2 9.49098412853888,4.62179852835042 8 38.08762181494826 14.112782656889301 9.49098412853888 BN2 TRUE 52 -04-14093_tumorA BN2 0.7471364901635413 2.206925621937667 52,1381,1131 0.133,0.255,0.259 BN2,BN2,ST2 11.4289950742863,3.86806941761744 5 25.22294206244177 15.297064491903711 11.428995074286275 Other FALSE 53 -04-16803_tumorB EZB 1 0.15663203453059468 1437,1241,1066,408,1372,958,2003,1242,54,1814 0.145,0.165,0.182,0.214,0.219,0.229,0.243,0.258,0.259,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.7075399442298 1 7.47252904391432 47.70753994422975 47.70753994422975 EZB TRUE 54 -04-21856_tumorA EZB 1 0.10588307612027845 55,1180,1305,1457,1517,1219,1853,1570,278,1590 0.125,0.13,0.143,0.151,0.185,0.206,0.215,0.215,0.216,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.1128841422838 1 6.1531709352063535 58.11288414228376 58.11288414228376 EZB TRUE 55 -04-21856_tumorB EZB 1 0.11045050880148016 75,259,662,178,1551,2013,2072,320,56,205 0.104,0.162,0.17,0.193,0.195,0.201,0.207,0.207,0.221,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.5289788176351 1 6.133203963634407 55.528978817635064 55.528978817635064 EZB TRUE 56 -04-24061_tumorB BN2 1 0.5520646080396314 57,2064,106,1014,1632,817,889 0.147,0.151,0.157,0.231,0.256,0.261,0.285 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.3663771249907 4 19.524525125289784 35.36637712499071 35.36637712499071 BN2 TRUE 57 -04-24061_tumorA BN2 1 0.5346643717343452 57,2064,106,817,1014,1632 0.106,0.108,0.207,0.256,0.282,0.296 BN2,BN2,BN2,BN2,BN2,BN2 34.4042835067503 4 18.394744626106927 34.40428350675027 34.40428350675027 Other FALSE 58 -04-24937T MCD 1 0 798,1331,23,2008,21,2034,1699,59,709,305,905 0.114,0.114,0.117,0.122,0.133,0.139,0.139,0.153,0.173,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.9172271950256 0 0 79.91722719502563 79.91722719502563 MCD TRUE 59 -04-28140T ST2 0.5671892619509608 5.64290503980861 1436,827 0.222,0.291 ST2,BN2 3.43471241489171,4.5011175286402 5 25.399378787134662 7.935829943531913 4.501117528640201 Other FALSE 60 -04-29264T ST2 0.746582025024119 3.450346524168387 35,1971,566 0.15,0.292,0.294 ST2,BN2,ST2 3.42273619742412,10.0835519723471 8 34.791748499059096 13.50628816977122 10.083551972347099 Other FALSE 61 -04-32952_tumorC EZB 0.8581292466805929 0.810714706744607 179,62,1053,993,2126,1453,1318 0.179,0.188,0.194,0.214,0.227,0.236,0.279 EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.68059136346981,28.3113485110719 4 22.952426605697983 32.991939874541664 28.311348511071852 EZB TRUE 62 -04-35039T N1 1 0.8938438126013311 63,2189,745,2175,891,814 0.146,0.177,0.193,0.197,0.241,0.247 N1,N1,N1,N1,N1,N1 31.0139232169994 5 27.721603372007667 31.013923216999384 31.013923216999384 N1 TRUE 63 -05-12939T BN2 0.4255834657449445 8.359533804108626 286,1425,147 0.197,0.267,0.322 BN2,N1,MCD 5.06840656582215,3.10082072091348,3.74008539520884 8 42.36951601995638 11.909312681944478 5.06840656582215 Other FALSE 64 -05-12963_tumorB EZB 0.39117033376552524 2.9433041741325545 65,2127,1108 0.162,0.205,0.213 EZB,ST2,BN2 4.69785244895444,6.15829472506386,4.88710887963544 3 18.12573456981895 15.743256053653742 6.158294725063859 EZB TRUE 65 -FL1003T2 EZB 1 0.14812375319544935 1750,1752,1671,109,818,398,1972,397 0.197,0.21,0.211,0.247,0.273,0.29,0.306,0.318 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.1797858569527 1 4.766590658157672 32.17978585695269 32.17978585695269 Other FALSE 66 -05-17793T EZB 1 0 1020,427,366,78,645,161,1090,689,1589,48,326 0.15,0.151,0.173,0.232,0.242,0.278,0.287,0.288,0.329,0.331,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0379274211425 0 0 47.03792742114254 47.03792742114254 Other FALSE 67 -05-18426T ST2 0.47021024415328855 1.2626817128016632 816,2138,781,911,955,1486,508 0.13,0.147,0.201,0.225,0.23,0.232,0.238 EZB,EZB,MCD,ST2,ST2,ST2,ST2 14.5195668372276,4.97789685934056,17.304802639079 4 21.850457836007067 36.802266335647246 17.304802639079043 ST2 TRUE 68 -05-21634T MCD 1 0.14438679306533414 69,900,799,2036,765,824,1526,1412,1225,296 0.135,0.188,0.212,0.215,0.224,0.233,0.266,0.296,0.301,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.5660466128713 1 6.43474855003268 44.56604661287127 44.56604661287127 MCD TRUE 69 -05-22052T BN2 1 1.1359935539138326 70,1841,1813,1148,804 0.124,0.141,0.164,0.182,0.207 BN2,BN2,BN2,BN2,BN2 31.5574451819552 6 35.849054304690256 31.55744518195521 31.55744518195521 BN2 TRUE 70 -05-23110T EZB 1 0 2097,644,1109,71,1236,240,1079,2016,1589,522,1217 0.179,0.213,0.228,0.246,0.283,0.295,0.316,0.33,0.334,0.335,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.7287448535846 0 0 40.72874485358456 40.72874485358456 EZB TRUE 71 -05-24006T EZB 1 0.07975469799940936 699,477,1450,1542,520,1160,72,1539,1289,1964 0.116,0.125,0.132,0.147,0.196,0.206,0.21,0.234,0.242,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7971687685592 1 4.609595740357336 57.79716876855923 57.79716876855923 EZB TRUE 72 -05-24065T BN2 0.6173370861909108 1.776335395786444 674,1664 0.147,0.237 BN2,ST2 6.79958654592668,4.21479554454749 2 12.078346258242846 11.014382090474172 6.799586545926678 Other FALSE 73 -05-24395T ST2 1 5.55637543793822 90,1946 0.194,0.228 ST2,ST2 9.55778131425701 9 53.10662133572251 9.5577813142570065 9.5577813142570065 Other FALSE 74 -05-24401T EZB 0.908875887109498 0.3188592221484468 319,320,2059,1551,259,178,149,1088,1541 0.143,0.15,0.176,0.176,0.192,0.212,0.216,0.219,0.226 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 44.1355189999257,4.4250376458118 2 14.073017257434305 48.560556645737506 44.1355189999257 EZB TRUE 75 -05-24666T EZB 1 0.07043073286633095 117,1048,76,2047,108,2063,1573,1281,172,1138 0.155,0.156,0.177,0.19,0.196,0.205,0.212,0.274,0.283,0.362 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4655026534399 1 3.4134608706168823 48.46550265343994 48.46550265343994 EZB TRUE 76 -05-24904T EZB 0.6747908854554445 0.7114379253797524 77,282,994,491,1975,1425 0.145,0.178,0.23,0.245,0.263,0.278 EZB,BN2,EZB,EZB,EZB,N1 5.6148782776113,19.1130246785607,3.59646402999518 3 13.597730625047257 28.324366986167213 19.113024678560734 EZB TRUE 77 -05-25439T EZB 1 0 366,1020,427,689,78,48,128,645,326,161,164 0.148,0.177,0.217,0.252,0.261,0.272,0.299,0.303,0.312,0.323,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.4734538749754 0 0 44.473453874975434 44.473453874975434 EZB TRUE 78 -05-25674T EZB 1 0 277,731,275,116,28,712,438,128,691,602,10 0.157,0.187,0.209,0.252,0.256,0.266,0.268,0.268,0.297,0.305,0.306 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5297895668937 0 0 45.529789566893655 45.529789566893655 EZB TRUE 79 -05-27675_tumorB EZB 0.919904497502712 0 187,404,2059,80,17,121,1537,1541,149,1088,319 0.132,0.15,0.152,0.154,0.16,0.163,0.189,0.193,0.195,0.202,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 59.5437180942268,5.1844338561894 0 0 64.72815195041618 59.543718094226776 EZB TRUE 80 -05-32150_tumorB MCD 0.5814716004085125 3.216178963677871 81,82,491,401 0.189,0.217,0.277,0.284 MCD,MCD,EZB,EZB 7.12333087158749,9.89661539380419 7 31.829286241163633 17.019946265391688 9.896615393804192 MCD TRUE 81 -05-32150T MCD 0.5922591795789616 3.3206861945662207 81,82,401,491 0.2,0.205,0.291,0.298 MCD,MCD,EZB,EZB 6.79497541310527,9.86996239245634 7 32.77504785751757 16.664937805561618 9.869962392456344 MCD TRUE 82 -05-32762T EZB 1 0.08324965194485405 137,435,2104,83,1511,43,2006,47,1539,138 0.202,0.206,0.211,0.223,0.226,0.229,0.274,0.281,0.282,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.0148121819066 1 3.4977184906721357 42.01481218190657 42.01481218190657 EZB TRUE 83 -05-32947T BN2 0.40009214616448496 2.580561275247409 636,2121,476,1190,15 0.252,0.276,0.324,0.344,0.364 ST2,BN2,MCD,BN2,EZB 6.53635605131023,2.74511974097415,3.08266112766982,3.97298969942966 4 16.86746730724024 16.337126619383863 6.536356051310227 Other FALSE 84 -06-10398T MCD 1 4.690241810280954 641,743 0.233,0.249 MCD,MCD 8.31426493142133 7 38.99591300310502 8.314264931421327 8.314264931421327 Other FALSE 85 -FL1018T2 EZB 1 0.08278331633934188 86,1041,168,138,1094,2006,329,2104,1511,164 0.115,0.122,0.147,0.168,0.178,0.179,0.185,0.238,0.241,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6981209581391 1 4.859225115802583 58.6981209581391 58.6981209581391 EZB TRUE 86 -06-11535T N1 1 1.635794572535611 697,3,2077 0.201,0.21,0.251 N1,N1,N1 13.710826157257 4 22.42809501302023 13.710826157256966 13.710826157256966 Other FALSE 87 -06-11677_tumorA BN2 0.910800349479615 0.09760687161765116 88,1477,454,872,836,1212,294,1213,669,560 0.165,0.167,0.188,0.206,0.215,0.241,0.249,0.256,0.26,0.268 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2 42.3635726487913,4.14889594325051 1 4.134975796795609 46.5124685920418 42.36357264879128 BN2 TRUE 88 -06-11677_tumorB ST2 0.6413180050290375 2.048647949415614 838,718,572,419,1317 0.202,0.223,0.254,0.315,0.321 ST2,ST2,BN2,ST2,BN2 7.0502164945597,12.6056809115269 6 25.82460235038707 19.65589740608657 12.605680911526871 ST2 TRUE 89 -06-14634T ST2 1 5.809576157145324 90,1946 0.216,0.254 ST2,ST2 8.55621567470737 9 49.707986579173 8.556215674707365 8.556215674707365 ST2 TRUE 90 -FL1012T2 BN2 1 7.057572307807639 1807 0.237 BN2 4.2168574920497 5 29.760776661861108 4.216857492049696 4.216857492049696 Other FALSE 91 -06-15256T ST2 0.7000843591547049 4.173396450982474 1653,451,1535 0.254,0.287,0.314 ST2,ST2,EZB 3.1828620002552,7.42966221250172 8 31.006925909653262 10.61252421275692 7.429662212501718 Other FALSE 92 -06-15922T EZB 1 0.10532161075759579 93,1081,39,1138,1560,2063,174,558,382,1889 0.18,0.184,0.196,0.207,0.213,0.216,0.23,0.233,0.237,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.088438657728 1 4.959430207492153 47.08843865772798 47.08843865772798 EZB TRUE 93 -06-16041_tumorB BN2 0.6481711894724355 6.558717323559022 101,446 0.174,0.321 BN2,MCD 5.7356875871993,3.11334439749557 9 37.61875354068647 8.849031984694863 5.735687587199296 Other FALSE 94 -06-16716T MCD 1 5.674780718395486 1631 0.222 MCD 4.51153615091356 4 25.601978359548475 4.511536150913563 4.511536150913563 Other FALSE 95 -06-18449_tumorB EZB 0.887426213574401 0.12445654388864115 166,341,365,96,105,1245,1244,340,1581,1243 0.117,0.147,0.158,0.169,0.173,0.194,0.204,0.226,0.235,0.239 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 6.33785085793574,49.9616755164789 1 6.218057461666706 56.29952637441465 49.96167551647891 EZB TRUE 96 -06-19919T BN2 1 1.4391892776139859 987,1381,754,1113 0.226,0.254,0.257,0.275 BN2,BN2,BN2,BN2 15.8981616775277 5 22.880463820071434 15.898161677527693 15.898161677527693 Other FALSE 97 -06-22057T BN2 1 0.0844124250876273 99,423,1973,100,1496,1957,687,41,2085 0.151,0.153,0.161,0.174,0.179,0.251,0.257,0.257,0.269 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.2104407986311 1 3.900735372180684 46.210440798631105 46.210440798631105 BN2 TRUE 98 -06-22314_tumorB BN2 1 0 1496,423,100,943,111,266,2085,1929,1973,99 0.15,0.175,0.176,0.219,0.238,0.239,0.24,0.245,0.266,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.6962396449445 0 0 46.696239644944455 46.696239644944455 BN2 TRUE 99 -06-22314_tumorA BN2 1 0 423,100,1496,1973,99,41,2085,687,266,943,1929 0.114,0.114,0.119,0.2,0.212,0.255,0.273,0.281,0.283,0.287,0.291 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 57.3254582903814 0 0 57.32545829038141 57.32545829038141 BN2 TRUE 100 -06-23907T BN2 0.36359346102250656 6.603219455516188 101,140,446 0.266,0.279,0.335 BN2,EZB,MCD 3.75864664673855,3.5904769731344,2.98837429928212 7 24.81916866415468 10.33749791915507 3.7586466467385513 BN2 TRUE 101 -06-24255_tumorD EZB 1 0 1691,1220,2061,448,385,47,102,118,5,1468,43 0.124,0.135,0.147,0.164,0.169,0.182,0.227,0.306,0.313,0.332,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.5882589997249 0 0 56.588258999724935 56.588258999724935 EZB TRUE 102 -06-24255_tumorC EZB 1 0 54,1242,1372,1253,408,958,468,1872,79,1287,1080 0.151,0.155,0.19,0.203,0.213,0.238,0.244,0.254,0.258,0.282,0.283 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.1636785569501 0 0 51.16367855695005 51.16367855695005 EZB TRUE 103 -06-24915T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 104 -06-24925T EZB 1 0.11270925882557234 105,1243,1245,505,1581,341,1782,166,1244,1668 0.154,0.157,0.162,0.175,0.185,0.196,0.213,0.214,0.253,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.3100129479647 1 5.895822788521191 52.31001294796468 52.31001294796468 EZB TRUE 105 -06-25674T BN2 1 0.5318528977377217 106,2064,57,1014,1632,889,817 0.146,0.161,0.162,0.22,0.232,0.282,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.0959101215921 4 18.665861496911376 35.09591012159206 35.09591012159206 BN2 TRUE 106 -06-29769_tumorB EZB 1 0.18069557627290878 443,1034,354,660,548,192,289,1060,333 0.146,0.167,0.182,0.192,0.25,0.28,0.287,0.322,0.324 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.765628480072 2 7.366168730333905 40.76562848007196 40.76562848007196 EZB TRUE 107 -06-30025T EZB 1 0.08618209102885231 108,172,1573,117,2063,1048,2089,76,676,2047 0.129,0.16,0.162,0.19,0.246,0.257,0.286,0.3,0.307,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.704906081603 1 4.0251264674187075 46.704906081603 46.704906081603 EZB TRUE 108 -06-30145T EZB 1 0.24645294216537156 109,818,1750,1527,1959,165,110,1752,1292 0.161,0.183,0.206,0.234,0.266,0.28,0.321,0.358,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.7926533362361 2 9.067657664785955 36.79265333623608 36.79265333623608 EZB TRUE 109 -06-33777T EZB 1 0 1573,108,117,172,1752,1750,1048,110,76,818,2063 0.149,0.193,0.2,0.217,0.303,0.304,0.307,0.313,0.317,0.322,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.9259189029181 0 0 43.92591890291808 43.92591890291808 EZB TRUE 110 -06-34043T BN2 1 0.24945926400743962 1014,106,889,1935,1982,111,1632,579,57 0.176,0.18,0.188,0.197,0.232,0.241,0.251,0.255,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.8944715511609 2 10.450964039133218 41.89447155116091 41.89447155116091 BN2 TRUE 111 -07-10483T EZB 1 0 112,1468,1483,396,211,1663,390,336,1384,337,210 0.121,0.212,0.218,0.237,0.245,0.254,0.26,0.261,0.268,0.273,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.5231882515835 0 0 48.523188251583505 48.523188251583505 EZB TRUE 112 -07-17613T MCD 1 7.836142198344437 311 0.215 MCD 4.66065235650872 6 36.521534602651414 4.660652356508719 4.660652356508719 Other FALSE 113 -FL1013T2 EZB 1 2.1245485815759455 532,1182,1967 0.17,0.185,0.318 EZB,EZB,EZB 14.4371280932338 8 30.672380012510033 14.437128093233765 14.437128093233765 EZB TRUE 114 -07-25012T BN2 0.6230622896039634 1.0740348762569252 1816,1666,591,610,115 0.159,0.205,0.236,0.237,0.246 BN2,EZB,BN2,BN2,EZB 14.7689744248348,8.93487456628677 3 15.862393618819151 23.703848991121585 14.768974424834813 EZB FALSE 115 -07-25994_tumorC EZB 1 0 10,116,602,503,277,691,275,128,48,118,28 0.149,0.164,0.175,0.2,0.215,0.227,0.235,0.253,0.283,0.288,0.315 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9896496879254 0 0 50.989649687925414 50.989649687925414 EZB TRUE 116 -07-30628T EZB 1 0.07537405108026556 1573,172,108,117,1752,1048,2063,1972,2089,1750 0.152,0.172,0.177,0.211,0.293,0.308,0.312,0.315,0.316,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.0345735077044 1 3.1683160907068886 42.0345735077044 42.0345735077044 EZB TRUE 117 -07-31833T EZB 1 0 10,116,602,691,503,275,277,118,128,368,48 0.135,0.156,0.193,0.204,0.216,0.241,0.249,0.25,0.29,0.292,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9626897535504 0 0 50.962689753550386 50.962689753550386 EZB TRUE 118 -07-32561_tumorB EZB 1 0.13406668113291045 119,1668,520,477,1295,505,1542,1450,699,1782 0.149,0.169,0.18,0.247,0.253,0.257,0.266,0.272,0.273,0.28 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7113757373131 1 5.994305753988111 44.71137573731315 44.71137573731315 EZB TRUE 119 -07-35482T Other 1 10 0 0 1 2 Other TRUE 120 -07-40648_tumorA EZB 1 0.2235018194623753 42,333,1083,589,231,332,122,1705,639 0.19,0.194,0.202,0.207,0.22,0.22,0.223,0.232,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.3967268272381 2 9.475745585137009 42.39672682723808 42.39672682723808 EZB TRUE 121 -07-40648_tumorB EZB 1 0.1154014223440891 42,231,575,1083,589,122,343,16,332 0.169,0.182,0.189,0.191,0.192,0.194,0.207,0.214,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3452251838716 1 5.34830490507588 46.34522518387159 46.34522518387159 EZB TRUE 122 -FL1005T2 EZB 0.5670988109592271 1.693437498486674 561,141,383,844,2042 0.117,0.117,0.211,0.211,0.243 EZB,BN2,EZB,BN2,EZB 13.2966162279313,17.4185136089585 5 29.497164113310745 30.7151298368898 17.418513608958484 Other FALSE 123 -07-41887_tumorB EZB 0.9075457687985699 0.21655136545597126 575,231,343,42,122,16,589,1083,308 0.151,0.166,0.172,0.184,0.186,0.191,0.198,0.2,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.53694399118939,44.5353800358716 2 9.644197357868595 49.07232402706098 44.53538003587159 Other FALSE 124 -07-41887_tumorA EZB 0.9004151136971338 0.32788571746491624 42,155,16,1933,639,1533,308,343,107 0.114,0.175,0.182,0.195,0.196,0.2,0.204,0.207,0.239 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.89826814801916,44.2885947371994 2 14.52159766091955 49.186862885218595 44.28859473719943 Other FALSE 125 -08-10249_tumorA EZB 1 13.301099499985503 1535 0.298 EZB 3.35275370318615 9 44.59531060502389 3.3527537031861536 3.3527537031861536 Other FALSE 126 -08-10249_tumorB EZB 1 7.147380398620256 1535 0.245 EZB 4.08254836008243 6 29.179526125272403 4.082548360082426 4.082548360082426 Other FALSE 127 -08-13706T EZB 1 0 602,128,503,277,10,48,116,275,712,691,689 0.16,0.168,0.17,0.209,0.212,0.232,0.251,0.299,0.304,0.316,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7461643438647 0 0 48.746164343864734 48.746164343864734 EZB TRUE 128 -08-15460_tumorB BN2 1 0.8381695611571016 284,130,2213,973,412,1210 0.133,0.204,0.238,0.246,0.257,0.263 BN2,BN2,BN2,BN2,BN2,BN2 28.412334838745 5 23.814354223239484 28.41233483874495 28.41233483874495 Other FALSE 129 -08-15460T BN2 0.7136510315418181 0 13,1086,932,412,157,1145,1076,590,130,838 0.165,0.199,0.201,0.204,0.22,0.221,0.223,0.25,0.257,0.265 BN2,BN2,EZB,BN2,BN2,BN2,EZB,BN2,BN2,ST2 32.9441928593449,9.45130549758838,3.76738995021331 0 0 46.16288830714659 32.944192859344895 BN2 TRUE 130 -08-15555T MCD 0.5228875362771097 1.1592878545333272 151,736,2135,661,150,1199 0.147,0.151,0.203,0.241,0.257,0.289 MCD,BN2,MCD,BN2,MCD,BN2 14.2310298769682,15.5963818110939 4 18.0806960082656 29.827411688062092 15.596381811093853 Other FALSE 131 -08-17645_tumorA BN2 0.4477160069886154 0.3628730903243692 2140,972,132,133,1093,24,1070,1183,384 0.107,0.11,0.115,0.197,0.209,0.255,0.267,0.307,0.309 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2,EZB 22.9167996006093,15.6720549651122,12.5971634521512 2 8.31588989141736 51.186018017872684 22.91679960060928 BN2 TRUE 132 -08-17645_tumorB BN2 0.4573734761895185 0.4094926647343321 2140,972,132,133,1093,24,1070,1183 0.113,0.128,0.13,0.186,0.223,0.24,0.262,0.288 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2 20.867929083142,12.4794465333507,12.2782020995511 2 8.545263887742902 45.62557771604392 20.86792908314204 BN2 TRUE 133 -08-19764T EZB 1 0.09155726236412462 1066,958,1437,1914,1372,1172,468,1242,2132,54 0.126,0.149,0.167,0.171,0.212,0.213,0.225,0.226,0.23,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.418054618328 1 4.890810841671399 53.41805461832804 53.41805461832804 Other FALSE 134 -08-24058_tumorB EZB 1 0 679,135,617,103,1080,513,162,1040,468,1867,1253 0.119,0.137,0.145,0.151,0.159,0.165,0.204,0.226,0.229,0.238,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.4771752087652 0 0 63.47717520876518 63.47717520876518 EZB TRUE 135 -08-24058_tumorA EZB 1 1.2321225078017801 136,1397,1268,1879,2114 0.203,0.285,0.298,0.306,0.323 EZB,EZB,EZB,EZB,EZB 18.1659636727475 6 22.382692717101644 18.165963672747466 18.165963672747466 EZB TRUE 136 -08-29440_tumorA EZB 1 0 1511,2006,138,2104,1041,168,86,137,435,9,329 0.169,0.181,0.19,0.195,0.196,0.201,0.203,0.207,0.224,0.226,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8621248701047 0 0 54.862124870104715 54.862124870104715 EZB TRUE 137 -08-29440_tumorB EZB 1 0 1511,2006,138,2104,1041,168,86,137,435,9,329 0.169,0.181,0.19,0.195,0.196,0.201,0.203,0.207,0.224,0.226,0.232 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8621248701047 0 0 54.862124870104715 54.862124870104715 EZB TRUE 138 -08-33625T EZB 0.8917329222499222 0.12102316942806936 11,343,308,16,143,1533,575,139,218,1058 0.139,0.163,0.178,0.185,0.186,0.205,0.221,0.225,0.233,0.25 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.620236410505,46.2906170760801 1 5.6022371933283175 51.91085348658506 46.29061707608006 EZB TRUE 139 -09-11467T MCD 0.5439302074064405 5.727052082592686 140,446,768 0.173,0.281,0.299 EZB,MCD,MCD 5.78871251761582,6.90388982444415 8 39.53893659707333 12.69260234205997 6.903889824444152 EZB FALSE 140 -09-12737T EZB 0.5873650490644317 1.4995027535634688 561,141,844,383,2042 0.107,0.129,0.188,0.193,0.245 EZB,BN2,BN2,EZB,EZB 13.0933706471529,18.6377529948611 4 27.947361936029964 31.731123642013948 18.63775299486107 BN2 FALSE 141 -09-12864T EZB 1 0 1937,316,1962,463,1033,1264,372,369,1886,428,142 0.139,0.146,0.155,0.157,0.158,0.172,0.174,0.175,0.18,0.183,0.188 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.7628219002591 0 0 66.76282190025907 66.76282190025907 EZB TRUE 142 -09-15842_tumorB EZB 0.8961958995041572 0.133546370341843 11,143,308,343,16,218,139,1533,1058,575 0.13,0.176,0.187,0.192,0.204,0.204,0.207,0.209,0.239,0.255 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.34601100127851,46.1549506730881 1 6.163826135697725 51.50096167436665 46.154950673088145 EZB TRUE 143 -09-15842_tumorA EZB 1 0.07244338629902349 139,1058,143,218,220,1587,219,144,11,1291 0.116,0.127,0.149,0.173,0.174,0.178,0.181,0.216,0.248,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.5394373070382 1 4.240795070561232 58.539437307038156 58.539437307038156 EZB TRUE 144 -09-16981T BN2 1 0.1251748083071783 1375,417,564,592,1027,560,1193,931,145 0.168,0.185,0.196,0.209,0.229,0.229,0.233,0.242,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.4480987688826 1 5.313432626399047 42.448098768882566 42.448098768882566 BN2 TRUE 145 -FL1019T2 EZB 1 0.2804740733422566 42,639,155,1933,1537,233,17,16,107 0.149,0.16,0.169,0.186,0.215,0.216,0.227,0.227,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7464114206909 2 13.111156425294153 46.74641142069087 46.74641142069087 Other FALSE 146 -09-21480T BN2 0.473864744101632 4.712000828648826 147,834,1763,286 0.256,0.282,0.303,0.341 MCD,BN2,EZB,BN2 6.48178680529214,3.29571900937525,3.90105306943047 7 30.542184797661584 13.678558884097864 6.481786805292139 MCD FALSE 147 -09-27193T EZB 1 0 668,148,1047,700,1237,1078,299,12,424,1238,439 0.109,0.142,0.148,0.159,0.16,0.172,0.194,0.197,0.232,0.243,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.1192002779169 0 0 64.11920027791692 64.11920027791692 EZB TRUE 148 -09-27866_tumorB EZB 1 0.0798552724996746 1705,121,404,149,80,17,333,187,1537,2059 0.134,0.149,0.151,0.16,0.17,0.184,0.19,0.197,0.22,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6889692266406 1 4.606768357818724 57.68896922664055 57.68896922664055 EZB TRUE 149 -09-31008_tumorB MCD 0.6361499490218393 1.4340530141164496 151,150,661,736,2135 0.146,0.174,0.2,0.239,0.29 MCD,MCD,BN2,BN2,MCD 9.17653391836192,16.0441136909817 5 23.008109597379367 25.220647609343665 16.044113690981746 MCD TRUE 150 -09-31008_tumorA MCD 0.6617488788839708 0.34186052404446454 151,150,736,661,2135 0.14,0.182,0.231,0.244,0.261 MCD,MCD,BN2,BN2,MCD 8.43829717091096,16.5085445219401 1 5.643619681481809 24.94684169285104 16.508544521940077 MCD TRUE 151 -09-31233T MCD 1 2.6953559889956287 768,801,1994 0.139,0.23,0.236 MCD,MCD,MCD 15.7578572295961 8 42.473034857529896 15.757857229596093 15.757857229596093 Other FALSE 152 -FL1020T2 EZB 1 0 2016,240,174,558,39,93,382,1138,153,1903,287 0.11,0.139,0.157,0.175,0.207,0.214,0.22,0.224,0.224,0.238,0.243 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.6796016452163 0 0 59.67960164521633 59.67960164521633 EZB TRUE 153 -09-31601_tumorA MCD 0.914384243223882 0.2444552132263783 1728,214,154,997,1531,789,1127,1501,1732 0.17,0.177,0.198,0.207,0.231,0.235,0.254,0.286,0.292 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.49824896534811,37.3616242294413 2 9.133243817491895 40.85987319478941 37.3616242294413 MCD TRUE 154 -09-31895T EZB 0.8998510686529435 0.11390678674468062 343,16,42,575,308,231,1533,11,122,589 0.143,0.156,0.17,0.172,0.185,0.199,0.203,0.218,0.221,0.234 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.39090420712618,48.4379697969759 1 5.517413496009416 53.82887400410209 48.437969796975914 EZB TRUE 155 -09-32452_tumorA MCD 1 0.09933158895356725 837,156,1491,1968,767,788,471,1422,950,1224 0.112,0.141,0.148,0.152,0.154,0.16,0.161,0.161,0.163,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.58192180386 1 6.613688088359578 66.58192180386 66.58192180386 MCD TRUE 156 -09-33003T BN2 0.7472353592774243 0.1154338436066498 932,157,412,13,1086,1076,590,130,1806,973 0.103,0.119,0.159,0.176,0.189,0.243,0.247,0.257,0.276,0.288 EZB,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2,BN2 40.7476367906905,13.7835578118955 1 4.703656332637139 54.53119460258604 40.74763679069052 BN2 TRUE 157 -09-33003_tumorB BN2 0.8627104306185912 1.217841602701515 158,594,922,1735,2041,903 0.178,0.185,0.213,0.233,0.266,0.275 BN2,BN2,BN2,BN2,ST2,BN2 23.6525037857372,3.76399999847877 5 28.805003118325796 27.41650378421593 23.65250378573716 BN2 TRUE 158 -09-37629T ST2 0.6528411662479223 1.9493280267073023 1123,234,1176,649,235,293 0.211,0.213,0.238,0.238,0.252,0.255 EZB,ST2,ST2,EZB,ST2,ST2 8.92804066408947,16.7894113955214 5 32.72807018520879 25.717452059610856 16.789411395521384 Other FALSE 159 -09-41082T EZB 0.7457307165496786 1.4229049411043808 160,1944,1439,388,242 0.157,0.172,0.183,0.258,0.283 ST2,EZB,EZB,EZB,EZB 18.6677965318511,6.36509552632053 6 26.562499924702113 25.032892058171605 18.66779653185107 ST2 FALSE 160 -09-41114T EZB 1 0.20645204620682625 333,1705,1083,589,332,42,121,122,231 0.158,0.2,0.212,0.218,0.221,0.222,0.231,0.24,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2792549090423 2 8.728638688071792 42.27925490904232 42.27925490904232 EZB TRUE 161 -10-10826T EZB 1 0 2132,1914,1172,468,162,1570,1779,278,958,1080,103 0.112,0.15,0.153,0.181,0.188,0.205,0.207,0.209,0.209,0.218,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2650050392373 0 0 61.265005039237295 61.265005039237295 EZB TRUE 162 -10-11584_tumorA EZB 0.6399018128462339 1.6050818727511709 1703,1392,1493,1122,1528,696 0.205,0.232,0.238,0.259,0.269,0.303 ST2,EZB,EZB,BN2,EZB,EZB 3.85506017029669,15.5256001666062,4.88181220954844 5 24.919859391002117 24.2624725464513 15.525600166606168 Other FALSE 163 -10-11584_tumorB EZB 1 0.1349977670255197 164,329,1094,326,83,138,2006,1796,43,435 0.147,0.148,0.161,0.17,0.177,0.178,0.185,0.192,0.199,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.5001777763551 1 7.762395603378356 57.50017777635514 57.50017777635514 EZB TRUE 164 -10-11741_tumorB EZB 0.769046627294242 0.3363632574885211 1292,1446,1177,165,1959,1527,1738,2080 0.147,0.158,0.183,0.232,0.246,0.318,0.319,0.348 EZB,EZB,ST2,EZB,EZB,EZB,EZB,ST2 27.7930871604197,8.34657742429462 3 9.348573332941164 36.139664584714325 27.793087160419713 EZB TRUE 165 -10-15025T EZB 0.905136465656894 0.08544080104358007 1244,340,1042,341,365,1245,166,1581,105,1243 0.129,0.132,0.166,0.192,0.197,0.216,0.22,0.224,0.236,0.25 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.07968616943378,48.4676142195796 1 4.141111783592089 53.54730038901334 48.46761421957956 EZB TRUE 166 -10-18191T EZB 0.7750152118397449 0.3897589255489414 11,1533,308,218,16,221,343,143,139 0.2,0.212,0.218,0.235,0.248,0.26,0.267,0.269,0.279 EZB,EZB,BN2,EZB,EZB,BN2,EZB,EZB,EZB 8.4314669226507,29.0442530653381 2 11.320256868117726 37.475719987988796 29.044253065338097 Other FALSE 167 -FL1016T2 EZB 1 0.07859940918827148 1511,2104,137,435,2006,138,83,1041,329,1094 0.12,0.13,0.157,0.162,0.171,0.189,0.202,0.227,0.228,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.5541594040899 1 4.523722925489069 57.554159404089944 57.554159404089944 EZB TRUE 168 -10-27154T MCD 0.539770557705657 2.018656284698435 446,140 0.151,0.177 MCD,EZB 5.65044235090661,6.62700413912657 2 13.377643554170387 12.277446490033174 6.6270041391265675 Other FALSE 169 -10-28165T EZB 0.8884255687822131 0 433,565,170,1181,780,250,368,44,5,367,196 0.143,0.19,0.218,0.234,0.236,0.25,0.258,0.268,0.269,0.307,0.319 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.25081757008414,41.8103012971485 0 0 47.061118867232636 41.8103012971485 EZB TRUE 170 -10-31625T ST2 0.5724958537757704 0.875691381551024 648,171,1414,531,268,1174,1038 0.153,0.23,0.234,0.254,0.28,0.293,0.303 ST2,BN2,BN2,BN2,ST2,ST2,ST2 12.5550420271132,16.8131924987994 3 14.723167767556937 29.36823452591257 16.813192498799374 BN2 FALSE 171 -10-32847T EZB 1 0 1972,1752,172,398,397,1573,1671,108,695,2089,1750 0.199,0.205,0.212,0.229,0.242,0.268,0.278,0.282,0.283,0.293,0.321 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.0535146676212 0 0 44.053514667621165 44.053514667621165 EZB TRUE 172 -10-36955_tumorB EZB 1 0 287,558,382,676,2016,1903,174,1138,240,93,153 0.168,0.17,0.18,0.184,0.189,0.192,0.201,0.203,0.22,0.222,0.229 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.623320111347 0 0 56.623320111346985 56.623320111346985 EZB TRUE 173 -10-36955_tumorA EZB 1 0 558,174,2016,382,1138,93,240,676,287,39,1903 0.134,0.16,0.16,0.167,0.179,0.187,0.194,0.199,0.21,0.214,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.9142225264264 0 0 60.91422252642642 60.91422252642642 EZB TRUE 174 -10-39294_tumorA N1 0.4284139207894127 6.64887733125974 175,2211,2127 0.196,0.253,0.349 N1,BN2,ST2 3.94985558941864,5.1108728723598,2.86902597510424 8 33.98156678398345 11.929754436882686 5.110872872359804 N1 TRUE 175 -10-39294_tumorB N1 0.4506674720811514 1.5034729148073458 1609,464,176,1008,1064,659 0.155,0.164,0.206,0.222,0.232,0.244 MCD,BN2,N1,N1,N1,MCD 6.1157058941014,10.5386071341942,13.6630488279022 4 20.542023846441143 30.317361856197735 13.663048827902156 N1 TRUE 176 -10-40676T MCD 0.7210890979834268 4.5043048096952 751,81,401 0.183,0.237,0.267 MCD,MCD,EZB 3.74196408912898,9.67437805480997 8 43.57634760309023 13.416342143938943 9.674378054809967 Other FALSE 177 -10-41170T EZB 0.8778708402401229 0.26132529789948544 1088,320,1541,1551,2059,178,259,319,206 0.16,0.169,0.173,0.179,0.182,0.2,0.212,0.217,0.251 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 41.5480960961948,5.78016017078658 2 10.857568589494559 47.3282562669814 41.54809609619482 EZB TRUE 178 -1001-2027_FFPE EZB 0.7709123073668266 0.8227350465721601 1053,62,179,1453,2126,993,1133 0.141,0.166,0.185,0.219,0.224,0.226,0.266 EZB,EZB,EZB,EZB,EZB,BN2,BN2 8.18953452874418,27.5589355641371 4 22.67370213483951 35.7484700928813 27.558935564137116 EZB TRUE 179 -1001-2028_plasma BN2 0.5105068601590714 6.725876978357978 793,1257 0.152,0.158 BN2,EZB 6.59646268187725,6.32493602336605 6 44.36699649063573 12.921398705243305 6.5964626818772505 Other FALSE 180 -1001-2030_FFPE MCD 0.8334894456011774 1.0492987913791716 467,1420,2028,755,647,713 0.184,0.24,0.241,0.254,0.27,0.274 MCD,BN2,MCD,MCD,MCD,MCD 4.16967424548557,20.8718269406702 5 21.900782782720466 25.041501186155763 20.871826940670193 Other FALSE 181 -1001-2030_plasma N1 0.5560050805980076 3.435472486576977 901,1387,870,1488 0.154,0.2,0.208,0.248 N1,EZB,N1,EZB 9.01524780321454,11.2895967102249 7 38.78509888252765 20.304844513439456 11.289596710224917 Other FALSE 182 -1001-2035_FFPE BN2 1 0.6689673445840012 1320,183,582,1362,489,666 0.165,0.183,0.2,0.218,0.311,0.323 BN2,BN2,BN2,BN2,BN2,BN2 27.4270524756267 5 18.347802464386056 27.42705247562671 27.42705247562671 BN2 TRUE 183 -1001-2035_plasma BN2 0.7138188106278891 2.1453168359463692 811,1232,150 0.169,0.221,0.239 BN2,BN2,MCD 10.4434300626945,4.18693538467758 4 22.404466338526966 14.63036544737208 10.443430062694503 Other FALSE 184 -1001-2036_FFPE MCD 1 0.21334644331821423 296,643,185,481,739,246,1968,465,1422 0.115,0.137,0.137,0.159,0.167,0.212,0.216,0.218,0.238 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.6589923119436 2 11.44795516179256 53.658992311943564 53.658992311943564 MCD TRUE 185 -1001-2037_plasma Other 1 10 0 0 1 2 Other TRUE 186 -1001-2038_FFPE EZB 1 0.08657248015022247 121,1705,404,80,17,149,187,333,1537,639 0.134,0.15,0.153,0.156,0.168,0.176,0.186,0.196,0.202,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6265146248437 1 5.075442773636005 58.62651462484366 58.62651462484366 EZB TRUE 187 -1001-2039_FFPE MCD 1 0 705,2094,1169,1567,515,516,540,621,1273,547 0.184,0.206,0.222,0.231,0.235,0.238,0.243,0.243,0.246,0.251 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.8913741301038 0 0 43.891374130103785 43.891374130103785 MCD TRUE 188 -1001-2040_FFPE BN2 0.746474438542931 1.661301851266305 190,189,2218 0.111,0.181,0.203 BN2,BN2,ST2 14.5221795117097,4.93217654106557 4 24.125723707224875 19.45435605277524 14.522179511709668 BN2 TRUE 189 -1001-2040_plasma BN2 0.7911250990385392 1.836482575085918 189,190,2218 0.112,0.159,0.248 BN2,BN2,ST2 15.2575528446613,4.02833868273928 6 28.020229937673136 19.285891527400626 15.257552844661344 BN2 TRUE 190 -1001-2046_FFPE EZB 1 0 31 0.151 EZB 6.63219425243297 0 0 6.632194252432974 6.632194252432974 Other FALSE 191 -1003-2043_FFPE EZB 1 0.11522367530226502 333,1083,589,332,42,122,231,1705 0.182,0.192,0.197,0.206,0.206,0.216,0.217,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.1356788544056 1 4.5093567530537495 39.1356788544056 39.1356788544056 EZB TRUE 192 -1003-2043_plasma EZB 1 0.25898302770630166 332,589,1083,122,192,231,193,575,333 0.174,0.196,0.198,0.205,0.224,0.228,0.23,0.271,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.019685597031 2 10.623402371479663 41.019685597031 41.019685597031 EZB TRUE 193 -1003-2046_FFPE MCD 0.7720014960227319 2.3003207801571643 311,194,1513,1480 0.187,0.201,0.232,0.234 MCD,MCD,EZB,MCD 4.30917214571404,14.5908296987879 6 33.56358875585604 18.900001844501915 14.590829698787871 MCD TRUE 194 -1004-2031_plasma MCD 1 1.5773304641543051 768,1994,801,990 0.143,0.186,0.208,0.234 MCD,MCD,MCD,MCD 21.4567342966488 6 33.844360667368655 21.456734296648804 21.456734296648804 Other FALSE 195 -1004-2033_FFPE EZB 1 0.12540725553558665 1788,1118,196,6,1142,438,367,44,1872,28 0.124,0.155,0.157,0.162,0.164,0.18,0.211,0.215,0.216,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2413071654907 1 7.178475234893702 57.241307165490724 57.241307165490724 EZB TRUE 196 -1005-2018_FFPE BN2 0.4564039015585308 1.3459870733569892 1763,597,834,1377 0.201,0.285,0.302,0.318 EZB,BN2,BN2,MCD 6.82008523630077,4.98053437688687,3.14247183062234 2 9.179746567253687 14.94309144380998 6.820085236300773 Other FALSE 197 -1006-2010_FFPE N1 1 1.0149383858456889 198 0.18 N1 5.55070196714242 1 5.633620494842018 5.55070196714242 5.55070196714242 N1 TRUE 198 -1007-2013_FFPE EZB 1 0 199 0.149 EZB 6.715679064374 0 0 6.715679064374003 6.715679064374003 EZB TRUE 199 -1010-2010_FFPE BN2 0.5222512830963066 4.4348885360203605 101,1631 0.306,0.335 BN2,MCD 3.26273500118426,2.98470776590721 3 14.469866052824441 6.247442767091464 3.2627350011842573 Other FALSE 200 -1010-2010_plasma MCD 0.38605177907518157 7.722841452687494 446,140,101 0.212,0.262,0.272 MCD,EZB,BN2 3.67973547980611,3.81056407035323,4.70991423802683 8 36.37392091603665 12.200213788186172 4.709914238026832 Other FALSE 201 -1011-2017_FFPE EZB 0.8761428122985854 0 202,727,1221,1132,2114,1879,1896,351,527,1268 0.156,0.17,0.191,0.199,0.218,0.235,0.236,0.243,0.252,0.26 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6815569337989,5.89237319346672 0 0 47.5739301272656 41.68155693379888 EZB TRUE 202 -1011-2018_FFPE BN2 0.7874729863421264 2.6738887375217577 554,1199,736,2135 0.223,0.283,0.323,0.334 BN2,BN2,BN2,MCD 11.1012518619364,2.9960594801359 7 29.68351232602424 14.097311342072322 11.10125186193642 Other FALSE 203 -1011-2018_plasma BN2 0.3386011025892545 1.9321765115856446 1609,176,464,554,1366,659 0.209,0.211,0.221,0.251,0.273,0.292 MCD,N1,BN2,BN2,EZB,MCD 8.50666254505482,3.6632608809523,8.20635281906585,4.74668549369598 3 16.43637356154029 25.122961738768954 8.506662545054823 Other FALSE 204 -1011-2019_FFPE EZB 1 0.2602081393428047 729,45,205,548,1060,1040,733,2100,443 0.178,0.226,0.273,0.275,0.283,0.285,0.311,0.332,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5316265027871 2 8.72520214142812 33.53162650278715 33.53162650278715 EZB TRUE 205 -1011-2020_FFPE EZB 0.8515088118563579 0.2655157725862657 206,1088,1541,2059,187,320,1551,1218,178 0.157,0.162,0.164,0.237,0.266,0.272,0.276,0.28,0.285 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 34.897089010503,6.08556263631264 2 9.265727549635384 40.98265164681564 34.897089010502995 EZB TRUE 206 -1012-2006_FFPE EZB 0.8401938399664398 0.13935283997732714 1272,1318,2083,207,1600,595,1293 0.122,0.144,0.155,0.181,0.188,0.247,0.255 EZB,EZB,BN2,EZB,EZB,EZB,EZB 6.46029087954542,33.9655029583687 1 4.7331892985069866 40.42579383791413 33.96550295836871 EZB TRUE 207 -1012-2008_FFPE EZB 1 5.7107280146291455 1601,2166 0.245,0.279 EZB,EZB 7.65904821407169 9 43.73874120149455 7.659048214071694 7.659048214071694 Other FALSE 208 -1012-2008_plasma EZB 1 3.284601446199224 1601,2166,1395 0.191,0.296,0.318 EZB,EZB,EZB 11.7618406275516 8 38.63295873522065 11.76184062755156 11.76184062755156 Other FALSE 209 -1013-2006_FFPE EZB 0.9295296580252009 0 1423,210,877,390,396,693,211,25,212,216,2073 0.153,0.172,0.175,0.184,0.217,0.261,0.263,0.269,0.284,0.286,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.51966698928167,46.4256985453476 0 0 49.945365534629225 46.42569854534756 EZB TRUE 210 -1013-2006_plasma EZB 0.9295296580252009 0 1423,210,877,390,396,693,211,25,212,216,2073 0.153,0.172,0.175,0.184,0.217,0.261,0.263,0.269,0.284,0.286,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.51966698928167,46.4256985453476 0 0 49.945365534629225 46.42569854534756 EZB TRUE 211 -1013-2010_FFPE EZB 0.6767397093669042 0 216,213,212,877,1049,693,211,390,1423,210,944 0.17,0.171,0.191,0.214,0.233,0.247,0.273,0.273,0.281,0.282,0.316 EZB,BN2,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 11.0926393479139,32.207196714251,4.29186909192028 0 0 47.59170515408516 32.20719671425099 BN2 FALSE 212 -1013-2010_plasma EZB 0.5791148294864172 0.11693904596774456 213,212,216,1049,877,211,693,390,210,1423 0.134,0.168,0.183,0.186,0.249,0.282,0.29,0.309,0.322,0.334 BN2,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 13.3889686991842,25.826235836469,5.38084882283661 1 3.020095379654665 44.5960533584899 25.826235836469042 BN2 FALSE 213 -11-13204_tumorA MCD 0.8081014671440307 0.10006187845183931 749,1732,883,2084,790,1127,758,1503,1271,154 0.132,0.165,0.168,0.174,0.181,0.194,0.234,0.245,0.286,0.291 MCD,MCD,MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD 9.84181881633377,41.4447578440548 1 4.147040321857728 51.28657666038859 41.44475784405483 MCD TRUE 214 -11-14210T ST2 0.7443762221325684 0.2809016432651016 1341,1943,486,1789,1130,1555,293,1644,1713 0.165,0.169,0.198,0.214,0.221,0.235,0.249,0.263,0.273 BN2,ST2,ST2,EZB,ST2,ST2,ST2,ST2,ST2 6.04950192395678,4.67102831199315,31.2181748617804 2 8.76923661841139 41.9387050977303 31.21817486178037 ST2 TRUE 215 -11-21727T EZB 0.8354711880066648 0.10017482681703417 693,944,216,1423,2073,213,940,877,384,210 0.13,0.2,0.237,0.286,0.295,0.302,0.324,0.333,0.341,0.361 EZB,EZB,EZB,EZB,EZB,BN2,ST2,EZB,EZB,EZB 3.31581449591407,32.4989049952922,3.08417428492829 1 3.255572179646643 38.898893776134564 32.4989049952922 EZB TRUE 216 -11-22591_tumorB EZB 0.8850392003985204 0.8266866643102037 1964,1289,2214,72,1542,199,940 0.244,0.272,0.303,0.352,0.37,0.396,0.402 EZB,EZB,EZB,EZB,EZB,EZB,ST2 19.1556863590045,2.48819828517733 4 15.835750458697925 21.643884644181863 19.15568635900453 Other FALSE 217 -11-31342_tumorA EZB 0.9144815229560894 0.11726758438551155 218,143,139,11,1058,219,308,220,1533,343 0.145,0.176,0.178,0.184,0.221,0.225,0.24,0.244,0.255,0.256 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.16387130196249,44.5258557125224 1 5.221439542105327 48.68972701448485 44.52585571252236 EZB TRUE 218 -11-31342_tumorB EZB 0.9211538791146086 0.10472518201561885 218,139,143,11,1058,219,220,1587,308,343 0.134,0.163,0.166,0.193,0.206,0.212,0.229,0.249,0.251,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.98706878886695,46.5806540628691 1 4.87816747514054 50.56772285173602 46.58065406286907 EZB TRUE 219 -11-31342_tumorC EZB 0.9211538791146086 0.10472518201561885 218,139,143,11,1058,219,220,1587,308,343 0.134,0.163,0.166,0.193,0.206,0.212,0.229,0.249,0.251,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.98706878886695,46.5806540628691 1 4.87816747514054 50.56772285173602 46.58065406286907 EZB TRUE 220 -11-35935T EZB 0.7379887262704616 0.3205633766358073 221,2148,219,220,218,1587,4,139,1058 0.237,0.25,0.254,0.278,0.297,0.312,0.327,0.328,0.344 BN2,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.21227179625953,23.1196208826296,3.99598545749937 2 7.411303736675465 31.3278781363885 23.11962088262959 BN2 FALSE 221 -POG885T BN2 0.6562425993610381 3.5071237336051633 2218,189,190 0.233,0.239,0.249 ST2,BN2,BN2 8.2092628780296,4.30023115058526 5 28.790900674941433 12.509494028614856 8.209262878029598 Other FALSE 222 -12-17272_tumorA MCD 0.7779605351417452 0.12000165042790409 749,790,2084,1503,758,1732,883,1271,1127,236 0.183,0.185,0.189,0.22,0.235,0.239,0.241,0.26,0.267,0.289 MCD,MCD,BN2,BN2,MCD,MCD,MCD,MCD,MCD,MCD 9.85042510116656,34.5129726734356 1 4.1416136819854295 44.3633977746022 34.512972673435634 MCD TRUE 223 -12-17272_tumorB MCD 0.8834972480703143 0.2853198408881835 1251,738,1501,786,789,300,301,1531,1826 0.145,0.222,0.225,0.256,0.263,0.264,0.27,0.271,0.288 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.44995771822128,33.7462019819978 2 9.628460980084117 38.19615970021909 33.74620198199781 MCD TRUE 224 -12-29259T ST2 1 2.52256506599319 670 0.171 ST2 5.83794200543987 2 14.72658856021684 5.837942005439869 5.837942005439869 Other FALSE 225 -13-22818T EZB 0.6479531236229452 4.701295286762656 793,342,402 0.229,0.247,0.251 BN2,EZB,EZB 4.36538510785561,8.0346258019987 8 37.7731484138381 12.400010909854307 8.034625801998697 Other FALSE 226 -13-26601T EZB 1 0.08294928892247773 86,1041,168,1094,138,329,2006,164,2104,1511 0.107,0.128,0.149,0.175,0.179,0.187,0.193,0.24,0.254,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6042486652174 1 4.778231465693373 57.604248665217426 57.604248665217426 EZB TRUE 227 -13-26835_tumorB BN2 1 2.0637159534371436 284,1210,2213,973 0.192,0.21,0.257,0.272 BN2,BN2,BN2,BN2 17.5477447270701 7 36.213560740097115 17.547744727070118 17.547744727070118 Other FALSE 228 -13-26835_tumorA BN2 1 1.2065128049007572 1210,284,2213,973,903 0.172,0.196,0.233,0.249,0.261 BN2,BN2,BN2,BN2,BN2 23.0485349408981 5 27.808352540396122 23.04853494089814 23.04853494089814 Other FALSE 229 -13-26835_tumorD BN2 1 1.2886343269310288 284,1210,2213,130,973 0.159,0.229,0.243,0.248,0.256 BN2,BN2,BN2,BN2,BN2 22.700923474231 6 29.253189241928457 22.700923474231 22.700923474231 Other FALSE 230 -FL1017T2 EZB 1 0.1408543609933946 122,231,589,1083,575,332,193,343,42,16 0.117,0.129,0.144,0.151,0.169,0.172,0.225,0.251,0.273,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1778310975869 1 8.053746862238853 57.17783109758693 57.17783109758693 EZB TRUE 231 -13-30451T BN2 0.8803198684566201 0.10721285603344374 232,933,981,1075,1525,692,1115,892,261,742 0.166,0.18,0.185,0.212,0.215,0.225,0.231,0.262,0.265,0.268 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.9261837146758,5.56394468198044 1 4.387813042599806 46.49012839665623 40.926183714675794 BN2 TRUE 232 -13-31210T EZB 1 0.09434198528388306 121,80,17,187,404,1537,639,1705,149,1606 0.118,0.123,0.129,0.156,0.161,0.161,0.188,0.191,0.204,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.3989126022998 1 5.9811792797403545 63.39891260229978 63.39891260229978 EZB TRUE 233 -13-38657_tumorA ST2 0.6973860890207622 0.14438557014873923 1484,619,1139,235,1176,2004,1147,234,1363,1394 0.121,0.148,0.153,0.157,0.167,0.19,0.199,0.199,0.205,0.225 ST2,BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,EZB 13.2739695153284,4.43841870800586,40.8189204201388 1 5.893663097717749 58.53130864347298 40.81892042013875 ST2 TRUE 234 -13-38657_tumorB ST2 0.7000999002908329 0.1541543825914595 1484,1139,235,1176,1147,234,619,1394,1363,2004 0.121,0.125,0.125,0.164,0.167,0.168,0.178,0.206,0.225,0.228 ST2,BN2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 13.6354131914339,4.85217175149927,43.1582263150885 1 6.653029731344952 61.645811258021745 43.15822631508853 ST2 TRUE 235 -13-42815T MCD 0.8759638667766102 0.3613956074161588 2014,291,790,761,236,749,267,2130 0.16,0.28,0.282,0.294,0.316,0.328,0.342,0.345 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 3.57175237224476,25.2243111571786 2 9.115955252302761 28.796063529423385 25.22431115717863 MCD TRUE 236 -14-10498_tumorA MCD 0.44420106862532593 2.406063469920699 879,1441,1416 0.135,0.208,0.223 MCD,EZB,ST2 4.80871731669865,7.41936714177927,4.47463794770644 3 17.85146824976504 16.702722406184357 7.419367141779267 Other FALSE 237 -14-10498_tumorB MCD 1 0.08021041695505225 883,1732,749,214,790,1127,1911,1543,485,1184 0.199,0.207,0.236,0.239,0.248,0.251,0.257,0.271,0.275,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.7492967509288 1 3.2685180830171534 40.74929675092878 40.74929675092878 Other FALSE 238 -14-11247T Other 1 10 0 0 1 2 Other TRUE 239 -14-13959T EZB 1 0.0685444864600743 174,39,558,93,2016,240,1138,382,1081,1889 0.118,0.139,0.161,0.161,0.162,0.182,0.192,0.206,0.247,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.7981088446431 1 3.9617416891195427 57.798108844643146 57.798108844643146 EZB TRUE 240 -14-14094T ST2 0.5784031146300327 8.761208779843884 1664,951 0.217,0.297 ST2,MCD 3.36569810660689,4.61751577234138 8 40.45501972570494 7.98321387894827 4.617515772341384 Other FALSE 241 -14-16281T EZB 0.8963907663776974 0.5395583284538098 242,1439,1269,388,578,399,160 0.167,0.175,0.187,0.223,0.226,0.236,0.287 EZB,EZB,EZB,EZB,EZB,EZB,ST2 30.1968253125443,3.49029691716055 4 16.29294859024808 33.68712222970482 30.19682531254427 EZB TRUE 242 -14-16707T EZB 1 0.08478451226521427 676,2089,382,287,695,1138,558,1903,93,172 0.141,0.142,0.197,0.199,0.2,0.225,0.238,0.257,0.266,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.2182775635117 1 4.172947657756282 49.21827756351175 49.21827756351175 Other FALSE 243 -14-19181_tumorA EZB 1 1.6909079752484582 115 0.197 EZB 5.08606419191175 1 8.600066504729181 5.086064191911749 5.086064191911749 Other FALSE 244 -14-19181_tumorB BN2 0.7552974102861254 1.5473760267917007 610,591,1666 0.132,0.133,0.204 BN2,BN2,EZB 15.1396125601883,4.90495843132755 4 23.42667353054997 20.04457099151589 15.139612560188345 Other FALSE 245 -14-20552_tumorA MCD 1 0.2717417444522819 185,643,739,481,1422,296,246,247,1968 0.141,0.155,0.169,0.171,0.189,0.189,0.192,0.2,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 50.9182660722313 2 13.836618446953578 50.91826607223132 50.91826607223132 MCD TRUE 246 -14-20552_tumorB MCD 1 0.35638747001707066 788,1310,1224,156,471,767,1802,1491 0.117,0.122,0.122,0.135,0.141,0.143,0.158,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.1194671420666 3 21.069437323518457 59.11946714206661 59.11946714206661 MCD TRUE 247 -14-20962T EZB 1 0 1510,248,930,1217,355,522,1069,1109,498,1238,161 0.153,0.161,0.178,0.185,0.234,0.263,0.309,0.313,0.315,0.318,0.335 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.610625733802 0 0 47.61062573380202 47.61062573380202 EZB TRUE 248 -14-23891T ST2 0.570860927464325 1.832152142967034 911,2138,1178,816,955 0.151,0.159,0.175,0.178,0.285 ST2,EZB,ST2,EZB,ST2 11.9305371486978,15.8705602396226 6 29.077280953111966 27.8010973883204 15.870560239622609 Other FALSE 249 -14-24534_tumorB EZB 0.9280638489033473 0 5,250,780,368,433,170,118,102,565,691,1181 0.161,0.17,0.172,0.175,0.19,0.192,0.232,0.253,0.262,0.284,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.8210027857192,49.2955836241561 0 0 53.11658640987527 49.295583624156066 EZB TRUE 250 -14-24648_tumorA MCD 0.494988438940862 3.9108560018575695 879,1416,1441 0.131,0.246,0.268 MCD,ST2,EZB 3.73326950723968,7.64987586663263,4.07151026010824 6 29.917562946485603 15.454655633980552 7.649875866632631 Other FALSE 251 -14-24648_tumorB MCD 0.494988438940862 3.9108560018575695 879,1416,1441 0.131,0.246,0.268 MCD,ST2,EZB 3.73326950723968,7.64987586663263,4.07151026010824 6 29.917562946485603 15.454655633980552 7.649875866632631 Other FALSE 252 -14-25466T MCD 1 0 305,479,1689,764,1699,709,310,1331,1449,21,1009 0.127,0.132,0.144,0.149,0.15,0.154,0.154,0.156,0.163,0.168,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.133022738429 0 0 73.13302273842903 73.13302273842903 MCD TRUE 253 -14-27873T MCD 0.8900937562011619 0.29054593584133137 1194,285,2030,1398,952,1063,1890,650 0.149,0.157,0.17,0.189,0.216,0.216,0.229,0.237 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.62488856242336,37.4554191850447 2 10.882519819448166 42.08030774746805 37.45541918504469 MCD TRUE 254 -14-27873_tumorB MCD 0.5499388727750518 1.9911248986261258 659,869,1008,464,1275,1609 0.194,0.232,0.272,0.289,0.315,0.317 MCD,MCD,N1,BN2,BN2,MCD 6.64119992098864,12.6010284806644,3.67127908887629 5 25.090221556147867 22.913507490529355 12.60102848066442 Other FALSE 255 -14-27873_tumorC BN2 0.5836406281455355 1.40358226272204 281,659,1199,1135,897,1203,869 0.276,0.297,0.298,0.315,0.328,0.336,0.339 BN2,MCD,BN2,BN2,MCD,BN2,MCD 13.1266995719114,9.36436588667442 4 18.42440268721579 22.491065458585787 13.126699571911367 Other FALSE 256 -14-29443_tumorA EZB 0.756017327804847 1.0251879334695353 1745,1492,625,207,330,1356,993 0.196,0.202,0.236,0.237,0.248,0.262,0.315 EZB,EZB,EZB,EZB,BN2,EZB,BN2 7.20784706543177,22.3346077350734 4 22.897170348772587 29.542454800505165 22.33460773507339 Other FALSE 257 -14-29443_tumorB EZB 0.8659610288273341 0.8449705151225316 1745,207,1492,330,625,1356,1272 0.183,0.219,0.22,0.252,0.253,0.268,0.302 EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.96118061688963,25.591274032937 4 21.623872002252636 29.55245464982662 25.59127403293699 Other FALSE 258 -14-32442T EZB 0.8984556326386964 0.31531976519591676 320,1551,2059,319,1088,259,178,1541,149 0.144,0.166,0.17,0.175,0.191,0.192,0.198,0.2,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 44.2746472281766,5.00396556002007 2 13.960671368120678 49.278612788196625 44.274647228176555 EZB TRUE 259 -14-33262T BN2 0.90310544489078 0 892,260,981,933,961,667,907,692,232,145,592 0.161,0.205,0.219,0.234,0.258,0.277,0.278,0.293,0.303,0.309,0.321 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.7976909304802,4.26991064985714 0 0 44.06760158033735 39.79769093048021 BN2 TRUE 260 -14-33436T BN2 0.9207553649908755 0.08931663883840861 1115,261,830,1525,981,232,1203,742,1075,933 0.153,0.173,0.224,0.236,0.266,0.278,0.278,0.283,0.284,0.298 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 39.0536269231741,3.36114295824941 1 3.48813869122709 42.41476988142349 39.05362692317408 BN2 TRUE 261 -14-33798_tumorB N1 1 5.401695161501329 262 0.179 N1 5.57458351095885 5 30.1122007785315 5.574583510958848 5.574583510958848 N1 TRUE 262 -14-35026T MCD 0.5335266260392112 10.126808562491954 751,401 0.189,0.216 MCD,EZB 4.61929872877442,5.28330019032855 8 53.502969605634505 9.90259891910297 5.283300190328547 Other FALSE 263 -14-35472_tumorB BN2 0.39196149648629086 7.429257586840452 286,1425,147 0.199,0.226,0.297 BN2,N1,MCD 5.02723897108582,3.37022815806094,4.42838187937096 7 37.34865326679928 12.825849008517721 5.027238971085816 Other FALSE 264 -14-35472_tumorA MCD 0.6545429390095517 4.085030470725605 743,1064,2108 0.189,0.223,0.31 MCD,N1,MCD 8.50158283154723,4.48699641187376 8 34.72922491626811 12.988579243420986 8.501582831547232 Other FALSE 265 -14-35632T BN2 1 0 266,1929,796,976,1724,943,111,1496,1982,100,857 0.146,0.153,0.177,0.195,0.24,0.256,0.268,0.279,0.282,0.283,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.0210004242436 0 0 50.02100042424363 50.02100042424363 BN2 TRUE 266 -14-36022T MCD 0.8167556961736179 0.6366422627217643 1994,850,801,569,2130,768,990 0.157,0.162,0.195,0.221,0.241,0.268,0.277 MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.17830879712458,27.5379304971583 4 17.531810382385565 33.71623929428292 27.53793049715834 MCD TRUE 267 -14-37722T ST2 0.6384492705580469 0.8793485420297159 2066,1152,629,1198,1174,268,1101 0.145,0.153,0.164,0.183,0.234,0.24,0.266 BN2,BN2,ST2,ST2,ST2,ST2,ST2 13.4437660758434,23.739857081531 3 20.8756087126381 37.183623157374406 23.73985708153099 ST2 TRUE 268 -14-41461T EZB 1 0.23280017744428186 1483,1663,170,1468,430,337,336,1181,112 0.168,0.183,0.191,0.203,0.21,0.25,0.252,0.266,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.798191495441 2 9.730626396988729 41.79819149544096 41.79819149544096 Other FALSE 269 -15-10535T BN2 0.527991152299494 6.110364190997689 1108,65 0.187,0.209 BN2,EZB 5.35928817270482,4.79104891033944 6 32.747202539733 10.150337083044263 5.359288172704825 Other FALSE 270 -15-11617T ST2 1 0.5482139573279067 271,1077,2198,1427,1955 0.123,0.188,0.223,0.237,0.244 ST2,ST2,ST2,ST2,ST2 26.2692499334572 3 14.401169462056398 26.26924993345716 26.26924993345716 ST2 TRUE 271 -15-13365T EZB 1 8.216100488598208 199,1182 0.34,0.35 EZB,EZB 5.79847653455456 9 47.64086588867898 5.7984765345545615 5.7984765345545615 Other FALSE 272 -15-13383T BN2 0.6082862762447929 1.0575661759173138 273,1275,897,34,1191,1112,274 0.137,0.178,0.188,0.196,0.204,0.213,0.246 BN2,BN2,MCD,EZB,BN2,BN2,MCD 22.5064517538563,5.09749016912048,9.39582715417235 4 23.802062114793287 36.9997690771491 22.50645175385626 BN2 TRUE 273 -15-13383_tumorB BN2 0.6829304781864383 0.2136885391199132 274,742,1112,34,1191,1407,817,897,1525,1203 0.176,0.198,0.214,0.224,0.224,0.233,0.245,0.245,0.245,0.25 MCD,BN2,BN2,EZB,BN2,BN2,BN2,MCD,BN2,BN2 30.653615528959,4.47232354265595,9.75947241325753 1 6.550326321126742 44.88541148487252 30.653615528959037 MCD FALSE 274 -15-15757T EZB 1 0.13292597389506602 196,1788,438,44,6,1118,367,28,1142,1872 0.118,0.153,0.171,0.176,0.178,0.195,0.197,0.199,0.202,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.2256774085693 1 7.4738529274438905 56.22567740856933 56.22567740856933 EZB TRUE 275 -15-21654T EZB 1 0 1022,1563,1103,2091,1110,29,2217,1055,1,4,1111 0.101,0.116,0.121,0.153,0.154,0.177,0.182,0.185,0.201,0.204,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 71.0368202489423 0 0 71.03682024894232 71.03682024894232 EZB TRUE 276 -15-21665_tumorA EZB 1 0 79,1872,1142,1118,1253,54,1242,6,408,1788,1372 0.159,0.17,0.212,0.22,0.231,0.249,0.255,0.266,0.271,0.271,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4924012667795 0 0 48.49240126677945 48.49240126677945 EZB TRUE 277 -15-21665_tumorB EZB 1 0.2033096254456403 689,712,128,366,2100,161,498,1020,427 0.115,0.261,0.272,0.312,0.314,0.321,0.333,0.334,0.34 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.650621976313 2 7.044804975462659 34.650621976312955 34.650621976312955 EZB TRUE 278 -POG638T ST2 0.5240502600528553 9.870376257239299 1710,1538 0.149,0.164 ST2,BN2 6.10702687666308,6.72421634944159 9 66.3705454040686 12.831243226104672 6.724216349441591 Other FALSE 279 -15-24058T BN2 1 0.6085745239110614 106,2064,57,1632,1014,2085 0.193,0.199,0.206,0.215,0.245,0.294 BN2,BN2,BN2,BN2,BN2,BN2 27.2141499097629 4 16.56183832497821 27.2141499097629 27.2141499097629 Other FALSE 280 -15-24306T BN2 0.677577154784005 1.736287198266176 661,151,1135,150,736 0.102,0.221,0.243,0.257,0.264 BN2,MCD,BN2,MCD,BN2 17.6576776508263,8.40234743436879 6 30.658799656240408 26.060025085195058 17.657677650826265 BN2 TRUE 281 -15-26538T ST2 0.31712511311458136 4.316149880425803 1074,147,282,1425 0.2,0.262,0.277,0.3 ST2,MCD,BN2,N1 3.61471742161734,3.8169747601693,3.33812967137764,5.00147397275965 5 21.587111289479328 15.771295825923927 5.0014739727596504 BN2 FALSE 282 -15-29858T N1 1 13.693269816313473 913 0.294 N1 3.40209439760669 10 46.5857965269968 3.402094397606686 3.402094397606686 Other FALSE 283 -15-31924T BN2 1 0.6316724460613692 284,2213,973,130,1210,412,157 0.112,0.194,0.202,0.224,0.246,0.247,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.327230372724 4 22.315238022112066 35.327230372724 35.327230372724 BN2 TRUE 284 -15-34472T MCD 1 0.8518566725615804 1194,650,285,2030,864,1063 0.196,0.201,0.211,0.213,0.222,0.239 MCD,MCD,MCD,MCD,MCD,MCD 28.1947744382073 5 24.017906736555577 28.194774438207308 28.194774438207308 MCD TRUE 285 -15-38154T MCD 0.5110662268254184 4.130106720238858 147,286,1074 0.123,0.228,0.294 MCD,BN2,ST2 4.37889224176699,8.12991737758996,3.39894744001759 7 33.57742639617097 15.907757059374543 8.12991737758996 BN2 FALSE 286 -POG721T EZB 1 0 1163,2215,142,1046,1033,1962,1264,1304,463,517,1516 0.126,0.128,0.157,0.163,0.169,0.172,0.179,0.194,0.208,0.215,0.218 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.8305924197706 0 0 64.83059241977057 64.83059241977057 EZB TRUE 287 -15-43657T EZB 1 0 463,348,1264,1886,1516,2215,1962,1033,349,316,1222 0.131,0.136,0.141,0.16,0.186,0.189,0.19,0.19,0.194,0.204,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.3992172642994 0 0 64.3992172642994 64.3992172642994 EZB TRUE 288 -15-43891T EZB 1 0.08380005817089328 289,498,660,192,548,1510,930,443,161,2100 0.174,0.195,0.257,0.29,0.292,0.303,0.315,0.315,0.316,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.5054818504136 1 3.142961560792039 37.50548185041357 37.50548185041357 EZB TRUE 289 -16-11636T MCD 1 0.42044416599068807 2053,765,1063,704,753,1526,799,650 0.119,0.234,0.239,0.239,0.24,0.244,0.248,0.253 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.2863136021294 3 15.676813025314537 37.28631360212938 37.28631360212938 Other FALSE 290 -16-12281_tumorB MCD 0.768633955125446 0.4791931205955297 267,291,2014,990,236 0.163,0.194,0.244,0.282,0.301 MCD,BN2,MCD,MCD,MCD 5.15167192755584,17.1146547080101 2 8.201224797446358 22.26632663556599 17.11465470801015 BN2 FALSE 291 -16-13732T ST2 0.6830272585365325 3.693337664086179 2080,292,1177 0.198,0.264,0.322 ST2,EZB,ST2 3.784600186245,8.15522835791135 7 30.120012053497657 11.939828544156345 8.155228357911346 EZB FALSE 292 -16-16192T ST2 0.8575226108661033 0.9556576895440493 234,1176,235,1484,293,649 0.165,0.167,0.192,0.23,0.232,0.232 ST2,ST2,ST2,ST2,ST2,EZB 4.30321555524917,25.8995807018125 5 24.751133453653736 30.20279625706162 25.899580701812454 ST2 TRUE 293 -16-16723T BN2 0.8935578253588875 0 669,1477,1212,560,1193,88,836,294,564,872,1213 0.138,0.158,0.16,0.18,0.182,0.187,0.201,0.217,0.218,0.235,0.246 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.4527141922828,6.24825926874168 0 0 58.70097346102453 52.45271419228285 BN2 TRUE 294 -16-17861T BN2 0.7865958057463226 1.6483813835479986 459,1003,1714,2210 0.202,0.217,0.285,0.294 BN2,BN2,ST2,BN2 12.9479849977023,3.51280071093397 5 21.343217424671252 16.460785708636276 12.947984997702303 BN2 TRUE 295 -16-18029T MCD 1 0.3552180139620431 185,296,643,481,739,246,465,1968 0.121,0.136,0.151,0.176,0.182,0.185,0.207,0.232 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.9453735558435 3 17.031060373174984 47.94537355584349 47.94537355584349 MCD TRUE 296 -16-18623T EZB 1 3.4967141785641034 2162 0.325 EZB 3.08139941512181 3 10.774773024675575 3.081399415121812 3.081399415121812 Other FALSE 297 -16-22216_tumorA BN2 1 1.708525776235103 1381,987,52,754 0.157,0.24,0.297,0.307 BN2,BN2,BN2,BN2 17.1576911379371 7 29.314357569846184 17.15769113793713 17.15769113793713 Other FALSE 298 -16-23208T EZB 1 0 1237,299,12,668,1047,700,1078,148,1110,439,1111 0.138,0.145,0.149,0.153,0.157,0.163,0.179,0.192,0.226,0.228,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 63.7189523162902 0 0 63.71895231629018 63.71895231629018 EZB TRUE 299 -16-27074_tumorA MCD 0.8853228532452329 0.3616481993657077 300,301,1501,224,1251,2108,1531,223,789 0.177,0.192,0.229,0.23,0.235,0.26,0.284,0.292,0.296 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.35860685967961,33.6490257246951 2 12.16910956374635 38.007632584374676 33.64902572469507 MCD TRUE 300 -16-27074_tumorB MCD 0.876401713285542 0.3815877185893459 300,224,1501,301,1251,1531,223,2108,789 0.19,0.197,0.21,0.21,0.259,0.262,0.265,0.274,0.281 MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.76148605281326,33.7623978891498 2 12.88331638462643 38.523883941963085 33.762397889149824 MCD TRUE 301 -16-27413T ST2 0.5156794450278864 4.230914081570616 1071,999 0.167,0.177 ST2,N1 5.63798906797407,6.00303878040767 4 25.398341308241296 11.64102784838174 6.003038780407667 Other FALSE 302 -16-29329T EZB 1 0.59007474949914 1745,1991,1293,207,367,625,6 0.141,0.243,0.259,0.264,0.28,0.284,0.315 EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.1147346216414 4 17.179869738599002 29.114734621641425 29.114734621641425 Other FALSE 303 -16-31791T ST2 0.543341575977337 9.632196652971608 1052,31 0.193,0.23 ST2,EZB 4.35169550022573,5.17773672154746 9 49.872978319257655 9.529432221773192 5.177736721547462 Other FALSE 304 -16-32248_tumorB MCD 0.8648704646822406 0.09846743509193762 1376,1694,767,1224,759,1310,1449,156,858,788 0.115,0.125,0.151,0.158,0.163,0.165,0.168,0.179,0.182,0.183 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.68643962395308,55.5958773656433 1 5.474383445880805 64.28231698959637 55.5958773656433 MCD TRUE 305 -16-32248_tumorA MCD 0.8630806459253626 1.1406614502364487 467,755,2028,1420,1897,713 0.105,0.179,0.218,0.222,0.224,0.24 MCD,MCD,MCD,BN2,MCD,MCD 4.49956399369028,28.3633137499301 5 32.352938595506615 32.862877743620324 28.36331374993005 Other FALSE 306 -16-43083_tumorB EZB 0.5122742123435852 6.395038833594785 401,81 0.17,0.179 EZB,MCD 5.88199598405106,5.60012792990066 5 37.615592737055124 11.482123913951725 5.881995984051064 Other FALSE 307 -16-43741_tumorA EZB 0.873947558708268 0.2166746765446053 343,16,308,1533,11,575,42,231,143 0.112,0.129,0.148,0.173,0.174,0.195,0.195,0.233,0.252 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 6.74596399673938,46.7711589372522 2 10.134125734345446 53.51712293399158 46.7711589372522 BN2 FALSE 308 -17-12136T EZB 1 7.3774411439054495 1863 0.259 EZB 3.86497140213895 6 28.513599042157843 3.8649714021389525 3.8649714021389525 Other FALSE 309 -17-16667_tumorA MCD 0.9176856978958592 0 1449,759,1009,764,479,310,1689,305,1376,1904,858 0.109,0.145,0.145,0.145,0.145,0.147,0.148,0.155,0.163,0.167,0.178 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.13031095248353,68.3441217497181 0 0 74.47443270220163 68.3441217497181 MCD TRUE 310 -17-23504T MCD 1 3.512014170863793 311,194 0.153,0.269 MCD,MCD 10.2380956804187 7 35.95633711228985 10.238095680418697 10.238095680418697 MCD TRUE 311 -17-23711_tumorB EZB 1 9.175297577984692 401 0.189 EZB 5.28834538253453 7 48.52214257991562 5.288345382534532 5.288345382534532 Other FALSE 312 -17-23711_tumorA MCD 1 5.038484109013871 751 0.182 MCD 5.49056317288703 4 27.664115296128074 5.490563172887028 5.490563172887028 Other FALSE 313 -17-33596_tumorA Other 1 10 0 0 1 2 Other TRUE 314 -17-33596_tumorB Other 1 10 0 0 1 2 Other TRUE 315 -17-33848_tumorB EZB 1 0 316,463,1937,1886,1222,1264,1962,1033,369,372,348 0.131,0.15,0.154,0.155,0.172,0.174,0.175,0.178,0.181,0.198,0.202 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.668203023038 0 0 65.66820302303802 65.66820302303802 EZB TRUE 316 -17-36275T ST2 1 1.1805481453566158 318 0.124 ST2 8.08455851425751 1 9.544210560033742 8.08455851425751 8.08455851425751 Other FALSE 317 -17-36275_tumorB ST2 0.6843459256770398 5.890333839694892 318,1381 0.16,0.346 ST2,BN2 2.88736389870215,6.25987712739643 9 36.87276607583522 9.147241026098577 6.259877127396426 ST2 TRUE 318 -17-40409_tumorB EZB 0.9011734205710711 0.32033728913201365 319,320,2059,1551,1088,259,1541,178,149 0.154,0.155,0.161,0.18,0.201,0.201,0.208,0.214,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 43.9304296842196,4.81760114028143 2 14.072554755447465 48.74803082450108 43.930429684219646 EZB TRUE 319 -17-40409_tumorA EZB 0.9011734205710711 0.32033728913201365 319,320,2059,1551,1088,259,1541,178,149 0.154,0.155,0.161,0.18,0.201,0.201,0.208,0.214,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB 43.9304296842196,4.81760114028143 2 14.072554755447465 48.74803082450108 43.930429684219646 EZB TRUE 320 -17-45529_tumorA BN2 0.5243168227680199 0.9372298993716832 897,1191,34,273,1112,274,1275 0.122,0.142,0.156,0.182,0.187,0.19,0.245 MCD,BN2,EZB,BN2,BN2,MCD,BN2 21.9469066498313,6.4293396515834,13.4818549396769 4 20.569297110941072 41.85810124109159 21.946906649831256 Other FALSE 321 -17-45529_tumorB BN2 0.7856790061971388 0.4954213257443236 281,1203,1135,830,1199,897,1191,274 0.223,0.237,0.252,0.283,0.302,0.316,0.318,0.331 BN2,BN2,BN2,BN2,BN2,MCD,BN2,MCD 22.6684672727412,6.18360474896346 3 11.23044210885324 28.852072021704625 22.668467272741164 Other FALSE 322 -18-14625_tumorA BN2 1 0.6159545947388791 931,1173,1375,1027,1126,1158,564 0.145,0.191,0.236,0.256,0.275,0.284,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.8877634725126 4 19.025459832101852 30.8877634725126 30.8877634725126 Other FALSE 323 -18-19313_tumorB EZB 1 0.5694342189724579 2100,712,689,1867,548,729,128,513 0.19,0.21,0.262,0.279,0.311,0.332,0.336,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.3656371738346 3 16.72179866871106 29.36563717383456 29.36563717383456 Other FALSE 324 -18-19313_tumorA EZB 1 0.5694342189724579 2100,712,689,1867,548,729,128,513 0.19,0.21,0.262,0.279,0.311,0.332,0.336,0.367 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 29.3656371738346 3 16.72179866871106 29.36563717383456 29.36563717383456 Other FALSE 325 -18-36878_tumorB EZB 1 0 366,1020,78,427,48,326,164,1796,645,1094,329 0.127,0.164,0.214,0.23,0.244,0.245,0.258,0.271,0.29,0.299,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7835105966512 0 0 48.78351059665125 48.78351059665125 EZB TRUE 326 -19-13976_tumorA ST2 0.6206723146708246 7.224957243852811 2107,1508 0.189,0.31 ST2,EZB 3.22574318328816,5.27810008480577 8 38.13404744149758 8.50384326809393 5.27810008480577 Other FALSE 327 -19-13976_tumorB ST2 0.6206723146708246 7.224957243852811 2107,1508 0.189,0.31 ST2,EZB 3.22574318328816,5.27810008480577 8 38.13404744149758 8.50384326809393 5.27810008480577 Other FALSE 328 -19-16466_tumorA EZB 1 0 1020,78,366,427,164,326,645,1094,1796,329,48 0.161,0.161,0.167,0.22,0.24,0.24,0.253,0.265,0.278,0.279,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7419212053776 0 0 49.74192120537756 49.74192120537756 EZB TRUE 329 -19-25121_tumorA EZB 0.5059366069941593 1.390883638173598 993,330,1053,1356,62,179,1133 0.13,0.231,0.236,0.239,0.261,0.262,0.271 BN2,BN2,EZB,EZB,EZB,EZB,BN2 15.7063465542195,16.0837977401461 4 22.370691116462666 31.790144294365618 16.083797740146075 BN2 FALSE 330 -2001-2024_FFPE EZB 0.9097223093025028 0.32587394553788784 331,595,651,408,1293,1241,1600,1372,2083 0.183,0.226,0.228,0.256,0.283,0.29,0.292,0.302,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.16716699309742,31.9153320011444 2 10.400375162364545 35.08249899424184 31.915332001144417 EZB TRUE 331 -2001-2026_FFPE EZB 1 0.09618325518700722 333,332,1083,589,1705,122,1034,354,231 0.15,0.192,0.206,0.212,0.213,0.241,0.242,0.244,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4555349336224 1 4.0835115506215 42.45553493362237 42.45553493362237 EZB TRUE 332 -2001-2026_plasma EZB 1 0.24104378560870418 332,1083,589,192,333,1034,122,354,231 0.173,0.206,0.21,0.216,0.23,0.233,0.235,0.256,0.257 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6998482628056 2 9.810445498966498 40.699848262805574 40.699848262805574 EZB TRUE 333 -2004-2026_plasma Other 1 10 0 0 1 2 Other TRUE 334 -2004-2027_FFPE EZB 1 13.088135406943005 1535 0.24 EZB 4.17400996931766 10 54.63000766835958 4.174009969317662 4.174009969317662 Other FALSE 335 -2004-2030_FFPE EZB 1 0.22709749627180037 336,337,1663,430,1384,1483,112,646,1468 0.126,0.133,0.148,0.162,0.188,0.201,0.225,0.239,0.253 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.2522246781019 2 11.639251902756731 51.252224678101946 51.252224678101946 EZB TRUE 336 -2004-2030_plasma EZB 1 0.22152974466059083 336,337,1663,430,1384,1483,112,646,2176 0.116,0.126,0.16,0.168,0.177,0.213,0.229,0.234,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.6024295047477 2 11.431473032052889 51.60242950474766 51.60242950474766 EZB TRUE 337 -2004-2033_FFPE ST2 1 0.38642884297632174 339,959,2052,1404,1101,1198,1970 0.14,0.154,0.177,0.181,0.215,0.24,0.253 ST2,ST2,ST2,ST2,ST2,ST2,ST2 37.5568597624804 3 14.513053863839295 37.55685976248045 37.55685976248045 Other FALSE 338 -2004-2033_plasma ST2 0.8756387663666526 0.5658420948748875 339,2052,444,959,1970,1128,1404 0.172,0.182,0.201,0.206,0.209,0.233,0.235 ST2,ST2,ST2,ST2,ST2,EZB,ST2 4.28986613929776,30.2053379847263 3 17.091451721681516 34.49520412402401 30.205337984726256 ST2 TRUE 339 -4002-2003_FFPE EZB 0.9238282080520466 0.09588797808615798 1245,1244,341,1581,340,105,1243,1042,166,365 0.145,0.146,0.149,0.155,0.164,0.176,0.177,0.192,0.195,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.50624618532015,54.6527425962379 1 5.24054098441649 59.15898878155802 54.65274259623787 EZB TRUE 340 -4002-2003_plasma EZB 0.9000915337748483 0.10694585056607105 341,1244,166,1245,365,340,105,1581,1042,96 0.132,0.153,0.16,0.172,0.174,0.175,0.178,0.201,0.212,0.216 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.75781573470526,51.8730933589201 1 5.5476120907629225 57.63090909362537 51.87309335892011 EZB TRUE 341 -7004-2002_FFPE EZB 1 1.1067635731375545 342,1260,1002,1102,1322,473 0.237,0.255,0.266,0.29,0.324,0.333 EZB,EZB,EZB,EZB,EZB,EZB 21.433678421308 5 23.722014515048087 21.433678421307953 21.433678421307953 EZB TRUE 342 -75-10141_tumorA EZB 0.9010403815019664 0.1146217452450829 343,16,42,575,308,231,1533,122,11,589 0.147,0.159,0.169,0.171,0.189,0.197,0.206,0.218,0.222,0.231 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.30006764052801,48.2578150693102 1 5.5313949849587924 53.55788270983818 48.25781506931017 EZB TRUE 343 -76-10146_tumorB Other 1 10 0 0 1 2 Other TRUE 344 -8002-2019_FFPE EZB 1 1.2356026834650136 1967,532,1782,1182,96 0.201,0.273,0.338,0.354,0.36 EZB,EZB,EZB,EZB,EZB 17.1890616765035 5 21.238850733733365 17.189061676503513 17.189061676503513 Other FALSE 345 -8003-2001_FFPE BN2 0.49636408027225043 2.0662028345293053 402,737,793,581 0.122,0.142,0.148,0.171 EZB,BN2,BN2,ST2 13.8198049716722,8.17928411682212,5.84298394002178 4 28.554520205111388 27.842073028516147 13.819804971672248 Other FALSE 346 -8005-2002_FFPE MCD 0.8904096057064737 0.3950374647748704 465,347,1526,246,2185,296,185,704 0.142,0.193,0.215,0.225,0.246,0.249,0.257,0.261 MCD,MCD,MCD,MCD,ST2,MCD,MCD,MCD 33.0704941892831,4.07027111394398 3 13.064084183386495 37.14076530322712 33.07049418928314 MCD TRUE 347 -8005-2003_FFPE EZB 1 0 1516,1281,1046,1163,351,1896,2215,348,1264,2047,463 0.155,0.18,0.211,0.217,0.222,0.225,0.232,0.237,0.245,0.261,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5350503868177 0 0 50.53505038681765 50.53505038681765 EZB TRUE 348 -8005-2003_plasma EZB 1 0 1281,1516,1046,1163,2047,1896,351,2215,76,1264,348 0.158,0.186,0.207,0.226,0.233,0.24,0.24,0.246,0.259,0.268,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.9791597992631 0 0 48.97915979926305 48.97915979926305 EZB TRUE 349 -8006-2002_FFPE EZB 1 0.2562030185580145 413,288,2015,2151,1435,1222,349,1120 0.148,0.162,0.167,0.198,0.203,0.213,0.215,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8248431361406 2 10.971854080752681 42.82484313614057 42.82484313614057 Other FALSE 350 -8006-2002_plasma EZB 1 0 351,1516,1896,348,1281,527,349,1264,463,2001,1163 0.164,0.172,0.175,0.208,0.229,0.229,0.249,0.257,0.262,0.263,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4128698536398 0 0 50.4128698536398 50.4128698536398 EZB TRUE 351 -81-52884T Other 1 10 0 0 1 2 Other TRUE 352 -89-62169T EZB 0.42175395955328854 2.794891451707747 353,1725,1738,1909 0.181,0.248,0.264,0.314 BN2,ST2,EZB,EZB 5.52972840672704,6.96837953915554,4.0242728908884 5 19.47586440624098 16.522380836770978 6.968379539155537 BN2 FALSE 353 -92-38267_tumorA EZB 1 0.1899990064513702 548,443,660,1034,354,729,289,2100,192 0.177,0.203,0.203,0.246,0.257,0.259,0.286,0.296,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.3000796764512 2 7.086978079082667 37.30007967645116 37.30007967645116 EZB TRUE 354 -92-38267_tumorB EZB 0.9257517570096536 0.1040146202710749 575,231,122,343,589,1083,16,332,42,308 0.107,0.148,0.17,0.189,0.196,0.2,0.224,0.231,0.241,0.248 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 4.03917077160394,50.3617228916859 1 5.2383554827758045 54.40089366328979 50.36172289168586 EZB TRUE 355 -92-38626_tumorA EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 356 -92-38626_tumorB BN2 0.5363283416780009 7.226529351027729 793,581 0.145,0.167 BN2,ST2 6.92025947629693,5.98276827466182 7 50.00945822218754 12.903027750958746 6.92025947629693 Other FALSE 357 -94-15772_tumorB EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 358 -94-15772_tumorA BN2 1 0.7204666472253565 1980,1148,804 0.152,0.153,0.164 BN2,BN2,BN2 19.2325614815759 2 13.85641908818656 19.23256148157595 19.23256148157595 Other FALSE 359 -94-25764_tumorB EZB 1 0 360,1294 0.144,0.171 EZB,EZB 12.7956264273476 0 0 12.795626427347566 12.795626427347566 EZB TRUE 360 -94-26795T ST2 1 2.1791339451099363 1052 0.168 ST2 5.95135791973542 2 12.968806062394304 5.951357919735417 5.951357919735417 Other FALSE 361 -95-32141_tumorB ST2 0.7716308756660343 0.2582486273214168 2082,1394,1045,1417,1147,1687,1917,1139,1939 0.135,0.154,0.161,0.198,0.202,0.225,0.232,0.235,0.249 ST2,EZB,ST2,ST2,ST2,ST2,ST2,BN2,ST2 4.26058520800005,6.50424634319393,36.3730273103724 2 9.393284374428076 47.13785886156637 36.37302731037239 Other FALSE 362 -95-32141_tumorA ST2 1 1.1484765857045414 874 0.181 ST2 5.51389405026123 1 6.332578212780607 5.513894050261235 5.513894050261235 Other FALSE 363 -95-32814T EZB 1 0.08324724746599496 137,1539,1511,2104,1450,699,435,520,1160,477 0.178,0.214,0.216,0.253,0.256,0.272,0.277,0.296,0.3,0.302 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0813077732149 1 3.3366585469575254 40.08130777321487 40.08130777321487 EZB TRUE 364 -FL1007T2 EZB 0.3981088443562669 5.5783073461638395 1725,353,2183,1527 0.191,0.215,0.273,0.348 ST2,BN2,EZB,EZB 4.6468630475492,6.5328672894117,5.23002137296456 7 36.442341592038744 16.409751709925462 6.532867289411701 BN2 FALSE 365 -96-31596T EZB 1 0 366,1020,427,78,48,689,645,326,128,164,1796 0.133,0.166,0.213,0.248,0.267,0.267,0.295,0.299,0.307,0.313,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0853084683072 0 0 46.085308468307154 46.085308468307154 EZB TRUE 366 -97-18502_tumorA EZB 1 0.09621931619662515 44,196,28,438,1788,275,367,433,6,691 0.125,0.17,0.17,0.197,0.229,0.234,0.236,0.249,0.255,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6216194512166 1 4.774558292165213 49.62161945121658 49.62161945121658 EZB TRUE 367 -97-18502_tumorB EZB 0.9181933249192705 0 433,780,44,250,368,565,170,5,691,196,28 0.125,0.201,0.22,0.22,0.228,0.25,0.259,0.265,0.272,0.276,0.286 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.00077016667432,44.9044097923722 0 0 48.90517995904655 44.90440979237223 EZB TRUE 368 -FL1004T2 EZB 1 0 2016,558,174,240,382,1138,93,287,1903,676,39 0.144,0.155,0.165,0.177,0.191,0.202,0.206,0.209,0.216,0.218,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6629614869566 0 0 58.662961486956604 58.662961486956604 EZB TRUE 369 -98-22532T EZB 1 2.0552858338868862 2050 0.116 EZB 8.6386081763156 2 17.754809009280883 8.638608176315602 8.638608176315602 Other FALSE 370 -98-28290T EZB 0.8292527246362427 0.3356238514049938 473,1864,1102,809,371,1049,1011,1865 0.113,0.163,0.174,0.246,0.28,0.299,0.304,0.316 EZB,EZB,EZB,EZB,EZB,ST2,EZB,BN2 3.16116314205511,31.6204659743226,3.34964864118785 3 10.612582573522708 38.131277757565556 31.620465974322595 EZB TRUE 371 -99-13280T EZB 1 0 142,1033,1962,1304,517,372,2215,1264,1937,428,276 0.125,0.129,0.129,0.151,0.158,0.163,0.177,0.181,0.182,0.184,0.191 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 69.9251841421404 0 0 69.92518414214044 69.92518414214044 EZB TRUE 372 -99-25549T MCD 0.5238374126071436 0.9446953273052487 150,151,661,811,736 0.135,0.182,0.223,0.272,0.278 MCD,MCD,BN2,BN2,BN2 11.7505091480618,12.9270053379032 2 12.212081538767137 24.677514485964934 12.927005337903175 Other FALSE 373 -99-27137T BN2 0.5624828745451326 7.641675144068769 1108,318 0.237,0.305 BN2,ST2 4.21802356906512,3.28091330519286 7 32.2327658648212 7.498936874257987 4.218023569065124 Other FALSE 374 -FL1010T2 EZB 1 0 375,1046,1163,1560,2215,1081,142,1304,1516,1033,1962 0.147,0.16,0.203,0.218,0.231,0.245,0.257,0.271,0.272,0.277,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3797544955643 0 0 49.37975449556432 49.37975449556432 EZB TRUE 375 -BLGSP-71-30-00639-01A-01E EZB 0.5374394629718922 3.60581781242441 816,2138,1178,781 0.227,0.228,0.257,0.272 EZB,EZB,ST2,MCD 8.7922555808858,3.67946037470823,3.88781137101322 7 31.703271784945944 16.359527326607257 8.792255580885799 Other FALSE 376 -BLGSP-71-30-00659-01A-01E EZB 0.7082982230851945 0.6964270563821087 994,1975,77,1425,282,141,491,383 0.122,0.196,0.215,0.277,0.309,0.353,0.359,0.361 EZB,EZB,EZB,N1,BN2,BN2,EZB,EZB 6.07216178844299,23.5206639432759,3.61446348160845 3 16.380426754168443 33.20728921332735 23.52066394327591 Other FALSE 377 -BLGSP-71-30-00668-01A-01E EZB 1 0.2342560564873601 818,110,1750,109,1573,117,1527,1752,108 0.16,0.192,0.219,0.297,0.329,0.349,0.353,0.356,0.375 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.6195369324175 2 7.875580142719292 33.61953693241754 33.61953693241754 Other FALSE 378 -BLGSP-71-30-00674-01A-01E ST2 0.6031449788561672 6.2452820852022874 1436,1901 0.174,0.265 ST2,BN2 3.77662758142923,5.7397634939415 7 35.846442122010956 9.516391075370738 5.739763493941503 Other FALSE 379 -CABN-0003P EZB 0.7507397975392421 0.2756098132499715 384,1330,944,757,1093,1107,400,216,132,972 0.183,0.206,0.236,0.263,0.268,0.282,0.32,0.338,0.344,0.346 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,BN2,BN2 5.80353098909094,28.1448851086008,3.54111782718271 1 7.757006528723358 37.489533924874394 28.14488510860075 EZB TRUE 380 -CABN0001_2015-08-11 EZB 0.8915345615917477 0.10922451591833257 1102,473,371,1864,1865,381,380,809 0.139,0.194,0.235,0.248,0.265,0.272,0.285,0.32 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.76796467258553,30.9708860422691 1 3.3827800355286803 34.738850714854586 30.970886042269054 EZB TRUE 381 -CABN0002_2015-08-19 EZB 1 0.1001116004854138 1138,382,93,558,676,174,39,2016,2089,287 0.108,0.124,0.15,0.151,0.177,0.189,0.215,0.225,0.249,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.7373427199119 1 5.880289387950644 58.73734271991185 58.73734271991185 EZB TRUE 382 -CABN0003_2016-07-05 EZB 0.605897034975147 1.3959104738877244 383,844,561,141,2042 0.108,0.158,0.195,0.199,0.321 EZB,BN2,EZB,BN2,EZB 11.3726260054018,17.4843657319849 5 24.406609254561303 28.85699173738664 17.48436573198488 EZB TRUE 383 -CABN0003_2013-11-28 EZB 0.6088602169897053 0.3403502305181175 384,1093,940,944,132,972,1330,2140,757 0.148,0.197,0.248,0.262,0.292,0.297,0.302,0.312,0.348 EZB,EZB,ST2,EZB,BN2,BN2,EZB,ST2,EZB 6.79285605525162,21.8256690412116,7.22823978024515 2 7.428371489388507 35.846764876708356 21.825669041211594 EZB TRUE 384 -CAGJ0001_Biopsy EZB 1 0 385,1691,1220,448,2061,47,102,1468,25,396,112 0.178,0.178,0.208,0.217,0.225,0.241,0.249,0.289,0.294,0.323,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.1906657489761 0 0 46.19066574897609 46.19066574897609 EZB TRUE 385 -CAHN0001_2011-05-25 EZB 0.37537867174430495 7.27123436662168 1835,921,70 0.18,0.191,0.249 EZB,MCD,BN2 4.01647840841043,5.55632763200194,5.22912078772325 8 40.40136043002218 14.80192682813562 5.556327632001942 Other FALSE 386 -CAHN0001_2016-07-13 MCD 1 0 888,1572,587,483,516,947,387,515,540,1567,59 0.109,0.11,0.119,0.128,0.133,0.139,0.143,0.143,0.144,0.153,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 82.3396767047713 0 0 82.33967670477135 82.33967670477135 MCD TRUE 387 -CALM0002_Biopsy EZB 0.905736523009142 0.48995435768759993 242,1269,1439,578,388,399,160 0.149,0.165,0.191,0.206,0.217,0.217,0.3 EZB,EZB,EZB,EZB,EZB,EZB,ST2 32.062057376797,3.33682139456661 4 15.708944728191574 35.39887877136366 32.06205737679705 EZB TRUE 388 -CAMP-0004P ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 389 -CAMP-0009P EZB 0.8679866089542307 0 877,390,210,396,1423,211,212,25,216,693,213 0.136,0.139,0.143,0.186,0.198,0.214,0.258,0.285,0.3,0.302,0.308 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,BN2 7.12355468288586,46.8372944889691 0 0 53.96084917185494 46.83729448896908 EZB TRUE 390 -CAMP0003_2016-05-06 ST2 0.6422041587780154 1.8699566131668057 618,701,744,1077,1427,1211 0.23,0.252,0.266,0.308,0.309,0.327 ST2,EZB,BN2,ST2,ST2,ST2 3.76008103311438,3.97573828195485,13.884944326702 5 25.964243467169286 21.620763641771205 13.884944326701978 Other FALSE 391 -CAMP0005_2016-06-20 BN2 0.5105068601590714 6.725876978357978 793,1257 0.152,0.158 BN2,EZB 6.59646268187725,6.32493602336605 6 44.36699649063573 12.921398705243305 6.5964626818772505 Other FALSE 392 -CAMP0005_2015-12-15 BN2 0.6377997402617273 4.204142031723825 2218,190,189 0.203,0.228,0.232 ST2,BN2,BN2 8.6908648485841,4.93545749049011 6 36.53763020196355 13.62632233907421 8.690864848584104 Other FALSE 393 -CAMP0006_2015-08-18 MCD 0.5339596058401412 6.5960236326861565 81,401 0.162,0.186 MCD,EZB 5.37225132499877,6.15518576483428 6 40.59975076842032 11.527437089833047 6.155185764834279 Other FALSE 394 -CAMP0007_2016-05-20 BN2 1 3.750755325719512 1901 0.21 BN2 4.76083385642487 3 17.856722941851356 4.760833856424873 4.760833856424873 Other FALSE 395 -CAMP0009_2016-07-04 EZB 1 0 396,210,390,112,25,1468,877,211,1423,385,1483 0.171,0.216,0.221,0.234,0.245,0.267,0.281,0.281,0.29,0.305,0.306 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.2432634561524 0 0 44.24326345615242 44.24326345615242 EZB TRUE 396 -CAMP0010_2017-07-18 EZB 0.9279161387644281 0.18951998975603238 398,397,1972,1671,1752,1493,695,1703 0.119,0.13,0.156,0.192,0.208,0.295,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 39.0098255874462,3.0304234800934 2 7.393141745717408 42.04024906753958 39.00982558744617 EZB TRUE 397 -CAMP0010-Biopsy EZB 0.9279161387644281 0.18951998975603238 398,397,1972,1671,1752,1493,695,1703 0.119,0.13,0.156,0.192,0.208,0.295,0.327,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 39.0098255874462,3.0304234800934 2 7.393141745717408 42.04024906753958 39.00982558744617 EZB TRUE 398 -CARM0001_2016-03-24 EZB 0.9033111883562182 0.49276667842506555 399,578,242,1269,1439,2093,388,365 0.196,0.197,0.235,0.238,0.264,0.284,0.309,0.32 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.12374674954804,29.1834736665685 3 14.38064338358035 32.307220416116586 29.183473666568545 EZB TRUE 399 -CASA0002_2016-08-24 EZB 1 4.616202296107246 401,380 0.145,0.217 EZB,EZB 11.5027086171145 9 53.0988299297767 11.50270861711453 11.50270861711453 EZB TRUE 400 -CASA0002_2015-03-10 MCD 1 10.730276640130027 751 0.216 MCD 4.62120521458593 9 49.58681036331843 4.621205214585926 4.621205214585926 EZB FALSE 401 -CATC0001_2016-01-11 BN2 0.41989618827576136 1.7660642377801627 737,402,581,1415,793 0.167,0.189,0.233,0.241,0.253 BN2,EZB,ST2,EZB,BN2 9.94035828730155,9.44721538962459,4.28579742015405 4 17.55531128192493 23.673371097080178 9.940358287301546 EZB FALSE 402 -CATC0002_2016-09-21 EZB 0.8863380302117354 0.610131369059192 388,1037,242,1269,1439,160,1944 0.148,0.163,0.219,0.225,0.248,0.264,0.278 EZB,EZB,EZB,EZB,EZB,ST2,EZB 29.5249607080034,3.78621369906819 4 18.01410469819299 33.31117440707163 29.524960708003444 Other FALSE 403 -CATC0003_Biopsy EZB 1 0.37450378908717397 1060,662,319,205,149,354,75,320 0.168,0.193,0.204,0.222,0.239,0.256,0.259,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.3405320160399 3 13.609666937450685 36.34053201603986 36.34053201603986 EZB TRUE 404 -CAVA0006_2016-08-23 Other 1 10 0 0 1 2 Other TRUE 405 -CAVA0006-Diagnostic Other 1 10 0 0 1 2 Other TRUE 406 -DFCIDL008-Tumor N1 0.762677990283213 3.343940309116601 901,870,1387 0.113,0.216,0.238 N1,N1,EZB 4.20264125238294,13.5059617440195 8 45.16312988921355 17.70860299640244 13.505961744019498 Other FALSE 407 -DFCIDL009-Tumor EZB 1 0.08753616143092323 54,408,1242,1372,79,1253,958,1872,468,1066 0.169,0.173,0.176,0.178,0.23,0.258,0.259,0.265,0.291,0.303 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5938772111464 1 3.991112995816604 45.59387721114641 45.59387721114641 EZB TRUE 408 -DLBCL-BWH_DLBCL64-Tumor EZB 0.4022471420065838 5.325385894713633 1835,921,1532 0.121,0.126,0.228 EZB,MCD,BN2 4.39259105227054,8.28743617287011,7.92281940175835 8 44.13379569834201 20.602846626899 8.287436172870109 Other FALSE 409 -DLBCL-DFCI_DLBCL_Goe05-Tumor MCD 0.8431399711452777 1.2593654662113636 569,713,2028,2130,850,801 0.227,0.246,0.25,0.254,0.277,0.332 MCD,MCD,MCD,MCD,BN2,MCD 3.612244464164,19.4162127568246 5 24.452107830557384 23.02845722098856 19.416212756824557 Other FALSE 410 -DLBCL-DFCI_DLBCL_Goe07-Tumor N1 0.5068342810404087 5.136473423073945 2077,1697 0.295,0.303 N1,BN2 3.29739204323191,3.38878243415931 3 17.406390909639132 6.686174477391223 3.3887824341593107 Other FALSE 411 -DLBCL-DFCI_DLBCL_Goe08-Tumor BN2 0.7668319393222718 0.11635947376479021 932,157,13,1086,412,590,1076,1806,130,973 0.131,0.135,0.181,0.182,0.187,0.233,0.236,0.252,0.285,0.289 EZB,BN2,BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 38.9652835575807,11.8480453603805 1 4.53397988985592 50.81332891796122 38.965283557580676 BN2 TRUE 412 -DLBCL-DFCI_DLBCL_Goe16-Tumor EZB 0.9105171808988074 0.09273685001309398 527,2001,1268,1132,351,727,1896,1879,349,2114 0.127,0.162,0.186,0.197,0.211,0.218,0.225,0.229,0.235,0.235 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB 46.5963693122914,4.57934740103221 1 4.321200512068698 51.17571671332356 46.59636931229135 EZB TRUE 413 -DLBCL-MC_F064AB-Tumor EZB 0.8901838470958318 0.1579003186383353 2059,319,1088,1541,320,404,149,1551,187 0.129,0.168,0.181,0.184,0.184,0.198,0.206,0.208,0.213 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 44.0629983812847,5.43576361606835 1 6.957561484565307 49.49876199735306 44.0629983812847 Other FALSE 414 -DLBCL-MC_F089_HCZ-Tumor EZB 0.882482988817308 1.27632947006241 1260,1322,1002,1011,809,1521 0.131,0.187,0.207,0.243,0.284,0.295 EZB,EZB,EZB,EZB,EZB,BN2 3.38887704722345,25.4484547834432 5 32.48061280765928 28.837331830666667 25.448454783443214 Other FALSE 415 -DLBCL-MC_F089_MLB-Tumor MCD 1 0 1699,798,1331,905,910,23,2008,305,21,479,2034 0.109,0.125,0.138,0.144,0.147,0.149,0.152,0.154,0.161,0.165,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 76.0233153557741 0 0 76.02331535577407 76.02331535577407 MCD TRUE 416 -DLBCL-MC_F132_MLM-Tumor BN2 1 0 592,145,417,1193,981,1375,669,560,564,1027,892 0.168,0.176,0.196,0.26,0.265,0.268,0.288,0.291,0.295,0.301,0.304 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.9981352280878 0 0 44.998135228087804 44.998135228087804 BN2 TRUE 417 -DLBCL-MC_F139_AD-Tumor BN2 0.8342121789790495 0 1758,863,1806,590,1444,684,960,795,1722,1086,1076 0.218,0.218,0.272,0.277,0.282,0.289,0.29,0.295,0.302,0.308,0.313 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,EZB 33.4186517400801,3.19845380506935,3.44302853366431 0 0 40.06013407881378 33.41865174008012 BN2 TRUE 418 -DLBCL-MC_F139_LJF-Tumor ST2 0.7537837740335606 0.9033279476145906 572,1302,419,1461,718,1482 0.138,0.202,0.207,0.219,0.238,0.279 BN2,ST2,ST2,ST2,ST2,ST2 7.22445651714181,22.1174623136933 4 19.979321838271588 29.341918830835077 22.117462313693263 ST2 TRUE 419 -DLBCL-MC_F210_GWC-Tumor BN2 1 11.87402028268948 2058 0.303 BN2 3.30061046784138 8 39.19151564040578 3.300610467841382 3.300610467841382 Other FALSE 420 -DLBCL-MC_F218_RAP-Tumor EZB 0.6946562786895626 1.5615876083575568 1983,292,2080 0.201,0.23,0.244 EZB,EZB,ST2 9.30906465942968,4.09190060211061 3 14.536920017564643 13.400965261540287 9.309064659429676 Other FALSE 421 -DLBCL-MC_F231_CAO-Tumor Other 1 10 0 0 1 2 Other TRUE 422 -DLBCL-MC_F262_WLC-Tumor BN2 1 0.08705993184488671 1973,99,423,100,1496,687,41,1957,519 0.131,0.139,0.165,0.177,0.192,0.227,0.229,0.229,0.287 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.3853428466669 1 4.212424650522297 48.3853428466669 48.3853428466669 BN2 TRUE 423 -DLBCL-MC_F300_TBF-Tumor EZB 0.9095900635352931 0.09791979475139946 143,343,11,575,139,1058,16,308,218,231 0.17,0.2,0.202,0.208,0.212,0.217,0.236,0.238,0.248,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.19333082923733,42.1879740716269 1 4.131037762071071 46.381304900864265 42.18797407162693 EZB TRUE 424 -DLBCL-MC_F307_JPS-Tumor Other 1 10 0 0 1 2 Other TRUE 425 -DLBCL-MC_F339_JPS2-Tumor MCD 0.8687950019906079 0.16487072482907925 285,1890,1398,1194,952,2030,1063,753 0.137,0.144,0.151,0.152,0.158,0.176,0.218,0.237 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 6.32738999851094,41.8978307972548 1 6.907725732309525 48.225220795765786 41.89783079725484 Other FALSE 426 -DLBCL-MC_F344_CJS-Tumor EZB 1 0 427,161,1020,645,1090,366,1589,78,689,498,48 0.168,0.202,0.227,0.233,0.235,0.261,0.271,0.284,0.316,0.318,0.417 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6438125608739 0 0 43.64381256087387 43.64381256087387 EZB TRUE 427 -DLBCL-MC_F349_BJJ-Tumor EZB 1 0 1,1279,369,1055,1563,1022,1937,1120,1111,429,316 0.128,0.143,0.148,0.161,0.169,0.184,0.187,0.189,0.191,0.193,0.194 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.3577739500436 0 0 65.35777395004361 65.35777395004361 EZB TRUE 428 -DLBCL-MC_F358_WDS-Tumor EZB 0.9279340142726902 0.08469213932747817 2091,1103,1022,29,1563,4,2217,1110,2148,1055 0.112,0.128,0.149,0.161,0.163,0.168,0.174,0.188,0.226,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 57.041436317617,4.42999962529946 1 4.830961272051096 61.4714359429165 57.04143631761704 EZB TRUE 429 -DLBCL-MC_F362_MJB1-Tumor EZB 1 0.22791417451804666 1663,1483,430,1468,336,337,112,170,1384 0.14,0.142,0.18,0.196,0.207,0.207,0.23,0.238,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7287568177402 2 10.650146036369797 46.72875681774018 46.72875681774018 EZB TRUE 430 -DLBCL-MC_F496DMP-Tumor EZB 1 6.513938878063861 2050 0.179 EZB 5.58545535601871 6 36.383314795260276 5.585455356018707 5.585455356018707 Other FALSE 431 -DLBCL-MC_F500_ECU-Tumor Other 1 10 0 0 1 2 Other TRUE 432 -DLBCL-MC_F502_RFK-Tumor EZB 1 0 275,28,691,116,438,44,196,731,780,1788,277 0.15,0.157,0.188,0.21,0.211,0.213,0.242,0.248,0.261,0.273,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.811765565305 0 0 51.811765565305 51.811765565305 EZB TRUE 433 -DLBCL-MC_F519_ALT-Tumor Other 1 10 0 0 1 2 Other TRUE 434 -DLBCL-MC_F606_DMJ-Tumor EZB 1 0.11465500449732624 2006,138,329,435,2104,1094,83,1041,1511,86 0.124,0.128,0.149,0.162,0.165,0.166,0.171,0.179,0.2,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.2741082404743 1 7.14003816037856 62.274108240474284 62.274108240474284 EZB TRUE 435 -DLBCL-MC_F648_JKW-Tumor EZB 0.5613678709448959 1.4615728679687008 141,561,383,844,2042 0.122,0.127,0.18,0.192,0.269 BN2,EZB,EZB,BN2,EZB 13.4055271840332,17.1565914936643 4 25.075608633962297 30.56211867769747 17.156591493664266 Other FALSE 436 -DLBCL-MC_F729_MAN-Tumor ST2 0.35709171600885536 8.053118699175224 581,793,1257 0.161,0.171,0.188 ST2,BN2,EZB 5.85316357110215,5.33054740678578,6.21178890346369 7 50.02427337381261 17.395499881351626 6.211788903463692 Other FALSE 437 -DLBCL-MC_F739_AMA3-Tumor EZB 1 0.10235581641696058 438,1788,196,28,44,1118,1142,6,731,367 0.125,0.147,0.159,0.169,0.197,0.202,0.212,0.215,0.223,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0005331204247 1 5.629624470909148 55.00053312042467 55.00053312042467 EZB TRUE 438 -DLBCL-MC_F815_BAT-Tumor EZB 1 0.09955551051171004 144,2217,4,439,1291,1587,2091,1058,1110,220 0.132,0.161,0.176,0.177,0.181,0.185,0.216,0.221,0.222,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.1928048611474 1 5.395192354013014 54.19280486114744 54.19280486114744 EZB TRUE 439 -DLBCL-MC_F927_JHS-Tumor BN2 0.7178460551354712 4.731338652367879 1875,674,951 0.286,0.39,0.419 BN2,BN2,MCD 6.06613675006079,2.38433352370284 8 28.70094727611188 8.450470273763633 6.066136750060789 Other FALSE 440 -DLBCL-MC_F959_ERM-Tumor BN2 0.5937132057538095 8.940598905675085 101,1631 0.214,0.313 BN2,MCD 4.66845360742395,3.19469237312846 9 41.7387712137295 7.86314598055241 4.668453607423953 Other FALSE 441 -DLBCL-MC_F990_MNA-Tumor MCD 0.915211713742439 0.10157309707868124 767,156,1224,788,1694,837,1310,471,1491,1376 0.109,0.124,0.13,0.139,0.144,0.146,0.152,0.162,0.165,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.99196225965483,64.6777319178296 1 6.569517542918624 70.66969417748442 64.67773191782958 Other FALSE 442 -DLBCL-RICOVER_1013-Tumor EZB 1 0.10353557191833908 333,1705,354,1034,149,332,404,121,1083,589 0.144,0.18,0.204,0.216,0.233,0.244,0.259,0.259,0.26,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.7573531468839 1 4.737513727532034 45.757353146883865 45.757353146883865 EZB TRUE 443 -DLBCL-RICOVER_1018-Tumor ST2 0.6564525696686803 0.9754843641246125 444,1970,1128,545,2052,339 0.17,0.21,0.213,0.244,0.315,0.332 ST2,ST2,EZB,BN2,ST2,ST2 4.09058150261745,4.70102361994547,16.7990538268701 4 16.387214340199556 25.590658949433056 16.79905382687014 ST2 TRUE 444 -DLBCL-RICOVER_102-Tumor ST2 0.8015274827424206 0.13554090971824614 1497,1776,1176,722,2004,293,235,1484,234,619 0.167,0.174,0.18,0.181,0.199,0.208,0.228,0.229,0.242,0.243 ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2 9.85082457748925,39.782367531949 1 5.392138286025993 49.6331921094383 39.78236753194905 Other FALSE 445 -DLBCL-RICOVER_1032-Tumor MCD 0.6670995350841592 4.04811546915588 446,140,990 0.174,0.206,0.253 MCD,EZB,MCD 4.84445680087217,9.70781125347518 8 39.298340906838405 14.552268054347351 9.70781125347518 MCD TRUE 446 -DLBCL-RICOVER_1045-Tumor BN2 0.7649960535429724 2.916061726668925 572,1317,915,874 0.281,0.306,0.319,0.327 BN2,BN2,BN2,ST2 9.95888634554382,3.05933289809885 7 29.04072731248608 13.018219243642662 9.958886345543815 Other FALSE 447 -DLBCL-RICOVER_1046-Tumor EZB 1 0 2061,448,118,1220,47,10,1691,102,385,368,5 0.194,0.216,0.224,0.231,0.248,0.249,0.253,0.26,0.265,0.286,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.1128260038872 0 0 45.112826003887236 45.112826003887236 EZB TRUE 448 -DLBCL-RICOVER_105-Tumor ST2 1 6.010811091935245 530,702 0.288,0.309 ST2,ST2 6.71064719262994 8 40.33643257952414 6.7106471926299385 6.7106471926299385 Other FALSE 449 -DLBCL-RICOVER_1060-Tumor EZB 0.5040138106660828 4.423244489655276 1535,1653 0.283,0.288 EZB,ST2 3.53148183551199,3.47523456943123 4 15.620607569246097 7.006716404943219 3.5314818355119875 Other FALSE 450 -DLBCL-RICOVER_1061-Tumor ST2 0.6515969861001726 1.326649559885773 1847,1822,1121 0.141,0.156,0.204 ST2,EZB,ST2 6.42974984215548,12.0251704244188 3 15.953187051106594 18.454920266574263 12.025170424418784 ST2 TRUE 451 -DLBCL-RICOVER_107-Tumor EZB 1 0.36101959455768895 809,1011,1864,1031,1701,473,466,1322 0.107,0.171,0.191,0.194,0.211,0.228,0.264,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2911794953006 3 15.267944474759881 42.291179495300625 42.291179495300625 Other FALSE 452 -DLBCL-RICOVER_1072-Tumor BN2 0.7218127325727216 3.387158285832756 1793,2058,566 0.191,0.212,0.26 BN2,BN2,ST2 9.96271242887066,3.83963820764562 7 33.74528395281824 13.802350636516284 9.962712428870661 Other FALSE 453 -DLBCL-RICOVER_1081-Tumor BN2 0.9300001527696008 0 454,872,936,1213,836,903,294,1735,1549,1477,1212 0.139,0.17,0.184,0.208,0.211,0.212,0.229,0.244,0.251,0.264,0.271 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 49.021387600999,3.68977320366337 0 0 52.71116080466237 49.021387600999 BN2 TRUE 454 -DLBCL-RICOVER_1106-Tumor MCD 1 0.18467329116074485 214,1728,896,1171,154,1127,997,883,1732 0.159,0.164,0.26,0.265,0.268,0.281,0.289,0.295,0.296 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.5250254072322 2 6.929869942844138 37.525025407232185 37.525025407232185 MCD TRUE 455 -DLBCL-RICOVER_111-Tumor MCD 0.40368192326826613 3.9324623683503592 921,1835,1532 0.111,0.121,0.199 MCD,EZB,BN2 5.0252879400664,8.24164531177735,8.98114838364247 6 35.31802804324468 22.24808163548623 8.981148383642473 Other FALSE 456 -DLBCL-RICOVER_1138-Tumor ST2 0.6416772102850898 1.008368734332538 444,1128,1970,545,2052,339 0.164,0.185,0.187,0.217,0.298,0.32 ST2,EZB,ST2,BN2,ST2,ST2 4.60647562168546,5.403524598779,17.9257060973744 4 18.07572156942648 27.93570631783886 17.9257060973744 ST2 TRUE 457 -DLBCL-RICOVER_1144-Tumor MCD 0.6155105712146044 4.819649985521322 1631,101 0.232,0.372 MCD,BN2 2.69104269444272,4.30796038073607 5 20.762861186641008 6.999003075178783 4.307960380736065 Other FALSE 458 -DLBCL-RICOVER_1150-Tumor ST2 1 4.313806614404737 1714,35 0.193,0.328 ST2,ST2 8.2288982496927 9 35.49787569878795 8.228898249692703 8.228898249692703 BN2 FALSE 459 -DLBCL-RICOVER_1165-Tumor BN2 0.4062601128013723 5.890172673539721 147,286,1074,834 0.156,0.281,0.308,0.329 MCD,BN2,ST2,BN2 6.5960107318134,6.39764919043091,3.24227002502645 7 38.851642166901996 16.235929947270755 6.596010731813395 Other FALSE 460 -DLBCL-RICOVER_1190-Tumor BN2 0.8615452256955253 0.11488004936521369 692,933,232,892,981,1935,1075,260,961,579 0.141,0.142,0.167,0.196,0.217,0.22,0.235,0.241,0.244,0.245 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.9049487488508,7.05575237209887 1 5.043802679645154 50.96070112094964 43.90494874885077 Other FALSE 461 -DLBCL-RICOVER_1192-Tumor MCD 0.8633121954757629 0.3824279769955255 1640,1736,188,792,708,556,1273,49 0.136,0.156,0.164,0.165,0.175,0.184,0.201,0.202 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 6.42400427974798,40.5736360884452 3 15.516493568656735 46.99764036819316 40.57363608844518 MCD TRUE 462 -DLBCL-RICOVER_1199-Tumor EZB 1 0 348,349,1886,463,2015,1222,1264,413,1516,316 0.13,0.14,0.15,0.176,0.186,0.186,0.196,0.205,0.213,0.216 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2879034372034 0 0 57.28790343720344 57.28790343720344 EZB TRUE 463 -DLBCL-RICOVER_120-Tumor MCD 0.5158682545857977 0.694845259772892 1008,659,869,464,1609 0.114,0.194,0.202,0.213,0.234 N1,MCD,MCD,BN2,MCD 4.69529425792169,14.3635445779472,8.78459686745817 2 9.980440863523253 27.843435703327085 14.363544577947224 BN2 FALSE 464 -DLBCL-RICOVER_1204-Tumor MCD 1 0.07394671614942 765,1526,799,900,69,2053,465,296,824 0.134,0.15,0.152,0.156,0.217,0.229,0.24,0.274,0.297 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 47.262695127822 1 3.4949211010736323 47.26269512782204 47.26269512782204 MCD TRUE 465 -DLBCL-RICOVER_1210-Tumor EZB 0.8623423488898558 0.2196963740969635 2176,1550,1384,336,337,466,1031,211,646 0.114,0.168,0.171,0.241,0.249,0.265,0.268,0.277,0.291 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.96931100844026,37.3941414426264 2 8.215357287414015 43.36345245106666 37.3941414426264 EZB TRUE 466 -DLBCL-RICOVER_1219-Tumor MCD 1 0.9014054431271853 755,761,467,1897,1911,986 0.142,0.177,0.205,0.209,0.214,0.23 MCD,MCD,MCD,MCD,MCD,MCD 31.4011656964215 5 28.305181679293018 31.40116569642153 31.40116569642153 MCD TRUE 467 -DLBCL-RICOVER_1235-Tumor EZB 1 0 1242,54,1372,958,408,468,1066,1914,1437,1253 0.117,0.123,0.138,0.171,0.182,0.21,0.228,0.232,0.247,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.583610129278 0 0 56.583610129278 56.583610129278 EZB TRUE 468 -DLBCL-RICOVER_1237-Tumor MCD 1 2.19864900225346 311 0.184 MCD 5.42054512392782 2 11.917876128393761 5.420545123927821 5.420545123927821 Other FALSE 469 -DLBCL-RICOVER_1247-Tumor BN2 1 3.6092107382229144 2058,1793 0.223,0.262 BN2,BN2 8.30046064194144 6 29.958111681091722 8.300460641941443 8.300460641941443 Other FALSE 470 -DLBCL-RICOVER_1248-Tumor MCD 0.9033980909390785 0.0989383953576424 1694,767,156,837,1224,1376,788,1310,1968,1491 0.135,0.145,0.162,0.164,0.168,0.174,0.179,0.188,0.193,0.196 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.74620313827279,53.7371258573165 1 5.316665003454571 59.48332899558932 53.73712585731653 MCD TRUE 471 -DLBCL-RICOVER_1257-Tumor ST2 1 1.8609818807379295 833,1664 0.132,0.244 ST2,ST2 11.6490628013303 4 21.678694800853965 11.649062801330327 11.649062801330327 Other FALSE 472 -DLBCL-RICOVER_126-Tumor EZB 0.8276713139708571 0.2590799567311587 1864,473,809,1102,1011,1031,1049,1550 0.115,0.139,0.206,0.216,0.268,0.269,0.292,0.294 EZB,EZB,EZB,EZB,EZB,EZB,ST2,BN2 3.39661302158466,32.7826708186575,3.42903683645077 2 8.493332937229608 39.60832067669294 32.782670818657515 EZB TRUE 473 -DLBCL-RICOVER_1263-Tumor EZB 1 0 728,1017,1295,1243,505,1581,1245,364,1668,474,105 0.173,0.195,0.205,0.221,0.225,0.239,0.267,0.28,0.286,0.288,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.697533389301 0 0 46.69753338930096 46.69753338930096 EZB TRUE 474 -DLBCL-RICOVER_1269-Tumor EZB 1 0.11259499305387771 1034,333,332,354,1083,589,443,192,1705 0.197,0.197,0.205,0.212,0.232,0.237,0.242,0.243,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.4971141232536 1 4.559772283409833 40.49711412325361 40.49711412325361 Other FALSE 475 -DLBCL-RICOVER_1278-Tumor BN2 0.6489736262589824 3.126267069833224 674,1664 0.165,0.305 BN2,ST2 6.05524786611398,3.27525128069965 4 18.930322003510042 9.330499146813631 6.055247866113983 MCD FALSE 476 -DLBCL-RICOVER_1283-Tumor EZB 1 0.08569672351678628 1160,72,1539,699,1450,477,1542,1964,1289,520 0.118,0.167,0.176,0.195,0.198,0.226,0.23,0.276,0.285,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6763808914332 1 4.257103078567719 49.67638089143323 49.67638089143323 EZB TRUE 477 -DLBCL-RICOVER_1287-Tumor ST2 1 12.200463606602614 670 0.377 ST2 2.65580305705212 10 32.402028543868376 2.6558030570521214 2.6558030570521214 Other FALSE 478 -DLBCL-RICOVER_1289-Tumor MCD 1 0 21,2034,1331,23,2008,59,798,709,480,1699,1233 0.101,0.116,0.123,0.126,0.133,0.135,0.147,0.15,0.162,0.167,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 81.0146768307213 0 0 81.01467683072134 81.01467683072134 MCD TRUE 479 -DLBCL-RICOVER_1293-Tumor MCD 1 0 798,1331,23,2008,1699,21,2034,59,905,910,846 0.106,0.121,0.124,0.126,0.134,0.141,0.147,0.159,0.17,0.173,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 78.8069140026974 0 0 78.8069140026974 78.8069140026974 MCD TRUE 480 -DLBCL-RICOVER_1299-Tumor MCD 1 0.2037280018000001 643,296,481,739,185,1968,1422,246,465 0.123,0.132,0.138,0.147,0.148,0.194,0.223,0.23,0.24 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 54.638125010714 2 11.13131603053137 54.638125010713985 54.638125010713985 MCD TRUE 481 -DLBCL-RICOVER_1329-Tumor ST2 1 2.1373376986210473 1946,90 0.19,0.228 ST2,ST2 9.63900068734979 4 20.601799546106896 9.639000687349792 9.639000687349792 Other FALSE 482 -DLBCL-RICOVER_134-Tumor MCD 1 0 387,515,1567,888,516,1572,59,587,483,540,2034 0.109,0.118,0.121,0.131,0.132,0.145,0.151,0.152,0.162,0.163,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4249231355605 0 0 79.42492313556049 79.42492313556049 MCD TRUE 483 -DLBCL-RICOVER_147-Tumor Other 1 10 0 0 1 2 Other TRUE 484 -DLBCL-RICOVER_150-Tumor MCD 0.8668850878475052 0.47570064573743276 1184,986,647,1420,1897,1543,1977,485 0.155,0.167,0.17,0.175,0.181,0.192,0.237,0.253 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.72137210927094,37.2593279246713 3 17.724286353508877 42.9807000339422 37.259327924671254 MCD TRUE 485 -DLBCL-RICOVER_151-Tumor ST2 0.41845134022361785 0.6249868793534881 486,1789,1123,854,1130,1341,1943 0.209,0.215,0.221,0.239,0.248,0.275,0.293 ST2,EZB,EZB,BN2,ST2,BN2,ST2 7.81796821436569,9.16940691559908,12.2232074144035 2 7.639344257618481 29.2105825443683 12.223207414403532 ST2 TRUE 486 -DLBCL-RICOVER_173-Tumor EZB 0.5892146715619988 5.693435507214928 140,446 0.192,0.276 EZB,MCD 5.20149298059983,3.62634724749693 6 29.614364826276283 8.827840228096756 5.201492980599831 Other FALSE 487 -DLBCL-RICOVER_174-Tumor EZB 0.5011891967451105 3.349357816653937 1266,1052,1608,2042 0.165,0.168,0.201,0.204 EZB,ST2,N1,EZB 10.9882906333564,4.97012628132938,5.96601941538283 6 36.80371712449752 21.924436330068612 10.988290633356407 Other FALSE 488 -DLBCL-RICOVER_181-Tumor BN2 0.8966669287022204 0.4127746016880262 582,1362,1320,183,489,2041,1173,922 0.14,0.163,0.221,0.233,0.269,0.27,0.318,0.321 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2 32.0866970037819,3.69771298914845 3 13.24457357522046 35.78440999293036 32.08669700378191 BN2 TRUE 489 -DLBCL-RICOVER_197-Tumor ST2 1 1.1372915927988911 670 0.149 ST2 6.73389335003346 1 7.658400293797412 6.7338933500334575 6.7338933500334575 Other FALSE 490 -DLBCL-RICOVER_208-Tumor EZB 0.6159024496904834 0.6171318848441328 77,282,491,994,1975,1425,1074 0.167,0.172,0.183,0.284,0.337,0.363,0.381 EZB,BN2,EZB,EZB,EZB,N1,ST2 5.82727235031142,17.9655637888601,2.75467637056595,2.62198307822216 3 11.087122243306709 29.16949558795959 17.96556378886006 EZB TRUE 491 -DLBCL-RICOVER_215-Tumor BN2 0.8951366402987163 0 981,933,261,892,232,1115,592,692,417,1525,1075 0.143,0.215,0.237,0.249,0.25,0.279,0.28,0.291,0.296,0.304,0.307 BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.7577420292015,4.65753519141807 0 0 44.4152772206196 39.75774202920154 Other FALSE 492 -DLBCL-RICOVER_216-Tumor EZB 1 1.2300887160413843 1967,199,1782,532 0.311,0.325,0.346,0.358 EZB,EZB,EZB,EZB 11.9783853751539 4 14.734476686371963 11.978385375153906 11.978385375153906 Other FALSE 493 -DLBCL-RICOVER_221-Tumor MCD 1 2.570876229205813 864,650,2030 0.233,0.246,0.261 MCD,MCD,MCD 12.1910523052056 6 31.341686580457836 12.191052305205611 12.191052305205611 Other FALSE 494 -DLBCL-RICOVER_224-Tumor ST2 1 11.130974388650513 1028 0.252 ST2 3.96923112077151 8 44.181409947942214 3.9692311207715067 3.9692311207715067 Other FALSE 495 -DLBCL-RICOVER_253-Tumor ST2 0.4309545266766602 6.333714128133038 2218,1257,190 0.142,0.204,0.228 ST2,EZB,BN2 4.39071871129008,4.90206830592203,7.03769526031199 8 44.57474989973295 16.3304822775241 7.0376952603119864 Other FALSE 496 -DLBCL-RICOVER_258-Tumor BN2 1 1.0432032936655753 2069,1765,1957,523,596 0.11,0.194,0.206,0.217,0.218 BN2,BN2,BN2,BN2,BN2 28.2683029090176 5 29.489586701023278 28.268302909017553 28.268302909017553 Other FALSE 497 -DLBCL-RICOVER_267-Tumor EZB 1 0.1022930048543147 144,2217,4,439,1587,1291,2091,1110,1103,220 0.141,0.151,0.167,0.187,0.187,0.191,0.205,0.214,0.219,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2520426668025 1 5.549604463871713 54.25204266680247 54.25204266680247 EZB TRUE 498 -DLBCL-RICOVER_269-Tumor EZB 1 0 2013,2072,499,75,1540,1726,1590,662,205,1853,56 0.107,0.147,0.183,0.199,0.21,0.218,0.226,0.231,0.232,0.234,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.3830878237049 0 0 57.38308782370489 57.38308782370489 EZB TRUE 499 -DLBCL-RICOVER_274-Tumor MCD 1 0.9198016881993809 755,761,467,1897,1911,986 0.138,0.183,0.199,0.205,0.216,0.227 MCD,MCD,MCD,MCD,MCD,MCD 31.6376106903234 5 29.10032772355425 31.63761069032341 31.63761069032341 Other FALSE 500 -DLBCL-RICOVER_288-Tumor EZB 0.6108334623303947 1.7722390209521872 2042,561,844,141,383 0.176,0.191,0.21,0.216,0.258 EZB,EZB,BN2,BN2,EZB 9.41002240174224,14.7699146968866 5 26.17581916195769 24.179937098628876 14.769914696886635 Other FALSE 501 -DLBCL-RICOVER_290-Tumor BN2 1 0.0870025962309671 571,995,907,1252,502,957,1056,418,585,812 0.131,0.133,0.177,0.192,0.204,0.211,0.213,0.217,0.231,0.232 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.5845834822555 1 4.661997880911222 53.584583482255475 53.584583482255475 BN2 TRUE 502 -DLBCL-RICOVER_299-Tumor EZB 1 0 10,602,116,503,277,128,691,275,48,118,28 0.154,0.163,0.178,0.188,0.21,0.238,0.242,0.245,0.271,0.302,0.326 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8186320636966 0 0 50.8186320636966 50.8186320636966 EZB TRUE 503 -DLBCL-RICOVER_314-Tumor EZB 0.8312731508877905 0.7628741591303965 628,1260,1322,1521,1002,1036 0.207,0.237,0.241,0.243,0.245,0.331 EZB,EZB,EZB,BN2,EZB,EZB 4.12262758893989,20.3111101986851 4 15.49482111382672 24.433737787625002 20.311110198685107 Other FALSE 504 -DLBCL-RICOVER_320-Tumor EZB 1 0.11155347465956056 1782,505,105,166,1245,1668,341,1243,119,1581 0.135,0.191,0.2,0.23,0.234,0.237,0.243,0.243,0.257,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.3496141273409 1 5.170460505034731 46.34961412734088 46.34961412734088 EZB TRUE 505 -DLBCL-RICOVER_325-Tumor MCD 1 2.424574326671671 951 0.18 MCD 5.56090441013083 2 13.482826065878475 5.560904410130827 5.560904410130827 Other FALSE 506 -DLBCL-RICOVER_335-Tumor MCD 0.5483194221224362 4.85661891504173 904,1733 0.195,0.236 MCD,EZB 4.23284919094953,5.13848399950906 5 24.95565858665498 9.37133319045859 5.13848399950906 Other FALSE 507 -DLBCL-RICOVER_336-Tumor MCD 0.569126521594143 3.0155450287183547 2134,68,781,508 0.125,0.177,0.215,0.255 MCD,ST2,MCD,ST2 12.6597748111455,9.58444388329356 7 38.176120996443736 22.244218694439084 12.659774811145525 ST2 FALSE 508 -DLBCL-RICOVER_338-Tumor BN2 0.535046038576323 4.086279664286107 572,838,1317,718 0.235,0.293,0.295,0.309 BN2,ST2,BN2,ST2 7.65176595323619,6.64936965295648 6 31.26725561058585 14.301135606192672 7.6517659532361915 Other FALSE 509 -DLBCL-RICOVER_342-Tumor ST2 0.5150901280077722 2.947294602511029 2017,1082,1983,2216 0.175,0.202,0.229,0.239 ST2,BN2,EZB,ST2 4.95123653573091,4.37616899289008,9.90793296902562 6 29.201597361650297 19.235338497646616 9.907932969025625 Other FALSE 510 -DLBCL-RICOVER_361-Tumor ST2 0.4414085335917521 1.9109843114823903 511,1848,1719 0.135,0.186,0.249 ST2,BN2,EZB 5.38547384788445,4.01892175279726,7.43151430168777 3 14.201507241082346 16.835909902369487 7.431514301687773 ST2 TRUE 511 -DLBCL-RICOVER_384-Tumor EZB 1 0 366,1020,427,78,48,326,164,689,645,1796,128 0.112,0.16,0.219,0.232,0.254,0.275,0.289,0.289,0.292,0.297,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.5078261756113 0 0 48.507826175611285 48.507826175611285 Other FALSE 512 -DLBCL-RICOVER_401-Tumor EZB 1 0 1287,1253,1872,1118,1142,731,1788,513,438,79,135 0.153,0.172,0.2,0.242,0.247,0.25,0.254,0.257,0.276,0.291,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.7158584252054 0 0 47.715858425205425 47.715858425205425 EZB TRUE 513 -DLBCL-RICOVER_408-Tumor EZB 0.8830965250756875 0.5159609432194588 1227,1671,1703,1226,398,397,1493,1752 0.192,0.267,0.312,0.315,0.316,0.322,0.345,0.348 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 24.1772737863547,3.2005644225348 3 12.474528987282692 27.37783820888955 24.177273786354746 Other FALSE 514 -DLBCL-RICOVER_417-Tumor MCD 1 0 846,2008,587,888,1572,59,483,23,387,707,2034 0.142,0.143,0.147,0.147,0.149,0.149,0.15,0.152,0.156,0.156,0.16 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.4214393769503 0 0 73.42143937695033 73.42143937695033 MCD TRUE 515 -DLBCL-RICOVER_431-Tumor MCD 1 0 59,2034,387,21,2008,1567,23,515,1331,888,516 0.115,0.131,0.134,0.146,0.15,0.15,0.15,0.155,0.164,0.164,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.0754006809142 0 0 75.07540068091417 75.07540068091417 MCD TRUE 516 -DLBCL-RICOVER_440-Tumor EZB 1 0 372,1937,428,1962,1033,142,517,429,369,1304,316 0.128,0.142,0.145,0.156,0.159,0.169,0.17,0.176,0.176,0.178,0.184 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 68.6892742217395 0 0 68.68927422173948 68.68927422173948 EZB TRUE 517 -DLBCL-RICOVER_449-Tumor MCD 0.8953958952672124 0.3878128404028103 1184,986,1897,1543,1420,647,1911,485 0.128,0.128,0.15,0.16,0.204,0.21,0.222,0.243 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.90257160867421,41.965298645537 3 16.274681666077903 46.86787025421119 41.96529864553698 MCD TRUE 518 -DLBCL-RICOVER_451-Tumor BN2 1 0.08091877539400466 519,41,1001,1409,687,98,1927,1973,991,1765 0.121,0.151,0.161,0.196,0.205,0.21,0.223,0.257,0.264,0.28 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.5849129083409 1 4.174187981349329 51.5849129083409 51.5849129083409 BN2 TRUE 519 -DLBCL-RICOVER_463-Tumor EZB 1 0.1057163696698689 1668,119,1782,505,520,1295,477,1542,728,105 0.137,0.139,0.21,0.22,0.252,0.288,0.308,0.317,0.324,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7940408837036 1 4.629747015398958 43.79404088370356 43.79404088370356 EZB TRUE 520 -DLBCL-RICOVER_467-Tumor BN2 0.47294246785795074 4.592076949657778 581,793,402,737 0.138,0.162,0.172,0.18 ST2,BN2,EZB,BN2 11.7189877414591,5.8233688616324,7.23652991673553 7 53.8144934808764 24.778886519827026 11.718987741459102 Other FALSE 521 -DLBCL-RICOVER_469-Tumor EZB 1 0 644,2097,240,1109,2016,71,1485,39,174,1889,1085 0.188,0.237,0.243,0.246,0.277,0.278,0.298,0.304,0.304,0.31,0.312 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.289530771369 0 0 41.28953077136899 41.28953077136899 EZB TRUE 522 -DLBCL-RICOVER_473-Tumor BN2 1 1.1935722450704889 523,1096,20,596,2069 0.219,0.236,0.242,0.294,0.317 BN2,BN2,BN2,BN2,BN2 19.4927560877044 6 23.266012646212758 19.492756087704382 19.492756087704382 BN2 TRUE 523 -DLBCL-RICOVER_478-Tumor Other 1 10 0 0 1 2 Other TRUE 524 -DLBCL-RICOVER_496-Tumor Other 1 10 0 0 1 2 Other TRUE 525 -DLBCL-RICOVER_506-Tumor MCD 0.8139039858176129 1.2900253777805009 920,254,2145,1358,815 0.202,0.202,0.22,0.244,0.285 MCD,MCD,MCD,EZB,MCD 4.1009378166449,17.9357395117889 5 23.137559139468102 22.03667732843378 17.935739511788878 Other FALSE 526 -DLBCL-RICOVER_522-Tumor EZB 1 0.08204420475930826 2089,695,676,172,287,382,1972,1138,108,397 0.138,0.153,0.216,0.229,0.265,0.268,0.285,0.29,0.309,0.312 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6759545931143 1 3.5833589616957213 43.67595459311432 43.67595459311432 EZB TRUE 527 -DLBCL-RICOVER_533-Tumor EZB 1 2.4728179931750893 1099 0.279 EZB 3.58324929518157 2 8.860723331156942 3.5832492951815698 3.5832492951815698 Other FALSE 528 -DLBCL-RICOVER_543-Tumor BN2 1 10.585190210204185 1096 0.292 BN2 3.420512288084 8 36.2067731857099 3.4205122880840024 3.4205122880840024 Other FALSE 529 -DLBCL-RICOVER_554-Tumor ST2 1 10.624841424274901 530 0.268 ST2 3.73686968749509 9 39.703647852815074 3.7368696874950933 3.7368696874950933 ST2 TRUE 530 -DLBCL-RICOVER_575-Tumor ST2 0.7309410160707627 0.5525044657280527 1038,666,648,955,1486,508,171 0.183,0.226,0.243,0.248,0.254,0.263,0.291 ST2,BN2,ST2,ST2,ST2,ST2,BN2 7.85611544782246,21.3423723078907 3 11.79175600934034 29.198487755713163 21.342372307890702 BN2 FALSE 531 -DLBCL-RICOVER_577-Tumor EZB 1 1.0447130934437654 532,1182,1967,1037 0.132,0.172,0.254,0.303 EZB,EZB,EZB,EZB 20.6583844560449 5 21.58208473062523 20.658384456044864 20.658384456044864 EZB TRUE 532 -DLBCL-RICOVER_583-Tumor Other 1 10 0 0 1 2 Other TRUE 533 -DLBCL-RICOVER_585-Tumor MCD 1 0 880,771,1239,2180,1240,929,1250,777,518,783,1598 0.117,0.12,0.131,0.137,0.143,0.148,0.148,0.157,0.164,0.164,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 76.8684796188538 0 0 76.86847961885377 76.86847961885377 MCD TRUE 534 -DLBCL-RICOVER_597-Tumor MCD 1 0 535,783,534,1565,1459,518,1811,777,1240,462,586 0.114,0.141,0.143,0.159,0.162,0.166,0.168,0.175,0.182,0.183,0.184 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.3652761625808 0 0 69.36527616258078 69.36527616258078 MCD TRUE 535 -DLBCL-RICOVER_617-Tumor BN2 0.6932689184642865 3.8080460791664628 915,1317,874 0.26,0.306,0.318 BN2,BN2,ST2 7.1096147101113,3.1455900463061 7 27.073740421223537 10.2552047564174 7.109614710111298 Other FALSE 536 -DLBCL-RICOVER_623-Tumor ST2 0.5753261167485851 0 537,1433 0.162,0.22 ST2,BN2 4.55222041505245,6.16711174683953 0 0 10.719332161891987 6.167111746839534 ST2 TRUE 537 -DLBCL-RICOVER_632-Tumor BN2 0.5791697997412985 9.66439393083916 1697,697 0.196,0.269 BN2,N1 5.10829007274041,3.71173140459173 9 49.36852757595832 8.820021477332139 5.108290072740406 Other FALSE 538 -DLBCL-RICOVER_678-Tumor BN2 0.5782067293300225 5.860916139163426 1089,1710 0.153,0.21 BN2,ST2 6.53846936645188,4.76971684927776 7 38.321420635263486 11.308186215729643 6.5384693664518805 Other FALSE 539 -DLBCL-RICOVER_685-Tumor MCD 1 0 888,387,59,1572,587,2008,483,2034,23,515,1567 0.136,0.137,0.137,0.143,0.145,0.146,0.151,0.153,0.153,0.158,0.161 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.9236441656326 0 0 74.92364416563261 74.92364416563261 MCD TRUE 540 -DLBCL-RICOVER_692-Tumor BN2 0.5482418318525007 1.2797201972434735 831,130,600,1145,89,284,412 0.216,0.221,0.247,0.27,0.28,0.285,0.318 ST2,BN2,ST2,BN2,ST2,BN2,BN2 14.8855889776802,12.2659126277008 4 19.049388862602168 27.151501605380993 14.88558897768019 Other FALSE 541 -DLBCL-RICOVER_694-Tumor ST2 1 13.826186214288185 511 0.299 ST2 3.34640639705252 10 46.268037994333405 3.346406397052524 3.346406397052524 Other FALSE 542 -DLBCL-RICOVER_701-Tumor ST2 0.8473533346597938 1.300699732284823 271,2198,1077,1427,1822 0.156,0.183,0.201,0.244,0.265 ST2,ST2,ST2,ST2,EZB 3.77755842347169,20.9695162345471 6 27.275044152417628 24.74707465801876 20.969516234547072 Other FALSE 543 -DLBCL-RICOVER_704-Tumor Other 1 10 0 0 1 2 Other TRUE 544 -DLBCL-RICOVER_711-Tumor ST2 0.4370077283285695 1.5098397428283696 545,1417,1687,649,1128,1394,2082 0.243,0.26,0.297,0.333,0.342,0.356,0.361 BN2,ST2,ST2,EZB,EZB,EZB,ST2 4.11740836175712,8.73297371986033,9.97476619877866 4 15.060298432337081 22.825148280396107 9.974766198778656 BN2 FALSE 545 -DLBCL-RICOVER_712-Tumor BN2 1 3.865978264032031 1791 0.249 BN2 4.01700484605902 3 15.529653421375517 4.017004846059023 4.017004846059023 Other FALSE 546 -DLBCL-RICOVER_720-Tumor MCD 0.9062743224925509 0.11567293724039235 1169,621,556,2094,705,1736,1273,540,188,708 0.124,0.13,0.171,0.172,0.177,0.186,0.198,0.21,0.216,0.231 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD 5.36287012147509,51.8559226799839 1 5.99832688970442 57.21879280145903 51.855922679983934 MCD TRUE 547 -DLBCL-RICOVER_744-Tumor EZB 1 0.16989946820770147 354,1034,149,1705,1060,443,333,319,404,662 0.161,0.2,0.238,0.241,0.246,0.253,0.256,0.259,0.288,0.289 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.2690950789879 1 7.181496775540811 42.269095078987874 42.269095078987874 EZB TRUE 548 -DLBCL-RICOVER_763-Tumor MCD 0.5595437185947215 2.38428280716079 632,1907,1480,1513,717 0.163,0.197,0.235,0.247,0.278 EZB,MCD,MCD,EZB,MCD 10.1716894151089,12.9218384661511 6 30.809317291753047 23.09352788126002 12.921838466151112 Other FALSE 549 -DLBCL-RICOVER_773-Tumor N1 0.6093039471654457 8.410887598740237 901,1387 0.193,0.301 N1,EZB 3.3212328857163,5.17957704471502 9 43.56484033211315 8.500809930431316 5.1795770447150185 Other FALSE 550 -DLBCL-RICOVER_780-Tumor EZB 1 0.20686417988781078 55,1180,1457,1305,1517,1853,1219,1590,278 0.126,0.135,0.139,0.143,0.18,0.192,0.196,0.2,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.294229492135 2 11.438395436418913 55.29422949213503 55.29422949213503 Other FALSE 551 -DLBCL-RICOVER_787-Tumor BN2 0.5390598526455814 10.4642113222737 1971,1667 0.251,0.294 BN2,ST2 3.977505986143,3.40109207977179 9 41.62146317460899 7.378598065914792 3.9775059861429996 Other FALSE 552 -DLBCL-RICOVER_822-Tumor ST2 0.6159991578695471 7.409426583808948 833,286 0.195,0.312 ST2,BN2 3.20182192340092,5.13623771635684 9 38.056576276336564 8.338059639757768 5.1362377163568445 Other FALSE 553 -DLBCL-RICOVER_826-Tumor MCD 0.6026401845385866 2.905626532119975 254,920,1358,554 0.161,0.228,0.28,0.293 MCD,MCD,EZB,BN2 3.41875607645475,3.56794996582744,10.5961137860622 7 30.788349354144447 17.58281982834435 10.596113786062158 BN2 FALSE 554 -DLBCL-RICOVER_829-Tumor BN2 0.7452547575481607 2.0848937323537315 1003,459,1714,1854 0.189,0.195,0.209,0.277 BN2,BN2,ST2,BN2 14.0197708948844,4.79228062560307 7 29.229732467779748 18.81205152048747 14.019770894884397 BN2 TRUE 555 -DLBCL-RICOVER_850-Tumor MCD 1 0.11232351164830678 708,49,1870,188,800,586,1660,547,1273,1811 0.117,0.118,0.134,0.135,0.138,0.142,0.147,0.148,0.154,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.4803432258508 1 8.141246676602124 72.48034322585079 72.48034322585079 MCD TRUE 556 -DLBCL-RICOVER_865-Tumor BN2 1 0.07798017622003081 841,555,557,935,2210,1758,1029,991,857,795 0.177,0.203,0.215,0.237,0.269,0.309,0.312,0.323,0.325,0.335 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.7413619167765 1 3.021058229274225 38.74136191677653 38.74136191677653 BN2 TRUE 557 -DLBCL-RICOVER_866-Tumor EZB 1 0.08004326970222009 382,558,1138,676,93,174,2016,287,39,240 0.134,0.137,0.156,0.164,0.181,0.181,0.195,0.205,0.228,0.23 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.069338352888 1 4.56801644150747 57.06933835288804 57.06933835288804 EZB TRUE 558 -DLBCL-RICOVER_910-Tumor BN2 1 12.646912079222597 1803 0.297 BN2 3.36193138339813 9 42.5180506222154 3.361931383398134 3.361931383398134 Other FALSE 559 -DLBCL-RICOVER_945-Tumor BN2 0.8680231597555237 0.3201838134275178 88,2041,1477,922,1362,594,564,560,582 0.219,0.224,0.254,0.268,0.288,0.291,0.293,0.296,0.297 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.3018133555129,4.45513543807191 2 9.381966340509486 33.75694879358479 29.301813355512884 BN2 TRUE 560 -DLBCL-RICOVER_948-Tumor EZB 0.5818948933952555 1.740841112455501 383,141,561,844,2042 0.158,0.169,0.182,0.208,0.324 EZB,BN2,EZB,BN2,EZB 10.7103631084474,14.9060738574192 6 25.94910619629356 25.616436965866612 14.906073857419234 EZB TRUE 561 -DLBCL-RICOVER_950-Tumor ST2 0.5719573579278916 1.544454930403322 1082,2017,1121,1863,1847 0.129,0.132,0.185,0.194,0.236 BN2,ST2,ST2,EZB,ST2 7.72363832799241,5.14884360129751,17.2004142358576 5 26.56526457154974 30.072896165147505 17.20041423585759 Other FALSE 562 -DLBCL-RICOVER_956-Tumor Other 1 10 0 0 1 2 Other TRUE 563 -DLBCL-RICOVER_977-Tumor BN2 1 0.0837865655664606 417,592,1375,145,564,1193,1027,560,669,1158 0.119,0.141,0.173,0.201,0.224,0.226,0.227,0.241,0.272,0.288 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.8804930704022 1 4.263101768697101 50.880493070402245 50.880493070402245 BN2 TRUE 564 -DLBCL-RICOVER_978-Tumor EZB 0.8635851487114643 0.08821763479013033 433,565,170,1181,780,250,368,5,44,367 0.163,0.169,0.207,0.212,0.252,0.264,0.271,0.275,0.287,0.318 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.9047935465631,37.3807687715284 1 3.2976430076610033 43.28556231809152 37.38076877152842 BN2 FALSE 565 -DLBCL-RICOVER_985-Tumor ST2 0.716440916161354 3.966506225748658 566,1667,1971 0.143,0.143,0.181 ST2,ST2,BN2 5.53593327927179,13.9870994669544 8 55.47991711584045 19.523032746226214 13.987099466954422 ST2 TRUE 566 -DLBCL-RICOVER_99-Tumor BN2 1 2.478039150091049 1803 0.219 BN2 4.56534167416804 2 11.31309540213061 4.565341674168038 4.565341674168038 Other FALSE 567 -DLBCL-c_D_1103-Tumor ST2 0.85163256623885 0.9614046591011175 1077,271,701,1427,1442,618,1284 0.245,0.252,0.255,0.27,0.27,0.277,0.289 ST2,ST2,EZB,ST2,ST2,ST2,ST2 3.92752245686165,22.5440714589864 4 21.673975335778064 26.47159391584806 22.544071458986412 Other FALSE 568 -DLBCL-c_D_1104-Tumor MCD 0.7808590959919052 0.5318713247705068 569,850,801,1994,2130 0.188,0.189,0.207,0.225,0.236 MCD,BN2,MCD,MCD,MCD 5.28872726848395,18.8451846199538 2 10.023213309359594 24.133911888437723 18.845184619953773 MCD TRUE 569 -DLBCL-c_D_1105-Tumor BN2 1 1.6531809889201894 1813,70,1841,1148 0.152,0.21,0.234,0.296 BN2,BN2,BN2,BN2 19.008356731496 6 31.42425397912237 19.008356731496043 19.008356731496043 Other FALSE 570 -DLBCL-c_D_1106-Tumor BN2 1 0 961,667,260,579,892,502,907,1982,1935,692,571 0.137,0.144,0.15,0.212,0.226,0.228,0.229,0.25,0.283,0.288,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.2466684073496 0 0 53.24666840734955 53.24666840734955 BN2 TRUE 571 -DLBCL-c_D_1107-Tumor ST2 1 0.5998754024682023 271,1077,1427,1955,2198,1442 0.151,0.186,0.232,0.242,0.27,0.274 ST2,ST2,ST2,ST2,ST2,ST2 27.7928937944654 3 16.67227335071094 27.792893794465417 27.792893794465417 BN2 FALSE 572 -DLBCL-c_D_1108-Tumor EZB 0.5205491445903789 10.238907391875909 31,1052 0.209,0.227 EZB,ST2 4.79144516781217,4.41315197273748 9 49.059163346480084 9.204597140549645 4.7914451678121655 Other FALSE 573 -DLBCL-c_D_1109-Tumor Other 1 10 0 0 1 2 Other TRUE 574 -DLBCL-c_D_1110-Tumor EZB 1 0.1897681257357857 231,122,589,1083,575,332,42,343,16 0.13,0.139,0.144,0.145,0.162,0.179,0.222,0.224,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.4780899625714 2 10.1484369001269 53.47808996257135 53.47808996257135 EZB TRUE 575 -DLBCL-c_D_1111-Tumor Other 1 10 0 0 1 2 Other TRUE 576 -DLBCL-c_D_1112-Tumor ST2 0.687877849763702 3.877995653676498 724,1944 0.119,0.263 ST2,EZB 3.79931703249416,8.37321551483648 6 32.47129337383251 12.172532547330649 8.373215514836485 Other FALSE 577 -DLBCL-c_D_1113-Tumor EZB 0.9046503002872133 0 2093,340,1244,1042,365,399,578,1162,341,1554,166 0.205,0.218,0.226,0.226,0.235,0.244,0.258,0.27,0.281,0.289,0.294 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 4.25837655252652,40.4022418380258 0 0 44.66061839055227 40.40224183802575 EZB TRUE 578 -DLBCL-c_D_1114-Tumor BN2 0.8927546593315688 0.13963072792236517 692,232,933,1935,1075,579,892,981,961,1525 0.107,0.162,0.183,0.186,0.207,0.237,0.241,0.257,0.261,0.265 BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.5411831319263,5.47079720993414 1 6.358948551156609 51.01198034186046 45.54118313192632 BN2 TRUE 579 -DLBCL-c_D_1115-Tumor Other 1 10 0 0 1 2 Other TRUE 580 -DLBCL-c_D_1117-Tumor ST2 1 3.1496217712509096 2218,581 0.153,0.195 ST2,ST2 11.6553185053344 6 36.70984491526491 11.655318505334423 11.655318505334423 ST2 TRUE 581 -DLBCL-c_D_1118-Tumor BN2 0.9140109865669415 0.25029812690352354 582,1362,183,1320,489,1173,2041,931 0.109,0.181,0.194,0.214,0.296,0.298,0.308,0.314 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2 34.4645516419965,3.24238202566828 2 8.62641272056149 37.70693366766481 34.464551641996536 BN2 TRUE 582 -DLBCL-c_D_1120-Tumor EZB 0.5478425248611235 5.702687054527026 901,1387,1488 0.166,0.269,0.28 N1,EZB,EZB 7.28898378039671,6.01590485022656 8 41.566793445125754 13.304888630623264 7.2889837803967055 Other FALSE 583 -DLBCL-c_D_1121-Tumor ST2 0.5999314985736212 0.7534369207272651 545,2082,1045,1128,1417,1394,1970,1687 0.177,0.203,0.222,0.225,0.227,0.233,0.249,0.273 BN2,ST2,ST2,EZB,ST2,EZB,ST2,ST2 5.64019746337557,8.73032481779172,21.5496319672465 3 16.236288352208053 35.92015424841381 21.54963196724652 Other FALSE 584 -DLBCL-c_D_1123-Tumor BN2 1 0.09658815628101129 1252,995,1056,957,585,571,812,1029,418,795 0.126,0.135,0.15,0.153,0.164,0.176,0.187,0.215,0.225,0.228 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 59.1404806221615 1 5.712269984867451 59.140480622161455 59.140480622161455 BN2 TRUE 585 -DLBCL-c_D_1124-Tumor MCD 0.8894783902687728 0.25481461352802254 188,708,1273,1736,49,800,547,556,1640 0.117,0.134,0.137,0.154,0.161,0.172,0.173,0.18,0.182 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 6.48221240217734,52.1688732808955 2 13.293391283263762 58.651085683072814 52.168873280895475 MCD TRUE 586 -DLBCL-c_D_1125-Tumor MCD 1 0 1689,310,479,305,1449,764,709,1009,759,1699,1904 0.122,0.129,0.137,0.141,0.142,0.149,0.161,0.163,0.175,0.178,0.182 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.2861305411564 0 0 73.28613054115642 73.28613054115642 MCD TRUE 587 -DLBCL-c_D_1126-Tumor BN2 0.37778920726701404 8.343483560662408 286,1425,1975 0.218,0.231,0.309 BN2,N1,EZB 4.5930865045963,3.24133290992708,4.32338291610275 7 38.32234174379962 12.157802330626136 4.593086504596304 Other FALSE 588 -DLBCL-c_D_1127-Tumor EZB 1 0.10056855259833969 333,332,1083,589,1705,122,231,1034,354 0.158,0.178,0.19,0.197,0.222,0.226,0.239,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.1555722921303 1 4.340093441972554 43.15557229213027 43.15557229213027 EZB TRUE 589 -DLBCL-c_D_1128-Tumor BN2 0.5504898334275351 1.2682561901966236 838,1317,1722,960,1076,915,590 0.176,0.199,0.211,0.224,0.253,0.265,0.27 ST2,BN2,BN2,ST2,EZB,BN2,BN2 17.2422261866763,3.94538469225128,10.133996060333 4 21.867560094022558 31.321606939260633 17.242226186676312 BN2 TRUE 590 -DLBCL-c_D_1129-Tumor BN2 0.5136299255437169 2.805990101614703 610,1666,591,2216 0.159,0.164,0.21,0.229 BN2,EZB,BN2,ST2 11.0514450106791,6.09553916348099,4.36937315058168 6 31.01024530850487 21.51635732474181 11.051445010679144 BN2 TRUE 591 -DLBCL-c_D_1130-Tumor BN2 1 0.35253972025974295 1375,1027,417,592,1158,931,564,145 0.123,0.174,0.19,0.218,0.231,0.245,0.253,0.283 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.5996603694481 3 13.960453189026053 39.599660369448074 39.599660369448074 BN2 TRUE 592 -DLBCL-c_D_1131-Tumor EZB 0.5377826678386013 1.997634170791435 1415,581,737,402 0.131,0.178,0.223,0.242 EZB,ST2,BN2,EZB 4.4771377951192,11.749309612031,5.62124370430304 5 23.470822364201464 21.84769111145328 11.749309612031041 Other FALSE 593 -DLBCL-c_D_1132-Tumor BN2 1 0.255220786113911 1735,903,594,158,454,1210,922 0.136,0.162,0.178,0.185,0.222,0.241,0.244 BN2,BN2,BN2,BN2,BN2,BN2,BN2 37.276993722279 2 9.513863641763365 37.27699372227897 37.27699372227897 BN2 TRUE 594 -DLBCL-c_D_1133-Tumor EZB 1 0.1692854037204382 196,6,367,1788,44,1118,438,1142,28 0.13,0.159,0.166,0.174,0.18,0.2,0.203,0.204,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.5694402390219 1 8.560668106779401 50.56944023902193 50.56944023902193 EZB TRUE 595 -DLBCL-c_D_1134-Tumor BN2 1 1.0239621216135375 523,20,1096,596 0.203,0.225,0.242,0.266 BN2,BN2,BN2,BN2 17.2569466318278 4 17.670459685698006 17.256946631827823 17.256946631827823 BN2 TRUE 596 -DLBCL-c_D_1135-Tumor BN2 0.4136850644911677 0.7711721980571692 597,1377,1185,1763,15,827,476 0.118,0.254,0.298,0.317,0.335,0.355,0.393 BN2,MCD,ST2,EZB,EZB,BN2,MCD 11.2755668746574,6.13417430573087,6.48611824400706,3.36054367666019 3 8.695403691070172 27.25640310105554 11.275566874657425 BN2 TRUE 597 -DLBCL-c_D_1136-Tumor MCD 0.7934728010441972 1.2514719779101549 801,850,1994,569,2130 0.185,0.209,0.215,0.219,0.266 MCD,BN2,MCD,MCD,MCD 4.78584170205797,18.3870949680518 5 23.010934107689607 23.17293667010975 18.387094968051773 Other FALSE 598 -DLBCL-c_D_1138-Tumor ST2 0.5888888185233533 7.872279597116689 2216,292 0.148,0.211 ST2,EZB 4.73063861194972,6.77631820434998 9 53.34507154367471 11.506956816299697 6.776318204349976 Other FALSE 599 -DLBCL-c_D_1139-Tumor ST2 0.7838698476190759 0.12651448161538978 1145,1411,718,89,457,600,675,419,130,838 0.204,0.213,0.248,0.267,0.288,0.296,0.31,0.318,0.335,0.341 BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 7.89492324083683,28.633636767404 1 3.622569712391486 36.528560008240866 28.633636767404035 ST2 TRUE 600 -DLBCL-c_D_1140-Tumor Other 1 10 0 0 1 2 Other TRUE 601 -DLBCL-c_D_1141-Tumor EZB 1 0 10,602,503,116,277,128,691,48,275,118,1796 0.145,0.147,0.172,0.192,0.222,0.237,0.253,0.255,0.262,0.304,0.338 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.3912283354475 0 0 51.39122833544745 51.39122833544745 EZB TRUE 602 -DLBCL-c_D_1142-Tumor Other 1 10 0 0 1 2 Other TRUE 603 -DLBCL-c_D_1144-Tumor ST2 0.5720397888818423 9.188866440192683 1664,951 0.221,0.295 ST2,MCD 3.38420370834401,4.5235494435247 8 41.56629167215638 7.907753151868707 4.523549443524698 Other FALSE 604 -DLBCL-c_D_1145-Tumor EZB 0.5538381977483537 3.359279555828533 292,1983,2080,2216 0.176,0.201,0.217,0.251 EZB,EZB,ST2,ST2 10.6728450202116,8.59784642650044 7 35.85307007892327 19.270691446712057 10.672845020211621 Other FALSE 605 -DLBCL-c_D_1146-Tumor ST2 1 0.9178571770333572 1249,1955,606,1625,271,2198 0.197,0.274,0.28,0.283,0.284,0.291 ST2,ST2,ST2,ST2,ST2,ST2 22.7836571665727 5 20.912143249406245 22.78365716657271 22.78365716657271 ST2 TRUE 606 -DLBCL-c_D_1147-Tumor N1 1 2.3479561645376745 63,2175,849,2189 0.278,0.308,0.335,0.342 N1,N1,N1,N1 12.7454622714714 7 29.92578671018366 12.745462271471416 12.745462271471416 Other FALSE 607 -DLBCL-c_D_1148-Tumor BN2 0.5083423886383214 0.8373667477930336 1987,1497,2004,608,619,722,1776,1363 0.17,0.207,0.216,0.259,0.292,0.299,0.3,0.314 BN2,ST2,ST2,BN2,BN2,ST2,BN2,ST2 16.516763068709,15.974651060559 3 13.830588174912926 32.491414129267966 16.516763068708983 BN2 TRUE 608 -DLBCL-c_D_1149-Tumor Other 1 10 0 0 1 2 Other TRUE 609 -DLBCL-c_D_1150-Tumor BN2 0.6043274649249739 3.127124246677717 1875,951 0.206,0.314 BN2,MCD 4.86431432625502,3.18482229019665 4 15.211315273093842 8.049136616451662 4.864314326255015 BN2 TRUE 610 -DLBCL-c_D_1151-Tumor BN2 0.794590305598639 1.4514156677341001 591,610,1666,1816 0.142,0.19,0.236,0.246 BN2,BN2,EZB,BN2 16.3704402909316,4.23192570269605 4 23.760313525963706 20.60236599362765 16.3704402909316 Other FALSE 611 -DLBCL-c_D_1152-Tumor EZB 1 0.12850259475663803 105,1245,1243,341,166,505,1581,1782,1244,96 0.135,0.159,0.172,0.18,0.191,0.192,0.194,0.207,0.246,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3796605182931 1 6.8594248838291225 53.37966051829305 53.37966051829305 Other FALSE 612 -DLBCL-c_D_1153-Tumor MCD 1 7.294627623878429 311 0.201 MCD 4.98413219518038 6 36.35738839202465 4.984132195180383 4.984132195180383 Other FALSE 613 -DLBCL-c_D_1154-Tumor BN2 0.39658199813027906 5.167262488845525 1532,921,1835 0.145,0.186,0.195 BN2,MCD,EZB 6.90107478118727,5.1260935872919,5.37421347366949 5 35.659664849546814 17.401381842148652 6.901074781187269 Other FALSE 614 -DLBCL-c_D_1155-Tumor BN2 1 1.0677449537034216 915,1317 0.214,0.239 BN2,BN2 8.86878961723221 2 9.469605359256988 8.868789617232206 8.868789617232206 Other FALSE 615 -DLBCL-c_D_1156-Tumor BN2 0.5307825223912699 8.110758795141527 1433,537 0.216,0.245 BN2,ST2 4.61939476462369,4.08359482104031 7 37.466796715402346 8.702989585664007 4.619394764623694 Other FALSE 616 -DLBCL-c_D_1157-Tumor EZB 1 0 2132,1172,1914,1570,468,278,162,1779,1517,958,1080 0.11,0.14,0.154,0.191,0.195,0.196,0.197,0.199,0.215,0.216,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.9285401468248 0 0 61.9285401468248 61.9285401468248 EZB TRUE 617 -DLBCL-c_D_1158-Tumor ST2 0.6366222677061613 1.8508937011578956 618,701,744,1211,1018,1427 0.215,0.257,0.267,0.331,0.349,0.352 ST2,EZB,BN2,ST2,ST2,ST2 3.75143283567068,3.8848700082218,13.3784764484102 5 24.762137789451767 21.014779292302727 13.37847644841024 ST2 TRUE 618 -DLBCL-c_D_1159-Tumor ST2 0.6894332968200055 0.16399452313906193 1139,1484,235,1147,1176,234,619,1394,1363,1687 0.118,0.12,0.131,0.161,0.171,0.173,0.175,0.199,0.22,0.229 BN2,ST2,ST2,ST2,ST2,ST2,BN2,EZB,ST2,ST2 14.2059581428402,5.03048951502486,42.7033786689651 1 7.003120221243718 61.93982632683014 42.70337866896508 BN2 FALSE 619 -DLBCL-c_D_1162-Tumor BN2 0.5200081640885238 1.7582835996829136 2196,938,628,1521 0.215,0.279,0.314,0.373 BN2,ST2,EZB,BN2 7.33090993564073,3.18765404800849,3.57911840272337 4 12.889818710589616 14.097682386372586 7.330909935640728 Other FALSE 620 -DLBCL-c_D_1163-Tumor MCD 1 0 540,947,1572,587,483,888,516,515,2094,839,387 0.128,0.129,0.14,0.142,0.143,0.148,0.149,0.17,0.177,0.178,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.8514725591579 0 0 72.85147255915787 72.85147255915787 MCD TRUE 621 -DLBCL-c_D_1166-Tumor ST2 0.5031384894369876 3.851256464686211 190,2218,581,1415 0.197,0.205,0.233,0.251 BN2,ST2,ST2,EZB 5.08326885513454,3.98125058110134,9.17903383470381 7 35.3508133954765 18.243553270939685 9.179033834703807 Other FALSE 622 -DLBCL-c_D_pair13-Tumor N1 1 0.8938438126013311 63,2189,745,2175,891,814 0.146,0.177,0.193,0.197,0.241,0.247 N1,N1,N1,N1,N1,N1 31.0139232169994 5 27.721603372007667 31.013923216999384 31.013923216999384 Other FALSE 623 -DLBCL-c_D_pair2-Tumor BN2 1 0.9550231536624303 1096,20,523 0.187,0.219,0.276 BN2,BN2,BN2 13.5354826328818 3 12.92669931039779 13.535482632881756 13.535482632881756 Other FALSE 624 -DLBCL-c_D_pair20-Tumor EZB 0.75938670060942 1.0854748044408802 1492,625,1745,330,1356,207,993 0.166,0.206,0.23,0.241,0.251,0.275,0.324 EZB,EZB,EZB,BN2,EZB,EZB,BN2 7.23690184824563,22.8399969207546 4 24.792241190986456 30.076898769000277 22.839996920754647 EZB TRUE 625 -DLBCL-c_D_pair22-Tumor EZB 1 0.23497182239058853 1663,430,1483,337,336,1468,112,1384,646 0.106,0.153,0.165,0.173,0.173,0.222,0.23,0.24,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.4611998645756 2 11.856960092195058 50.46119986457564 50.46119986457564 Other FALSE 626 -DLBCL-c_D_pair23-Tumor BN2 0.6915735845649462 2.7116428006228364 811,150,1232 0.147,0.213,0.266 BN2,MCD,BN2 10.5419497687631,4.70147479811766 6 28.58600219499396 15.243424566880734 10.541949768763072 Other FALSE 627 -DLBCL-c_D_pair5-Tumor EZB 0.8352057437118978 0.947807707575971 1260,1002,628,1322,1521,1011 0.185,0.193,0.214,0.219,0.22,0.315 EZB,EZB,EZB,EZB,BN2,EZB 4.54031509023533,23.0111007934451 5 21.810098691834853 27.551415883680477 23.01110079344515 EZB TRUE 628 -DLBCL-c_D_pair9-Tumor ST2 0.6638409731722162 0.9813224104695397 629,2066,1152,1198,1174,268,1101 0.154,0.17,0.192,0.217,0.26,0.278,0.297 ST2,BN2,BN2,ST2,ST2,ST2,ST2 11.0912387560086,21.9027845212265 4 21.4936933023649 32.99402327723507 21.902784521226486 ST2 TRUE 629 -DLBCL10450T MCD 1 5.25620890109995 1480,864 0.26,0.285 MCD,MCD 7.36636906765172 9 38.7191746621783 7.366369067651718 7.366369067651718 Other FALSE 630 -DLBCL10451T EZB 1 0.0991969104201324 44,196,433,367,28,438,780,6,1788,250 0.144,0.196,0.205,0.216,0.23,0.251,0.261,0.262,0.267,0.282 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8210993634875 1 4.446114578491726 44.821099363487534 44.821099363487534 Other FALSE 631 -DLBCL10452T EZB 0.34777132395559285 1.8484239677123533 554,1366,1358,176,920,934 0.169,0.202,0.247,0.267,0.268,0.286 BN2,EZB,EZB,N1,MCD,MCD 5.90013376200425,8.99628483533895,7.22986972502947,3.74210339266043 4 16.628948510007696 25.8683917150331 8.99628483533895 EZB TRUE 632 -DLBCL10453T EZB 0.5978246508248192 5.786299509078485 140,446 0.19,0.283 EZB,MCD 5.25769508145505,3.53701600013217 6 30.422598468707704 8.79471108158722 5.257695081455047 Other FALSE 633 -DLBCL10454T BN2 1 0 1108 0.197 BN2 5.0778386066419 0 0 5.077838606641898 5.077838606641898 Other FALSE 634 -DLBCL10455T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 635 -DLBCL10456T BN2 0.45985899052629775 1.1980940323749418 636,1190,476,2121,826,15 0.179,0.271,0.307,0.321,0.322,0.358 ST2,BN2,MCD,BN2,BN2,EZB 9.90704772016862,2.79382656916738,3.25634159318704,5.5864484238554 3 11.869574751987793 21.543664306378428 9.907047720168617 ST2 FALSE 636 -DLBCL10457T Other 1 10 0 0 1 2 Other TRUE 637 -DLBCL10458T BN2 1 0.0936961554809364 418,907,571,995,638,1996,502,1252,260,812 0.207,0.22,0.238,0.239,0.261,0.269,0.275,0.285,0.287,0.288 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.4110179780211 1 3.6926608681306483 39.411017978021135 39.411017978021135 BN2 TRUE 638 -DLBCL10459T EZB 1 0.2583623432164357 42,639,333,155,1083,589,121,1705,231 0.17,0.206,0.21,0.226,0.227,0.232,0.235,0.235,0.238 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.3700513866035 2 10.688463415227243 41.37005138660353 41.37005138660353 EZB TRUE 639 -DLBCL10460T ST2 0.42926163131726647 3.1172309763568524 1183,1070,133,2140,24 0.196,0.249,0.264,0.348,0.356 ST2,EZB,BN2,ST2,EZB 3.79244203175053,6.82243164962181,7.983619540437 6 24.886786134898063 18.59849322180933 7.9836195404369965 Other FALSE 640 -DLBCL10461T MCD 1 1.1210738683253358 743,641 0.186,0.218 MCD,MCD 9.95823082351835 2 11.163912350998316 9.958230823518354 9.958230823518354 MCD TRUE 641 -DLBCL10462T MCD 1 1.059359718878089 467,2028,755,713 0.16,0.164,0.185,0.192 MCD,MCD,MCD,MCD 22.9209896433282 4 24.281573144963723 22.920989643328173 22.920989643328173 Other FALSE 642 -DLBCL10463T MCD 1 0.2664694838298949 2036,69,824,1225,1399,900,799,1412,765 0.18,0.217,0.222,0.239,0.267,0.295,0.301,0.308,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.6955266455967 2 9.51176856028841 35.69552664559669 35.69552664559669 MCD TRUE 643 -DLBCL10464T EZB 1 0 375,1560,1046,1304,276,1163,1085,1889,1081,142,1485 0.153,0.216,0.22,0.239,0.239,0.24,0.243,0.248,0.249,0.25,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4120557718347 0 0 48.41205577183473 48.41205577183473 EZB TRUE 644 -DLBCL10465T EZB 1 0 427,1020,161,366,645,1090,78,689,1589,498,48 0.166,0.205,0.221,0.23,0.253,0.271,0.278,0.279,0.31,0.324,0.381 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5198903615842 0 0 43.51989036158417 43.51989036158417 EZB TRUE 645 -DLBCL10466T EZB 1 0.21887752136900937 336,337,1384,430,1663,646,2176,1483,112 0.128,0.128,0.156,0.182,0.195,0.203,0.236,0.255,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4706301219046 2 10.828008901645523 49.470630121904556 49.470630121904556 EZB TRUE 646 -DLBCL10467T MCD 0.85693169902323 0.2569593213536922 1897,986,1420,1184,647,1543,467 0.134,0.141,0.172,0.179,0.194,0.209,0.21 MCD,MCD,BN2,MCD,MCD,MCD,MCD 5.81607920437343,34.836386538669 1 8.951534243391286 40.652465743042434 34.836386538669004 MCD TRUE 647 -DLBCL10469T BN2 0.5706418431222059 0.9661140091527787 648,489,666,1038,922,2041,594 0.212,0.212,0.22,0.24,0.256,0.283,0.3 ST2,BN2,BN2,ST2,BN2,ST2,BN2 16.5003701209983,12.4150876567179 4 15.94123873010243 28.915457777716284 16.500370120998344 ST2 FALSE 648 -DLBCL10470T ST2 0.7078107488221468 0.14391635692901597 235,234,1139,1176,1484,1147,649,1687,619 0.119,0.127,0.154,0.16,0.162,0.172,0.196,0.212,0.219 ST2,ST2,BN2,ST2,ST2,ST2,EZB,ST2,BN2 11.0666056842435,5.10032083641576,39.1633994769203 1 5.636253777674098 55.33032599757958 39.1633994769203 EZB FALSE 649 -DLBCL10471T MCD 0.8063794610160391 1.6790462044820775 1890,753,952,1412,1194 0.208,0.254,0.256,0.262,0.273 MCD,MCD,BN2,MCD,MCD 3.90106516277922,16.246927313899 6 27.279341640898245 20.14799247667818 16.24692731389896 MCD TRUE 650 -DLBCL10472T EZB 1 0.19941832420455258 1814,1241,651,331,1437,2003,1066,408,1372 0.152,0.162,0.188,0.21,0.255,0.291,0.298,0.305,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.8658693619692 2 7.949984861121508 39.865869361969175 39.865869361969175 EZB TRUE 651 -DLBCL10473T BN2 1 0.8446523366203109 2064,57,1113,106,817,1407 0.191,0.197,0.224,0.289,0.314,0.317 BN2,BN2,BN2,BN2,BN2,BN2 24.589738149636 5 20.769779784971607 24.589738149635952 24.589738149635952 Other FALSE 652 -DLBCL10474T EZB 1 10.371427976123565 199 0.267 EZB 3.75005877439714 7 38.893464484890174 3.750058774397143 3.750058774397143 Other FALSE 653 -DLBCL10475T EZB 1 0.20213674813683324 654,1539,25,1160,72,2073,1450,699,47,1423 0.177,0.253,0.261,0.267,0.304,0.305,0.353,0.355,0.362,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8756082401281 1 7.04964203895363 34.87560824012805 34.87560824012805 EZB TRUE 654 -DLBCL10476T EZB 0.44803323197721084 2.444690237990901 1123,854,649,234,1341 0.134,0.144,0.315,0.324,0.325 EZB,BN2,EZB,ST2,BN2 10.0166338539885,10.6342715981725,3.08454727584883 6 25.997499964196322 23.735452728009825 10.634271598172548 Other FALSE 655 -DLBCL10477T Other 1 10 0 0 1 2 Other TRUE 656 -DLBCL10478T EZB 0.5689283954234057 1.5519994162833228 2214,940,1964,826,1190,1289 0.158,0.277,0.368,0.387,0.4,0.413 EZB,ST2,EZB,BN2,BN2,EZB 5.08442960876949,11.4836347878973,3.61661065255621 4 17.822594487627455 20.184675049222996 11.48363478789729 Other FALSE 657 -DLBCL10479T MCD 0.8662291048837701 1.2003024477560196 713,2028,2130,569,850,755 0.165,0.188,0.189,0.195,0.254,0.27 MCD,MCD,MCD,MCD,BN2,MCD 3.93760202227201,25.4978145446183 5 30.60508921033435 29.435416566890286 25.497814544618272 Other FALSE 658 -DLBCL10480T MCD 0.5681377143853601 1.2274583468857083 659,869,1008,464,1609,1275 0.134,0.179,0.187,0.23,0.26,0.316 MCD,MCD,N1,BN2,MCD,BN2 7.5028986093508,16.8901900788607,5.3359534985642 5 20.732004792783734 29.729042186775693 16.89019007886069 MCD TRUE 659 -DLBCL10481T EZB 1 0.19966609652800618 1034,354,443,333,660,192,1705,332,149 0.129,0.146,0.19,0.231,0.246,0.257,0.261,0.275,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.9968872005664 2 8.585020630192076 42.99688720056636 42.99688720056636 EZB TRUE 660 -DLBCL10482T BN2 0.8295378804948987 1.5937878480812109 661,1135,151,281,1199 0.125,0.223,0.242,0.257,0.268 BN2,BN2,MCD,BN2,BN2 20.1101831095028,4.1324507500928 6 32.051365462613575 24.242633859595593 20.110183109502795 BN2 TRUE 661 -DLBCL10483T EZB 1 0.0796778662117477 205,2013,662,1060,499,75,1540,45,2072,259 0.147,0.185,0.193,0.198,0.217,0.218,0.228,0.23,0.234,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.3161831204414 1 3.8497303745328337 48.316183120441416 48.316183120441416 EZB TRUE 662 -DLBCL10484T EZB 1 13.206066320578888 1535 0.293 EZB 3.40924920461968 9 45.02277109958827 3.4092492046196763 3.4092492046196763 Other FALSE 663 -DLBCL10485T BN2 1 3.0745616885656486 1381,987,754 0.247,0.289,0.325 BN2,BN2,BN2 10.5863911560057 8 32.54851266842543 10.586391156005732 10.586391156005732 Other FALSE 664 -DLBCL10486T BN2 1 5.660725821361762 2032,523 0.339,0.353 BN2,BN2 5.78899620518279 9 32.76992029844346 5.788996205182788 5.788996205182788 Other FALSE 665 -DLBCL10487T ST2 0.7051907348315548 0.6140289547328851 911,2138,955,816,1486,508,1178 0.171,0.19,0.193,0.2,0.2,0.215,0.257 ST2,EZB,ST2,EZB,ST2,ST2,ST2 10.2669280289158,24.5587346687935 3 15.07977417824156 34.82566269770936 24.558734668793534 BN2 FALSE 666 -DLBCL10488T BN2 1 0.07667039845133464 1982,889,579,1935,111,1014,943,1929,961,266 0.138,0.139,0.179,0.187,0.198,0.205,0.214,0.251,0.256,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.7653470064053 1 3.9688697809526956 51.765347006405285 51.765347006405285 BN2 TRUE 667 -DLBCL10489T EZB 1 0 299,12,1110,2217,1237,1111,700,1055,1022,1563,1047 0.135,0.139,0.159,0.183,0.186,0.199,0.21,0.211,0.212,0.215,0.216 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 60.2639141530857 0 0 60.26391415308574 60.26391415308574 EZB TRUE 668 -DLBCL10490T BN2 0.9145902476931468 0 145,892,981,592,417,260,933,1193,907,669,418 0.225,0.241,0.248,0.249,0.277,0.284,0.295,0.31,0.316,0.324,0.332 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 36.2403989913838,3.38433906239739 0 0 39.62473805378122 36.24039899138383 BN2 TRUE 669 -DLBCL10491T EZB 1 9.83157154819241 2162 0.189 EZB 5.29489005853032 8 52.05709045025351 5.294890058530317 5.294890058530317 ST2 FALSE 670 -DLBCL10492T BN2 0.6795918699524844 2.7511341734370545 811,150,1232 0.15,0.206,0.278 BN2,MCD,BN2 10.2741661994739,4.84397551724827 6 28.26560973494442 15.118141716722125 10.274166199473852 Other FALSE 671 -DLBCL10493T Other 1 10 0 0 1 2 Other TRUE 672 -DLBCL10494T EZB 1 0.5534914557168562 1601,1395 0.192,0.206 EZB,EZB 10.0563312709461 1 5.566093434326888 10.056331270946078 10.056331270946078 Other FALSE 673 -DLBCL10495T BN2 0.7212407645165446 2.208724186079343 286,147,834 0.136,0.236,0.277 BN2,MCD,BN2 10.9520436855176,4.23296002521813 6 24.19004377520033 15.18500371073576 10.952043685517626 BN2 TRUE 674 -DLBCL10497T ST2 0.8077094139883653 0.3910721227380466 89,1411,675,600,457,1145,718,608 0.176,0.184,0.202,0.219,0.241,0.276,0.319,0.33 ST2,ST2,ST2,ST2,ST2,BN2,ST2,BN2 6.64887465307102,27.9283492816916 3 10.921998838160722 34.57722393476259 27.92834928169157 ST2 TRUE 675 -DLBCL10498T EZB 1 0.10172569227242338 382,1138,676,558,93,174,2089,287,2016,39 0.116,0.137,0.149,0.166,0.178,0.21,0.22,0.233,0.238,0.242 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.1910177359534 1 5.71607017868188 56.1910177359534 56.1910177359534 EZB TRUE 676 -DLBCL10499T EZB 0.4073994433095862 4.064315455890879 65,2127,1108 0.154,0.206,0.218 EZB,ST2,BN2 4.58332316365941,6.49014530266461,4.85719960219539 5 26.377997864597365 15.930668068519413 6.49014530266461 Other FALSE 677 -DLBCL10500T EZB 0.4156574478292495 2.951075105419559 1441,879,1416 0.153,0.205,0.231 EZB,MCD,ST2 6.5554148563567,4.88587169213183,4.32990891689295 4 19.345521588291806 15.771195465381492 6.5554148563567045 Other FALSE 678 -DLBCL10501T EZB 1 0.4626887812622263 2100,1867,729,548,712,135,513,679 0.181,0.213,0.23,0.268,0.285,0.294,0.298,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.7153738116759 3 14.674347656200252 31.715373811675903 31.715373811675903 EZB TRUE 679 -DLBCL10502T ST2 0.5086304248416844 11.082150078082442 724,1471 0.325,0.337 ST2,EZB 2.9699053071109,3.07423225707186 9 34.0691032477525 6.044137564182763 3.0742322570718623 Other FALSE 680 -DLBCL10503T Other 1 10 0 0 1 2 Other TRUE 681 -DLBCL10504T MCD 1 0 880,518,771,783,1239,1240,1598,2180,1459,777,929 0.129,0.139,0.142,0.153,0.154,0.154,0.16,0.161,0.162,0.166,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.1809263997427 0 0 72.18092639974275 72.18092639974275 Other FALSE 682 -DLBCL10505T ST2 0.5647124752436234 3.8258180269698885 1500,1226,2080,1099 0.229,0.344,0.369,0.393 ST2,EZB,ST2,EZB 5.45557928567033,7.07769808937903 7 27.077984939796632 12.533277375049364 7.07769808937903 Other FALSE 683 -DLBCL10506T ST2 0.7085226183438205 2.2787679432600894 1479,1710,951 0.298,0.412,0.42 ST2,ST2,MCD 2.37926345213515,5.78350869373817 5 13.179274210856565 8.162772145873312 5.783508693738165 BN2 FALSE 684 -DLBCL10507T MCD 1 0 751 0.141 MCD 7.0817535719006 0 0 7.081753571900604 7.081753571900604 Other FALSE 685 -DLBCL10508T MCD 0.7816031222898273 0.6237716250638063 715,236,686,1325,291,1271,1503 0.128,0.154,0.174,0.221,0.225,0.273,0.289 MCD,MCD,MCD,MCD,BN2,MCD,BN2 7.90059509774956,28.2748080517139 4 17.637022966784752 36.17540314946342 28.274808051713862 MCD TRUE 686 -DLBCL10509T BN2 1 1.3773615890738373 1957,99,1973,2069 0.202,0.212,0.244,0.254 BN2,BN2,BN2,BN2 17.7150641333361 5 24.400048885236743 17.715064133336092 17.715064133336092 BN2 TRUE 687 -DLBCL10510T ST2 1 0.9929827663588061 90,1946 0.114,0.155 ST2,ST2 15.2231786864012 2 15.116354084797042 15.223178686401162 15.223178686401162 Other FALSE 688 -DLBCL10512T EZB 1 0.09804396336271859 689,128,712,366,48,503,1020,277,602 0.158,0.207,0.256,0.275,0.3,0.309,0.31,0.315,0.316 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 34.8131510779915 1 3.413219308831384 34.81315107799148 34.81315107799148 EZB TRUE 689 -DLBCL10513T EZB 0.8748495412399255 0.8183543621117276 62,179,1053,2126,1453,993 0.151,0.155,0.163,0.2,0.202,0.24 EZB,EZB,EZB,EZB,EZB,BN2 4.16932019466826,29.1451417415941 4 23.851053878598087 33.31446193626232 29.145141741594053 Other FALSE 690 -DLBCL10514T EZB 1 0 731,438,28,275,277,1788,196,116,1287,44,1118 0.121,0.191,0.199,0.201,0.235,0.243,0.271,0.28,0.282,0.286,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.6241893717564 0 0 49.62418937175642 49.62418937175642 EZB TRUE 691 -DLBCL10515T BN2 0.8565565948320637 0.10103896286621017 933,892,692,232,981,260,961,1935,579,1075 0.139,0.164,0.172,0.193,0.203,0.217,0.229,0.24,0.248,0.264 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.9766791462408,7.19709735088231 1 4.342319088370054 50.17377649712312 42.97667914624081 BN2 TRUE 692 -DLBCL10516T EZB 0.5013907400769715 1.7717922691225159 1797,1388,1415,737,402,938 0.306,0.313,0.393,0.4,0.422,0.437 ST2,EZB,EZB,BN2,EZB,ST2 2.5010514150899,8.10687123639743,5.56084671021428 3 14.363691783420661 16.16876936170161 8.106871236397431 EZB TRUE 693 -DLBCL10517T EZB 1 0.2166479328044984 178,259,1551,320,75,56,662,1218,319 0.121,0.123,0.124,0.149,0.174,0.207,0.223,0.232,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.8374097232914 2 11.880411456904373 54.83740972329135 54.83740972329135 Other FALSE 694 -DLBCL10518T EZB 1 0 695,2089,676,287,382,1903,2201,172,1138,558,173 0.137,0.159,0.215,0.219,0.271,0.271,0.289,0.293,0.299,0.308,0.309 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.8856871249275 0 0 46.88568712492748 46.88568712492748 EZB TRUE 695 -DLBCL10519T EZB 0.6531622547726014 0.23549103554838574 696,1392,1122,1227,1703 0.158,0.188,0.205,0.229,0.275 EZB,EZB,BN2,EZB,ST2 4.87145459312715,16.0259725147615,3.63854752576643 1 3.7739728631711587 24.53597463365511 16.025972514761524 EZB TRUE 696 -DLBCL10520T N1 1 1.7071822383991986 3,697,2077 0.187,0.267,0.295 N1,N1,N1 12.4940198924381 5 21.329568846576652 12.494019892438137 12.494019892438137 N1 TRUE 697 -DLBCL10521T EZB 0.5217165541727116 7.34836725568776 140,768 0.219,0.239 EZB,MCD 4.56733489780582,4.18710247106062 7 33.56245420879628 8.75443736886644 4.567334897805819 Other FALSE 698 -DLBCL10522T EZB 1 0.10733965769934511 520,477,1450,699,1542,1539,1160,119,72,1668 0.137,0.155,0.157,0.162,0.191,0.261,0.264,0.266,0.275,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0258645875386 1 5.369759180940183 50.02586458753859 50.02586458753859 EZB TRUE 699 -DLBCL10523T EZB 1 0 2097,1109,1217,1589,248,1090,522,644,161,1510,71 0.196,0.229,0.232,0.236,0.256,0.269,0.298,0.308,0.329,0.334,0.348 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.137930472908 0 0 41.13793047290798 41.13793047290798 EZB TRUE 700 -DLBCL10524T ST2 0.7957386761694797 1.3225384155278141 618,701,1211,1018,1130 0.235,0.286,0.3,0.324,0.337 ST2,EZB,ST2,ST2,ST2 3.50216560853512,13.6433494740996 5 18.04385379596789 17.14551508263469 13.643349474099576 EZB FALSE 701 -DLBCL10527T ST2 1 3.356090777425447 530,702 0.212,0.238 ST2,ST2 8.91352468637643 6 29.91459799430199 8.91352468637643 8.91352468637643 ST2 TRUE 702 -DLBCL10528T BN2 0.6141911922793254 2.987561750857352 2218,190,189 0.148,0.166,0.211 ST2,BN2,BN2 10.7678254470173,6.7638903808454 5 32.169543445417425 17.53171582786272 10.767825447017323 Other FALSE 703 -DLBCL10529T MCD 0.8867288696991388 0.5626796721217948 465,347,1526,704,2053,246,2185 0.177,0.211,0.216,0.218,0.232,0.269,0.283 MCD,MCD,MCD,MCD,MCD,MCD,ST2 27.6435159735184,3.53119471668125 4 15.554444504272917 31.17471069019963 27.643515973518376 MCD TRUE 704 -DLBCL10530T MCD 1 0 1567,515,516,387,540,888,2094,705,1572,59,587 0.124,0.129,0.141,0.149,0.166,0.169,0.178,0.179,0.18,0.185,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.0570861337582 0 0 69.05708613375823 69.05708613375823 MCD TRUE 705 -DLBCL10531T EZB 1 3.0093872112614894 1601,1649 0.209,0.305 EZB,EZB 8.07583915535868 6 24.303327074341208 8.075839155358683 8.075839155358683 Other FALSE 706 -DLBCL10532T MCD 1 0 709,21,480,1331,2034,1233,23,1689,2008,59,310 0.115,0.136,0.138,0.145,0.149,0.15,0.159,0.161,0.167,0.167,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.9450590046452 0 0 73.94505900464523 73.94505900464523 MCD TRUE 707 -DLBCL10533T MCD 0.8663709664744967 0.2455953248838852 1736,188,1640,708,556,1273,792,49,800 0.138,0.144,0.154,0.159,0.17,0.178,0.187,0.188,0.205 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.22215298200944,46.8241331540875 2 11.499788194384422 54.04628613609695 46.82413315408751 MCD TRUE 708 -DLBCL10534T MCD 1 0 305,1699,479,1331,764,798,905,910,21,709,1689 0.128,0.132,0.137,0.144,0.152,0.153,0.159,0.159,0.161,0.162,0.162 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.8304848138622 0 0 73.8304848138622 73.8304848138622 MCD TRUE 709 -DLBCL10535T ST2 0.4853869533071614 2.542993210210247 1388,1797,938,1841 0.174,0.19,0.372,0.372 EZB,ST2,ST2,BN2 2.68631464659672,5.75838750263162,7.96510751941148 5 20.255214340457982 16.40980966863982 7.965107519411482 Other FALSE 710 -DLBCL10536T N1 1 1.2267208728454966 814,745,891,2189,63 0.304,0.338,0.355,0.356,0.385 N1,N1,N1,N1,N1 14.4653682294374 5 17.744969140446997 14.465368229437429 14.465368229437429 Other FALSE 711 -DLBCL10537T EZB 1 0.23746204499157522 689,712,2100,128,498,161,366,427,1020 0.153,0.259,0.278,0.3,0.313,0.321,0.348,0.365,0.366 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.9594019573011 2 7.589144945488478 31.95940195730113 31.95940195730113 EZB TRUE 712 -DLBCL10538T MCD 0.8635413469203368 1.1616013713478066 713,2130,569,2028,850,755 0.168,0.171,0.187,0.197,0.243,0.265 MCD,MCD,MCD,MCD,BN2,MCD 4.11323170934655,26.029464385897 5 30.235861526106813 30.142696095243522 26.02946438589697 MCD TRUE 713 -DLBCL10539T ST2 1 3.3916175127990877 1667,566 0.185,0.213 ST2,ST2 10.0843742217619 6 34.202340216147434 10.08437422176193 10.08437422176193 Other FALSE 714 -DLBCL10540T MCD 0.6730835004856938 0.4334309965742236 2014,236,790,1503,749,2084,291,1271,758 0.225,0.237,0.237,0.253,0.258,0.26,0.267,0.282,0.288 MCD,MCD,MCD,BN2,MCD,BN2,BN2,MCD,MCD 11.5437015791744,23.7671548515187 2 10.301421613027655 35.310856430693164 23.767154851518725 MCD TRUE 715 -DLBCL10541T ST2 0.5265024179274038 5.652353567205613 833,286 0.231,0.257 ST2,BN2 3.88587602709348,4.32087343524633 5 24.42310437515858 8.206749462339813 4.320873435246333 Other FALSE 716 -DLBCL10542T MCD 0.5163645662482238 2.5331152900741443 1278,952,1398,2030,1890 0.187,0.302,0.314,0.319,0.343 EZB,BN2,MCD,MCD,MCD 3.30928902234086,5.33474040335377,9.22899811209149 6 23.378116229804363 17.87302753778612 9.228998112091489 MCD TRUE 717 -DLBCL10543T ST2 0.9000880686348081 0.19714911984250558 457,1411,419,1302,718,608,1482,675,1461 0.15,0.172,0.202,0.24,0.243,0.249,0.255,0.304,0.309 ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.01158733896524,36.1396466943589 2 7.124899537211965 40.151234033324094 36.13964669435886 ST2 TRUE 718 -DLBCL10544T ST2 0.8685739828371077 0.6368735552370425 444,339,2052,1970,959,1128,1404 0.186,0.198,0.208,0.209,0.23,0.234,0.257 ST2,ST2,ST2,ST2,ST2,EZB,ST2 4.27465914605044,28.2505534285065 3 17.992030399426955 32.52521257455694 28.2505534285065 Other FALSE 719 -DLBCL10545T BN2 1 1.1229929823468272 2032 0.211 BN2 4.74210912692761 1 5.325355271062547 4.742109126927611 4.742109126927611 Other FALSE 720 -DLBCL10546T BN2 1 1.019155187692765 1135,661,1126,281,830,1158 0.216,0.226,0.239,0.254,0.278,0.284 BN2,BN2,BN2,BN2,BN2,BN2 24.2820065738701 5 24.747132967349533 24.2820065738701 24.2820065738701 Other FALSE 721 -DLBCL10547T ST2 0.6381022052041397 0.33366234589366167 1341,1943,486,1555,293,1789,1130,722,1776 0.142,0.193,0.215,0.221,0.225,0.232,0.243,0.254,0.258 BN2,ST2,ST2,ST2,ST2,EZB,ST2,ST2,BN2 10.9311331810924,4.30600838153511,26.8662969820105 2 8.964271676493425 42.10343854463802 26.866296982010496 ST2 TRUE 722 -DLBCL10548T BN2 1 1.7176785406176867 915,1119,684 0.226,0.247,0.258 BN2,BN2,BN2 12.3516233934538 5 21.216118444727044 12.351623393453824 12.351623393453824 Other FALSE 723 -DLBCL10549T EZB 0.6562544870670588 3.8640951098294916 1944,160,1471 0.241,0.253,0.294 EZB,ST2,EZB 7.55227920580018,3.95587708819017 7 29.182725147199434 11.50815629399035 7.552279205800181 ST2 FALSE 724 -DLBCL10550T BN2 1 2.6113224285545202 1381,52,1108 0.175,0.249,0.323 BN2,BN2,BN2 12.8299701679959 8 33.50318885737301 12.829970167995867 12.829970167995867 Other FALSE 725 -DLBCL10552T ST2 0.690989962055712 0.49740400890213854 1776,722,293,1497,1176,1341,1555 0.119,0.129,0.14,0.202,0.22,0.227,0.24 BN2,ST2,ST2,ST2,ST2,BN2,ST2 12.7850945650746,28.5892719445974 3 14.220418476836162 41.37436650967197 28.589271944597353 ST2 TRUE 726 -DLBCL10553T EZB 1 0 172,1573,108,1972,1752,2089,117,398,695,397 0.162,0.223,0.232,0.247,0.252,0.271,0.28,0.28,0.281,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.8485328685839 0 0 40.84853286858388 40.84853286858388 ST2 FALSE 727 -DLBCL10554T EZB 1 0.14561293433805073 1032,474,1579,1545,1017,227,1581,728,1243,1042 0.139,0.185,0.21,0.236,0.278,0.282,0.294,0.298,0.306,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.984413850185 1 6.113473697188533 41.98441385018498 41.98441385018498 EZB TRUE 728 -DLBCL10555T EZB 1 0.3385557460207684 548,2100,660,289,729,443,498,689 0.176,0.179,0.266,0.271,0.287,0.312,0.314,0.352 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 31.4469550215704 3 10.646547317409302 31.44695502157036 31.44695502157036 EZB TRUE 729 -DLBCL10564T EZB 0.4960124196820567 2.060878584437799 2183,1725,353,1909 0.233,0.244,0.304,0.337 EZB,ST2,BN2,EZB 3.28943114889535,7.26574854188819,4.093140096341 4 14.97382556988753 14.648319787124532 7.265748541888189 Other FALSE 730 -DLBCL10565T EZB 1 0.18146824444713897 277,128,712,602,503,731,275,116,689 0.163,0.178,0.205,0.266,0.279,0.28,0.287,0.288,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.8358657923167 2 6.866008142469266 37.835865792316675 37.835865792316675 EZB TRUE 731 -DLBCL10566T EZB 1 8.73065473051215 2162 0.267 EZB 3.74110957854424 6 32.662336039281556 3.7411095785442368 3.7411095785442368 Other FALSE 732 -DLBCL10780T EZB 1 0.17941917561783294 2132,1172,278,1570,1779,1914,1517,162,733 0.132,0.135,0.167,0.17,0.176,0.178,0.188,0.208,0.22 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.8275049419587 2 9.478267386633227 52.827504941958715 52.827504941958715 EZB TRUE 733 -DLBCL10781T N1 1 9.20939888680807 913 0.316 N1 3.16489182700849 7 29.146751268519917 3.1648918270084865 3.1648918270084865 Other FALSE 734 -DLBCL10782T BN2 1 1.1038831054802167 1210,903,1735,158,2213 0.114,0.194,0.207,0.223,0.242 BN2,BN2,BN2,BN2,BN2 27.4104524758564 6 30.257935401666295 27.41045247585643 27.41045247585643 Other FALSE 735 -DLBCL10833T MCD 0.6400054338811402 1.1833257797800962 2135,151,736,150,1278 0.212,0.228,0.251,0.276,0.315 MCD,MCD,BN2,MCD,EZB 3.99128264085302,3.17027868951982,12.7319648624872 4 15.066062249035499 19.893526192860076 12.731964862487239 BN2 FALSE 736 -DLBCL10834T EZB 0.3910017378429145 3.0122457451765805 581,737,1415,402,793 0.145,0.167,0.175,0.185,0.224 ST2,BN2,EZB,EZB,BN2 10.4543785250712,11.129160692189,6.87966085337522 6 33.523766942432914 28.46320007063551 11.12916069218905 BN2 FALSE 737 -DLBCL10835T MCD 1 0 485,1171,455,1977,214,2207,1543,2010,1728,896 0.193,0.209,0.245,0.25,0.265,0.27,0.273,0.282,0.291,0.293 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.6083187156748 0 0 39.60831871567482 39.60831871567482 MCD TRUE 738 -DLBCL10836T MCD 1 0.10065441232024418 837,156,767,1491,788,471,1224,1968,950,1422 0.116,0.131,0.142,0.15,0.152,0.158,0.158,0.164,0.165,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.2531437469398 1 6.769325660537128 67.25314374693978 67.25314374693978 MCD TRUE 739 -DLBCL10837T BN2 0.6814315172267115 2.492645925506224 811,150,1232 0.16,0.21,0.255 BN2,MCD,BN2 10.1673179572363,4.75320993178067 5 25.343523679431307 14.920527889016963 10.16731795723629 Other FALSE 740 -DLBCL10838T Other 1 10 0 0 1 2 Other TRUE 741 -DLBCL10839T BN2 0.9123622700034913 0.3686339394004162 742,1525,1075,817,1115,232,274,1203 0.11,0.149,0.188,0.212,0.227,0.254,0.275,0.276 BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2 37.8546735391682,3.63616270400891 3 13.954517431460266 41.4908362431771 37.85467353916819 BN2 TRUE 742 -DLBCL10842T MCD 0.6864857602281158 5.2718472453626175 743,1064 0.128,0.28 MCD,N1 7.82931221501377,3.57560347094617 9 41.274938033804226 11.404915685959933 7.8293122150137675 MCD TRUE 743 -DLBCL10843T BN2 0.8259613349694882 1.4037181516243407 1614,744,591,1427 0.208,0.306,0.461,0.464 BN2,BN2,BN2,ST2 10.2372497413669,2.15709525753681 6 14.370213284668344 12.394344998903733 10.237249741366927 BN2 TRUE 744 -DLBCL10844T N1 1 0.6549029885091987 891,814,745,2189,63,2175 0.12,0.146,0.165,0.188,0.262,0.276 N1,N1,N1,N1,N1,N1 34.0204500855218 4 22.280094431436275 34.02045008552184 34.02045008552184 N1 TRUE 745 -DLBCL10845T MCD 1 2.1160619044916644 801,1994,768 0.137,0.176,0.212 MCD,MCD,MCD 17.686342675697 6 37.42539596582755 17.686342675696977 17.686342675696977 Other FALSE 746 -DLBCL10846T BN2 1 1.5638463525173991 987,754,1113,1381 0.193,0.222,0.252,0.265 BN2,BN2,BN2,BN2 17.3989385885237 6 27.20926664933694 17.398938588523652 17.398938588523652 Other FALSE 747 -DLBCL10847T EZB 0.5915227197901995 3.529754936546928 140,446 0.144,0.209 EZB,MCD 6.940492719655,4.79277210255951 4 24.498238439270267 11.733264822214519 6.940492719655005 Other FALSE 748 -DLBCL10848T MCD 1 0 1911,761,790,1897,986,1543,749,755,1184,883 0.16,0.196,0.227,0.26,0.262,0.264,0.267,0.282,0.283,0.286 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.4953536326717 0 0 41.49535363267165 41.49535363267165 MCD TRUE 749 -DLBCL10849T BN2 0.6144597533267385 1.6089763385980342 147,286,834 0.179,0.191,0.272 MCD,BN2,BN2 8.90516544658269,5.58750945746001 3 14.328200494852345 14.492674904042698 8.90516544658269 Other FALSE 750 -DLBCL10850T MCD 0.7188625556418341 4.308669770341578 751,81,401 0.205,0.235,0.28 MCD,MCD,EZB 3.57178096763126,9.13296910856582 8 39.350947911541034 12.704750076197087 9.132969108565824 MCD TRUE 751 -DLBCL10851T MCD 0.709194605658743 4.025433594920878 81,401,82 0.159,0.233,0.238 MCD,EZB,MCD 4.29258871473429,10.4684466658439 8 42.14003689532552 14.761035380578194 10.468446665843906 Other FALSE 752 -DLBCL10852T MCD 1 1.3795461935104951 650,2053,1063,864,753 0.167,0.2,0.207,0.212,0.238 MCD,MCD,MCD,MCD,MCD 24.7558438683167 6 34.15183017567643 24.755843868316695 24.755843868316695 MCD TRUE 753 -DLBCL10853T BN2 1 0.7801681580991618 754,987,1113,1204 0.15,0.164,0.205,0.267 BN2,BN2,BN2,BN2 21.413368104816 3 16.706027953033672 21.413368104816044 21.413368104816044 BN2 TRUE 754 -DLBCL10854T MCD 0.875598146836821 1.242655430556482 467,755,2028,713,1897,1420 0.13,0.155,0.195,0.213,0.232,0.249 MCD,MCD,MCD,MCD,MCD,BN2 4.01666204339965,28.2711370630249 5 35.13128199937452 32.287799106424544 28.271137063024895 MCD TRUE 755 -DLBCL10855T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 756 -DLBCL10856T EZB 0.3645903404039752 0.5883098848700333 757,1330,972,2140,132,82,133,1070,1107 0.201,0.239,0.258,0.264,0.265,0.272,0.283,0.284,0.289 EZB,EZB,BN2,ST2,BN2,MCD,BN2,EZB,ST2 11.1821652928043,12.676923137779,3.67201346074683,7.2392190220892 2 7.457959191693032 34.77032091341933 12.676923137779012 EZB TRUE 757 -DLBCL10857T MCD 0.7671760607103316 0.12351670064061385 2084,1127,749,1732,883,758,1503,790,1271,154 0.138,0.179,0.181,0.184,0.191,0.192,0.216,0.229,0.254,0.259 BN2,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 11.8724008895611,39.1206410020205 1 4.832052503515495 50.99304189158167 39.12064100202054 MCD TRUE 758 -DLBCL10858T MCD 0.8492175205179433 0 1376,1694,767,1224,759,1310,1449,858,156 0.112,0.129,0.151,0.155,0.16,0.161,0.168,0.177,0.177 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.96621213653231,50.4983368437708 0 0 59.46454898030309 50.49833684377079 MCD TRUE 759 -DLBCL10859T MCD 0.6703308952894761 5.651588538894485 751,401,81 0.197,0.211,0.22 MCD,EZB,MCD 4.73517043832162,9.62823326151698 8 54.41481275059204 14.3634036998386 9.628233261516982 Other FALSE 760 -DLBCL10860T MCD 1 0.196507462413082 761,790,1911,749,755,2014,1897,883,986 0.175,0.223,0.224,0.276,0.284,0.306,0.31,0.317,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.5972028601534 2 6.798608540639365 34.59720286015339 34.59720286015339 MCD TRUE 761 -DLBCL10861T Other 1 10 0 0 1 2 Other TRUE 762 -DLBCL10862T MCD 0.7158934522972052 4.035585269850525 743,1064 0.11,0.277 MCD,N1 9.08996186764926,3.60740509175064 8 36.68331621658834 12.6973669593999 9.089961867649263 Other FALSE 763 -DLBCL10863T MCD 1 0.08775492942519349 1905,918,2036,1399,1826,896,1728,1233,480,738 0.215,0.226,0.312,0.333,0.346,0.354,0.388,0.402,0.412,0.414 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 30.9220227672957 1 2.713559925628259 30.922022767295687 30.922022767295687 MCD TRUE 764 -DLBCL10864T MCD 1 0.1658397353119751 1526,765,799,2053,900,465,69,704,296 0.151,0.16,0.18,0.187,0.199,0.229,0.258,0.29,0.291 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.9492325382877 2 7.28852909131407 43.94923253828766 43.94923253828766 MCD TRUE 765 -DLBCL10865T EZB 0.421804334234771 2.982271264949275 1983,1082,2017,1863,1121 0.194,0.21,0.211,0.216,0.255 EZB,BN2,ST2,EZB,ST2 4.76437489637644,9.79654310861215,8.66440784754169 6 29.21594900865085 23.225325852530272 9.796543108612147 Other FALSE 766 -DLBCL10866T MCD 1 0.0992486981651572 837,156,767,788,1491,1224,471,1694,1968,950 0.123,0.132,0.139,0.153,0.155,0.157,0.162,0.165,0.167,0.171 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.3656808542789 1 6.586707427631481 66.36568085427892 66.36568085427892 MCD TRUE 767 -DLBCL10867T MCD 0.5067789911196383 4.570923209005617 768,140 0.226,0.232 MCD,EZB 4.30679378716375,4.42518175649852 4 20.22716599484732 8.731975543662266 4.425181756498518 MCD TRUE 768 -DLBCL10868T MCD 0.7471438585356942 1.5588634190641166 1907,717,632 0.123,0.216,0.231 MCD,MCD,EZB 4.32697092078248,12.7854112255428 3 19.93070985719035 17.112382146325256 12.785411225542777 Other FALSE 769 -DLBCL10869T Other 1 10 0 0 1 2 Other TRUE 770 -DLBCL10870T MCD 1 0.20924621415955405 2180,1250,1239,771,880,929,1240,1977,777 0.139,0.145,0.164,0.169,0.172,0.175,0.19,0.197,0.202 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 52.9574346116053 2 11.081142704080552 52.95743461160534 52.95743461160534 MCD TRUE 771 -DLBCL10871T BN2 0.47246557545934953 6.4941895633331015 286,147,833 0.176,0.315,0.316 BN2,MCD,ST2 5.68369823022535,3.17785996347451,3.16830921981368 8 36.91101372786427 12.029867413513532 5.683698230225348 Other FALSE 772 -DLBCL10872T EZB 1 2.2451099995562105 31 0.171 EZB 5.86188434639605 2 13.160575162335805 5.861884346396055 5.861884346396055 Other FALSE 773 -DLBCL10873T ST2 0.680795028976003 0.6158442810965242 1789,486,1130,1943,1341,1713,1211 0.121,0.132,0.15,0.215,0.269,0.293,0.311 EZB,ST2,ST2,ST2,BN2,ST2,ST2 3.72362647038447,8.23800107503416,25.5115593759699 4 15.711147943545452 37.4731869213885 25.511559375969863 Other FALSE 774 -DLBCL10874T EZB 0.4891592100824958 1.2130371032991352 1797,1415,1388,737,581,402 0.338,0.415,0.428,0.481,0.505,0.507 ST2,EZB,EZB,BN2,ST2,EZB 2.07946233320672,6.71600065745866,4.93421953197591 3 8.146757983278741 13.729682522641287 6.71600065745866 Other FALSE 775 -DLBCL10875T ST2 0.5939874222392578 2.1272181148441374 744,1427,1077,618,701 0.236,0.261,0.266,0.282,0.295 BN2,ST2,ST2,ST2,EZB 4.23923899932323,3.38488230337794,11.1539208573473 5 23.72682249928698 18.77804216004844 11.153920857347277 Other FALSE 776 -DLBCL10876T MCD 0.8768187047595247 0.32593720936961845 986,1897,1184,1543,1420,647,1911 0.119,0.129,0.154,0.183,0.19,0.204,0.221 MCD,MCD,MCD,MCD,BN2,MCD,MCD 5.26616727649252,37.4851876772923 2 12.21781746423305 42.751354953784784 37.48518767729227 MCD TRUE 777 -DLBCL10877T BN2 0.6237577419418868 3.6934811948362647 1603,2096,596 0.197,0.21,0.272 MCD,BN2,BN2 8.43310869316318,5.0867374363761 7 31.14752837220843 13.519846129539278 8.43310869316318 Other FALSE 778 -DLBCL10878T BN2 1 0.7482809600095612 1204,871,1275,273,754 0.194,0.194,0.25,0.253,0.266 BN2,BN2,BN2,BN2,BN2 22.0084237466905 3 16.468484449470786 22.008423746690493 22.008423746690493 Other FALSE 779 -DLBCL10879T EZB 0.9242299442345111 0 433,780,44,250,368,691,196,28,5,565,170 0.143,0.194,0.206,0.214,0.222,0.254,0.264,0.266,0.267,0.271,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.69093849332107,45.0214249335273 0 0 48.71236342684838 45.021424933527314 EZB TRUE 780 -DLBCL10880T N1 1 2.474559423924395 913 0.196 N1 5.09007382337646 2 12.595690148107096 5.090073823376461 5.090073823376461 MCD FALSE 781 -DLBCL10881T MCD 0.7721201494031461 2.468752273186658 254,1890,952,1398 0.198,0.238,0.268,0.294 MCD,MCD,BN2,MCD 3.73487575816001,12.6547951512151 6 31.241554296273833 16.38967090937513 12.654795151215124 Other FALSE 782 -DLBCL10883T MCD 0.8264446698600141 0.4636865944814753 647,1420,986,1184,1897,1543,467,1977 0.122,0.137,0.203,0.203,0.208,0.24,0.252,0.265 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 7.30468928559341,34.7838439775545 3 16.1288021569272 42.08853326314786 34.783843977554454 MCD TRUE 783 -DLBCL10884T EZB 0.7457632442101885 2.97799603553086 904,1400,816,2138 0.308,0.313,0.315,0.318 MCD,EZB,EZB,EZB 9.5217248773612,3.24603345785325 6 28.355658936197223 12.767758335214447 9.521724877361201 Other FALSE 784 -DLBCL10885T BN2 1 0.0757059217422146 1193,564,560,145,669,417,592,1375,1477,88 0.149,0.149,0.158,0.188,0.196,0.2,0.204,0.238,0.263,0.264 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.8438977036282 1 3.9248900623622522 51.84389770362815 51.84389770362815 Other FALSE 785 -DLBCL10887T MCD 0.9159945736719849 0.23551518018201822 1251,786,1197,738,934,1826,2145,1501,988 0.171,0.179,0.218,0.264,0.274,0.288,0.329,0.331,0.331 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.02534503834269,32.9883408696326 2 7.769255043817356 36.01368590797529 32.988340869632594 MCD TRUE 786 -DLBCL10888T BN2 1 3.5364292542169897 894,2211 0.197,0.279 BN2,BN2 8.65914268515598 7 30.622445508224654 8.659142685155977 8.659142685155977 Other FALSE 787 -DLBCL10889T MCD 1 0.1577035103379182 2036,69,900,481,296,739,1968,799,643,765 0.174,0.227,0.241,0.286,0.288,0.292,0.293,0.295,0.299,0.301 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.1663706000567 1 6.018970620486864 38.16637060005673 38.16637060005673 MCD TRUE 788 -DLBCL10890T MCD 0.90941800394791406 0.2832223925247621 1127,154,1732,997,214,883,1531,2084,789 0.146,0.156,0.202,0.205,0.208,0.209,0.222,0.244,0.255 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 4.09842590708321,41.1470542733994 2 11.653767156658429 45.24548018048266 41.14705427339945 MCD TRUE 789 -DLBCL10891T MCD 0.8078303048154267 0.09777815550904957 749,790,1732,883,2084,1127,758,1503,1271 0.119,0.152,0.18,0.18,0.195,0.22,0.255,0.258,0.3 MCD,MCD,MCD,MCD,BN2,MCD,MCD,BN2,MCD 9.01072026405775,37.8786722356453 1 3.703706704333243 46.88939249970303 37.87867223564528 MCD TRUE 790 -DLBCL10892T BN2 0.4794139194483298 4.344999164716219 1835,921,70,804 0.186,0.195,0.205,0.208 EZB,MCD,BN2,BN2 9.67600963033196,5.3886234953749,5.11836373317934 7 42.04225376157848 20.182996858886206 9.676009630331965 Other FALSE 791 -DLBCL10893T MCD 0.8861070291526828 0.6016842726211622 704,347,465,2053,1526,246,2185 0.188,0.207,0.211,0.237,0.25,0.289,0.293 MCD,MCD,MCD,MCD,MCD,MCD,ST2 26.5623379453462,3.41410628932572 4 15.982140985763134 29.976444234671934 26.562337945346215 MCD TRUE 792 -DLBCL10894T BN2 0.6628680822387302 2.624571624461786 793,402,737 0.135,0.151,0.178 BN2,EZB,BN2 13.0129357910296,6.61832439437636 5 34.15338202807937 19.63126018540592 13.012935791029562 BN2 TRUE 793 -DLBCL10895T BN2 0.6453256844375596 3.8241450890136868 114,1657,2170 0.253,0.273,0.282 EZB,BN2,BN2 7.19904854880922,3.95663411254902 7 27.53020615349989 11.155682661358238 7.199048548809221 Other FALSE 794 -DLBCL10896T BN2 1 0 795,585,1029,1444,557,1056,1252,957,935,812 0.116,0.188,0.188,0.188,0.214,0.215,0.226,0.226,0.243,0.251 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 50.8569379546921 0 0 50.85693795469206 50.85693795469206 BN2 TRUE 795 -DLBCL10897T BN2 0.8949776287696195 0.10267362137550759 692,1935,579,933,961,892,260,232,1982,981 0.156,0.186,0.191,0.192,0.198,0.202,0.213,0.214,0.235,0.262 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2 44.4316245253404,5.21388961608376 1 4.561955793613519 49.64551414142417 44.43162452534042 BN2 TRUE 796 -DLBCL10898T ST2 0.5006521848369643 8.764845744026001 1664,951 0.286,0.287 ST2,MCD 3.48797938405862,3.49709049738225 5 30.651458762454556 6.985069881440871 3.497090497382247 Other FALSE 797 -DLBCL10899T MCD 1 0 1699,305,910,905,479,764,798,1331,1009,1904,1449 0.124,0.124,0.133,0.134,0.135,0.143,0.157,0.161,0.162,0.164,0.177 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.9988386695093 0 0 75.99883866950934 75.99883866950934 MCD TRUE 798 -DLBCL10900T MCD 1 0.25281266546894476 824,69,1412,799,1225,765,900 0.144,0.164,0.207,0.209,0.227,0.23,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.4832010843717 2 8.97060264551055 35.48320108437166 35.48320108437166 MCD TRUE 799 -DLBCL10901T MCD 1 0.10394603071277377 1273,547,705,1169,800,2094,49,708,188,621 0.178,0.184,0.187,0.189,0.198,0.208,0.212,0.218,0.219,0.221 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 49.8871966412803 1 5.1855760742487105 49.88719664128034 49.88719664128034 MCD TRUE 800 -DLBCL10902T MCD 0.5663907241999885 4.282295287228473 140,768,446 0.168,0.254,0.261 EZB,MCD,MCD 5.94656095141171,7.76753900745165 6 33.262895684973536 13.714099958863361 7.767539007451651 MCD TRUE 801 -DLBCL10903T BN2 0.6119994848489703 3.160934530498542 2218,190,189 0.156,0.184,0.215 ST2,BN2,BN2 10.0876202619329,6.39543325635941 5 31.886307216500445 16.483053518292305 10.087620261932898 Other FALSE 802 -DLBCL10904T MCD 1 0.10008644095151151 803,1598,1647,1459,518,880,792,2180,783,771 0.18,0.196,0.21,0.227,0.229,0.242,0.247,0.267,0.271,0.274 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 43.4431110855154 1 4.348066372410393 43.44311108551541 43.44311108551541 MCD TRUE 803 -DLBCL10905T BN2 1 0.8163972746507866 70,1841,1813,1148,804 0.117,0.149,0.166,0.179,0.2 BN2,BN2,BN2,BN2,BN2 31.8511742171516 4 26.003211825309958 31.851174217151588 31.851174217151588 BN2 TRUE 804 -DLBCL10906T MCD 0.7914386491994155 1.2389950253743298 1890,952,1398,285,1194 0.165,0.204,0.227,0.239,0.254 MCD,BN2,MCD,MCD,MCD 4.89700965739807,18.5829382744713 5 23.02416807890819 23.479947931869383 18.582938274471314 Other FALSE 805 -DLBCL10907T MCD 1 0.25256391418830126 765,799,1526,900,2053,69,465,753 0.131,0.151,0.166,0.185,0.202,0.232,0.252,0.289 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.3405069067762 3 10.693684153092201 42.3405069067762 42.3405069067762 Other FALSE 806 -DLBCL10908T EZB 1 4.835531836672584 1300,2050 0.153,0.169 EZB,EZB 12.4708600986625 9 60.30324103777219 12.470860098662472 12.470860098662472 Other FALSE 807 -DLBCL10909T MCD 1 3.0199400602230524 864,717,650 0.167,0.204,0.21 MCD,MCD,MCD 15.6587266512561 8 47.288415906210766 15.658726651256135 15.658726651256135 Other FALSE 808 -DLBCL10910T EZB 1 0.37005449377335875 809,1011,1031,1701,1864,1322,473,466 0.129,0.137,0.202,0.203,0.224,0.229,0.255,0.262 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.4680584185439 3 15.34544136583834 41.468058418543926 41.468058418543926 EZB TRUE 809 -DLBCL10911T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 810 -DLBCL10912T BN2 0.7335580793280144 1.429822826080897 811,150,661,1126 0.187,0.204,0.238,0.252 BN2,MCD,BN2,BN2 13.5288089826073,4.9139147278323 4 19.3437998930202 18.4427237104396 13.528808982607305 BN2 TRUE 811 -DLBCL10913T BN2 1 0.10530649101974109 1252,995,957,1056,585,812,571,418,1444,1029 0.124,0.138,0.142,0.144,0.165,0.172,0.185,0.207,0.225,0.23 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.1973197467393 1 6.339168511322487 60.197319746739325 60.197319746739325 BN2 TRUE 812 -DLBCL10914T BN2 1 0 1444,795,1758,585,945,1056,957,812,1806,557,1029 0.19,0.205,0.24,0.245,0.264,0.264,0.27,0.274,0.277,0.28,0.281 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.0714850563735 0 0 44.07148505637347 44.07148505637347 Other FALSE 813 -DLBCL10915T N1 1 0.7038883705026121 814,745,891,2189,63,2175 0.144,0.175,0.188,0.2,0.256,0.292 N1,N1,N1,N1,N1,N1 30.3209117180265 5 21.342537141355226 30.320911718026494 30.320911718026494 N1 TRUE 814 -DLBCL10916T MCD 1 0.9516918715817807 753,1412,1063,824,1890,1194 0.174,0.184,0.221,0.268,0.269,0.27 MCD,MCD,MCD,MCD,MCD,MCD 26.8435755170435 4 25.546812623761973 26.843575517043476 26.843575517043476 MCD TRUE 815 -DLBCL10917T EZB 0.5887108620323257 1.1173432552660445 781,1400,816,2138,2134 0.152,0.176,0.204,0.222,0.253 MCD,EZB,EZB,EZB,MCD 15.0901520723134,10.542383431142 3 16.860879638938332 25.632535503455408 15.090152072313428 EZB TRUE 816 -DLBCL10918T BN2 1 0.7303696926140945 817,742,57,1075,2064,1525 0.125,0.226,0.23,0.231,0.239,0.263 BN2,BN2,BN2,BN2,BN2,BN2 29.0618575764061 4 21.225899984874307 29.061857576406087 29.061857576406087 BN2 TRUE 817 -DLBCL10919T EZB 1 0.09204130219247082 1750,818,1752,1573,110,172,108,117,1671,109 0.193,0.23,0.243,0.26,0.292,0.299,0.303,0.305,0.312,0.322 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.150348179079 1 3.419366423306117 37.15034817907898 37.15034817907898 EZB TRUE 818 -DLBCL10920T Other 1 10 0 0 1 2 Other TRUE 819 -DLBCL10921T N1 0.5026495329699907 1.8136486052917897 1609,1064,464,176,1008,743 0.2,0.202,0.21,0.225,0.234,0.267 MCD,N1,BN2,N1,N1,MCD 4.76352895833576,8.73934584639643,13.6467424166335 4 24.750395350703606 27.14961722136567 13.646742416633472 Other FALSE 820 -DLBCL10922T MCD 0.4139724404738713 8.567049498059548 147,1074,286 0.209,0.257,0.347 MCD,ST2,BN2 2.8787968845145,4.78015535471701,3.8880854071519 7 40.951827532275 11.547037646383412 4.7801553547170075 Other FALSE 821 -DLBCL10923T BN2 1 1.0582794144824703 1108 0.157 BN2 6.38426244337464 1 6.756333520476942 6.384262443374642 6.384262443374642 Other FALSE 822 -DLBCL10924T MCD 1 0 823,2203,910,905,1904,253,764,1009,1699,858,305 0.107,0.13,0.146,0.149,0.162,0.166,0.182,0.182,0.187,0.188,0.189 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.7918396615104 0 0 69.79183966151035 69.79183966151035 MCD TRUE 823 -DLBCL10925T MCD 1 0.22531862701797692 2036,918,1399,1905,69,1225,824,900,1826 0.194,0.268,0.297,0.329,0.33,0.342,0.349,0.387,0.398 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 29.2157112498422 2 6.58284394616811 29.21571124984221 29.21571124984221 MCD TRUE 824 -DLBCL10926T ST2 0.6587963052998417 3.805068846115495 702,1697,530 0.216,0.22,0.243 ST2,BN2,ST2 4.53648107980578,8.7590404818594 6 33.32875205938765 13.295521561665174 8.759040481859397 Other FALSE 825 -DLBCL10927T BN2 0.5059656497133177 0.8788256571263758 24,826,1190,132,1093,2140,972,2214 0.181,0.199,0.25,0.293,0.295,0.298,0.3,0.32 EZB,BN2,BN2,BN2,EZB,ST2,BN2,EZB 15.7732778087319,12.0503784239244,3.35094587258365 2 13.861961235295702 31.17460210523998 15.77327780873191 BN2 TRUE 826 -DLBCL10928T BN2 1 6.756910762585228 2032 0.296 BN2 3.38041309872143 5 22.841149648734884 3.380413098721426 3.380413098721426 BN2 TRUE 827 -DLBCL10929T ST2 0.562552901696659 1.3605054839071467 1789,486,1130,854,1123,1943,1211 0.282,0.294,0.295,0.341,0.358,0.374,0.405 EZB,ST2,ST2,BN2,EZB,ST2,ST2 2.93569291690538,6.34217993779078,11.9312582395112 4 16.232542264767265 21.20913109420732 11.931258239511163 ST2 TRUE 828 -DLBCL10930T BN2 0.5662077616063375 0.8742968274864823 897,1191,273,34,1275,1112,274 0.183,0.203,0.208,0.217,0.218,0.247,0.251 MCD,BN2,BN2,EZB,BN2,BN2,MCD 18.3517782253912,4.60914700763635,9.450813831317 3 16.04490148119507 32.4117390643446 18.351778225391243 Other FALSE 829 -DLBCL10931T BN2 0.9292242683525783 0.10109244864924764 830,1115,1203,261,1525,742,1075,1158,232,274 0.162,0.163,0.204,0.22,0.256,0.283,0.318,0.331,0.336,0.342 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 38.3542224039928,2.92130570074665 1 3.8773222588574643 41.275528104739436 38.354222403992786 BN2 TRUE 830 -DLBCL10932T BN2 0.597774535282264 1.1661325825989624 130,831,284,1145,600,412,89 0.192,0.233,0.249,0.27,0.282,0.29,0.313 BN2,ST2,BN2,BN2,ST2,BN2,ST2 16.3805546982447,11.0220088627975 4 19.10189855466761 27.40256356104213 16.38055469824466 ST2 FALSE 831 -DLBCL10933T BN2 1 0 19 0.181 BN2 5.51070958457825 0 0 5.5107095845782466 5.5107095845782466 Other FALSE 832 -DLBCL10934T ST2 1 2.8149833178203663 833,1664 0.124,0.26 ST2,ST2 11.930139460707 7 33.583143561160725 11.930139460707021 11.930139460707021 ST2 TRUE 833 -DLBCL10935T BN2 0.5724845087181609 8.305882388097928 834,1763 0.261,0.349 BN2,EZB 3.83246614639914,2.86197900982062 9 31.832013068358183 6.694445156219765 3.832466146399144 BN2 TRUE 834 -DLBCL10936T Other 1 10 0 0 1 2 Other TRUE 835 -DLBCL10937T BN2 0.9157942421417414 0.1298392362678342 88,1477,560,564,669,1193,1212,836,872,294 0.14,0.16,0.173,0.188,0.197,0.205,0.239,0.258,0.276,0.285 BN2,BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2 45.4961337456813,4.1832938507194 1 5.907183258678496 49.67942759640068 45.49613374568128 BN2 TRUE 836 -DLBCL10938T MCD 1 0.09138390065935136 739,481,1968,643,1422,837,185,296,1491,247 0.122,0.127,0.131,0.152,0.179,0.186,0.193,0.195,0.205,0.21 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.1640093524854 1 5.589405754595166 61.16400935248544 61.16400935248544 MCD TRUE 837 -DLBCL10939T BN2 1 1.3102213692774614 1317,915 0.228,0.24 BN2,BN2 8.55597332519675 2 11.210219085640723 8.555973325196753 8.555973325196753 ST2 FALSE 838 -DLBCL10940T MCD 1 0 2008,23,798,1331,2034,21,59,1699,846,387,888 0.106,0.113,0.127,0.132,0.134,0.138,0.138,0.16,0.163,0.172,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4279947578727 0 0 79.42799475787272 79.42799475787272 MCD TRUE 839 -DLBCL10941T N1 1 11.008255573541392 697 0.258 N1 3.87145453945042 10 42.617961011617176 3.871454539450417 3.871454539450417 Other FALSE 840 -DLBCL10942T BN2 0.9088760746946924 0.09198790244618194 863,1758,684,1119,960,590,1722,2210,795,1444 0.172,0.182,0.244,0.271,0.277,0.281,0.285,0.302,0.307,0.312 BN2,BN2,BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 35.9618204472075,3.60553251594286 1 3.308052431084836 39.56735296315037 35.961820447207515 BN2 TRUE 841 -DLBCL10943T EZB 0.6105388342012874 0.9065661666948344 207,1272,1318,993,2083,330,1356 0.157,0.208,0.211,0.228,0.245,0.245,0.264 EZB,EZB,EZB,BN2,BN2,BN2,EZB 12.549433430372,19.6731205298808 4 17.834985465699503 32.222553960252796 19.67312052988082 Other FALSE 842 -DLBCL10945T BN2 1 1.085644191114206 1173,1126,931,811,1158,1027 0.198,0.211,0.214,0.241,0.27,0.271 BN2,BN2,BN2,BN2,BN2,BN2 26.0046093044734 5 28.23175303359599 26.00460930447341 26.00460930447341 Other FALSE 843 -DLBCL10947T EZB 0.6084506398049997 1.796716804905044 561,2042,141,844,383 0.166,0.18,0.188,0.212,0.248 EZB,EZB,BN2,BN2,EZB 10.0451523764069,15.6097289683211 5 28.046262357395584 25.654881344727983 15.609728968321093 BN2 FALSE 844 -DLBCL10948T MCD 0.9004843366827219 0.22540936235884373 1251,786,738,1826,1197,1501,789,1531 0.158,0.166,0.195,0.225,0.262,0.278,0.281,0.306 MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 3.59884835780234,32.5647889816626 2 7.3404083197068735 36.16363733946498 32.56478898166263 Other FALSE 845 -DLBCL10949T MCD 1 0 798,1331,23,2008,21,1699,2034,59,905,846,305 0.11,0.117,0.119,0.122,0.136,0.138,0.142,0.154,0.174,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4321606599702 0 0 79.43216065997021 79.43216065997021 MCD TRUE 846 -DLBCL10950T BN2 0.8908149916103685 0.5675670192992588 742,1525,817,1075,1115,274,232 0.123,0.178,0.189,0.202,0.257,0.259,0.274 BN2,BN2,BN2,BN2,BN2,MCD,BN2 31.5445915653874,3.86634321060098 4 17.903669809779466 35.41093477598838 31.544591565387396 MCD FALSE 847 -DLBCL10951T MCD 0.7852080501887585 1.5169408724829243 1890,952,1398,285,254 0.17,0.199,0.225,0.243,0.258 MCD,BN2,MCD,MCD,MCD 5.01374134522927,18.3285736234431 6 27.80336246371329 23.34231496867237 18.328573623443102 Other FALSE 848 -DLBCL10952T N1 1 0.8007664288126405 63,2189,2175,745,849,891 0.122,0.166,0.172,0.188,0.225,0.234 N1,N1,N1,N1,N1,N1 34.0461243268659 4 27.262993392135606 34.04612432686594 34.04612432686594 N1 TRUE 849 -DLBCL10953T MCD 0.7344611769177855 0 850,569,1994,801,2130 0.131,0.168,0.191,0.199,0.206 BN2,MCD,MCD,MCD,MCD 7.62601236540372,21.092998575767 0 0 28.719010941170723 21.092998575767005 BN2 FALSE 850 -DLBCL10954T ST2 1 4.6802789608432125 1052 0.212 ST2 4.71086260360404 4 22.048151131071084 4.710862603604044 4.710862603604044 Other FALSE 851 -DLBCL10955T BN2 0.6093241263284839 3.0292507472262438 2218,190,189 0.145,0.164,0.214 ST2,BN2,BN2 10.7832040236598,6.91378770492082 5 32.6650288461644 17.696991728580585 10.78320402365977 Other FALSE 852 -DLBCL10956T Other 1 10 0 0 1 2 Other TRUE 853 -DLBCL10957T BN2 0.7521065116129825 1.4337344195436927 1813,1841,1797,70 0.276,0.295,0.301,0.325 BN2,BN2,ST2,BN2 10.0838925607732,3.32364002280608 4 14.457623847361175 13.407532583579313 10.083892560773235 BN2 TRUE 854 -DLBCL10958T ST2 0.5758679649231996 4.518846975543084 1667,1971 0.134,0.181 ST2,BN2 5.5149253203212,7.48792488721517 5 33.83678672968605 13.002850207536362 7.487924887215168 Other FALSE 855 -DLBCL10959T BN2 1 1.125782624234604 871,1204,754,1275,987 0.146,0.248,0.274,0.286,0.309 BN2,BN2,BN2,BN2,BN2 21.2684213435454 6 23.943619193463753 21.268421343545356 21.268421343545356 Other FALSE 856 -DLBCL10960T BN2 1 0 857,935,1724,976,555,796,557,1029,841,1409,98 0.173,0.202,0.209,0.239,0.243,0.253,0.266,0.27,0.295,0.308,0.31 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.1083391883439 0 0 45.10833918834394 45.10833918834394 BN2 TRUE 857 -DLBCL10961T MCD 0.9113859379568929 0.09524672345066647 858,759,1009,1904,1406,764,1449,1376,479,2203 0.116,0.118,0.13,0.132,0.138,0.151,0.153,0.16,0.167,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.26363302787349,64.4207807486499 1 6.135868288442675 70.6844137765234 64.4207807486499 MCD TRUE 858 -DLBCL10962T Other 1 10 0 0 1 2 Other TRUE 859 -DLBCL10963T ST2 1 1.0663811083547394 874 0.212 ST2 4.7095571900874 1 5.02218281622543 4.709557190087397 4.709557190087397 Other FALSE 860 -DLBCL10964T MCD 0.5763119988823973 1.9543341553397684 267,291,990,1416 0.156,0.247,0.269,0.295 MCD,BN2,MCD,ST2 4.05373304057672,10.1178875066284,3.38464676329567 5 19.773733134089387 17.556267310500772 10.117887506628389 Other FALSE 861 -DLBCL10965T BN2 0.6650416075835202 0.734408768658513 130,1145,412,600,284,831,89,157 0.173,0.209,0.263,0.272,0.274,0.283,0.291,0.317 BN2,BN2,BN2,ST2,BN2,ST2,ST2,BN2 21.1638565909941,10.6595005488616 3 15.542921859057358 31.82335713985571 21.163856590994136 Other FALSE 862 -DLBCL10966T BN2 0.5230624875164384 0.40171396842017687 838,1076,13,1086,590,960,1722,1145,932 0.157,0.204,0.222,0.232,0.239,0.252,0.256,0.28,0.291 ST2,EZB,BN2,BN2,BN2,ST2,BN2,BN2,EZB 20.4672024951529,8.32631636743071,10.3360363695906 2 8.221961136787211 39.12955523217417 20.467202495152886 BN2 TRUE 863 -DLBCL10967T MCD 1 1.3301936243247985 864,717,650,704 0.149,0.154,0.188,0.229 MCD,MCD,MCD,MCD 22.8787083666518 5 30.433112002106604 22.878708366651768 22.878708366651768 MCD TRUE 864 -DLBCL10968T BN2 1 0 865,964,1549,954,1806,973,936,2213,945 0.154,0.162,0.167,0.176,0.183,0.21,0.224,0.224,0.243 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.6413489166743 0 0 47.6413489166743 47.6413489166743 BN2 TRUE 865 -DLBCL10969T BN2 1 2.782793002915591 19,2211,894 0.23,0.261,0.279 BN2,BN2,BN2 11.7599620815165 6 32.7255401949967 11.759962081516468 11.759962081516468 Other FALSE 866 -DLBCL10970T ST2 0.5057219511233866 2.9834757552042426 1131,2211,2127,175 0.224,0.229,0.248,0.254 ST2,BN2,ST2,N1 4.37050869026872,3.94415398831807,8.50717008838356 5 25.380935704091087 16.821832766970346 8.50717008838356 Other FALSE 867 -DLBCL10971T ST2 1 3.365250832876903 1946,90 0.19,0.211 ST2,ST2 10.0095033915209 6 33.6844896249999 10.009503391520902 10.009503391520902 Other FALSE 868 -DLBCL10972T MCD 0.5425187023582727 2.115718828455687 659,869,897,1008,1191,1275 0.246,0.279,0.313,0.326,0.329,0.329 MCD,MCD,MCD,N1,BN2,BN2 6.0790068642551,10.8453965505574,3.066421792528 4 22.945809684082725 19.990825207340535 10.845396550557435 MCD TRUE 869 -DLBCL10973T N1 1 11.549292378404655 262 0.193 N1 5.19101721071467 9 59.95257550787437 5.191017210714674 5.191017210714674 N1 TRUE 870 -DLBCL10974T BN2 1 1.2451754428758584 987,754,1204,871,1113 0.174,0.19,0.277,0.28,0.291 BN2,BN2,BN2,BN2,BN2 21.612766450075 6 26.91168603624457 21.61276645007495 21.61276645007495 BN2 TRUE 871 -DLBCL10975T BN2 1 0.08743649817740932 936,903,2213,973,1549,1210,454,872,1735,964 0.157,0.206,0.209,0.217,0.217,0.218,0.224,0.246,0.248,0.249 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.3683231742576 1 4.054283804715499 46.36832317425758 46.36832317425758 BN2 TRUE 872 -DLBCL10977T EZB 1 10.923068260934544 65 0.242 EZB 4.12555166393883 8 45.0636824392159 4.125551663938827 4.125551663938827 Other FALSE 873 -DLBCL10978T BN2 0.6985132601197312 3.0679756931185853 1793,2058,874 0.207,0.255,0.265 BN2,BN2,ST2 8.74266871439116,3.77344116288245 5 26.822295108740402 12.516109877273616 8.742668714391163 ST2 FALSE 874 -DLBCL10979T Other 1 10 0 0 1 2 Other TRUE 875 -DLBCL10980T ST2 1 3.1175638101240915 1188,828,1638 0.188,0.244,0.251 ST2,ST2,ST2 13.4048794105405 8 41.7905669293787 13.404879410540524 13.404879410540524 Other FALSE 876 -DLBCL10981T EZB 0.7928190471565788 0 877,390,211,210,396,212,1423,112,1049,213,216 0.136,0.147,0.163,0.17,0.193,0.232,0.248,0.287,0.295,0.304,0.312 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,ST2,BN2,EZB 7.60724185285356,42.0671457435708,3.38582317227919 0 0 53.060210768703534 42.06714574357078 EZB TRUE 877 -DLBCL10982T MCD 1 2.7268488513218387 801,1994,768 0.164,0.202,0.213 MCD,MCD,MCD 15.7125493469187 7 42.84574713798302 15.71254934691872 15.71254934691872 Other FALSE 878 -DLBCL10983T MCD 0.6031490668507639 2.369294062481878 267,1416,879,291,990 0.216,0.242,0.257,0.279,0.312 MCD,ST2,MCD,BN2,MCD 3.57918539555501,11.7230925372758,4.13419835732719 6 27.775453542493132 19.436476290157987 11.723092537275786 MCD TRUE 879 -DLBCL10984T MCD 1 0 2180,1250,1239,771,880,929,1240,777,1598,1565 0.115,0.128,0.138,0.14,0.144,0.153,0.164,0.177,0.186,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 66.587896328477 0 0 66.58789632847703 66.58789632847703 MCD TRUE 880 -DLBCL10985T EZB 0.5504037489015415 0.7399646482340434 881,1494,1036,1322,466,1701,1011 0.148,0.17,0.22,0.348,0.362,0.368,0.392 N1,BN2,EZB,EZB,EZB,EZB,EZB 5.88029620221954,15.4539321793256,6.74321878368131 3 11.435363488907422 28.07744716522643 15.453932179325587 N1 FALSE 881 -DLBCL10986T BN2 1 2.2798155101591777 1807,1854,1006 0.141,0.252,0.293 BN2,BN2,BN2 14.4601516272037 7 32.96647795895248 14.460151627203706 14.460151627203706 Other FALSE 882 -DLBCL10987T MCD 0.8470553630569861 0.08813601051233852 749,883,1732,790,1127,2084,214,758,1911,1503 0.131,0.149,0.154,0.159,0.211,0.232,0.284,0.292,0.294,0.302 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,BN2 7.62722678919204,42.2419738682582 1 3.723039052914736 49.869200657450264 42.24197386825823 MCD TRUE 883 -DLBCL10988T ST2 1 1.9283568666984585 833,1664 0.191,0.236 ST2,ST2 9.48174559340998 3 18.284189223339986 9.48174559340998 9.48174559340998 Other FALSE 884 -DLBCL10989T BN2 0.3468194404222955 6.972535090125205 793,581,1257 0.164,0.172,0.176 BN2,ST2,EZB 6.10930134024627,5.69507057856458,5.81084980234596 6 42.597317971016054 17.615221721156807 6.109301340246269 Other FALSE 885 -DLBCL10990T N1 1 1.7357726998532101 814,3,891,745,2189 0.309,0.313,0.326,0.368,0.394 N1,N1,N1,N1,N1 14.7573863356503 6 25.61546832260866 14.757386335650338 14.757386335650338 Other FALSE 886 -DLBCL10991T BN2 1 1.3139795309109679 1126,1158,811,661,1027 0.128,0.24,0.251,0.275,0.283 BN2,BN2,BN2,BN2,BN2 23.1042930231624 6 30.358568108604437 23.10429302316237 23.10429302316237 Other FALSE 887 -DLBCL10992T MCD 1 0 587,888,1572,483,846,387,59,2008,707,947,23 0.134,0.135,0.136,0.138,0.148,0.15,0.154,0.155,0.156,0.159,0.163 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.6762256539954 0 0 74.67622565399543 74.67622565399543 MCD TRUE 888 -DLBCL10993T BN2 1 0.0737379337024628 111,943,889,1014,1929,1982,1632,266,106,579 0.103,0.119,0.165,0.187,0.215,0.215,0.217,0.22,0.261,0.265 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.6334832502212 1 4.102298099541885 55.6334832502212 55.6334832502212 BN2 TRUE 889 -DLBCL10994T BN2 1 10.98494828657852 674 0.265 BN2 3.77942159142539 7 41.51675073498622 3.7794215914253915 3.7794215914253915 Other FALSE 890 -DLBCL10995T N1 1 0.44870832634355445 2189,745,891,814,63,2175 0.127,0.131,0.148,0.188,0.197,0.208 N1,N1,N1,N1,N1,N1 37.4374763611023 3 16.79850736051659 37.43747636110229 37.43747636110229 N1 TRUE 891 -DLBCL10996T BN2 0.830409757388718 0.10287048061815254 933,981,232,892,692,1075,260,1525,961,1935 0.12,0.156,0.179,0.188,0.198,0.252,0.259,0.279,0.281,0.281 MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 40.8786458710281,8.34843209536511 1 4.205205947771915 49.22707796639318 40.87864587102807 BN2 TRUE 892 -DLBCL10997T BN2 0.41559960221613024 3.3907733725137703 150,1232,811,1283,151 0.249,0.261,0.296,0.315,0.341 MCD,BN2,BN2,ST2,MCD 7.2017025460673,6.95189205550674,3.17486891875939 6 24.419341229969632 17.32846352033344 7.201702546067302 Other FALSE 893 -DLBCL10998T BN2 0.6589613414236586 4.347174960738846 1131,19,52 0.233,0.237,0.245 ST2,BN2,BN2 8.29119392303105,4.29102206116958 7 36.043270616830654 12.582215984200634 8.291193923031049 BN2 TRUE 894 -DLBCL10999T MCD 1 0.09108916986326007 1968,837,739,1422,156,1491,481,767,950,471 0.125,0.147,0.174,0.176,0.178,0.18,0.181,0.186,0.193,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 58.6142727176111 1 5.339125443985929 58.614272717611115 58.614272717611115 MCD TRUE 895 -DLBCL11000T MCD 1 0 1171,214,485,1728,896,455,2207,1977,2010,1543,883 0.209,0.225,0.236,0.247,0.267,0.271,0.281,0.295,0.304,0.309,0.319 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 41.5457153639829 0 0 41.5457153639829 41.5457153639829 MCD TRUE 896 -DLBCL11001T BN2 0.6209376253593256 0.6499345771161187 273,34,1112,897,1191,274,1407,1275 0.137,0.15,0.152,0.17,0.174,0.199,0.201,0.238 BN2,EZB,BN2,MCD,BN2,MCD,BN2,BN2 28.8145507069665,6.68804160358397,10.9023113869699 3 18.727572828523265 46.40490369752047 28.81455070696655 MCD FALSE 897 -DLBCL11002T ST2 0.5877072339190105 0.9408842689274162 1082,2017,1121,1863,1847 0.114,0.114,0.169,0.206,0.21 BN2,ST2,ST2,EZB,ST2 8.75939096533658,4.86529890278359,19.4214632274784 3 18.273349230286673 33.046153095598534 19.42146322747836 Other FALSE 898 -DLBCL11003T N1 1 4.835134973880531 198 0.265 N1 3.77881434339092 3 18.271077391530817 3.778814343390917 3.778814343390917 Other FALSE 899 -DLBCL11004T MCD 1 0.17641533872962994 2036,69,900,799,765,824,296,481,1526,739 0.168,0.208,0.232,0.28,0.288,0.292,0.296,0.301,0.307,0.307 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.7499826856686 1 6.836091321259519 38.749982685668584 38.749982685668584 MCD TRUE 900 -DLBCL11005T Other 1 10 0 0 1 2 N1 FALSE 901 -DLBCL11180T EZB 1 8.924658045509826 2050 0.147 EZB 6.79219405515095 8 60.61800932096693 6.792194055150949 6.792194055150949 Other FALSE 902 -DLBCL11181T BN2 0.8649469624176396 0.11632947617961574 594,2041,922,1735,454,903,88,158,1477 0.186,0.191,0.197,0.242,0.244,0.262,0.262,0.269,0.283 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 33.5793800972694,5.24309290548056 1 3.906271697151561 38.82247300274994 33.57938009726938 BN2 TRUE 903 -DLBCL11182T MCD 1 3.3121888582605696 904 0.222 MCD 4.49944338929356 3 14.903006262392289 4.499443389293555 4.499443389293555 MCD TRUE 904 -DLBCL11187T MCD 1 0 479,764,305,1449,1009,1689,310,759,1904,1699,910 0.116,0.124,0.125,0.13,0.138,0.147,0.153,0.154,0.156,0.171,0.174 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 77.5543984705094 0 0 77.55439847050941 77.55439847050941 MCD TRUE 905 -DLBCL11188T ST2 1 5.685075990297036 1710 0.149 ST2 6.69562015635925 5 38.06510939106684 6.695620156359247 6.695620156359247 Other FALSE 906 -DLBCL11191T BN2 0.9064950532286004 0 892,260,961,933,692,981,667,579,232,1935,907 0.128,0.152,0.19,0.204,0.234,0.235,0.236,0.246,0.265,0.276,0.278 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.5073111855289,4.9003782070667 0 0 52.40768939259564 47.50731118552894 BN2 TRUE 907 -DLBCL11193T EZB 0.8680541104268369 0.2509625709105192 16,1533,308,42,343,155,11,1933,575 0.133,0.142,0.146,0.164,0.172,0.2,0.204,0.217,0.242 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 6.85779135361128,45.1164791279915 2 11.322547592391537 51.974270481602815 45.11647912799154 Other FALSE 908 -DLBCL11194T MCD 1 0.360673198465011 824,1225,69,1412,2036,1399,799,765 0.153,0.172,0.217,0.238,0.255,0.256,0.281,0.302 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.8332375421752 3 12.924088395692838 35.8332375421752 35.8332375421752 Other FALSE 909 -DLBCL11195T MCD 0.8951147919561917 0.3955760840342102 1406,858,759,1904,1009,2203,1376,1310 0.109,0.117,0.15,0.152,0.161,0.167,0.175,0.18 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD 5.69963026603252,48.6419720660323 3 19.241600829582495 54.34160233206481 48.641972066032295 MCD TRUE 910 -DLBCL11196T EZB 0.5254656318224508 2.3344272389635576 1178,1733,911,2138,816 0.152,0.208,0.238,0.272,0.289 ST2,EZB,ST2,EZB,EZB 11.9479070480469,10.7898446229967 5 27.891519661565376 22.737751671043597 11.94790704804691 ST2 FALSE 911 -DLBCL11202T EZB 0.8402532014003598 0.17452189370568236 1102,371,380,381,1865,473 0.198,0.221,0.225,0.234,0.238,0.263 EZB,EZB,EZB,EZB,BN2,EZB 4.20364059920375,22.1107559086034 1 3.858810992433575 26.31439650780717 22.110755908603423 Other FALSE 912 -DLBCL11203T EZB 0.4258950414259634 1.7154913903281084 1763,1377,834,597 0.13,0.256,0.289,0.334 EZB,MCD,BN2,BN2 6.45283072695032,7.6833278551825,3.9042671280551 3 13.180682784633706 18.040425710187918 7.683327855182498 N1 FALSE 913 -DLBCL11204T BN2 0.45390225261971406 5.105161857278512 147,286,834,1074 0.185,0.285,0.296,0.346 MCD,BN2,BN2,ST2 6.88407097900543,5.39426844625423,2.88807787995034 7 35.14429658481649 15.166417305210004 6.884070979005434 Other FALSE 914 -DLBCL11205T BN2 1 1.2630340923092547 915,1317 0.201,0.254 BN2,BN2 8.9242670486658 2 11.271653531337 8.9242670486658 8.9242670486658 BN2 TRUE 915 -DLBCL11206T EZB 0.37765897824355954 1.405226528526885 1183,1185,15,476,133,24,1070 0.196,0.282,0.31,0.31,0.333,0.341,0.348 ST2,ST2,EZB,MCD,BN2,EZB,EZB 3.00343920769889,9.02551893461213,3.22196983449328,8.64766693172558 3 12.682898640638667 23.898594908529873 9.025518934612126 Other FALSE 916 -DLBCL11208T BN2 0.8069262618708926 2.4420962190060744 1799,1433,537,1791 0.135,0.187,0.253,0.269 BN2,BN2,ST2,BN2 16.5099890976539,3.95035514665759 7 40.31898195121215 20.460344244311514 16.50998909765392 Other FALSE 917 -DLBCL11242T MCD 0.9075921188134087 0 1694,767,1968,837,1376,156,1224,310,788,1449,1689 0.191,0.216,0.22,0.224,0.226,0.232,0.24,0.243,0.251,0.251,0.253 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 4.41585335447198,43.3706914485133 0 0 47.78654480298531 43.37069144851333 MCD TRUE 918 -DLBCL11255T EZB 0.7241639281266607 1.8224764884934779 1387,1488,901 0.16,0.17,0.216 EZB,EZB,N1 12.1324495898965,4.62128408651283 4 22.111104125418727 16.75373367640934 12.13244958989651 Other FALSE 919 -DLBCL11256T MCD 0.6404189430289536 1.5665071115164728 920,254,1358,2145,554 0.165,0.203,0.218,0.262,0.268 MCD,MCD,EZB,MCD,BN2 3.72731780361063,4.57953761512692,14.7946268693197 5 23.175888203022065 23.101482288057287 14.794626869319742 MCD TRUE 920 -DLBCL11257T EZB 0.5215871104147224 2.0162476963952454 1835,921 0.125,0.137 EZB,MCD 7.96995441176203,7.31024376151144 2 16.069402223090318 15.280198173273472 7.96995441176203 MCD FALSE 921 -DLBCL11258T BN2 0.6342352922797466 0.3020156675085839 666,1038,489,648,922,1320,1362,2041 0.155,0.186,0.199,0.26,0.291,0.292,0.295,0.301 BN2,ST2,BN2,ST2,BN2,BN2,BN2,ST2 21.7304457555895,12.5319896845447 1 6.562935080133428 34.26243544013422 21.730445755589475 BN2 TRUE 922 -DLBCL11259T ST2 1 7.5654366848038785 833 0.276 ST2 3.62967475882719 6 27.46007457433793 3.629674758827195 3.629674758827195 Other FALSE 923 -DLBCL11260T EZB 1 10.243408965855814 401 0.187 EZB 5.35653905697952 9 54.86922020222082 5.356539056979517 5.356539056979517 Other FALSE 924 -DLBCL11261T ST2 0.3817910383903363 4.904494419585468 2127,65,175 0.201,0.244,0.253 ST2,EZB,N1 4.09732710814606,3.9449654016794,4.96672710853354 5 24.359285387406622 13.009019618359007 4.966727108533542 Other FALSE 925 -DLBCL11262T EZB 1 1.0395276372566387 2050 0.119 EZB 8.41797067294369 1 8.750713164140835 8.417970672943694 8.417970672943694 Other FALSE 926 -DLBCL11271T ST2 0.43162726936808715 4.685973412556138 1388,1797,1841,938 0.187,0.278,0.294,0.328 EZB,ST2,BN2,ST2 3.4038420667319,5.34498718376808,6.64393816952225 6 31.13331761704817 15.392767420022233 6.643938169522253 Other FALSE 927 -DLBCL11346T MCD 0.7631665055325884 0.423090255498058 1420,647,1897,986,467,1184 0.121,0.152,0.186,0.191,0.208,0.213 BN2,MCD,MCD,MCD,MCD,MCD 8.28721024335711,26.7045051894337 2 11.298415923546727 34.991715432790826 26.704505189433718 Other FALSE 928 -DLBCL11353T MCD 1 0.23085183913873217 2180,1250,880,647,1239,771,1598,929,1240 0.169,0.191,0.199,0.207,0.208,0.209,0.213,0.221,0.234 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 44.05261445382 2 10.169627065533852 44.05261445382004 44.05261445382004 MCD TRUE 929 -DLBCL11428T EZB 1 0.08752008522270717 193,424,1078,1291,700,439,1238,148,122,231 0.151,0.178,0.237,0.252,0.252,0.255,0.271,0.274,0.283,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6938796302205 1 3.7365719837249003 42.69387963022051 42.69387963022051 EZB TRUE 930 -DLBCL11429T BN2 1 0.37929565517768316 1027,1158,1375,1126,417,261,592 0.131,0.137,0.189,0.225,0.252,0.263,0.274 BN2,BN2,BN2,BN2,BN2,BN2,BN2 36.1046564437613 3 13.694339320801589 36.104656443761264 36.104656443761264 BN2 TRUE 931 -DLBCL11430T BN2 0.698442225594901 0 1086,590,1076,13,932,157,960,1722,412,1806 0.148,0.158,0.178,0.18,0.202,0.216,0.223,0.242,0.259,0.276 BN2,BN2,EZB,BN2,EZB,BN2,ST2,BN2,BN2,BN2 34.8666101925431,10.5729267530189,4.48099894821978 0 0 49.92053589378177 34.86661019254308 EZB FALSE 932 -DLBCL11432T BN2 0.8747225374187714 0.13370838366733478 232,692,933,1075,981,1935,892,1525,742,579 0.123,0.146,0.156,0.191,0.222,0.23,0.236,0.237,0.276,0.277 BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 44.8020080301637,6.41652826406561 1 5.990404078764139 51.21853629422929 44.80200803016368 MCD FALSE 933 -DLBCL11433T MCD 0.36795899081604605 1.3870732643085215 1366,554,1358,920,934,176,2145 0.178,0.182,0.196,0.223,0.262,0.298,0.324 EZB,BN2,EZB,MCD,MCD,N1,MCD 5.50733649084399,10.7145364740832,11.3964416180317,3.35372823289156 4 15.807699476624766 30.97204281585054 11.396441618031734 MCD TRUE 934 -DLBCL11434T BN2 1 0 555,1724,991,98,1409,857,976,1001,935,796,557 0.181,0.212,0.228,0.228,0.231,0.255,0.258,0.26,0.27,0.277,0.314 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.4480707298726 0 0 45.44807072987256 45.44807072987256 BN2 TRUE 935 -DLBCL11435T BN2 1 0.09262068773609192 903,936,454,1210,1735,2213,872,973,1549,158 0.165,0.187,0.2,0.203,0.207,0.241,0.242,0.252,0.256,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 46.0286618831149 1 4.263206319186144 46.02866188311492 46.02866188311492 BN2 TRUE 936 -DLBCL11436T ST2 1 11.871470755733496 1028 0.261 ST2 3.83643156510779 9 45.544085131550055 3.8364315651077936 3.8364315651077936 Other FALSE 937 -DLBCL11437T ST2 0.4622683963676368 3.6487788481492127 1388,938,1797,2196 0.174,0.224,0.334,0.341 EZB,ST2,ST2,BN2 2.93251253349604,5.73173764165187,7.44834227175519 7 27.177353734956004 16.1125924469031 7.448342271755193 ST2 TRUE 938 -DLBCL11438T EZB 1 10.1724471675787 401 0.192 EZB 5.22013155724759 8 53.1015124739114 5.220131557247587 5.220131557247587 Other FALSE 939 -DLBCL11439T EZB 0.7054158559450692 0.673256848350345 2214,940,384,944,826,1964,1093,2073 0.184,0.201,0.386,0.393,0.395,0.397,0.404,0.412 EZB,ST2,EZB,EZB,BN2,EZB,EZB,EZB 2.52959598038486,17.9847513318942,4.98089947062666 3 12.108357000075774 25.495246782905735 17.98475133189422 ST2 FALSE 940 -DLBCL11440T MCD 0.8799397133626384 0.21578617363268549 1325,1271,223,1503,758,715,686,236,224 0.125,0.135,0.166,0.166,0.203,0.217,0.222,0.226,0.243 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD 6.00905862489909,44.041285191531 2 9.50350041334632 50.05034381643005 44.04128519153096 Other FALSE 941 -DLBCL11441T Other 1 10 0 0 1 2 Other TRUE 942 -DLBCL11443T BN2 1 0.2686130455794272 106,1014,889,1935,57,1632,2064,111,1982 0.163,0.172,0.198,0.214,0.24,0.242,0.245,0.245,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 42.1790034554696 2 11.329830577678862 42.179003455469555 42.179003455469555 BN2 TRUE 943 -DLBCL11444T EZB 0.8166229755411253 0.12445557770696954 944,693,940,216,384,2073,213,1423,1093,877 0.166,0.189,0.272,0.273,0.289,0.326,0.338,0.342,0.384,0.391 EZB,EZB,ST2,EZB,EZB,EZB,BN2,EZB,EZB,EZB 2.96100400943181,29.5877805070516,3.68308917950906 1 3.6823643160721162 36.23187369599245 29.58778050705158 EZB TRUE 944 -DLBCL11445T BN2 1 0 1549,936,964,954,865,1213,973,872,2213,294,454 0.135,0.137,0.167,0.182,0.185,0.218,0.232,0.235,0.236,0.25,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 56.9046198662042 0 0 56.90461986620421 56.90461986620421 BN2 TRUE 945 -DLBCL11446T EZB 0.4456341669389855 2.995830137417641 353,1725,1738,1527,1909 0.148,0.207,0.264,0.362,0.363 BN2,ST2,EZB,EZB,EZB 6.75662064121667,9.30660435688856,4.82073105224578 6 27.88100580938908 20.88395605035102 9.306604356888563 Other FALSE 946 -DLBCL11447T MCD 1 0 798,1699,2008,23,1331,905,846,910,21,2034,707 0.123,0.137,0.146,0.148,0.149,0.157,0.16,0.162,0.169,0.172,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 71.7821576737497 0 0 71.78215767374965 71.78215767374965 MCD TRUE 947 -DLBCL11448T ST2 0.5752227086754167 0.822235855802289 1071,999 0.106,0.144 ST2,N1 6.93756987900025,9.39468238751528 1 7.724644712889316 16.332252266515532 9.394682387515278 Other FALSE 948 -DLBCL11449T MCD 0.538933581066942 1.6561668714290965 659,464,1609,1008,869,176 0.176,0.217,0.241,0.269,0.28,0.317 MCD,BN2,MCD,N1,MCD,N1 4.60097842833752,13.4099762681199,6.8714737496821 5 22.209158441910617 24.88242844613955 13.409976268119932 Other FALSE 949 -DLBCL11450T MCD 0.8695328141652399 0.2292460528990749 759,1376,858,1449,1406,1009,1904,1694,1310 0.125,0.126,0.142,0.15,0.151,0.152,0.163,0.166,0.169 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.95143740913261,52.9944422636558 2 12.148766714530998 60.945879672788365 52.994442263655756 MCD TRUE 950 -DLBCL11451T MCD 0.5775847451235009 2.682173288160081 951,1479 0.149,0.204 MCD,ST2 6.70433957687537,4.90320309712969 3 17.982200527849585 11.60754267400506 6.704339576875373 MCD TRUE 951 -DLBCL11452T MCD 0.8467764065182629 0.5585282686043226 2030,1398,285,952,1194,1890 0.131,0.167,0.17,0.193,0.205,0.232 MCD,MCD,MCD,BN2,MCD,MCD 5.18766589451831,28.66916892209 3 16.01254128037976 33.85683481660828 28.669168922089966 BN2 FALSE 952 -DLBCL11453T BN2 0.6608134198929266 1.2821735361114746 844,383,189,561,141,190 0.211,0.25,0.285,0.305,0.323,0.353 BN2,EZB,BN2,EZB,BN2,BN2 14.1743233656618,7.27548824373093 5 18.173942311738134 21.449811609392768 14.174323365661836 Other FALSE 953 -DLBCL11454T BN2 1 0 1549,964,865,954,936,973,1806,945,1213,2213,872 0.109,0.132,0.147,0.151,0.175,0.231,0.234,0.238,0.239,0.24,0.266 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 60.7854496507468 0 0 60.785449650746784 60.785449650746784 BN2 TRUE 954 -DLBCL11455T ST2 0.6151110777528513 0.5705575795650993 816,2138,955,1486,508,911 0.158,0.16,0.192,0.195,0.204,0.207 EZB,EZB,ST2,ST2,ST2,ST2 12.5811546668013,20.1066000062795 2 11.471973032866453 32.687754673080775 20.106600006279518 ST2 TRUE 955 -DLBCL11456T EZB 1 9.483416417159733 2162 0.211 EZB 4.72989052596728 8 44.85552146532635 4.729890525967276 4.729890525967276 Other FALSE 956 -DLBCL11457T BN2 1 0.0868928122830552 1444,795,585,1056,957,1252,812,1029,995,945 0.141,0.147,0.148,0.17,0.179,0.188,0.197,0.209,0.246,0.246 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 55.4522789688145 1 4.81840446710481 55.45227896881452 55.45227896881452 BN2 TRUE 957 -DLBCL11458T EZB 1 0.09058769930364553 1066,958,1914,1437,1172,1372,468,2132,1242,54 0.127,0.15,0.167,0.169,0.209,0.215,0.224,0.226,0.229,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.3592837349476 1 4.833694750039338 53.3592837349476 53.3592837349476 EZB TRUE 958 -DLBCL11459T ST2 0.8667043704257463 0.7336088710842473 2052,339,1884,1970,959,1128 0.117,0.16,0.189,0.204,0.206,0.218 ST2,ST2,ST2,ST2,ST2,EZB 4.58940740563165,29.8408842722742 4 21.891537423138743 34.43029167790584 29.840884272274195 ST2 TRUE 959 -DLBCL11460T BN2 0.5320649412010293 0.3531368591764273 1076,590,960,838,1722,1086,13,932,1317 0.143,0.173,0.181,0.182,0.188,0.193,0.203,0.275,0.292 EZB,BN2,ST2,ST2,BN2,BN2,BN2,EZB,BN2 24.643131573143,10.6321455870654,11.0407449357337 2 8.702398084011179 46.316022095942145 24.64313157314303 ST2 FALSE 960 -DLBCL11461T BN2 0.9120568418138598 0 260,892,961,933,667,579,692,981,907,1935,232 0.137,0.143,0.175,0.216,0.221,0.236,0.239,0.251,0.268,0.273,0.275 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2,BN2,BN2 47.9754161755449,4.62592836361061 0 0 52.60134453915548 47.975416175544865 BN2 TRUE 961 -DLBCL11462T BN2 1 1.1452266776259652 2064,57,106,1632,1113 0.197,0.207,0.244,0.276,0.305 BN2,BN2,BN2,BN2,BN2 20.9106099144574 6 23.947388319466665 20.910609914457442 20.910609914457442 Other FALSE 962 -DLBCL11463T ST2 0.563723610046993 4.86599413056673 1971,1667,566 0.157,0.239,0.247 BN2,ST2,ST2 6.37192866983625,8.23332803571822 8 40.06332589683535 14.60525670555447 8.233328035718218 Other FALSE 963 -DLBCL11464T BN2 1 0.12855332129812816 903,1210,1735,936,454,2213,973,158 0.153,0.167,0.193,0.216,0.23,0.231,0.245,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 39.1265493688241 1 5.029847872297513 39.12654936882406 39.12654936882406 BN2 TRUE 964 -DLBCL11466T N1 1 0.5504961761455962 745,814,2189,891,63,2175 0.138,0.146,0.162,0.164,0.223,0.256 N1,N1,N1,N1,N1,N1 34.7526362026362 4 19.131193340530267 34.752636202636246 34.752636202636246 Other FALSE 965 -DLBCL11467T ST2 1 7.935528726795499 318 0.215 ST2 4.6584511630505 7 36.96727302676115 4.6584511630505006 4.6584511630505006 Other FALSE 966 -DLBCL11468T EZB 1 0.1484268029496227 1241,1437,1814,1066,2003,651,331,408,1372,958 0.112,0.181,0.207,0.225,0.236,0.247,0.25,0.261,0.273,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.0437137471057 1 6.982548030360122 47.04371374710575 47.04371374710575 Other FALSE 967 -DLBCL11469T N1 1 1.62458605925895 63,2175,849,2189 0.153,0.165,0.198,0.227 N1,N1,N1,N1 22.050436260851 6 35.822831349956594 22.050436260851008 22.050436260851008 Other FALSE 968 -DLBCL11470T BN2 0.5807498551673445 0.7831152869546278 648,489,922,666,2041,594,1038 0.214,0.215,0.228,0.253,0.262,0.266,0.277 ST2,BN2,BN2,BN2,ST2,BN2,ST2 16.7493974715924,12.0915868567911 3 13.116709207283183 28.840984328383513 16.749397471592378 Other FALSE 969 -DLBCL11471T BN2 0.47816674243565693 3.7615588911621423 581,793,402,737 0.14,0.158,0.159,0.167 ST2,BN2,EZB,BN2 12.3258714476745,6.30723117308753,7.14424731555874 6 46.36449133532156 25.777349936320764 12.325871447674489 Other FALSE 970 -DLBCL11472T Other 1 10 0 0 1 2 Other TRUE 971 -DLBCL11473T BN2 0.45568242125553043 0.29704822852449414 2140,972,132,133,1070,1093,24,1183 0.135,0.15,0.156,0.156,0.23,0.25,0.257,0.274 ST2,BN2,BN2,BN2,EZB,EZB,EZB,ST2 19.5045178882143,12.2329595219801,11.0653979662004 1 5.7937824869183725 42.80287537639484 19.504517888214323 BN2 TRUE 972 -DLBCL11474T BN2 0.880110557517446 0.42166413314291806 412,130,284,157,932,973,2213,13 0.163,0.17,0.186,0.201,0.214,0.241,0.244,0.274 BN2,BN2,BN2,BN2,EZB,BN2,BN2,BN2 34.2464923644398,4.66508763187327 3 14.440517516037056 38.91157999631304 34.24649236443977 BN2 TRUE 973 -DLBCL11475T Other 1 10 0 0 1 2 Other TRUE 974 -DLBCL11476T Other 1 10 0 0 1 2 Other TRUE 975 -DLBCL11477T BN2 1 0 1982,579,889,1929,961,266,111,1935,943,667,1014 0.133,0.161,0.192,0.21,0.216,0.22,0.221,0.226,0.229,0.256,0.263 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.8949860409904 0 0 53.894986040990425 53.894986040990425 BN2 TRUE 976 -DLBCL11478T EZB 1 0.20940392439429784 595,408,331,1293,79,1372,651,54,1242,1241 0.219,0.224,0.24,0.26,0.265,0.268,0.286,0.289,0.295,0.314 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0861317606122 1 7.975385455670512 38.08613176061225 38.08613176061225 Other FALSE 977 -DLBCL11479T EZB 1 4.690274512135857 1508 0.29 EZB 3.44579708996619 4 16.161734265060325 3.44579708996619 3.44579708996619 Other FALSE 978 -DLBCL11480T N1 1 3.6174674136150617 901,870 0.162,0.233 N1,N1 10.4778647357435 7 37.903334245818584 10.477864735743523 10.477864735743523 Other FALSE 979 -DLBCL11481T Other 1 10 0 0 1 2 Other TRUE 980 -DLBCL11482T BN2 0.9011210977339955 0.09310969784016183 1115,261,981,1525,232,933,1075,742,692,830 0.186,0.21,0.21,0.218,0.221,0.235,0.244,0.272,0.283,0.286 BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 38.8163039842497,4.25926497297384 1 3.6141743352453557 43.075568957223496 38.81630398424966 BN2 TRUE 981 -DLBCL11483T Other 1 10 0 0 1 2 Other TRUE 982 -DLBCL11484T ST2 1 7.2522430289106605 2107 0.261 ST2 3.83839876369643 6 27.837000676196748 3.838398763696432 3.838398763696432 Other FALSE 983 -DLBCL11486T N1 1 10.000777844883713 913 0.274 N1 3.64790781394613 7 36.48191564589059 3.647907813946125 3.647907813946125 Other FALSE 984 -DLBCL11488T BN2 0.788795572688273 1.3574474891432349 661,151,736,1199,1135 0.156,0.193,0.208,0.24,0.249 BN2,MCD,BN2,BN2,BN2 19.366089693662,5.18537885434334 5 26.288449829184138 24.551468548005325 19.366089693661984 Other FALSE 985 -DLBCL11489T MCD 1 0.5184535659682165 1911,761,1897,986,755,1543,1184 0.152,0.178,0.186,0.199,0.202,0.238,0.239 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.9419334822003 4 18.63422358163916 35.941933482200255 35.941933482200255 MCD TRUE 986 -DLBCL11490T BN2 1 1.093812332220477 987,754,1381,52,1113 0.128,0.206,0.265,0.275,0.293 BN2,BN2,BN2,BN2,BN2 23.4905606817682 5 25.694264964491552 23.490560681768237 23.490560681768237 BN2 TRUE 987 -DLBCL11491T MCD 0.6088768192756671 0.39681506256153437 934,988,1366,1251,176,301,1064,1197,1358 0.148,0.193,0.23,0.286,0.299,0.309,0.313,0.313,0.32 MCD,MCD,EZB,MCD,N1,MCD,N1,MCD,EZB 7.48373535282983,21.8403540823014,6.5458162516761 2 8.666581471534505 35.869905686807364 21.84035408230143 MCD TRUE 988 -DLBCL11492T Other 1 10 0 0 1 2 Other TRUE 989 -DLBCL11493T MCD 0.8699369311996448 0.640471948369461 990,267,1994,850,2014,768,801 0.182,0.22,0.265,0.282,0.294,0.302,0.313 MCD,MCD,MCD,BN2,MCD,MCD,MCD 3.54503288187375,23.7112275967662 4 15.186376137132566 27.256260478639927 23.71122759676618 MCD TRUE 990 -DLBCL11494T BN2 1 0.09898363588714591 991,98,555,1409,295,1001,1927,519,1006,1724 0.129,0.181,0.193,0.199,0.224,0.236,0.278,0.289,0.3,0.3 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 45.90835746181 1 4.544176139176739 45.90835746181 45.90835746181 BN2 TRUE 991 -DLBCL11495T MCD 1 1.8691810344660635 864,650,717 0.123,0.144,0.214 MCD,MCD,MCD 19.7616182885345 6 36.93804211528644 19.761618288534525 19.761618288534525 Other FALSE 992 -DLBCL11496T EZB 0.5140222537204949 1.8184872332482656 993,1053,62,179,1133,330 0.171,0.188,0.218,0.227,0.253,0.268 BN2,EZB,EZB,EZB,BN2,BN2 13.5281662246011,14.3088413918351 5 26.02044539362656 27.83700761643626 14.308841391835148 BN2 FALSE 993 -DLBCL11497T EZB 0.5156070060163884 3.229954968980665 141,561,2042,1052 0.179,0.18,0.214,0.249 BN2,EZB,EZB,ST2 5.58256452711108,10.2215009993953,4.02014337702901 6 33.01498794343776 19.824208903535414 10.221500999395325 EZB TRUE 994 -DLBCL11498T BN2 0.9226861349025091 0.10783573421120729 418,638,1996,812,995,957,1252,1056,571,1212 0.135,0.174,0.18,0.229,0.239,0.249,0.257,0.261,0.266,0.279 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 42.7781322785598,3.58447214388526 1 4.6130113024426445 46.36260442244508 42.77813227855982 BN2 TRUE 995 -DLBCL11499T EZB 0.8554540074967736 1.0683729585781963 1527,818,109,1959,165,353 0.213,0.306,0.307,0.308,0.322,0.337 EZB,EZB,EZB,EZB,EZB,BN2 2.96565505123935,17.5513790074749 5 18.751418717343164 20.517034058714206 17.55137900747486 Other FALSE 996 -DLBCL11500T MCD 1 0.17954923773830594 1399,918,1225,1826,2036,786,1905,824,815 0.163,0.185,0.271,0.277,0.298,0.316,0.323,0.328,0.329 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.5405227705442 2 6.201724534533816 34.54052277054423 34.54052277054423 MCD TRUE 997 -DLBCL11501T BN2 0.7581419463436394 1.8330401137026702 190,189,2218 0.144,0.173,0.247 BN2,BN2,ST2 12.694973392937,4.04987690081366 4 23.27039547164152 16.744850293750613 12.694973392936951 Other FALSE 998 -DLBCL11503T MCD 1 4.007583061133663 311,194 0.13,0.255 MCD,MCD 11.6209038490401 9 46.57173742047589 11.620903849040051 11.620903849040051 N1 FALSE 999 -DLBCL11504T EZB 0.5326926031766777 10.190107013508953 65,2127 0.214,0.244 EZB,ST2 4.66387684828609,4.09141057352785 9 47.52540418186216 8.755287421813946 4.663876848286094 Other FALSE 1000 -DLBCL11506T BN2 1 0 98,1409,991,1001,555,1724,519,976,1927,41,796 0.146,0.147,0.175,0.18,0.226,0.229,0.249,0.272,0.283,0.287,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.8015281200607 0 0 51.80152812006067 51.80152812006067 BN2 TRUE 1001 -DLBCL11507T EZB 0.7736203262988727 0.7314113959179602 1002,342,1521,1260 0.186,0.215,0.242,0.244 EZB,EZB,BN2,EZB 4.1350862505187,14.1310689343233 2 10.335624855066364 18.266155184842045 14.131068934323348 EZB TRUE 1002 -DLBCL11508T BN2 0.7338404746656302 0.8609387690233193 1003,1714,459,1854 0.179,0.186,0.202,0.232 BN2,ST2,BN2,BN2 14.8605239376633,5.38982263041666 2 12.79400118593345 20.250346568080005 14.860523937663345 BN2 TRUE 1003 -DLBCL11509T ST2 1 10.547634157520125 451 0.319 ST2 3.13954997807344 7 33.11482458796902 3.1395499780734437 3.1395499780734437 Other FALSE 1004 -DLBCL11510T Other 1 10 0 0 1 2 Other TRUE 1005 -DLBCL11511T BN2 1 2.015905235004477 1807,1006,1927,1854 0.197,0.23,0.297,0.315 BN2,BN2,BN2,BN2 15.9611258626882 7 32.17611718315858 15.961125862688245 15.961125862688245 BN2 TRUE 1006 -DLBCL11512T BN2 0.36232880963556297 1.2030297436437756 476,15,2121,1185,636,1190,826 0.176,0.213,0.229,0.267,0.29,0.338,0.375 MCD,EZB,BN2,ST2,ST2,BN2,BN2 9.9858644904606,4.69278274962532,5.68575002350284,7.19583042160296 3 12.013291998020303 27.560227685191727 9.985864490460605 Other FALSE 1007 -DLBCL11513T MCD 0.6233017747160112 0.6324855464203482 659,464,1609,1008,869 0.136,0.191,0.219,0.226,0.246 MCD,BN2,MCD,N1,MCD 5.24694750313055,16.0127792342746,4.43052457235624 2 10.127851423698594 25.69025130976143 16.012779234274632 N1 FALSE 1008 -DLBCL11514T MCD 0.9124584954465184 0.09240209207934026 759,858,1009,1904,1406,764,1449,1376,479,305 0.116,0.119,0.127,0.13,0.142,0.148,0.15,0.16,0.164,0.173 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.23347190416167,64.9724313523155 1 6.003588584435274 71.20590325647721 64.97243135231554 MCD TRUE 1009 -DLBCL11515T EZB 0.5742549067456566 1.4382729496523101 1480,1513,632 0.123,0.171,0.196 MCD,EZB,EZB 10.9223951642662,8.09772122649171 2 15.709385510177345 19.020116390757963 10.922395164266248 Other FALSE 1010 -DLBCL11516T EZB 0.8867131115733871 0.3750035914276733 1036,1322,1701,1011,466,881,1031,809 0.179,0.205,0.211,0.218,0.238,0.248,0.255,0.275 EZB,EZB,EZB,EZB,EZB,N1,EZB,EZB 31.5340929636505,4.02881069940269 3 11.825398113783056 35.56290366305318 31.534092963650487 EZB TRUE 1011 -DLBCL11518T EZB 0.48274507514003706 2.165259682662179 1509,1494,881,2196,1036,628 0.315,0.338,0.339,0.385,0.411,0.431 EZB,BN2,N1,BN2,EZB,EZB 5.55248621366425,7.93629548717953,2.95114880466646 4 17.18414064808363 16.43993050551024 7.936295487179528 BN2 FALSE 1012 -DLBCL11519T ST2 0.7434926086071293 0.537905518310335 1555,1341,1644,1943,293,722,1776,486 0.166,0.178,0.198,0.215,0.225,0.239,0.246,0.267 ST2,BN2,ST2,ST2,ST2,ST2,BN2,ST2 9.69630852130123,28.1049745865614 3 15.117820922083101 37.80128310786263 28.104974586561397 ST2 TRUE 1013 -DLBCL11520T BN2 0.8986054240006325 0.2877758129200703 692,1935,232,1075,933,579,817,1982,1525 0.155,0.164,0.193,0.202,0.231,0.239,0.258,0.259,0.268 BN2,BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2 38.348764687784,4.32710134143886 2 11.035846932507521 42.67586602922284 38.34876468778398 BN2 TRUE 1014 -DLBCL11523T MCD 1 2.6518534949375927 864,650,717 0.176,0.203,0.237 MCD,MCD,MCD 14.8279714276686 7 39.32160785329785 14.82797142766864 14.82797142766864 Other FALSE 1015 -DLBCL11525T EZB 0.6210298863758215 1.0718030635846245 1703,1392,1122,1493,696,1227 0.172,0.182,0.221,0.239,0.246,0.31 ST2,EZB,BN2,EZB,EZB,EZB 4.51831435329154,16.9547235262445,5.82794054066228 4 18.17212461765921 27.30097842019837 16.954723526244546 Other FALSE 1016 -DLBCL11526T EZB 1 0 728,1017,1295,505,1243,364,1581,9,1668,119,474 0.143,0.17,0.177,0.235,0.249,0.252,0.269,0.274,0.281,0.286,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.6454217567903 0 0 48.64542175679035 48.64542175679035 EZB TRUE 1017 -DLBCL11527T ST2 0.6276566288787618 1.3449163213119997 618,701,1211,1018,744 0.217,0.285,0.332,0.356,0.373 ST2,EZB,ST2,ST2,BN2 2.6800733401575,3.50455760583509,10.4253893354718 4 14.021276273308079 16.61002028146438 10.425389335471795 ST2 TRUE 1018 -DLBCL11528T EZB 1 14.314903571621906 1535 0.31 EZB 3.22091438137584 9 46.107078781845374 3.22091438137584 3.22091438137584 Other FALSE 1019 -DLBCL11530T EZB 1 0 366,1020,427,689,161,78,645,48,128,1090,326 0.185,0.188,0.197,0.239,0.276,0.276,0.291,0.319,0.325,0.328,0.356 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.7296132755986 0 0 42.729613275598595 42.729613275598595 EZB TRUE 1020 -DLBCL11531T EZB 1 0 1972,695,397,398,2089,172,1752,1671,676,1493,108 0.185,0.197,0.208,0.22,0.242,0.262,0.272,0.311,0.321,0.335,0.35 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6687073979041 0 0 43.668707397904086 43.668707397904086 Other FALSE 1021 -DLBCL11532T EZB 1 0 1937,369,316,1,428,372,1279,1222,1055,429,1120 0.129,0.129,0.13,0.173,0.176,0.18,0.184,0.187,0.19,0.194,0.196 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.4548365774755 0 0 66.45483657747552 66.45483657747552 EZB TRUE 1022 -DLBCL11533T MCD 0.6777735402782542 3.9948670850952173 81,401,82 0.136,0.172,0.205 MCD,EZB,MCD 5.80836306697421,12.2173542251127 8 48.80670626085163 18.025717292086892 12.217354225112679 Other FALSE 1023 -DLBCL11534T Other 1 10 0 0 1 2 Other TRUE 1024 -DLBCL11535T Other 1 10 0 0 1 2 Other TRUE 1025 -DLBCL11536T MCD 0.6537351537561876 2.5361023607960305 904,1733 0.122,0.23 MCD,EZB 4.33948773556226,8.1927914797143 4 20.777757813213032 12.53227921527656 8.192791479714296 Other FALSE 1026 -DLBCL11537T BN2 0.9329767812664944 0.07294028873365689 145,592,417,1193,560,564,669,1375,1027,1212 0.137,0.147,0.169,0.207,0.237,0.24,0.242,0.243,0.29,0.31 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,ST2 44.9008133240256,3.22558620230964 1 3.2750782882304526 48.1263995263352 44.900813324025556 BN2 TRUE 1027 -DLBCL11538T ST2 0.6302997618782207 1.6593018113989328 1847,2017,1082,1121,610 0.18,0.183,0.19,0.197,0.239 ST2,ST2,BN2,ST2,BN2 9.42897402975705,16.0754023743803 5 26.673944278775874 25.504376404137314 16.07540237438026 ST2 TRUE 1028 -DLBCL11539T BN2 1 0 1029,935,557,795,857,585,1252,841,1056,995,957 0.14,0.146,0.193,0.208,0.222,0.24,0.252,0.254,0.26,0.271,0.272 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.7497510658789 0 0 51.74975106587889 51.74975106587889 BN2 TRUE 1029 -DLBCL11540T Other 1 10 0 0 1 2 Other TRUE 1030 -DLBCL11541T EZB 0.8987785101000821 0.20879710506426388 466,1701,1031,646,2176,1550,1036,1384,337 0.156,0.216,0.223,0.245,0.246,0.256,0.261,0.263,0.296 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.90457672726938,34.6700059145195 2 7.238996867512581 38.57458264178889 34.670005914519514 EZB TRUE 1031 -DLBCL11542T EZB 1 0.10034280248779753 1581,1243,1032,474,1545,1017,1245,728,1042,340 0.211,0.218,0.228,0.252,0.252,0.255,0.255,0.256,0.269,0.281 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6722608530796 1 4.081168637512748 40.67226085307962 40.67226085307962 EZB TRUE 1032 -DLBCL11558T EZB 1 0.13176962256037258 93,2063,1138,1081,382,39,558,174,1560,1048 0.185,0.189,0.195,0.222,0.223,0.224,0.234,0.244,0.249,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.5370580317872 1 6.000400949358385 45.53705803178722 45.53705803178722 EZB TRUE 1033 -DLBCL11559T EZB 1 0.18548962357190563 1034,443,354,660,192,548,333,289,1705 0.139,0.15,0.154,0.207,0.273,0.279,0.294,0.298,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.4004653680081 2 7.864846360385436 42.400465368008064 42.400465368008064 EZB TRUE 1034 -DLBCL11560T ST2 0.8082723711620188 1.5300360219897895 1884,1045,2082,2052,1128 0.198,0.225,0.23,0.232,0.232 ST2,ST2,ST2,ST2,EZB 4.30402649345971,18.1446238108572 6 27.761928036065193 22.448650304316928 18.14462381085722 Other FALSE 1035 -DLBCL11562T EZB 0.9050771766004391 0.3468717658096254 1701,466,1036,1011,1031,1322,809,881 0.162,0.188,0.195,0.202,0.206,0.233,0.246,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,N1 34.7839386308432,3.64807526823133 3 12.065566214694224 38.43201389907452 34.783938630843195 EZB TRUE 1036 -DLBCL11563T EZB 0.6847818570592594 1.4225833193918715 160,1037,1944,388,1439,724 0.184,0.186,0.192,0.196,0.233,0.266 ST2,EZB,EZB,EZB,EZB,ST2 19.9854449507161,9.19968129743243 5 28.43096061751318 29.1851262481485 19.985444950716065 EZB TRUE 1037 -DLBCL11564T ST2 0.6424497529284393 1.3718633888485299 2134,508,1486,781,955,68 0.173,0.202,0.212,0.213,0.219,0.219 MCD,ST2,ST2,MCD,ST2,ST2 10.4593968702646,18.7935457746645 4 25.782177394911184 29.25294264492906 18.79354577466448 ST2 TRUE 1038 -DLBCL11565T Other 1 10 0 0 1 2 Other TRUE 1039 -DLBCL11566T EZB 1 0.37198836676007047 2100,548,729,660,289,1867,443,712 0.147,0.176,0.254,0.291,0.309,0.327,0.331,0.344 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 32.0732698640974 3 11.930883273400578 32.0732698640974 32.0732698640974 EZB TRUE 1040 -DLBCL11567T EZB 1 0.15169100725566625 329,1094,138,2006,164,1041,83,435,86,326 0.118,0.137,0.144,0.154,0.167,0.177,0.185,0.192,0.196,0.198 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.5652205536942 1 9.338890317707122 61.56522055369421 61.56522055369421 EZB TRUE 1041 -DLBCL11568T EZB 0.9166659241212828 0 340,1244,1042,341,1245,1581,365,166,105,1243 0.112,0.121,0.148,0.196,0.212,0.212,0.215,0.23,0.237,0.241 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.65538383649503,51.2087244218167 0 0 55.864108258311774 51.20872442181675 EZB TRUE 1042 -DLBCL11569T EZB 1 0.08195256281348061 80,121,17,187,404,1537,149,639,1705,2059 0.104,0.118,0.12,0.135,0.149,0.159,0.198,0.2,0.2,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.8394216862115 1 5.477661904155965 66.83942168621148 66.83942168621148 Other FALSE 1043 -DLBCL11570T BN2 1 1.7240777237654195 2032,2064,57,1632,2085 0.293,0.33,0.34,0.345,0.347 BN2,BN2,BN2,BN2,BN2 15.1670994796789 6 26.149258347048544 15.16709947967894 15.16709947967894 Other FALSE 1044 -DLBCL11571T ST2 0.6846710766461268 0.294185102236411 2082,1394,1045,1417,1147,1687,1139,1917,545 0.141,0.155,0.167,0.192,0.201,0.22,0.235,0.238,0.249 ST2,EZB,ST2,ST2,ST2,ST2,BN2,ST2,BN2 8.27358200852404,6.46127503656343,31.9936729240822 2 9.412061940089423 46.7285299691697 31.993672924082222 ST2 TRUE 1045 -DLBCL11572T EZB 1 0.09215670155216699 172,108,2089,1573,695,676,117,382,2063,1138 0.126,0.206,0.219,0.225,0.256,0.262,0.265,0.289,0.296,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5250672219157 1 4.011126630008092 43.52506722191571 43.52506722191571 EZB TRUE 1046 -DLBCL11573T EZB 1 0 12,1237,299,1111,1047,429,428,1055,668,372,1110 0.148,0.152,0.154,0.173,0.175,0.179,0.2,0.202,0.207,0.209,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2150015201613 0 0 61.215001520161266 61.215001520161266 EZB TRUE 1047 -DLBCL11575T EZB 1 0.16222361436949453 1138,382,676,93,2089,172,2063,108,558,174 0.193,0.197,0.203,0.224,0.227,0.227,0.227,0.238,0.24,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8486991283946 1 7.2755180723781665 44.84869912839457 44.84869912839457 EZB TRUE 1048 -DLBCL11576T EZB 0.7041695002137258 0 216,213,877,212,693,1049,1423,390,210,211,396 0.177,0.192,0.203,0.207,0.231,0.254,0.259,0.26,0.266,0.273,0.31 EZB,BN2,EZB,BN2,EZB,ST2,EZB,EZB,EZB,EZB,EZB 10.0360471606089,33.2520821705845,3.93357248987664 0 0 47.2217018210701 33.252082170584515 ST2 FALSE 1049 -DLBCL11577T MCD 1 0 904 0.211 MCD 4.72814497551971 0 0 4.728144975519705 4.728144975519705 Other FALSE 1050 -DLBCL11578T BN2 1 4.944689987238244 1803 0.239 BN2 4.18500855430925 4 20.69356989499935 4.18500855430925 4.18500855430925 Other FALSE 1051 -DLBCL11579T ST2 0.5602785613931073 5.479655423692083 1052,2042 0.178,0.227 ST2,EZB 4.40296025274363,5.61010680783205 5 30.74145219702878 10.01306706057568 5.610106807832052 ST2 TRUE 1052 -DLBCL11581T EZB 0.8921433518888187 0.8008203097109667 62,1453,179,2126,1053,1087 0.113,0.144,0.15,0.159,0.167,0.238 EZB,EZB,EZB,EZB,EZB,BN2 4.20025568227685,34.7426908660631 5 27.82265245955305 38.942946548339975 34.74269086606313 EZB TRUE 1053 -DLBCL11584T EZB 1 0 1474,1673,2201,153,1054,1903,173,287,1277,1079,1545 0.133,0.168,0.186,0.187,0.191,0.209,0.209,0.265,0.29,0.302,0.308 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.7935660793881 0 0 52.79356607938809 52.79356607938809 EZB TRUE 1054 -DLBCL11585T EZB 1 0 316,369,1937,1,1222,1279,1120,1886,428,372,1055 0.125,0.139,0.146,0.166,0.173,0.174,0.18,0.189,0.192,0.197,0.2 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.701754148347 0 0 65.70175414834705 65.70175414834705 EZB TRUE 1055 -DLBCL11586T BN2 0.6955250069124519 0 590,1086,1076,13,932,960,157,1722,412,1806 0.157,0.162,0.182,0.194,0.216,0.217,0.229,0.236,0.274,0.275 BN2,BN2,EZB,BN2,EZB,ST2,BN2,BN2,BN2,BN2 33.6255307117022,10.1098279942406,4.61017938041781 0 0 48.34553808636065 33.6255307117022 BN2 TRUE 1056 -DLBCL11587T BN2 0.5322145612616535 3.2276023115208115 2170,114,1657,1719 0.197,0.249,0.273,0.273 BN2,EZB,BN2,EZB 8.74262058555936,7.68425162333763 5 28.217702410700824 16.426872208896995 8.7426205855593615 ST2 FALSE 1057 -DLBCL11589T EZB 0.8358362320759486 0.12586470047482343 218,139,143,11,1058,219,220,308,221,1533 0.141,0.177,0.178,0.188,0.22,0.222,0.241,0.244,0.256,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,BN2,EZB 8.00152769350471,40.7396031582587 1 5.127677948977401 48.74113085176341 40.739603158258696 EZB TRUE 1058 -DLBCL11590T EZB 0.4079965709363233 4.33225943459348 1388,1797,1841 0.198,0.259,0.289 EZB,ST2,BN2 3.45595257737565,5.04508744618125,3.86447431055413 4 21.856627687067853 12.365514334111026 5.045087446181252 EZB TRUE 1059 -DLBCL11591T EZB 1 0.19232897208833394 354,149,1705,333,1034,319,404,121,1060,80 0.188,0.193,0.196,0.221,0.224,0.231,0.241,0.266,0.266,0.285 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.1011668888786 1 8.48193209563409 44.1011668888786 44.1011668888786 EZB TRUE 1060 -DLBCL11595T EZB 1 17.76704165061437 2166 0.329 EZB 3.03863001802654 10 53.98746609108461 3.038630018026539 3.038630018026539 Other FALSE 1061 -DLBCL11596T EZB 1 10.156294496611709 2162 0.311 EZB 3.21667685690072 8 32.66951745911903 3.2166768569007194 3.2166768569007194 Other FALSE 1062 -DLBCL11646T MCD 1 1.2719415255595548 650,1063,864,1194,753 0.15,0.185,0.202,0.211,0.23 MCD,MCD,MCD,MCD,MCD 26.1268715786891 6 33.23185289389639 26.1268715786891 26.1268715786891 MCD TRUE 1063 -DLBCL11648T MCD 0.5622328220913498 1.7215068039763564 1064,988,176,2108,1609,743 0.133,0.212,0.24,0.283,0.293,0.301 N1,MCD,N1,MCD,MCD,MCD 14.9772196236125,11.6616015819895 5 25.78338548669705 26.638821205601914 14.977219623612458 N1 FALSE 1064 -DLBCL11649T BN2 0.8444295993069658 1.0867488283758027 1614,1816,591,610,1666 0.268,0.369,0.428,0.475,0.499 BN2,BN2,BN2,BN2,EZB 10.8819655043249,2.00479913923553 4 11.825963262251024 12.886764643560463 10.881965504324937 Other FALSE 1065 -DLBCL11651T EZB 1 0 1914,958,1066,1172,2132,468,1437,1570,1242,1372,54 0.124,0.148,0.168,0.177,0.184,0.19,0.211,0.224,0.234,0.235,0.241 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.9640048237265 0 0 58.96400482372649 58.96400482372649 EZB TRUE 1066 -DLBCL11652T BN2 1 0 1532 0.124 BN2 8.06824837427792 0 0 8.06824837427792 8.06824837427792 Other FALSE 1067 -DLBCL11655T BN2 0.6900347695184301 0.8626531534550874 648,922,489,594,666,2041,1414 0.197,0.237,0.24,0.265,0.273,0.277,0.286 ST2,BN2,BN2,BN2,BN2,ST2,BN2 19.3280063336406,8.68218559788983 4 16.67336561371495 28.010191931530414 19.32800633364058 Other FALSE 1068 -DLBCL11656T EZB 1 0.14447356573977033 4,2217,2091,1587,144,220,1103,1022,219,1110 0.116,0.17,0.17,0.181,0.19,0.198,0.199,0.221,0.227,0.231 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.4773345994133 1 7.870534781575803 54.47733459941331 54.47733459941331 EZB TRUE 1069 -DLBCL11657T BN2 0.3986600165137226 1.3034439775739983 1070,133,1183,2140,972,132,1074 0.174,0.213,0.229,0.304,0.319,0.324,0.326 EZB,BN2,ST2,ST2,BN2,BN2,ST2 10.9176168755431,5.74811831478757,10.7200482424238 3 14.230501965886928 27.38578343275444 10.917616875543116 EZB FALSE 1070 -DLBCL11658T MCD 0.8624213119332564 0.7636016202878455 2030,285,1398,1194,952,1890,650 0.191,0.225,0.228,0.25,0.253,0.292,0.301 MCD,MCD,MCD,MCD,BN2,MCD,MCD 3.95828064801148,24.8127499791392 4 18.94705608786786 28.771030627150633 24.812749979139152 ST2 FALSE 1071 -DLBCL11659T EZB 1 0 2132,1914,1172,468,1570,958,278,162,1779,1066,1517 0.127,0.135,0.137,0.19,0.192,0.199,0.208,0.21,0.217,0.22,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 61.2801886440991 0 0 61.280188644099134 61.280188644099134 Other FALSE 1072 -DLBCL11660T ST2 0.7466598656343291 0.6670467895286004 508,1486,955,171,2134,68,1174 0.164,0.18,0.189,0.232,0.239,0.243,0.249 ST2,ST2,ST2,BN2,MCD,ST2,ST2 4.31890962781472,4.18202413071582,25.0544828745889 3 16.712512364793795 33.55541663311939 25.054482874588857 Other FALSE 1073 -DLBCL11661T BN2 0.4055518783368154 0.9328063754350081 15,1185,2121,476,597,827,1377 0.196,0.214,0.237,0.242,0.285,0.297,0.425 EZB,ST2,BN2,MCD,BN2,BN2,MCD 11.0903467114807,5.09525645039849,6.48067204647212,4.68003312823598 3 10.345146118253854 27.346308336587267 11.090346711480679 ST2 FALSE 1074 -DLBCL11662T BN2 0.8944533410773117 0.2987767964475576 232,1075,692,933,1525,1935,742,817,981 0.146,0.151,0.163,0.208,0.212,0.221,0.237,0.247,0.269 BN2,BN2,BN2,MCD,BN2,BN2,BN2,BN2,BN2 40.8049690446251,4.81503947977498 2 12.191577930294844 45.62000852440009 40.8049690446251 BN2 TRUE 1075 -DLBCL11664T BN2 0.8202305225241888 0.4304218605497936 2041,489,922,1362,594,1320,582,666 0.148,0.167,0.18,0.215,0.248,0.276,0.279,0.29 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.8579739945739,6.76312537589471 3 13.28194657954165 37.62109937046863 30.857973994573914 EZB FALSE 1076 -DLBCL11665T ST2 0.6784038302324997 1.207612539398397 1822,1427,1847,1077 0.146,0.186,0.218,0.222 EZB,ST2,ST2,ST2 6.86091729193236,14.473034840932 2 17.47781835705932 21.333952132864322 14.473034840931962 ST2 TRUE 1077 -DLBCL11666T EZB 1 0.08123449350656657 1237,1047,12,299,668,1111,429,276,517,428 0.134,0.151,0.156,0.161,0.186,0.199,0.203,0.213,0.221,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.7640535923635 1 4.5299646494486865 55.76405359236354 55.76405359236354 EZB TRUE 1078 -DLBCL11667T EZB 1 0 240,2016,153,1673,174,1903,558,1474,287,39,382 0.154,0.163,0.177,0.197,0.214,0.22,0.228,0.246,0.248,0.259,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6633086955053 0 0 52.66330869550529 52.66330869550529 EZB TRUE 1079 -DLBCL11669T EZB 1 0 617,513,679,1080,135,1253,103,1287,468,162,1867 0.175,0.175,0.18,0.181,0.186,0.19,0.19,0.211,0.219,0.24,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.6894816559056 0 0 55.68948165590564 55.68948165590564 EZB TRUE 1080 -DLBCL11670T EZB 1 0.10134616943230179 93,1081,39,1138,1560,174,2063,558,382,1889 0.174,0.18,0.186,0.204,0.211,0.221,0.226,0.227,0.235,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.0131190061824 1 4.86594569377383 48.01311900618239 48.01311900618239 EZB TRUE 1081 -DLBCL11671T EZB 0.5527796335114438 3.0774517338066656 1983,1863,2017,1082 0.184,0.258,0.265,0.265 EZB,EZB,ST2,BN2 3.76746724446993,9.3176833062003,3.77090336544679 6 28.67472064572754 16.856053916117016 9.317683306200301 BN2 FALSE 1082 -DLBCL11672T EZB 0.8861074604119302 0.2066952323295722 343,16,575,308,11,1533,42,231,143 0.116,0.157,0.172,0.174,0.181,0.2,0.213,0.216,0.237 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.7616415125478,44.8267599173148 2 9.265477555691344 50.58840142986263 44.82675991731483 EZB TRUE 1083 -DLBCL11673T ST2 1 5.272615045333875 2107 0.306 ST2 3.26300277768004 4 17.204557538561993 3.2630027776800374 3.2630027776800374 Other FALSE 1084 -DLBCL11675T EZB 1 0 240,2016,174,558,39,93,153,1138,382,1673,1903 0.112,0.126,0.165,0.197,0.2,0.226,0.237,0.244,0.246,0.256,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.543528092941 0 0 57.543528092941 57.543528092941 EZB TRUE 1085 -DLBCL11676T EZB 0.4383470721865005 2.8832879963057643 1123,854,649,234,1687 0.17,0.172,0.28,0.302,0.338 EZB,BN2,EZB,ST2,ST2 5.82268402120512,9.44094938166094,6.27397909804319 6 27.22097602587333 21.537612500909255 9.440949381660944 BN2 FALSE 1086 -DLBCL11677T EZB 0.8696073454696892 0.6063469923902671 2126,1453,179,62,1087,1059,1053 0.123,0.132,0.171,0.178,0.186,0.225,0.237 EZB,EZB,EZB,EZB,BN2,EZB,EZB 5.37319905836831,35.8346364422064 4 21.7282240301305 41.207835500574696 35.83463644220638 BN2 FALSE 1087 -DLBCL11678T EZB 0.8881637723430585 0 1541,1088,187,206,2059,17,1537,80,1606,404,121 0.155,0.169,0.172,0.186,0.189,0.2,0.2,0.205,0.207,0.222,0.223 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0938591016991,6.43366081381012 0 0 57.52751991550918 51.09385910169906 EZB TRUE 1088 -DLBCL11679T EZB 0.5369265597122261 3.3834549723960063 2042,844,561,141 0.218,0.266,0.287,0.312 EZB,BN2,EZB,BN2 6.97299375306198,8.08507943016499 7 27.3555022002084 15.058073183226972 8.085079430164988 BN2 FALSE 1089 -DLBCL11680T EZB 1 0 2097,71,1236,1079,1589,1109,1090,644,1054,240,1579 0.181,0.182,0.215,0.247,0.267,0.295,0.307,0.308,0.336,0.357,0.36 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.8957984434221 0 0 41.89579844342209 41.89579844342209 EZB TRUE 1090 -DLBCL11681T BN2 1 0.7502167486734478 1204,273,871,1275,1407,1112,754 0.181,0.215,0.228,0.233,0.27,0.277,0.282 BN2,BN2,BN2,BN2,BN2,BN2,BN2 29.6979914460476 4 22.27993058478568 29.697991446047585 29.697991446047585 Other FALSE 1091 -DLBCL11683T Other 1 10 0 0 1 2 Other TRUE 1092 -DLBCL11684T EZB 0.6708355459038603 0.9621125512732438 491,77,282,751,383,994 0.261,0.273,0.3,0.309,0.324,0.358 EZB,EZB,BN2,MCD,EZB,EZB 3.33569844340565,13.3881412162589,3.23357210542043 4 12.880898702381344 19.95741176508501 13.388141216258926 EZB TRUE 1093 -DLBCL11685T EZB 1 0.09562168984887888 1094,86,1041,329,164,78,138,168,326,2006 0.164,0.177,0.18,0.183,0.196,0.202,0.21,0.219,0.225,0.228 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.9784571982631 1 4.874646223186664 50.97845719826313 50.97845719826313 EZB TRUE 1094 -DLBCL_P01-Tumor ST2 0.64398828866145 1.0347463193117425 1847,1121,1082,1822,2017 0.138,0.184,0.207,0.209,0.211 ST2,ST2,BN2,EZB,ST2 4.8411828073779,4.77908574389845,17.4020687620261 3 18.00672659991632 27.02233731330241 17.40206876202606 Other FALSE 1095 -DLBCL_P04-Tumor BN2 1 3.2795665508139966 1096,523 0.29,0.315 BN2,BN2 6.62459477575775 6 21.725799439272265 6.62459477575775 6.62459477575775 BN2 TRUE 1096 -DLBCL_P07-Tumor ST2 1 14.016899216734158 1028 0.257 ST2 3.88925809517028 10 54.51533874786924 3.889258095170277 3.889258095170277 Other FALSE 1097 -DLBCL_P10-Tumor BN2 1 10.467923618812291 1803 0.291 BN2 3.43513513300666 8 35.95873219261233 3.435135133006661 3.435135133006661 Other FALSE 1098 -SP124957 EZB 0.5546830654108883 0 1099,1500 0.205,0.255 EZB,ST2 4.88388929217018,3.92093926078443 0 0 8.80482855295461 4.8838892921701795 EZB TRUE 1099 -SP124959 EZB 0.6663158731346609 2.3160277858904204 115,2216,1666 0.165,0.176,0.189 EZB,ST2,EZB 11.3597740366268,5.68885784300281 4 26.309552310264362 17.048631879629657 11.359774036626849 Other FALSE 1100 -SP124969 ST2 0.75407442573606 0.7658079750151477 600,89,831,1145,675,130,1411 0.161,0.192,0.247,0.266,0.267,0.285,0.328 ST2,ST2,ST2,BN2,ST2,BN2,ST2 7.25857322632517,22.2567516765452 4 17.044397931830094 29.515324902870397 22.256751676545225 ST2 TRUE 1101 -SP124971 EZB 0.9179287169103686 0.20001924152783254 1102,473,1864,809,371,1865,381,1011,380 0.12,0.158,0.211,0.272,0.278,0.311,0.32,0.32,0.334 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.2146078340342,35.9538773293265 2 7.19146727339662 39.16848516336071 35.9538773293265 EZB TRUE 1102 -SP124973 EZB 0.9265329202222824 0 29,1022,1563,1103,2091,1279,1,1120,1110,2148,1055 0.12,0.172,0.173,0.173,0.182,0.184,0.189,0.222,0.226,0.229,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 55.1306482888515,4.37144503733623 0 0 59.50209332618774 55.13064828885151 EZB TRUE 1103 -SP124975 MCD 0.4325153200949604 4.245636939965954 68,2134,781,1400 0.114,0.179,0.28,0.304 ST2,MCD,MCD,EZB 3.28745714835304,9.16235403383654,8.73407204262869 7 38.90002874310247 21.18388322481827 9.162354033836536 Other FALSE 1104 -SP124977 BN2 0.7029980965641499 2.9609955660308342 52,1381,1131 0.208,0.249,0.268 BN2,BN2,ST2 8.82215681686867,3.72717562085242 5 26.12236721757683 12.549332437721091 8.82215681686867 Other FALSE 1105 -SP124981 Other 1 10 0 0 1 2 Other TRUE 1106 -SP192765 EZB 0.48471925832721646 0.374648676546756 1093,1330,972,132,757,384,2140,133,1107 0.192,0.206,0.216,0.217,0.222,0.227,0.231,0.302,0.304 EZB,EZB,BN2,BN2,EZB,EZB,ST2,BN2,ST2 12.5467439553207,18.9692931854715,7.61856058683927 2 7.106820586964306 39.134597727631544 18.969293185471532 ST2 FALSE 1107 -SP192767 BN2 0.37872324894774345 4.927952901641618 1108,65,2127 0.182,0.204,0.243 BN2,EZB,ST2 5.49674431380925,4.89980140536964,4.11733566803876 5 27.087697090818345 14.513881387217644 5.4967443138092476 BN2 TRUE 1108 -SP193976 EZB 1 0.1828174367848319 276,1304,517,1047,142,1237,1085,375,372,668 0.178,0.214,0.215,0.241,0.257,0.258,0.269,0.274,0.274,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.5232401841678 1 7.591172337470496 41.52324018416785 41.52324018416785 EZB TRUE 1109 -SP192997 EZB 1 0 369,1,1279,1937,1055,316,428,429,1120,1111,1563 0.124,0.138,0.153,0.162,0.163,0.17,0.182,0.185,0.188,0.188,0.192 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 66.8052005742785 0 0 66.80520057427847 66.80520057427847 EZB TRUE 1110 -SP192993 EZB 1 0 372,428,1937,429,369,517,1111,1962,1055,1033,1304 0.108,0.121,0.144,0.152,0.167,0.171,0.173,0.179,0.181,0.182,0.185 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 70.6185114857347 0 0 70.61851148573471 70.61851148573471 EZB TRUE 1111 -SP192970 BN2 1 0.6939094301436548 817,57,2064,742,1075,106,1407 0.148,0.207,0.216,0.249,0.261,0.27,0.276 BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.3652367140104 4 21.76463353453978 31.365236714010376 31.365236714010376 BN2 TRUE 1112 -SP193005 BN2 1 1.241540839079802 987,1381,52,754 0.19,0.22,0.226,0.269 BN2,BN2,BN2,BN2 17.9608288528104 4 22.299102524486965 17.96082885281042 17.96082885281042 BN2 TRUE 1113 -SP193684 EZB 0.5836677039482706 7.531586983596391 2166,1901 0.213,0.299 EZB,BN2 3.34432037476217,4.68849477428559 7 35.311806214669026 8.032815149047753 4.688494774285587 Other FALSE 1114 -SP193025 BN2 0.911546277380667 0.20925055034580892 1115,1525,742,1075,232,261,830,981,933 0.144,0.168,0.218,0.218,0.23,0.24,0.266,0.267,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD 38.1147869018618,3.69854484843051 2 7.975540135527812 41.81333175029231 38.114786901861805 BN2 TRUE 1115 -SP192940 MCD 0.43676407420808794 3.2363166348334347 2135,1278,254,952,736 0.256,0.289,0.293,0.334,0.335 MCD,EZB,MCD,BN2,BN2 5.97455033138583,3.45848565147562,7.31489423767369 5 23.673313903430603 16.747930220535146 7.314894237673691 Other FALSE 1116 -SP192870 MCD 1 3.793025214731644 904 0.287 MCD 3.48358675350341 3 13.213332393743595 3.4835867535034133 3.4835867535034133 Other FALSE 1117 -SP193794 EZB 1 0.20752038249276425 6,1142,1118,1788,1872,196,1991,367,79 0.131,0.137,0.14,0.169,0.197,0.198,0.203,0.205,0.217 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5606367159511 2 10.907403435357411 52.56063671595115 52.56063671595115 EZB TRUE 1118 -SP192856 BN2 0.8651541528314151 0.42611723830944404 684,1722,960,863,1317,1119,915,590 0.178,0.188,0.2,0.216,0.226,0.238,0.247,0.261 BN2,BN2,ST2,BN2,BN2,BN2,BN2,BN2 32.0498515480228,4.99539806805118 3 13.656994229871117 37.045249616073946 32.04985154802276 BN2 TRUE 1119 -SP192850 EZB 1 0 316,1222,1886,1937,369,1120,463,1,1279,2015,349 0.122,0.139,0.156,0.17,0.175,0.178,0.186,0.19,0.193,0.21,0.21 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.3429189441507 0 0 64.34291894415068 64.34291894415068 EZB TRUE 1120 -SP192833 EZB 0.40674572370669987 5.711356792704715 1863,1121,1082 0.147,0.186,0.22 EZB,ST2,BN2 4.53522041428891,6.80000889686465,5.3828540036401 7 38.8372770035604 16.718083314793653 6.800008896864648 ST2 FALSE 1121 -SP192815 EZB 0.6390297307608582 0 1122,696,1392 0.161,0.167,0.199 BN2,EZB,EZB 6.22259696406521,11.0159334478196 0 0 17.23853041188486 11.015933447819648 BN2 FALSE 1122 -SP193512 EZB 0.41869441956110554 2.1223318469553236 1123,854,1341,649,234,293 0.122,0.158,0.31,0.311,0.315,0.318 EZB,BN2,BN2,EZB,ST2,ST2 9.56506050758269,11.4409838575394,6.31933501187649 5 24.28156440135772 27.32537937699862 11.44098385753944 EZB TRUE 1123 -SP193656 EZB 0.5096904348303333 3.2311802841585093 1178,2138,816,911 0.209,0.215,0.221,0.246 ST2,EZB,EZB,ST2 9.19350662490242,8.84392550380286 6 29.705877348665346 18.037432128705287 9.193506624902422 Other FALSE 1124 -SP193528 ST2 1 13.023047065108381 1946 0.267 ST2 3.73981884528815 9 48.70383683716686 3.7398188452881502 3.7398188452881502 Other FALSE 1125 -SP193337 BN2 1 1.3768538199369253 1126,811,1158,1027,661 0.134,0.234,0.246,0.283,0.291 BN2,BN2,BN2,BN2,BN2 22.7945409557911 6 31.38475078868965 22.794540955791085 22.794540955791085 BN2 TRUE 1126 -SP192798 MCD 0.9135557558239127 0.24742998283594925 1728,214,154,997,1531,789,1127,1501,1732 0.176,0.178,0.192,0.202,0.226,0.23,0.249,0.28,0.289 MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD 3.5690160190816,37.7178973329688 2 9.332538689704563 41.28691335205039 37.71789733296879 MCD TRUE 1127 -SP193546 ST2 0.6042256355094994 0.9002191288219875 545,1128,1970,444,2052,2082,1417 0.142,0.162,0.185,0.22,0.291,0.292,0.296 BN2,EZB,ST2,ST2,ST2,ST2,ST2 7.05574844813604,6.16594067357872,20.1854496623641 3 18.17132790993347 33.40713878407883 20.185449662364075 EZB FALSE 1128 -SP192800 MCD 0.7572159720893612 0.5106504066447656 2014,291,236,267,715,1325,790,1503,686 0.154,0.178,0.229,0.286,0.291,0.334,0.335,0.338,0.342 MCD,BN2,MCD,MCD,MCD,MCD,MCD,BN2,MCD 8.5684558390767,26.7240463605758 2 13.646645141221576 35.29250219965245 26.724046360575752 Other FALSE 1129 -SP193375 ST2 0.8431112037477578 0.4869045720581796 1130,1789,1943,486,1713,1018,1211 0.133,0.166,0.173,0.177,0.212,0.23,0.231 ST2,EZB,ST2,ST2,ST2,ST2,ST2 6.02136851691572,32.3584818022537 3 15.755492734378727 38.379850319169414 32.35848180225369 ST2 TRUE 1130 -SP193420 BN2 1 1.4419119796730935 987,52,754,1381 0.188,0.213,0.26,0.264 BN2,BN2,BN2,BN2 17.648365160272 5 25.447389146241452 17.648365160272 17.648365160272 ST2 FALSE 1131 -SP194216 EZB 0.9067389784692703 0 351,1896,527,727,1132,1268,2001,2114,1879,1516,1281 0.135,0.143,0.176,0.198,0.199,0.236,0.247,0.249,0.254,0.258,0.267 EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.9984107129314,5.03964420299275 0 0 54.038054915924164 48.99841071293141 EZB TRUE 1132 -SP194228 EZB 0.58892364426975 0.6954705291435269 1509,1133,1012,1850,1356,330,1494,1492 0.121,0.31,0.312,0.315,0.36,0.38,0.384,0.397 EZB,BN2,BN2,EZB,EZB,BN2,BN2,EZB 11.6706824221667,16.71986415022 3 11.628172767761406 28.390546572386683 16.719864150220022 BN2 FALSE 1133 -SP194234 MCD 0.44420106862532593 2.406063469920699 879,1441,1416 0.135,0.208,0.223 MCD,EZB,ST2 4.80871731669865,7.41936714177927,4.47463794770644 3 17.85146824976504 16.702722406184357 7.419367141779267 Other FALSE 1134 -SP193725 BN2 0.8937172731352914 0.8893718489233517 281,1135,1199,661,736,151 0.152,0.154,0.166,0.242,0.284,0.314 BN2,BN2,BN2,BN2,BN2,MCD 26.7566860366888,3.18196105113397 5 23.796643331511532 29.938647087822755 26.756686036688784 BN2 TRUE 1135 -SP193300 EZB 0.4070330700329336 2.716012451974231 65,2127,1108 0.151,0.195,0.222 EZB,ST2,BN2 4.50210919323995,6.61025490391365,5.12772888442848 3 17.953534629753204 16.24009298158208 6.610254903913653 Other FALSE 1136 -SP193914 EZB 0.5072055975475537 1.4673462642980029 491,282,77,1074,81,994 0.164,0.17,0.266,0.283,0.364,0.377 EZB,BN2,EZB,ST2,MCD,EZB 5.87318642217345,12.5115905853493,2.7442406871163,3.53867257898072 5 18.358835705838324 24.667690273619737 12.511590585349277 Other FALSE 1137 -SP193957 EZB 1 0.0919998312526624 93,1138,558,382,174,39,2016,676,240,1081 0.11,0.132,0.149,0.16,0.166,0.177,0.212,0.215,0.242,0.249 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.8037686109732 1 5.409936789230139 58.80376861097319 58.80376861097319 EZB TRUE 1138 -SP193967 ST2 0.7860378552948143 0.11602310300028787 1484,1176,619,2004,235,1139,234,1363,1497,1147 0.154,0.161,0.162,0.165,0.177,0.185,0.213,0.221,0.223,0.231 ST2,ST2,BN2,ST2,ST2,BN2,ST2,ST2,ST2,ST2 11.5480391182387,42.4243078787055 1 4.922199842726974 53.97234699694419 42.42430787870552 BN2 FALSE 1139 -SP194143 BN2 1 1.1084321449743202 754,987,1204,1113,871 0.144,0.166,0.242,0.244,0.293 BN2,BN2,BN2,BN2,BN2 24.6356107057903 6 27.306902817371476 24.635610705790306 24.635610705790306 Other FALSE 1140 -SP194195 ST2 0.6021912819715227 1.5017730562674527 1128,1970,545,444,2052,1884 0.126,0.146,0.196,0.204,0.233,0.27 EZB,ST2,BN2,ST2,ST2,ST2 5.10415904693656,7.94177306961334,19.7485530852953 5 29.65784492376391 32.79448520184516 19.74855308529527 Other FALSE 1141 -SP194035 EZB 1 0.10856961497828012 438,1788,196,28,44,1118,6,1142,367,731 0.136,0.143,0.148,0.175,0.191,0.197,0.205,0.207,0.233,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.3756439408838 1 6.012112341836087 55.37564394088382 55.37564394088382 EZB TRUE 1142 -SP194053 N1 1 3.7684840256577465 3,697 0.262,0.315 N1,N1 6.99399762237826 7 26.356768315420737 6.993997622378261 6.993997622378261 Other FALSE 1143 -SP194080 BN2 1 1.225644396525998 523,20,596,1096,2069 0.181,0.245,0.245,0.264,0.289 BN2,BN2,BN2,BN2,BN2 20.9256732678121 6 25.64743418422782 20.92567326781214 20.92567326781214 Other FALSE 1144 -SP193934 BN2 0.6297235641716348 0 13,1076,1086,838,590,932,1145,412,960,157,1722 0.174,0.189,0.194,0.209,0.223,0.239,0.253,0.257,0.26,0.26,0.27 BN2,EZB,BN2,ST2,BN2,EZB,BN2,BN2,ST2,BN2,BN2 30.7909475757072,9.46644338129717,8.63858392399109 0 0 48.89597488099541 30.790947575707158 BN2 TRUE 1145 -SP59280 ST2 1 4.681893743673735 1028 0.205 ST2 4.88186809475933 4 22.85638769029411 4.881868094759327 4.881868094759327 Other FALSE 1146 -SP59304 ST2 0.6794807384473429 0.292985227034343 234,1147,235,1139,649,1687,1484,1176,1394 0.147,0.153,0.154,0.164,0.165,0.173,0.187,0.199,0.207 ST2,ST2,ST2,BN2,EZB,ST2,ST2,ST2,EZB 6.11396996872831,10.8731806735404,36.0116942944745 2 10.550894428757967 52.998844936743225 36.01169429447451 ST2 TRUE 1147 -SP59312 BN2 1 1.4244816822361894 1148,804,1980,1841 0.124,0.163,0.18,0.207 BN2,BN2,BN2,BN2 24.5809470816127 6 35.015108849774386 24.580947081612685 24.580947081612685 BN2 TRUE 1148 -SP59324 N1 1 6.367329721651783 697 0.235 N1 4.25438785364591 6 27.089090227953953 4.2543878536459125 4.2543878536459125 Other FALSE 1149 -SP59360 Other 1 10 0 0 1 2 Other TRUE 1150 -SP59364 EZB 0.8631881290473808 0.5212782043896503 1600,2083,1318,1272,1059,651,331,595,179 0.199,0.224,0.246,0.281,0.291,0.322,0.323,0.334,0.342 EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.46881201863648,28.1951080602295 2 14.697495302208585 32.663920078865964 28.195108060229487 Other FALSE 1151 -SP59368 ST2 0.6384096854269461 0.6004868186477054 1152,1174,2066,629,68,268 0.185,0.202,0.224,0.23,0.245,0.247 BN2,ST2,BN2,ST2,ST2,ST2 9.87077184843882,17.4274478510951 2 10.464952717252903 27.298219699533945 17.427447851095128 BN2 FALSE 1152 -SP59372 BN2 0.6330361705099534 2.3088261393483815 114,1657,2170 0.205,0.208,0.276 EZB,BN2,BN2 8.42999588221002,4.88677221876451 4 19.463394847445723 13.316768100974535 8.429995882210024 Other FALSE 1153 -SP59376 Other 1 10 0 0 1 2 Other TRUE 1154 -SP59400 MCD 0.3857628777564817 3.7117203602440347 921,1835,1532 0.132,0.133,0.219 MCD,EZB,BN2 4.57395590049619,7.4926953237418,7.5782884696773 5 28.128487608703832 19.644939693915283 7.578288469677297 Other FALSE 1155 -SP59412 EZB 0.7554525310850035 0.2491839165416679 944,384,1330,693,216,1093,940,757,213,1107 0.166,0.197,0.276,0.286,0.29,0.301,0.316,0.333,0.336,0.341 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,BN2,ST2 2.97388491547197,28.0112969306834,6.09364942131386 1 6.979964676599293 37.07883126746924 28.011296930683407 Other FALSE 1156 -SP59448 BN2 0.6849403303281856 0 610,1666,591 0.121,0.152,0.164 BN2,EZB,BN2 14.3105152894058,6.58256788259693 0 0 20.893083172002743 14.310515289405814 Other FALSE 1157 -SP59452 BN2 1 0 261,830,1115,1158,1027,1203,981,1525,1375,1126,417 0.142,0.192,0.219,0.239,0.271,0.295,0.314,0.314,0.325,0.328,0.344 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.4596759343514 0 0 43.459675934351445 43.459675934351445 BN2 TRUE 1158 -SP59456 BN2 0.5493605145048053 1.4549225087324922 897,1191,34,274,273,1275,1112 0.216,0.23,0.255,0.269,0.274,0.286,0.288 MCD,BN2,EZB,MCD,BN2,BN2,BN2 14.9796434402832,3.92520005034783,8.36257570819124 4 21.794220414055083 27.267419198822292 14.97964344028322 Other FALSE 1159 -SP59460 EZB 1 0 1539,1450,699,477,520,137,1160,1542,1511,72 0.182,0.191,0.207,0.237,0.243,0.243,0.248,0.27,0.28,0.287 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8028161527974 0 0 42.80281615279739 42.80281615279739 EZB TRUE 1160 -SP116610 EZB 0.8840622814616396 0.17678155200001427 206,1541,1088,1606,2059,187,1537,17,233,80 0.111,0.197,0.202,0.265,0.267,0.268,0.283,0.293,0.297,0.301 EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.8045925072874,5.08891287236856 1 6.859936088166387 43.893505379655934 38.804592507287374 Other FALSE 1161 -SP116618 EZB 1 0 2093,1162,2005,1554,1277,1528,1042,399,340,578,1244 0.125,0.218,0.238,0.239,0.263,0.274,0.281,0.285,0.286,0.297,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.6974921757627 0 0 45.69749217576272 45.69749217576272 EZB TRUE 1162 -SP116620 EZB 1 0 1046,1163,1516,2215,1281,1264,375,142,348,1033,1962 0.156,0.18,0.184,0.205,0.205,0.239,0.245,0.26,0.261,0.262,0.264 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.705114941938 0 0 50.70511494193801 50.70511494193801 EZB TRUE 1163 -SP116624 BN2 0.5513025723364164 4.720821457485995 318,1381,1108 0.199,0.316,0.332 ST2,BN2,BN2 6.17498202856739,5.02573122476981 7 29.15098766005134 11.200713253337202 6.174982028567391 Other FALSE 1164 -SP116627 N1 1 1.7645224003607864 870,262,901 0.152,0.186,0.22 N1,N1,N1 16.4767306496611 5 29.073560316038183 16.47673064966112 16.47673064966112 Other FALSE 1165 -SP116630 EZB 0.7563780617762772 3.106529764857696 2042,1266,1052 0.117,0.241,0.244 EZB,EZB,ST2 12.7326095177419,4.10104835943218 8 39.554230451175506 16.833657877174048 12.732609517741867 Other FALSE 1166 -SP116635 ST2 1 11.75338241721894 670 0.294 ST2 3.39744321126602 7 39.93144930279389 3.3974432112660193 3.3974432112660193 Other FALSE 1167 -SP116642 EZB 0.765754261415552 0.32628007614995563 2148,219,220,1587,4,218,221,2091,139 0.2,0.204,0.218,0.246,0.252,0.273,0.275,0.28,0.285 ST2,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 3.63813686001558,28.2554699281346,5.00526710894356 2 9.21919687980455 36.89887389709377 28.255469928134634 Other FALSE 1168 -SP116726 MCD 1 0 705,1567,2094,515,516,540,387,888,1169,1572,621 0.154,0.163,0.164,0.166,0.169,0.18,0.188,0.202,0.21,0.212,0.214 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.6714348113907 0 0 60.67143481139069 60.67143481139069 MCD TRUE 1169 -SP116648 ST2 1 4.221928526707437 318 0.256 ST2 3.91373920959108 3 16.52352721506601 3.9137392095910837 3.9137392095910837 Other FALSE 1170 -SP116657 MCD 1 0.1850900335199017 214,1127,883,1732,154,1728,749,997,1531 0.139,0.205,0.205,0.207,0.24,0.242,0.268,0.281,0.304 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.6449579313547 2 7.5229766259294415 40.64495793135473 40.64495793135473 MCD TRUE 1171 -SP116659 EZB 1 0.08597330462945471 1372,408,958,1242,54,1066,1437,1914,468,1241 0.137,0.165,0.168,0.171,0.172,0.188,0.195,0.23,0.242,0.251 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.9062657851123 1 4.634499809779815 53.90626578511234 53.90626578511234 EZB TRUE 1172 -SP116663 BN2 1 1.4251178141852985 811,1173,1126,931,1232 0.157,0.221,0.244,0.276,0.287 BN2,BN2,BN2,BN2,BN2 22.1083399876529 6 31.506989158469302 22.10833998765288 22.10833998765288 BN2 TRUE 1173 -SP116668 ST2 0.5598326930043394 0.8065846042758461 1152,1174,268,2066,171,1198,629 0.156,0.16,0.201,0.221,0.23,0.243,0.244 BN2,ST2,ST2,BN2,BN2,ST2,ST2 15.2761681558644,19.42919935571 3 15.671293073721872 34.70536751157439 19.429199355709997 ST2 TRUE 1174 -SP116670 ST2 1 0.4376660411707736 1404,959,1101,339,2052,1198,1884 0.108,0.133,0.151,0.18,0.216,0.223,0.25 ST2,ST2,ST2,ST2,ST2,ST2,ST2 42.0427752250254 4 18.40069499256955 42.04277522502541 42.04277522502541 Other FALSE 1175 -SP116676 ST2 0.7581225404914695 0.300563484446366 234,235,649,1176,1139,1147,1484,1687 0.119,0.157,0.167,0.183,0.188,0.19,0.2,0.201 ST2,ST2,EZB,ST2,BN2,ST2,ST2,ST2 5.32311978616659,6.00212026847335,35.496981733416 2 10.669096517124524 46.822221788055955 35.49698173341602 ST2 TRUE 1176 -SP116688 ST2 1 8.995984657796582 1177 0.224 ST2 4.46038269091262 7 40.125534255351404 4.460382690912625 4.460382690912625 ST2 TRUE 1177 -SP116690 ST2 0.5010107557782449 1.06880213572438 1178,2138,816,911 0.176,0.18,0.193,0.197 ST2,EZB,EZB,ST2 10.7225938539667,10.7660333622173 2 11.506759450817775 21.48862721618398 10.766033362217298 ST2 TRUE 1178 -SP116697 ST2 1 0.5463689178518155 959,339,1404,2052,1101,1884 0.132,0.134,0.154,0.164,0.202,0.21 ST2,ST2,ST2,ST2,ST2,ST2 37.3800272568346 4 20.423285041588063 37.38002725683455 37.38002725683455 Other FALSE 1179 -SP116701 EZB 1 0 1305,1219,55,1180,1926,1590,1726,1457,1853 0.127,0.164,0.186,0.193,0.202,0.203,0.207,0.208,0.213 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.7318440343827 0 0 48.731844034382696 48.731844034382696 EZB TRUE 1180 -SP116709 EZB 1 0.2419788007812092 1663,430,1483,337,336,1468,112,1384,170 0.111,0.152,0.165,0.178,0.18,0.222,0.236,0.247,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.5034292307386 2 11.97878043981159 49.503429230738625 49.503429230738625 EZB TRUE 1181 -DOHH-2 EZB 1 0 1182,532 0.158,0.211 EZB,EZB 11.0576260286425 0 0 11.057626028642494 11.057626028642494 EZB TRUE 1182 -Farage BN2 0.44028139663286364 1.0724850142895916 133,1183,1070,2140,972,132,24 0.155,0.187,0.201,0.231,0.247,0.251,0.268 BN2,ST2,EZB,ST2,BN2,BN2,EZB 14.4700602269138,8.70809783737775,9.68732373343228 3 15.51892274923291 32.865481797723845 14.470060226913812 ST2 FALSE 1183 -HBL-1 MCD 1 0.10218915337535538 1911,761,1897,986,1543,1184,755,790 0.113,0.206,0.214,0.215,0.22,0.236,0.261,0.271 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.3168626980128 1 4.017756912485014 39.316862698012756 39.316862698012756 MCD TRUE 1184 -HT BN2 0.3307831700761498 0.8488234898884465 1185,15,597,476,2121,827,1377,1183 0.16,0.208,0.224,0.263,0.355,0.39,0.394,0.397 ST2,EZB,BN2,MCD,BN2,BN2,MCD,ST2 9.85061553573278,4.80635122040104,6.3491955295533,8.77351342594314 3 8.361433856590049 29.779675711630265 9.850615535732782 ST2 FALSE 1185 -HTMCP-01-01-00012-01A-01D ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1186 -HTMCP-01-01-00451-01A-01D MCD 0.5397948994521631 2.9563594805719413 2135,736,151,1199 0.165,0.207,0.293,0.308 MCD,BN2,MCD,BN2 8.08093644453905,9.4784874621481 7 28.0218162702038 17.559423906687144 9.478487462148095 Other FALSE 1187 -HTMCP-01-02-00013-01A-01D ST2 1 1.5367331088447944 1188,1638,828 0.154,0.247,0.276 ST2,ST2,ST2 14.1474397905 5 21.74083913144968 14.147439790500044 14.147439790500044 ST2 TRUE 1188 -HTMCP-01-06-00036-01E BN2 0.8307399312079529 0.2939378462007821 1362,1320,489,582,666,2041,183,922,1038 0.139,0.155,0.199,0.206,0.251,0.266,0.27,0.3,0.306 BN2,BN2,BN2,BN2,BN2,ST2,BN2,BN2,ST2 34.5157076961308,7.03244281343071 2 10.145472780296455 41.54815050956154 34.515707696130825 Other FALSE 1189 -HTMCP-01-06-00105-01A-01D BN2 0.4561389521283627 1.0031670461073996 826,1190,24,636,1183,2214,476 0.124,0.161,0.221,0.254,0.341,0.352,0.353 BN2,BN2,EZB,ST2,ST2,EZB,MCD 14.3202023425145,7.36835668166352,2.83095175226509,6.87487524495645 3 14.365555083600569 31.3943860213996 14.320202342514532 BN2 TRUE 1190 -HTMCP-01-06-00136-01A-01D BN2 0.5410079840614617 0.956011124615244 897,1191,34,273,1112,274,1275 0.132,0.15,0.153,0.164,0.182,0.197,0.231 MCD,BN2,EZB,BN2,BN2,MCD,BN2 22.5834823300988,6.51579503028586,12.644066537352 4 21.590060340126254 41.74334389773663 22.58348233009881 BN2 TRUE 1191 -HTMCP-01-06-00146-01A-01D EZB 1 1.1150946572862872 1102,1260,809,473,1011,1322 0.242,0.243,0.262,0.263,0.265,0.276 EZB,EZB,EZB,EZB,EZB,EZB 23.273821863715 5 25.952514414861383 23.273821863715007 23.273821863715007 Other FALSE 1192 -HTMCP-01-06-00175-01A-01D BN2 1 0.3081984080914163 1027,1375,1158,417,592,261,1126,931 0.116,0.15,0.176,0.206,0.228,0.262,0.27,0.297 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.0937945845782 3 12.66504207340267 41.09379458457822 41.09379458457822 BN2 TRUE 1193 -HTMCP-01-06-00185-01A-01D MCD 1 1.4482260468902362 650,864,1194,1063 0.149,0.178,0.218,0.226 MCD,MCD,MCD,MCD 21.3516863263102 6 30.922068282792495 21.351686326310176 21.351686326310176 MCD TRUE 1194 -HTMCP-01-06-00206-01A-01D BN2 0.5111272867087627 0.7256122166796479 2121,15,827,1185,476,597 0.207,0.243,0.253,0.267,0.279,0.314 BN2,EZB,BN2,ST2,MCD,BN2 11.973053511745,4.11836600415881,3.58267406388652,3.75070455384398 2 8.68779389908133 23.424798133634315 11.973053511744997 Other FALSE 1195 -HTMCP-01-06-00232-01A-01D EZB 1 7.2056946798437345 1535 0.242 EZB 4.13231798984954 6 29.77622175488135 4.132317989849535 4.132317989849535 Other FALSE 1196 -HTMCP-01-06-00242-01A-01D MCD 1 1.244263117461521 1412,1225,824,815,753 0.187,0.229,0.237,0.251,0.3 MCD,MCD,MCD,MCD,MCD 21.2318299108416 6 26.41798287427659 21.23182991084164 21.23182991084164 MCD TRUE 1197 -HTMCP-01-06-00253-01A-01D ST2 0.6607733427170249 0.3460216167803125 1198,1101,1152,268,2066,531,1404,629,1174 0.165,0.189,0.193,0.202,0.203,0.222,0.236,0.252,0.254 ST2,ST2,BN2,ST2,BN2,BN2,ST2,ST2,ST2 14.6148442647721,28.4679853153948 2 9.850538305311101 43.08282958016689 28.46798531539479 ST2 TRUE 1198 -HTMCP-01-06-00299-01A-01D BN2 0.7184365398888908 1.096580697527942 281,897,1203,1191,1135,1199,659 0.276,0.297,0.301,0.307,0.313,0.316,0.321 BN2,MCD,BN2,BN2,BN2,BN2,MCD 16.5573750825178,6.4890238173467 4 18.15649791721909 23.04639889986446 16.557375082517762 BN2 TRUE 1199 -HTMCP-01-06-00306-01A-01D BN2 0.7786585270663833 1.8965954976413293 1414,648,531,158 0.135,0.238,0.248,0.297 BN2,ST2,BN2,BN2 14.7837558307296,4.20243043300758 7 28.038804746790476 18.986186263737167 14.783755830729582 Other FALSE 1200 -HTMCP-01-06-00307-01A-01D EZB 1 1.4043434539105635 199,1289,1782,1967,1668 0.304,0.357,0.363,0.383,0.383 EZB,EZB,EZB,EZB,EZB 14.0697505787737 6 19.758762123455273 14.069750578773746 14.069750578773746 Other FALSE 1201 -HTMCP-01-06-00422-01A-01D N1 0.5355273170736593 3.920623859633503 999,1071 0.146,0.168 N1,ST2 6.86255223299878,5.9520176576626 4 26.905486022676207 12.814569890661378 6.862552232998784 Other FALSE 1202 -HTMCP-01-06-00443-01A-01D BN2 0.5535100955916062 1.008989089543749 897,1191,1203,274,34,1112,273 0.207,0.207,0.209,0.222,0.237,0.265,0.294 MCD,BN2,BN2,MCD,EZB,BN2,BN2 16.7892507983347,4.22012422388998,9.32295483418003 4 16.940170877133397 30.332329856404716 16.789250798334706 BN2 TRUE 1203 -HTMCP-01-06-00497-01A-01D BN2 1 0.624735319514068 1204,754,1113,871,1407,273,987 0.123,0.217,0.231,0.264,0.265,0.266,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2 31.8197792725712 4 19.8789399707169 31.819779272571218 31.819779272571218 BN2 TRUE 1204 -HTMCP-01-06-00500-01A-01D BN2 0.5800634545470482 9.493984546815053 1697,697 0.19,0.262 BN2,N1 5.27125872468305,3.81612418724198 9 50.04524887440489 9.087382911925033 5.271258724683049 Other FALSE 1205 -HTMCP-01-06-00526-01A-01D BN2 1 1.0959220475485771 523,596,2069,1096,20 0.182,0.265,0.271,0.283,0.284 BN2,BN2,BN2,BN2,BN2 20.0087710826432 5 21.928053373821136 20.008771082643236 20.008771082643236 Other FALSE 1206 -HTMCP-01-06-00594-01A-01D ST2 1 0.39507512276348705 2052,339,959,1884,1404 0.122,0.136,0.174,0.184,0.198 ST2,ST2,ST2,ST2,ST2 31.7391842554012 2 12.539362116115548 31.73918425540116 31.73918425540116 Other FALSE 1207 -HTMCP-01-06-00606-01A-01D MCD 1 3.6741519829928992 311,194 0.137,0.214 MCD,MCD 11.9501293032541 8 43.90659127657249 11.95012930325407 11.95012930325407 Other FALSE 1208 -HTMCP-01-06-00611-01A-01D BN2 1 2.6779643356298695 1210,284,158 0.211,0.251,0.259 BN2,BN2,BN2 12.588020699248 6 33.71027048875669 12.588020699247991 12.588020699247991 Other FALSE 1209 -HTMCP-01-06-00634-01A-01D BN2 1 0.12509244763919167 903,1210,1735,936,454,2213,973,158 0.158,0.174,0.199,0.208,0.225,0.228,0.242,0.255 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.789134724207 1 4.852227804457419 38.789134724207024 38.789134724207024 BN2 TRUE 1210 -HTMCP-01-10-00160-01A-01D ST2 0.5935689205642372 0.779120572828252 1789,486,1130,1943,1341,1713,1123 0.139,0.15,0.163,0.233,0.284,0.306,0.311 EZB,ST2,ST2,ST2,BN2,ST2,EZB 3.52255786471987,10.4109489283784,20.3490751699785 4 15.85438310295878 34.282581963076765 20.349075169978462 ST2 TRUE 1211 -HTMCP-01-10-00778-01A-01D BN2 0.8817144277039662 0 294,1212,836,1213,1996,872,638,669,1477,418,1193 0.145,0.153,0.172,0.172,0.204,0.208,0.227,0.229,0.243,0.258,0.274 BN2,ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.6079963123332,6.52096016727341 0 0 55.12895647960662 48.60799631233321 ST2 FALSE 1212 -HTMCP-01-15-00366-01A-01E BN2 1 0.09842847769219877 638,1996,418,812,945,957,1056,1252,995,294 0.114,0.13,0.146,0.195,0.224,0.228,0.24,0.246,0.251,0.259 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 53.3543192381552 1 5.251584420915207 53.35431923815516 53.35431923815516 BN2 TRUE 1213 -HTMCP-01-15-00367-01A-01E BN2 1 5.694289569427237 2058,1793 0.27,0.307 BN2,BN2 6.96287287422657 8 39.64861438095619 6.9628728742265675 6.9628728742265675 Other FALSE 1214 -HTMCP-01-16-00265-01A-01E ST2 0.5452217102086364 1.0993832862725317 1152,1174,2066,629,268,1198,171 0.144,0.188,0.194,0.214,0.215,0.222,0.258 BN2,ST2,BN2,ST2,ST2,ST2,BN2 15.9814189554463,19.159702145075 4 21.06385630825546 35.14112110052132 19.15970214507503 Other FALSE 1215 -HTMCP-01-20-00272-01A-01E BN2 0.9083537686705491 0.3745019837282277 1320,1362,582,183,489,666,2041 0.122,0.16,0.183,0.224,0.25,0.281,0.311 BN2,BN2,BN2,BN2,BN2,BN2,ST2 31.8888422334914,3.2173502361709 3 11.942434675239003 35.10619246966226 31.888842233491363 Other FALSE 1216 -Karpas422 EZB 1 0 1237,299,1047,12,668,700,1078,148,1110,1111,439 0.127,0.145,0.146,0.148,0.149,0.172,0.188,0.19,0.229,0.233,0.24 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 64.2913264218177 0 0 64.29132642181771 64.29132642181771 EZB TRUE 1217 -LY_RELY_028_tumorB EZB 1 0.2139794496316679 178,56,259,75,1551,320,2072,1218,662 0.153,0.161,0.161,0.163,0.18,0.203,0.21,0.226,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.0821131503559 2 10.50256355867241 49.08211315035592 49.08211315035592 EZB TRUE 1218 -LY_RELY_109_tumorB EZB 1 0.29501009478143597 1926,1219,1726,1590,2072,1305,1853,56,2013 0.142,0.203,0.211,0.239,0.246,0.257,0.267,0.278,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.6453270309454 2 11.695771685040214 39.64532703094535 39.64532703094535 EZB TRUE 1219 -LY_RELY_116_tumorA EZB 1 0 5,102,368,250,118,780,448,170,385,2061,1468 0.129,0.165,0.196,0.199,0.208,0.217,0.225,0.237,0.237,0.26,0.268 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.8287257547481 0 0 53.82872575474815 53.82872575474815 EZB TRUE 1220 -LY_RELY_128_tumorA EZB 1 0.791398041412613 110,818,2087,1750,202,109 0.159,0.265,0.31,0.339,0.354,0.384 EZB,EZB,EZB,EZB,EZB,EZB 21.6589414685499 5 17.140843857280778 21.658941468549852 21.658941468549852 EZB TRUE 1221 -LY_RELY_128_tumorB EZB 1 0 1886,463,348,1264,1222,316,349,1962,1033,1937,2015 0.124,0.134,0.154,0.162,0.169,0.171,0.178,0.195,0.197,0.203,0.208 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.5051064401127 0 0 65.50510644011271 65.50510644011271 EZB TRUE 1222 -MD903 ST2 0.5990276542989001 1.5287271852675075 629,2066,1152,68,1198 0.209,0.241,0.257,0.267,0.289 ST2,BN2,BN2,ST2,ST2 8.03578211658769,12.0049568589073 4 18.35230390817525 20.040738975495017 12.004956858907324 Other FALSE 1223 -OCI-Ly10 MCD 0.8946471512224767 0 1694,1376,767,1224,156,1310,1449,759,788,837,310 0.115,0.148,0.155,0.173,0.181,0.187,0.188,0.192,0.193,0.195,0.207 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.75984774433082,57.404034132331 0 0 64.16388187666179 57.40403413233098 MCD TRUE 1224 -OCI-Ly3 MCD 1 0.11883334327995665 765,799,2053,1526,900,753,69 0.154,0.157,0.202,0.219,0.225,0.238,0.239 MCD,MCD,MCD,MCD,MCD,MCD,MCD 35.2022417453282 1 4.183200077546605 35.20224174532819 35.20224174532819 MCD TRUE 1225 -OZM073-003_LowGradeDiagnosis EZB 1 1.3956065483215665 1226,1227 0.189,0.276 EZB,EZB 8.91047934257402 3 12.435523319180351 8.910479342574021 8.910479342574021 EZB TRUE 1226 -OZM073-003_Transformation EZB 0.8754314408851125 0.5857505237072698 1227,1671,1703,398,397,1493,1972,1752 0.213,0.25,0.278,0.282,0.284,0.304,0.319,0.327 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 25.2511508510603,3.5930848843274 3 14.790874835219858 28.844235735387727 25.25115085106033 EZB TRUE 1227 -OZM073-005P MCD 0.5209079579943119 2.3843527468330987 82,81,401,1107,757 0.115,0.191,0.215,0.243,0.245 MCD,MCD,EZB,ST2,EZB 8.73110924034305,13.9723815082636,4.11963854675374 6 33.31508622902826 26.823129295360374 13.97238150826358 Other FALSE 1228 -OZM073-006_LowGradeDiagnosis ST2 0.5379213228644167 9.732587933862954 1664,951 0.266,0.31 ST2,MCD 3.22978392279396,3.75990004794307 9 36.5935578391415 6.989683970737033 3.759900047943074 Other FALSE 1229 -OZM073-006_Transformation MCD 0.5674655518127071 9.371596362035255 951,1479 0.226,0.297 MCD,ST2 4.41928944192555,3.36847745918168 9 41.41579685673026 7.787766901107222 4.419289441925546 Other FALSE 1230 -OZM073-006_Relapse ST2 1 3.8618191771942647 1664,833 0.25,0.31 ST2,ST2 7.23131716901249 7 27.926039319666558 7.231317169012486 7.231317169012486 Other FALSE 1231 -OZM073-007_Relapse MCD 0.5215056397013654 2.2065802626710806 150,151,811,661,1232 0.134,0.24,0.247,0.288,0.319 MCD,MCD,BN2,BN2,BN2 10.6538631660266,11.6115260422754 6 25.62176418437624 22.265389208302057 11.611526042275443 BN2 FALSE 1232 -OZM073-008_Diagnosis MCD 1 0 709,1331,21,23,2034,1699,305,1689,798,480,2008 0.132,0.134,0.138,0.153,0.154,0.155,0.157,0.157,0.157,0.16,0.161 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.3282567127493 0 0 73.32825671274932 73.32825671274932 MCD TRUE 1233 -OZM073-008_Relapse N1 0.46189574990779414 1.9057034587320265 1064,176,988,1609,464,743 0.124,0.205,0.229,0.256,0.281,0.307 N1,N1,MCD,MCD,BN2,MCD 3.56069009820212,11.54041202737,12.9624229682819 5 24.70253428420223 28.06352509385396 12.962422968281874 Other FALSE 1234 -OZM073-009_Diagnosis Other 1 10 0 0 1 2 Other TRUE 1235 -OZM073-011_LowGradeDiagnosis EZB 1 0 644,2097,1109,71,240,1236,2016,1485,1079,522,1085 0.197,0.197,0.227,0.26,0.279,0.296,0.314,0.327,0.329,0.33,0.335 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6675160057095 0 0 40.66751600570947 40.66751600570947 EZB TRUE 1236 -OZM073-012_Relapse EZB 1 0 299,12,700,1078,439,1237,2217,1110,1291,668,144 0.156,0.163,0.174,0.184,0.189,0.191,0.192,0.196,0.199,0.206,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.1621755169881 0 0 59.16217551698806 59.16217551698806 EZB TRUE 1237 -OZM073-012_LowGradeDiagnosis EZB 1 0.09991114802914804 193,575,424,231,1291,122,439,1078,589,143 0.173,0.23,0.239,0.242,0.242,0.246,0.249,0.264,0.273,0.277 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.7403800800407 1 4.170329292969844 41.74038008004065 41.74038008004065 EZB TRUE 1238 -OZM073-013_Relapse MCD 1 0.10097524244264183 1598,2180,880,1250,771,1239,647,929,1240 0.194,0.195,0.21,0.232,0.236,0.24,0.247,0.257,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.4612479635242 1 3.984609080206067 39.461247963524244 39.461247963524244 MCD TRUE 1239 -OZM073-013_Diagnosis MCD 1 0.10097524244264183 1598,2180,880,1250,771,1239,647,929,1240 0.194,0.195,0.21,0.232,0.236,0.24,0.247,0.257,0.264 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.4612479635242 1 3.984609080206067 39.461247963524244 39.461247963524244 MCD TRUE 1240 -OZM073-014_LowGradeDiagnosis2 EZB 1 0.25046718962158177 331,651,1241,408,1372,1814,1437,595,54 0.162,0.199,0.218,0.223,0.264,0.271,0.272,0.296,0.301 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.0849132627476 2 9.539021191902108 38.08491326274765 38.08491326274765 EZB TRUE 1241 -OZM073-014_LowGradeDiagnosis EZB 1 0.16965192958915684 408,1372,54,1242,958,1437,1066,1241,1914 0.12,0.13,0.169,0.172,0.209,0.226,0.229,0.258,0.273 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.8937634140018 2 8.29492130806113 48.893763414001825 48.893763414001825 EZB TRUE 1242 -OZM073-015_PlasmaEx-T3 EZB 1 0.08602666129706092 1581,1243,1245,105,341,1244,340,1042,166,505 0.11,0.133,0.136,0.177,0.178,0.189,0.202,0.218,0.221,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 58.6313548418742 1 5.043859704369701 58.631354841874156 58.631354841874156 EZB TRUE 1243 -OZM073-015_Relapse EZB 0.9221231502590342 0.10760989297016137 341,1245,105,1581,1244,166,1243,340,365,1042 0.127,0.132,0.153,0.163,0.163,0.173,0.175,0.184,0.212,0.215 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.71296395830804,55.8051999631181 1 6.005191595209597 60.51816392142619 55.80519996311815 EZB TRUE 1244 -OZM073-015_Diagnosis EZB 1 0.08448052162352077 1581,1245,1243,341,1244,105,340,1042,166,505 0.113,0.143,0.144,0.18,0.181,0.185,0.192,0.207,0.225,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.6666851199757 1 4.871711639234873 57.66668511997573 57.66668511997573 EZB TRUE 1245 -OZM073-016_Diagnosis EZB 1 7.508976076552813 65 0.206 EZB 4.85436944947219 6 36.451344062835496 4.854369449472187 4.854369449472187 Other FALSE 1246 -OZM073-016_Relapse EZB 1 7.508976076552813 65 0.206 EZB 4.85436944947219 6 36.451344062835496 4.854369449472187 4.854369449472187 Other FALSE 1247 -OZM073-017_Diagnosis ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1248 -OZM073-018_Diagnosis ST2 1 2.7971738610604246 2198,271,1077 0.183,0.227,0.286 ST2,ST2,ST2 13.3719197304746 8 37.403584342281796 13.37191973047463 13.37191973047463 ST2 TRUE 1249 -OZM073-021_Diagnosis MCD 1 0.178039900706257 1911,1543,986,1897,761,1184,790,755,485 0.121,0.21,0.214,0.217,0.221,0.23,0.271,0.274,0.285 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 42.0362854067688 2 7.484136079881004 42.03628540676884 42.03628540676884 MCD TRUE 1250 -OZM073-021_Relapse MCD 0.9264534478378721 0.10655095700023128 786,1251,1197,738,1826,934,2145,815,1399,1501 0.145,0.189,0.203,0.252,0.263,0.301,0.318,0.319,0.33,0.336 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 2.97892356139191,37.5249950291776 1 3.9983241317877973 40.503918590569526 37.524995029177624 MCD TRUE 1251 -OZM073-022_Diagnosis BN2 1 0.09150703074138683 418,907,571,995,638,1996,260,502,145,892 0.221,0.228,0.252,0.256,0.273,0.279,0.281,0.284,0.289,0.29 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.0377982112212 1 3.4807259702488915 38.037798211221244 38.037798211221244 BN2 TRUE 1252 -OZM073-023_Relapse EZB 1 0 54,1242,1372,1253,408,1872,79,958,468,1287,1142 0.168,0.173,0.202,0.203,0.218,0.236,0.237,0.258,0.264,0.28,0.291 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4011698222552 0 0 49.40116982225517 49.40116982225517 EZB TRUE 1253 -OZM073-025P MCD 0.6684110882370056 6.601879174449466 743,1008,869 0.274,0.29,0.302 MCD,N1,MCD 6.95739605710529,3.45146187410595 8 45.93188813780021 10.40885793121123 6.957396057105285 Other FALSE 1254 -OZM073-025_Diagnosis MCD 1 7.3493914864896395 743,641 0.299,0.315 MCD,MCD 6.52034880588183 9 47.920596002890825 6.520348805881833 6.520348805881833 Other FALSE 1255 -OZM073-025_Progression MCD 1 7.3493914864896395 743,641 0.299,0.315 MCD,MCD 6.52034880588183 9 47.920596002890825 6.520348805881833 6.520348805881833 Other FALSE 1256 -OZM073-026_Transformation BN2 0.5024092807582259 3.3022147836696933 793,1257 0.153,0.155 BN2,EZB 6.51743630961438,6.45492817333943 3 21.52197453323425 12.972364482953811 6.517436309614379 EZB FALSE 1257 -OZM073-027_LowGradeDiagnosis BN2 1 4.272265176752989 1096,20 0.271,0.334 BN2,BN2 6.69090629556083 7 28.585325967441896 6.6909062955608345 6.6909062955608345 Other FALSE 1258 -OZM073-028_Transformation Other 1 10 0 0 1 2 Other TRUE 1259 -OZM073-029_Transformation EZB 0.8605957088412093 0.6765300805222193 1260,1322,1002,628,1521,1011 0.167,0.179,0.211,0.254,0.258,0.274 EZB,EZB,EZB,EZB,BN2,EZB 3.87103460035884,23.8973688553818 3 16.167288876000647 27.768403455740675 23.89736885538183 EZB TRUE 1260 -OZM073-030_Transformation EZB 1 1.658614443692417 2087,1221,202,2114 0.198,0.206,0.223,0.266 EZB,EZB,EZB,EZB 18.1414335143436 6 30.089643656176026 18.14143351434363 18.14143351434363 Other FALSE 1261 -OZM073-032_Diagnosis EZB 1 9.116184594608844 31 0.149 EZB 6.73363332740811 8 61.38504440506252 6.7336333274081115 6.7336333274081115 Other FALSE 1262 -OZM073-033_Diagnosis ST2 1 12.25998564676955 2107 0.289 ST2 3.45773842814218 8 42.39182349930669 3.457738428142185 3.457738428142185 Other FALSE 1263 -OZM073-036_Transformation EZB 1 0 558,382,287,676,2016,1138,174,1903,93,240,153 0.163,0.172,0.174,0.18,0.187,0.195,0.196,0.2,0.215,0.219,0.236 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.2486870136044 0 0 57.24868701360443 57.24868701360443 EZB TRUE 1264 -QC2-11T EZB 0.517103157365798 3.1043764671140335 1835,921 0.134,0.143 EZB,MCD 7.47296865130129,6.97863262951418 3 23.198908020580614 14.451601280815463 7.472968651301287 Other FALSE 1265 -QC2-12T EZB 0.60891050920471 3.6796765662621196 1052,2042,1266 0.161,0.204,0.211 ST2,EZB,EZB 9.64412483907678,6.19420393549609 6 35.48726017245725 15.838328774572869 9.644124839076776 EZB TRUE 1266 -QC2-13T MCD 1 5.884227873538137 879,1631 0.304,0.304 MCD,MCD 6.5763453567419 7 38.69671465415378 6.576345356741898 6.576345356741898 Other FALSE 1267 -QC2-15T EZB 0.8938557531362521 0.11096042742470302 1268,527,1132,1879,2001,2114,727,136 0.123,0.156,0.173,0.174,0.178,0.186,0.202,0.217 EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 41.6620689667625,4.94731830918634 1 4.622840979949423 46.60938727594885 41.662068966762504 EZB TRUE 1268 -QC2-17T EZB 0.9227769479915345 0.2139498888666421 1269,242,578,399,388,1439,96,365,1037 0.126,0.152,0.186,0.199,0.211,0.255,0.282,0.295,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.38730410647055,40.4765942810112 2 8.659962848122506 43.86389838748174 40.476594281011195 EZB TRUE 1269 -QC2-18T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1270 -QC2-19T MCD 0.6794592323887763 0.4012479912069851 790,2014,749,236,2084,1503,291,758,1271 0.218,0.229,0.244,0.258,0.259,0.262,0.284,0.293,0.294 MCD,MCD,MCD,MCD,BN2,BN2,BN2,MCD,MCD 11.2001847101509,23.7413448607003 2 9.526166933908291 34.94152957085128 23.741344860700337 MCD TRUE 1271 -QC2-20T EZB 0.8908261454464651 0.2205828362626619 207,1272,1293,1745,1318,2083,595,1600 0.133,0.175,0.211,0.217,0.225,0.232,0.238,0.26 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB 4.30178931310906,35.1013198901079 1 7.742748697922994 39.403109203216985 35.10131989010792 EZB TRUE 1272 -QC2-22T MCD 0.8658254519787383 0.14309126489672955 556,1736,1169,188,1273,621,708,1640,49,547 0.128,0.131,0.171,0.176,0.178,0.183,0.194,0.218,0.219,0.224 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.61845408829037,49.1617192057022 1 7.034612585641773 56.7801732939926 49.16171920570223 MCD TRUE 1273 -QC2-23T MCD 1 2.2603770027061345 801,1994,768 0.156,0.188,0.199 MCD,MCD,MCD 16.7753865525918 6 37.91869797498415 16.775386552591755 16.775386552591755 Other FALSE 1274 -QC2-25T BN2 0.5625295097708443 1.4040193452264238 897,1191,1203,34,274,281,273 0.256,0.267,0.28,0.295,0.298,0.304,0.323 MCD,BN2,BN2,EZB,MCD,BN2,BN2 13.7023962062118,3.39514433864156,7.26099703976811 4 19.23842934947853 24.358537584621473 13.702396206211803 BN2 TRUE 1275 -QC2-26T EZB 0.4263297840935787 1.4513879739709592 1415,581,1797,737,402 0.376,0.481,0.483,0.486,0.512 EZB,ST2,ST2,BN2,EZB 2.05891745418976,4.61433623074423,4.1501426690289 3 6.697192113160657 10.823396353962895 4.614336230744228 Other FALSE 1276 -QC2-3T EZB 0.9314085841870083 0 1162,1042,1277,2093,1554,340,1244,1545,2005,365,173 0.198,0.202,0.206,0.207,0.211,0.224,0.245,0.247,0.28,0.31,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.22551519537394,43.799541178561 0 0 47.025056373934945 43.799541178561 EZB TRUE 1277 -QC2-30T MCD 0.6981782245620822 2.182017069303387 194,1513,311 0.149,0.2,0.206 MCD,EZB,MCD 4.99435565511891,11.5530112400381 4 25.20886772761702 16.547366895157012 11.553011240038098 EZB FALSE 1278 -QC2-32T EZB 1 0 316,369,1937,1222,1,1886,1279,1120,463,428,372 0.116,0.149,0.149,0.165,0.174,0.179,0.18,0.181,0.196,0.198,0.201 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 65.6699416077587 0 0 65.66994160775866 65.66994160775866 EZB TRUE 1279 -QC2-33T BN2 0.6422743437372785 2.997117826414485 1657,114,1791 0.208,0.214,0.278 BN2,EZB,BN2 8.39479614114601,4.67562496940031 5 25.160193163744225 13.070421110546317 8.394796141146006 Other FALSE 1280 -QC2-34T EZB 1 0 2047,1281,76,1048,2063,117,1896,1046,1516,351,108 0.13,0.161,0.162,0.181,0.236,0.271,0.28,0.284,0.289,0.289,0.31 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.7832265798335 0 0 50.783226579833524 50.783226579833524 EZB TRUE 1281 -QC2-35T EZB 0.8385103364934512 1.2700992491306622 1260,1002,1322,1521,628 0.14,0.143,0.234,0.235,0.269 EZB,EZB,EZB,BN2,EZB 4.25932739846424,22.1159049599307 5 28.08939428345309 26.375232358394957 22.115904959930717 Other FALSE 1282 -QC2-36T MCD 0.43858067465890643 3.513305124984858 1278,2030,1398,952 0.198,0.309,0.33,0.338 EZB,MCD,MCD,BN2 2.9623504881266,5.05555056481062,6.26358284159365 6 22.005877698138207 14.281483894530869 6.263582841593655 ST2 FALSE 1283 -QC2-39T ST2 0.8733985653077084 0.28350482116028236 1713,1401,2076,1018,1211,1284,1943,726,1942 0.125,0.163,0.175,0.175,0.199,0.208,0.221,0.227,0.244 ST2,BN2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 6.1343949496103,42.3199923527147 2 11.997921863460904 48.454387302325024 42.31999235271472 ST2 TRUE 1284 -QC2-4T EZB 1 9.949151851825656 532 0.283 EZB 3.53388456376385 7 35.15915415170919 3.533884563763848 3.533884563763848 Other FALSE 1285 -QC2-40T MCD 0.8859452844524389 0.3824159227763751 1325,715,236,1271,686,1503,223,758 0.148,0.166,0.169,0.19,0.192,0.206,0.224,0.252 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 4.84327045182035,37.6211767967522 3 14.386937040663135 42.464447248572526 37.621176796752174 MCD TRUE 1286 -QC2-42T EZB 1 0 1872,1253,1142,79,1118,1287,1788,54,6,1242 0.14,0.194,0.2,0.2,0.203,0.24,0.246,0.264,0.265,0.269 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7264912196443 0 0 46.72649121964434 46.72649121964434 EZB TRUE 1287 -QC2-5T BN2 1 0 1697 0.139 BN2 7.20951505616804 0 0 7.209515056168045 7.209515056168045 Other FALSE 1288 -QC2-7T EZB 1 0.08623854404782674 1964,72,1289,1160,1542,699,477,1450,2073,1539 0.173,0.197,0.235,0.242,0.28,0.304,0.308,0.318,0.326,0.334 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.5533036542376 1 3.3247807753752054 38.55330365423756 38.55330365423756 EZB TRUE 1289 -QC2-9T MCD 0.5927584474653894 3.6529250431964675 951,1479 0.152,0.221 MCD,ST2 6.59784973211189,4.53290641370775 4 24.101450517678636 11.13075614581964 6.597849732111892 Other FALSE 1290 -Reddy_1008T EZB 1 0.09495808296552909 575,193,143,231,1291,439,122,1058,343,139 0.21,0.217,0.232,0.236,0.237,0.246,0.248,0.249,0.254,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.9445252739604 1 3.98297171091446 41.944525273960366 41.944525273960366 EZB TRUE 1291 -Reddy_1016T EZB 0.8514913181269089 0.27377983905903547 1446,1292,1177,1738,165,1959,1527 0.15,0.18,0.196,0.227,0.227,0.232,0.256 EZB,EZB,ST2,EZB,EZB,EZB,EZB 29.2922895071287,5.10887100213545 2 8.019638306932363 34.40116050926413 29.29228950712868 EZB TRUE 1292 -Reddy_2043T EZB 0.8725964617629686 0.15315401636026224 1272,207,1318,2083,1600,1293,595,1745 0.128,0.155,0.179,0.185,0.214,0.227,0.232,0.265 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB 5.39977837803307,36.9834901932597 1 5.6641700621181 42.38326857129282 36.98349019325974 EZB TRUE 1293 -Reddy_2044T EZB 1 8.466862207179497 1294 0.17 EZB 5.88054761205566 7 49.78978633403368 5.880547612055656 5.880547612055656 EZB TRUE 1294 -Reddy_2045T EZB 1 0.08519140332157582 1243,1581,1245,505,728,105,1017,1295,341,1244 0.169,0.182,0.214,0.221,0.231,0.243,0.25,0.259,0.263,0.274 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3790818357656 1 3.780716259711929 44.379081835765625 44.379081835765625 EZB TRUE 1295 -Reddy_2046T BN2 0.5132594981375155 2.1860220214443697 2170,1719,1848,114,511,1657 0.225,0.265,0.287,0.296,0.297,0.317 BN2,EZB,BN2,EZB,ST2,BN2 11.0899868878832,7.15363608916981,3.36335541911088 5 24.242955554442048 21.60697839616391 11.089986887883228 Other FALSE 1296 -Reddy_2047T EZB 1 0.40618464407825605 2151,1435,288,413,1120,2015,1297 0.128,0.131,0.172,0.218,0.22,0.235,0.235 EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.8518955359984 3 15.78104336005508 38.85189553599836 38.85189553599836 EZB TRUE 1297 -Reddy_2048T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1298 -Reddy_2057T BN2 0.6089849895666785 5.174926254405789 1089,1608 0.153,0.239 BN2,N1 6.52125302502259,4.18714395806688 7 33.747003490812595 10.708396983089473 6.521253025022594 Other FALSE 1299 -Reddy_2060T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 EZB TRUE 1300 -Reddy_2072T Other 1 10 0 0 1 2 Other TRUE 1301 -Reddy_2073T ST2 0.7212235215305182 1.0174226857783915 572,1302,419,1461,718,1482 0.123,0.218,0.22,0.233,0.239,0.295 BN2,ST2,ST2,ST2,ST2,ST2 8.11158436353837,20.9855059220983 4 21.351129797679636 29.097090285636718 20.985505922098344 ST2 TRUE 1302 -Reddy_2074T MCD 0.758551127245738 0.47575835254180715 2014,291,236,715,1325,1503,1271,686,790 0.186,0.189,0.198,0.262,0.295,0.296,0.311,0.312,0.313 MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD,MCD 8.67112528408728,27.2417584050093 2 12.96049409910913 35.91288368909653 27.241758405009254 Other FALSE 1303 -Reddy_2075T EZB 1 0 1560,1081,375,1889,2063,1485,39,1046,93,1048 0.156,0.159,0.192,0.233,0.24,0.258,0.259,0.26,0.27,0.278 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.227657395638 0 0 45.22765739563798 45.22765739563798 EZB TRUE 1304 -Reddy_2076T EZB 1 0.33789521713654536 55,1457,1180,1305,1517,1853,278,1590 0.13,0.135,0.139,0.15,0.176,0.185,0.195,0.196 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.250976773345 3 16.979564708152914 50.25097677334502 50.25097677334502 EZB TRUE 1305 -Reddy_2077T MCD 0.8622746917990568 0.11977268439678804 556,1736,188,621,1169,1640,1273,708,792,49 0.124,0.144,0.211,0.212,0.214,0.222,0.226,0.229,0.247,0.258 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 6.93782606591826,43.4365470725021 1 5.202511843801024 50.37437313842038 43.43654707250212 Other FALSE 1306 -Reddy_2078T EZB 1 0 1471 0.192 EZB 5.22068216676074 0 0 5.220682166760742 5.220682166760742 Other FALSE 1307 -Reddy_2079T ST2 1 13.252580378074267 1071 0.241 ST2 4.14156256859336 9 54.88639083110728 4.141562568593364 4.141562568593364 Other FALSE 1308 -Reddy_2080T BN2 0.5311837044224742 9.134570627339627 1538,1479 0.2,0.227 BN2,ST2 4.99752712893652,4.41075683634447 9 45.6502645213165 9.408283965280994 4.997527128936523 Other FALSE 1309 -Reddy_2081T MCD 1 0 1009,1904,759,764,858,1449,479,305,1406,910,2203 0.107,0.118,0.125,0.127,0.138,0.142,0.143,0.151,0.162,0.17,0.173 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.4810258052397 0 0 79.48102580523967 79.48102580523967 MCD TRUE 1310 -Reddy_2083T EZB 0.7568686202766444 2.983392285933021 2183,1725,2087 0.133,0.292,0.318 EZB,ST2,EZB 10.6788128751873,3.43038994958308 8 31.859087954755935 14.109202824770357 10.678812875187273 Other FALSE 1311 -Reddy_2084T EZB 0.43781743472426965 2.537113601113526 1074,1070,282,491 0.181,0.268,0.329,0.339 ST2,EZB,BN2,EZB 3.03819571379806,6.67238951133917,5.52953310304523 5 16.92861018114584 15.24011832818246 6.672389511339169 Other FALSE 1312 -Reddy_2085T MCD 0.8660494946452881 1.2383297006833742 713,2028,569,2130,850,755 0.173,0.187,0.207,0.208,0.266,0.28 MCD,MCD,MCD,MCD,BN2,MCD 3.76486804599883,24.3415436172432 5 30.142856421712047 28.106411663242017 24.341543617243182 Other FALSE 1313 -Reddy_2087T MCD 0.8994646361536652 0.10562491626635864 621,556,1169,1736,2094,2039,540,705,947,839 0.157,0.171,0.182,0.201,0.206,0.215,0.219,0.221,0.236,0.249 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD 4.98093843294148,44.5632044674084 1 4.70698474043063 49.54414290034986 44.56320446740838 Other FALSE 1314 -Reddy_2088T ST2 0.4686051726124937 0.8942669024475971 1341,1123,486,1789,293,854,1130,1943 0.2,0.204,0.221,0.237,0.25,0.256,0.268,0.274 BN2,EZB,ST2,EZB,ST2,BN2,ST2,ST2 8.90385113988751,9.13058823367247,15.9034885927746 3 14.221963481971216 33.93792796633455 15.903488592774577 Other FALSE 1315 -Reddy_2089T ST2 0.4727471045895638 3.758654438463251 1733,1178,911,904 0.176,0.22,0.303,0.327 EZB,ST2,ST2,MCD 5.67703495770737,3.06008724068612,7.83390524298669 7 29.444942712052438 16.571027441380174 7.833905242986686 Other FALSE 1316 -Reddy_2091T BN2 1 5.2025400909795865 915 0.258 BN2 3.88037095661293 4 20.187785469651587 3.8803709566129316 3.8803709566129316 BN2 TRUE 1317 -Reddy_2092T EZB 0.7203123132438068 0.5919188926352248 1318,2083,1600,1272,207,179,993 0.153,0.177,0.197,0.214,0.263,0.278,0.28 EZB,BN2,EZB,EZB,EZB,EZB,BN2 9.20619064440884,23.709776272769 3 14.034264516006338 32.91596691717782 23.709776272768973 EZB TRUE 1318 -Reddy_2093T BN2 1 12.053798413567398 1791 0.317 BN2 3.15891409080576 8 38.07691365635023 3.158914090805765 3.158914090805765 Other FALSE 1319 -Reddy_2095T BN2 0.8940233443537149 0.5279756659481588 1320,1362,582,183,666,489,1038 0.133,0.209,0.235,0.25,0.256,0.266,0.299 BN2,BN2,BN2,BN2,BN2,BN2,ST2 28.2398714286957,3.34752682778961 4 14.909964923855975 31.587398256485272 28.239871428695665 BN2 TRUE 1320 -Reddy_2097T BN2 1 9.816545003233209 2058 0.285 BN2 3.50603925236696 8 34.41719210396237 3.5060392523669592 3.5060392523669592 Other FALSE 1321 -Reddy_2100T EZB 0.8806484706666996 0.23660960923700086 466,1031,1701,1550,2176,809,1384,1011,1036 0.154,0.161,0.181,0.195,0.211,0.254,0.266,0.267,0.297 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.11568426361408,37.7466426138912 2 8.931218358881516 42.862326877505254 37.746642613891176 EZB TRUE 1322 -Reddy_2101T EZB 1 3.1588593335482975 1395,2166,1601 0.246,0.29,0.306 EZB,EZB,EZB 10.7787226985951 8 34.04846880018604 10.778722698595105 10.778722698595105 Other FALSE 1323 -Reddy_2102T EZB 0.742540602164638 0.4498248546561965 1227,696,1226,1392,1122,1703 0.138,0.278,0.3,0.309,0.331,0.331 EZB,EZB,EZB,EZB,BN2,ST2 3.02488270695354,17.4299046673175,3.01854811271272 2 7.840404333647442 23.473335486983736 17.42990466731747 Other FALSE 1324 -Reddy_2103T MCD 0.7522229837198902 0.14170969486367277 1503,2084,1271,758,1325,223,236,749,790,715 0.166,0.186,0.2,0.201,0.226,0.238,0.238,0.24,0.251,0.274 BN2,BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 11.3999965484247,34.6090995314841 1 4.904444934113093 46.009096079908815 34.60909953148411 MCD TRUE 1325 -Reddy_2106T EZB 0.5131949461300642 5.785628339981264 1835,921 0.166,0.175 EZB,MCD 6.02453339164199,5.71473535422584 5 34.85571112584732 11.739268745867829 6.024533391641987 Other FALSE 1326 -Reddy_2107T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 1327 -Reddy_2109T ST2 1 4.05716434043974 1052 0.215 ST2 4.66159446331039 3 18.912854826134247 4.661594463310391 4.661594463310391 Other FALSE 1328 -Reddy_2110T BN2 1 5.463206783006061 1096,20 0.246,0.319 BN2,BN2 7.20561362781423 9 39.36575724719562 7.20561362781423 7.20561362781423 Other FALSE 1329 -Reddy_2111T EZB 0.6326523163190454 0.1690031836717498 1330,384,1093,757,132,972,2140,1107,944,400 0.184,0.197,0.201,0.219,0.252,0.252,0.268,0.287,0.323,0.336 EZB,EZB,EZB,EZB,BN2,BN2,ST2,ST2,EZB,EZB 7.93877656410994,26.0995893243082,7.21587226274074 1 4.410913688333299 41.25423815115888 26.099589324308198 EZB TRUE 1330 -Reddy_2112T MCD 0.8943145536182163 0.4284516269757691 1406,858,759,1904,2203,1009,1310,1376 0.109,0.128,0.16,0.162,0.17,0.172,0.177,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.53108075247733,46.804230700876 3 20.053348793139566 52.33531145335333 46.804230700876005 MCD TRUE 1331 -Reddy_2113T MCD 1 0.08903598410189376 1598,803,880,2180,1647,518,1459,771,1239,1250 0.214,0.258,0.265,0.276,0.283,0.283,0.29,0.3,0.31,0.316 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.1686584555351 1 3.22031209923385 36.168658455535116 36.168658455535116 Other FALSE 1332 -Reddy_2114T MCD 1 3.324397525710624 311 0.213 MCD 4.70380262118217 3 15.637309795289141 4.703802621182166 4.703802621182166 Other FALSE 1333 -Reddy_2115T MCD 1 0.22072853248691365 69,799,765,824,900,1412,1526 0.152,0.159,0.179,0.19,0.205,0.219,0.257 MCD,MCD,MCD,MCD,MCD,MCD,MCD 37.0483947111261 2 8.177637795582786 37.04839471112605 37.04839471112605 Other FALSE 1334 -Reddy_2120T EZB 0.55438840010664725 3.1551469701625967 1387,901,1488,870 0.154,0.196,0.202,0.244 EZB,N1,EZB,N1 11.4323458703136,9.18920008576048 7 36.07073143457089 20.621545956074094 11.432345870313618 Other FALSE 1335 -Reddy_2121T BN2 0.5763068560610556 1.8548076767994826 1848,1719,511,2170,1657 0.176,0.204,0.245,0.296,0.319 BN2,EZB,ST2,BN2,BN2 12.2101068921109,4.90244709260649,4.07426127471677 6 22.647399998029563 21.186815259434155 12.210106892110897 Other FALSE 1336 -Reddy_2122T BN2 1 1.4731988985216309 2069,523,1957,596,1765 0.184,0.197,0.229,0.258,0.283 BN2,BN2,BN2,BN2,BN2 22.2874718151158 6 32.83387892886048 22.287471815115794 22.287471815115794 Other FALSE 1337 -Reddy_2123T EZB 1 8.714650371552592 1300 0.126 EZB 7.95688137992726 8 69.34143927398296 7.956881379927256 7.956881379927256 Other FALSE 1338 -Reddy_2124T ST2 0.760418326491399 0.296718002994885 234,1176,235,1484,1139,649,1147 0.13,0.142,0.15,0.19,0.193,0.213,0.215 ST2,ST2,ST2,ST2,BN2,EZB,ST2 5.18076227404029,4.7057377558333,31.379177286295 2 9.31076682001191 41.2656773161686 31.37917728629501 Other FALSE 1339 -Reddy_2125T MCD 0.5807410831377178 1.5975481865154881 1278,150,151,2135,736 0.23,0.293,0.298,0.306,0.344 EZB,MCD,MCD,MCD,BN2 2.9060812426195,4.35047597200708,10.0514997467718 5 16.05775519221622 17.308056961398407 10.051499746771826 Other FALSE 1340 -Reddy_2127T ST2 0.5474811806431125 0.486087516299867 1341,486,1789,1943,293,1130,1123 0.167,0.195,0.214,0.233,0.239,0.24,0.248 BN2,ST2,EZB,ST2,ST2,ST2,EZB 5.98340314913132,8.71771758735416,17.7861927356447 2 8.645646251300288 32.48731347213021 17.78619273564474 BN2 FALSE 1341 -Reddy_2128T EZB 0.9115363808764263 0.40664547406921975 809,1011,1864,1031,1701,473,466,1550 0.116,0.179,0.186,0.19,0.21,0.226,0.261,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.67775331977718,37.8958715919481 3 15.410184668774006 41.57362491172527 37.89587159194809 Other FALSE 1342 -Reddy_2130T ST2 1 10.325876460134841 90 0.235 ST2 4.26032556360195 8 43.991595449708065 4.260325563601949 4.260325563601949 Other FALSE 1343 -Reddy_2133T MCD 1 2.9307135485301474 311,1480,194 0.22,0.254,0.271 MCD,MCD,MCD 12.1806405325125 8 35.69796823840996 12.180640532512538 12.180640532512538 Other FALSE 1344 -Reddy_2134T N1 0.5068342810404087 5.136473423073945 2077,1697 0.295,0.303 N1,BN2 3.29739204323191,3.38878243415931 3 17.406390909639132 6.686174477391223 3.3887824341593107 Other FALSE 1345 -Reddy_2135T N1 1 10.771654970141187 999 0.207 N1 4.83947742115465 9 52.129181016466546 4.83947742115465 4.83947742115465 Other FALSE 1346 -Reddy_2136T Other 1 10 0 0 1 2 Other TRUE 1347 -Reddy_2137T BN2 0.7118894919828159 3.4348111029285318 1793,2058,874 0.191,0.235,0.26 BN2,BN2,ST2 9.48555013584704,3.83891980319795 6 32.58107292399265 13.324469939044988 9.485550135847038 Other FALSE 1348 -Reddy_2138T N1 0.5593886109640178 7.484093802334156 262,2050 0.137,0.174 N1,EZB 5.74821160887027,7.29777801351973 8 54.61725520179351 13.04598962239 7.297778013519734 Other FALSE 1349 -Reddy_2140T Other 1 10 0 0 1 2 Other TRUE 1350 -Reddy_2141T EZB 0.5846920560437975 5.346859937033938 1500,1099,1226 0.218,0.258,0.386 ST2,EZB,EZB 6.46142218141578,4.58956117747524 7 34.54831939807448 11.050983358891022 6.4614221814157835 Other FALSE 1351 -Reddy_2142T EZB 1 1.0383006714722411 1649 0.159 EZB 6.28523311064902 1 6.525961759146445 6.285233110649025 6.285233110649025 Other FALSE 1352 -Reddy_2143T ST2 1 7.5497870508345155 828 0.169 ST2 5.90164720200727 6 44.55617962430827 5.9016472020072746 5.9016472020072746 Other FALSE 1353 -Reddy_2144T Other 1 10 0 0 1 2 Other TRUE 1354 -Reddy_2145T MCD 1 3.1487545300882442 743,641 0.173,0.249 MCD,MCD 9.77986974590246 6 30.794409166083337 9.77986974590246 9.77986974590246 Other FALSE 1355 -Reddy_2146T BN2 0.5247414650271768 0.6470694307023062 1133,1012,1509,1850,1356,330,993,1053 0.183,0.198,0.213,0.235,0.259,0.277,0.301,0.335 BN2,BN2,EZB,EZB,EZB,BN2,BN2,EZB 17.4340933116998,15.7900646282429 3 11.281068834012496 33.22415793994269 17.434093311699836 EZB FALSE 1356 -Reddy_2147T N1 1 4.55319890857855 262 0.155 N1 6.45000978047359 4 29.368177492773345 6.450009780473595 6.450009780473595 Other FALSE 1357 -Reddy_2148T MCD 0.5813831135196665 0.5872700013066162 920,1358,2145,554,1366,254,934,1197 0.125,0.129,0.235,0.238,0.244,0.292,0.3,0.308 MCD,EZB,MCD,BN2,EZB,MCD,MCD,MCD 4.19865792311152,11.8334366751614,22.2656786546609 3 13.075965132615393 38.29777325293384 22.26567865466088 EZB FALSE 1358 -Reddy_2149T MCD 0.7510287992367203 2.259551550556059 194,1513,1480,311 0.188,0.203,0.21,0.21 MCD,EZB,MCD,MCD 4.92250423354,14.8488758234662 6 33.55180039092751 19.77138005700624 14.848875823466235 Other FALSE 1359 -Reddy_2150T BN2 0.7095488309320471 1.876102862474715 190,2218,189 0.123,0.182,0.189 BN2,ST2,BN2 13.4033312416204,5.48660368225803 4 25.146028109100747 18.8899349238784 13.403331241620368 Other FALSE 1360 -Reddy_2151T BN2 0.6198844070788248 2.4363614189700207 1714,1003,1854,459 0.147,0.255,0.279,0.28 ST2,BN2,BN2,BN2 11.0586485237019,6.78120741946537 6 26.942864609097036 17.83985594316725 11.05864852370188 Other FALSE 1361 -Reddy_2152T BN2 0.8103829886595751 0.32909290952540626 489,1362,1320,666,2041,582,922,1038,183 0.155,0.166,0.196,0.232,0.235,0.241,0.262,0.287,0.313 BN2,BN2,BN2,BN2,ST2,BN2,BN2,ST2,BN2 33.0857145042111,7.74154244368826 2 10.888274049917753 40.82725694789933 33.08571450421106 BN2 TRUE 1362 -Reddy_2153T ST2 0.7260835212097562 0.1180038422703941 1045,2082,1917,1394,1939,1487,1363,1472,1147,619 0.114,0.137,0.16,0.167,0.178,0.18,0.189,0.215,0.217,0.224 ST2,ST2,ST2,EZB,ST2,EZB,ST2,ST2,ST2,BN2 4.47364028309229,11.5440957532977,42.4589795928871 1 5.010322730840933 58.47671562927712 42.45897959288712 ST2 TRUE 1363 -Reddy_2154T Other 1 10 0 0 1 2 Other TRUE 1364 -Reddy_2155T EZB 1 2.669328455071931 2050,1300 0.124,0.131 EZB,EZB 15.6980346898584 5 41.903210686345304 15.698034689858401 15.698034689858401 Other FALSE 1365 -Reddy_2156T MCD 0.5505131035884784 0.40662017551070023 1358,920,2145,1366,554,934,1197,254 0.108,0.148,0.223,0.229,0.252,0.278,0.285,0.313 EZB,MCD,MCD,EZB,BN2,MCD,MCD,MCD 3.96268434458595,13.63311930301,21.5506181681946 2 8.76291614191537 39.14642181579059 21.550618168194593 EZB FALSE 1366 -Reddy_2157T EZB 1 0.9735709420537068 1294,360 0.106,0.143 EZB,EZB 16.4525208071791 2 16.0176961814036 16.452520807179134 16.452520807179134 Other FALSE 1367 -Reddy_2158T ST2 1 5.395149768113102 828,1188 0.206,0.224 ST2,ST2 9.32300850732705 9 50.29902718642203 9.323008507327053 9.323008507327053 Other FALSE 1368 -Reddy_2159T BN2 0.5059115449962679 11.690897650008287 19,641 0.288,0.295 BN2,MCD 3.47005047032946,3.38895582167967 8 40.56800488898485 6.859006292009131 3.4700504703294617 Other FALSE 1369 -Reddy_2160T MCD 0.8561898505127097 0.6715254050703173 2130,569,850,713,761,2028,755 0.172,0.219,0.236,0.248,0.26,0.283,0.289 MCD,MCD,BN2,MCD,MCD,MCD,MCD 4.23788768007091,25.2307395008323 4 16.943082563520033 29.46862718090316 25.23073950083225 Other FALSE 1370 -Reddy_2161T EZB 0.5452490389425476 2.8537054180983543 1099,1500 0.187,0.224 EZB,ST2 5.34308337840489,4.45626150217891 3 15.24758598630529 9.799344880583796 5.343083378404889 Other FALSE 1371 -Reddy_2163T EZB 1 0.11084837540619573 79,408,54,1372,1242,1872,595,1142,1253,1293 0.186,0.209,0.23,0.233,0.238,0.256,0.282,0.285,0.293,0.293 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.766254665414 1 4.518873101056386 40.76625466541397 40.76625466541397 EZB TRUE 1372 -Reddy_2164T Other 1 10 0 0 1 2 Other TRUE 1373 -Reddy_2165T Other 1 10 0 0 1 2 Other TRUE 1374 -Reddy_2166T BN2 1 0.0790368636996721 261,1158,1027,830,1375,1115,417,592,1126,981 0.15,0.193,0.213,0.247,0.265,0.27,0.288,0.296,0.299,0.307 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.5342644744036 1 3.282738000129567 41.534264474403564 41.534264474403564 BN2 TRUE 1375 -Reddy_2190T MCD 0.8989761038052664 0.21053547733948264 858,759,1009,1406,1904,1449,1376,764,479 0.116,0.118,0.135,0.135,0.138,0.154,0.154,0.156,0.172 MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.49521139314863,57.7986006434458 2 12.168655976021979 64.29381203659437 57.79860064344575 BN2 FALSE 1376 -Reddy_2191T BN2 0.39336130467392777 1.8229054564910243 1377,1763,597,834,1185,827 0.173,0.197,0.279,0.372,0.459,0.46 MCD,EZB,BN2,BN2,ST2,BN2 8.44515994498309,5.07744240737617,5.76629815965052,2.18031835786256 5 15.394728144649122 21.469218869872346 8.445159944983095 MCD FALSE 1377 -Reddy_2192T N1 1 0.7092961645573458 814,891,745,2189,63 0.163,0.217,0.22,0.247,0.307 N1,N1,N1,N1,N1 22.6063889926101 4 16.034625006949764 22.60638899261013 22.60638899261013 Other FALSE 1378 -Reddy_2193T BN2 0.5851724788781775 2.6402180888550535 610,591,1666,2017 0.135,0.178,0.189,0.252 BN2,BN2,EZB,ST2 13.0390462293956,5.28090094236627,3.96245109548489 7 34.42592571626749 22.28239826724674 13.03904622939558 Other FALSE 1379 -Reddy_2194T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1380 -Reddy_2195T BN2 1 2.428977112203452 1381,52,987 0.143,0.305,0.331 BN2,BN2,BN2 13.2760996296091 8 32.24734213965329 13.276099629609124 13.276099629609124 BN2 TRUE 1381 -Reddy_2196T N1 1 3.708140931340138 913 0.234 N1 4.27140674203328 3 15.838978174535828 4.271406742033279 4.271406742033279 Other FALSE 1382 -Reddy_2198T EZB 0.843561018363707 0.7043544084179322 1453,62,2126,1053,1087,179 0.166,0.184,0.201,0.202,0.208,0.218 EZB,EZB,EZB,EZB,BN2,EZB 4.81441220967059,25.9606040894234 3 18.285465935578 30.77501629909403 25.96060408942344 Other FALSE 1383 -Reddy_2199T EZB 0.9032681823023524 0.0954827868352229 466,646,2176,1384,1031,1701,1550,337,336,1036 0.184,0.222,0.226,0.232,0.241,0.242,0.246,0.264,0.266,0.292 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 4.06384025644834,37.9475718432456 1 3.6233399132229276 42.011412099693956 37.94757184324561 EZB TRUE 1384 -Reddy_2200T Other 1 10 0 0 1 2 Other TRUE 1385 -Reddy_2201T ST2 1 3.0150065721469113 451,1667 0.245,0.304 ST2,ST2 7.3684133887695 6 22.215814793435328 7.368413388769497 7.368413388769497 Other FALSE 1386 -Reddy_2203T EZB 0.7762288247219821 1.9756825488656942 1488,1387,1087 0.113,0.16,0.23 EZB,EZB,BN2 4.3482175440256,15.0833179905368 6 29.799848132895534 19.4315355345624 15.083317990536804 EZB TRUE 1387 -Reddy_2204T ST2 0.516329586771963 2.3871455174145884 1797,1388,938,1841 0.209,0.21,0.384,0.464 ST2,EZB,ST2,BN2 2.15510248225072,4.75654417729525,7.37834601008487 5 17.613185603907912 14.289992669630838 7.37834601008487 EZB FALSE 1388 -Reddy_2205T ST2 1 3.2774085596396123 874,1057 0.243,0.282 ST2,ST2 7.6624051533523 5 25.112832237023493 7.662405153352297 7.662405153352297 Other FALSE 1389 -Reddy_2206T BN2 1 1.6630844347334914 1841,1148,70,1813 0.171,0.222,0.235,0.252 BN2,BN2,BN2,BN2 18.5858178459972 6 30.909784366469893 18.5858178459972 18.5858178459972 Other FALSE 1390 -Reddy_2208T ST2 0.6937804098815206 0.14886368089555532 1394,1147,1417,1687,2082,1139,1045,649,1484,235 0.14,0.15,0.165,0.169,0.179,0.193,0.2,0.22,0.224,0.224 EZB,ST2,ST2,ST2,ST2,BN2,ST2,EZB,ST2,ST2 5.18568445104786,11.656246666687,38.1575909938927 1 5.6802794494579585 54.99952211162753 38.1575909938927 Other FALSE 1391 -Reddy_2209T EZB 0.6520994407024013 0.4547073734376042 1392,1122,696,1703,1227,1493 0.15,0.192,0.199,0.204,0.273,0.279 EZB,BN2,EZB,ST2,EZB,EZB 5.20234887879093,18.9265860842459,4.8951446525803 2 8.606058246508159 29.02407961561712 18.92658608424589 EZB TRUE 1392 -Reddy_2210T MCD 1 0.9763197639990236 755,467,1897,986,761,1911 0.153,0.166,0.175,0.199,0.226,0.226 MCD,MCD,MCD,MCD,MCD,MCD 32.1580138155254 5 31.396504459051073 32.15801381552537 32.15801381552537 Other FALSE 1393 -Reddy_2211T ST2 0.7120722178246467 0.22809348737692808 1417,2082,545,1394,1687,1045,1147 0.17,0.197,0.203,0.204,0.217,0.222,0.234 ST2,ST2,BN2,EZB,ST2,ST2,ST2 4.92967154445075,4.9023473959875,24.3154984201837 1 5.5462068319678854 34.147517360621954 24.3154984201837 EZB FALSE 1394 -Reddy_2212T EZB 1 1.8460473974584566 1395,1601 0.182,0.242 EZB,EZB 9.62536636578023 4 17.768882529132757 9.62536636578023 9.62536636578023 EZB TRUE 1395 -Reddy_2213T ST2 0.8458719920635447 0.7988676500852534 1249,606,215,1625,1396 0.127,0.26,0.273,0.288,0.291 ST2,ST2,ST2,ST2,EZB 3.436523426033,18.8600304063491 4 15.06666817125657 22.296553832382145 18.860030406349146 EZB FALSE 1396 -Reddy_2214T EZB 1 1.4180142775061484 1397,136 0.133,0.315 EZB,EZB 10.7028745433669 4 15.176828912851427 10.702874543366946 10.702874543366946 EZB TRUE 1397 -Reddy_2215T MCD 0.8945521760063777 0.3891123524318337 1194,285,2030,1063,1398,952,1890,753 0.124,0.156,0.184,0.189,0.192,0.215,0.216,0.23 MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 4.6415152585269,39.3756591386198 3 15.321555355982373 44.01717439714668 39.37565913861978 MCD TRUE 1398 -Reddy_2216T MCD 1 0.2841881937014713 69,2036,824,900,799,765,1225,1412,1526 0.158,0.187,0.22,0.228,0.244,0.259,0.274,0.296,0.306 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.8458546599226 2 11.039533268593273 38.84585465992255 38.84585465992255 MCD TRUE 1399 -Reddy_2217T EZB 0.613606964682863 1.6352850281684004 1400,781,2134,816,2138 0.155,0.223,0.286,0.314,0.331 EZB,MCD,MCD,EZB,EZB 12.675208258279,7.98167634020344 6 20.727578293680075 20.656884598482414 12.675208258278975 EZB TRUE 1400 -Reddy_2219T ST2 1 1.0348766694445515 1077,1427,271 0.186,0.205,0.232 ST2,ST2,ST2 14.5963074149697 2 15.10537800379268 14.596307414969726 14.596307414969726 BN2 FALSE 1401 -Reddy_2220T BN2 1 13.016844643694625 2170 0.322 BN2 3.10920869459573 9 40.47208654237721 3.109208694595732 3.109208694595732 Other FALSE 1402 -Reddy_2221T BN2 1 1.2383036437044421 1148,804,1980,70 0.119,0.143,0.169,0.202 BN2,BN2,BN2,BN2 26.2117352288616 5 32.458087241715376 26.211735228861574 26.211735228861574 Other FALSE 1403 -Reddy_2222T ST2 0.6129778524069809 1.6363451285119062 68,1152,1174,629,2066 0.207,0.229,0.236,0.252,0.259 ST2,BN2,ST2,ST2,BN2 8.23332607057671,13.0402008368152 5 21.338269114139504 21.273526907391954 13.040200836815242 ST2 TRUE 1404 -Reddy_2224T EZB 0.6108963786311047 2.0226284921204396 1037,1944,160,724,388 0.187,0.203,0.205,0.23,0.236 EZB,EZB,ST2,ST2,EZB 14.4916846670384,9.23031659856306 5 29.311294306376848 23.7220012656015 14.491684667038438 Other FALSE 1405 -Reddy_2225T MCD 0.8813055156133556 0.20613089407931223 1694,767,1376,1224,156,1310,788,837,759 0.119,0.126,0.138,0.139,0.153,0.154,0.161,0.176,0.186 MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 7.24787735447243,53.815426403288 2 11.09302195976919 61.06330375776047 53.81542640328804 MCD TRUE 1406 -Reddy_2226T BN2 0.5230810370682542 0.923177371662163 897,1191,34,273,1112,274,1275 0.119,0.139,0.151,0.178,0.182,0.187,0.245 MCD,BN2,EZB,BN2,BN2,MCD,BN2 22.3523583555025,6.64012465334013,13.7396307010754 4 20.635191437083574 42.73211370991804 22.35235835550249 BN2 TRUE 1407 -Reddy_2227T BN2 1 1.9633201309259207 523,2069,1957,596 0.215,0.215,0.243,0.285 BN2,BN2,BN2,BN2 16.9029462135292 7 33.18589457298001 16.902946213529233 16.902946213529233 Other FALSE 1408 -Reddy_2228T BN2 1 0.07680249340082003 555,98,991,1409,1724,1001,976,857,796,935 0.183,0.197,0.198,0.201,0.222,0.234,0.268,0.284,0.287,0.301 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.3967148915893 1 3.3329759090785545 43.39671489158928 43.39671489158928 BN2 TRUE 1409 -Reddy_2229T EZB 0.6404275050790176 1.1374395294790742 1975,1425,994,77,282 0.142,0.169,0.187,0.251,0.307 EZB,N1,EZB,EZB,BN2 3.26252471151044,16.3677468830217,5.92726107421573 4 18.617322313256743 25.55753266874784 16.367746883021663 Other FALSE 1410 -Reddy_2231T ST2 1 0.6480295773846831 675,600,89,1747,1472,1939,1917 0.208,0.235,0.24,0.267,0.274,0.31,0.329 ST2,ST2,ST2,ST2,ST2,ST2,ST2 26.8804482748571 4 17.419325535466477 26.88044827485709 26.88044827485709 ST2 TRUE 1411 -Reddy_2232T MCD 1 0.11035778583012097 296,465,185,643,1526,481,246,739,900 0.142,0.17,0.177,0.192,0.203,0.212,0.216,0.22,0.228 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.9753680798596 1 5.184097609848244 46.97536807985958 46.97536807985958 MCD TRUE 1412 -Reddy_2233T ST2 0.5685479067338427 1.4691306576338745 1847,1121,1822,1082,2017,1863 0.122,0.151,0.187,0.203,0.221,0.227 ST2,ST2,EZB,BN2,ST2,EZB 4.92085814774325,9.7571000405399,19.341944408941 5 28.415843509425383 34.01990259722419 19.341944408941032 Other FALSE 1413 -Reddy_2234T ST2 0.8543680864666354 0.5511142064882205 955,1486,508,171,1038,648,1174 0.186,0.188,0.19,0.217,0.267,0.27,0.281 ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.59891861014878,26.9801391565759 3 14.869137982218119 31.579057766724723 26.980139156575945 BN2 FALSE 1414 -Reddy_2236T BN2 0.4911000880631006 2.8616768783884345 737,402,581,793 0.128,0.141,0.152,0.187 BN2,EZB,ST2,BN2 13.175313023465,7.09221275628505,6.56063683660882 6 37.703488644779945 26.828162616358917 13.175313023465048 EZB FALSE 1415 -Reddy_2237T MCD 0.692966724554949 0.48727355412793444 2014,291,236,790,749,1503,715,2084,267 0.154,0.239,0.261,0.277,0.313,0.324,0.325,0.332,0.336 MCD,BN2,MCD,MCD,MCD,BN2,MCD,BN2,MCD 10.2714222708099,23.1823532390963 2 11.296147655863667 33.45377550990619 23.18235323909626 ST2 FALSE 1416 -Reddy_2238T ST2 0.6918709941424603 0.18322808770199975 1147,1139,1394,235,1484,1687,234,2082,649,1417 0.107,0.138,0.156,0.169,0.17,0.184,0.191,0.207,0.209,0.209 ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2,EZB,ST2 7.23321506170511,11.196430700285,41.3818144109962 1 7.582310720165891 59.811460172986315 41.38181441099621 ST2 TRUE 1417 -Reddy_2239T EZB 0.5884559009616778 4.006141827552904 1480,632,1513 0.145,0.196,0.211 MCD,EZB,EZB 9.8451246563172,6.88531281610496 7 39.440965683144746 16.730437472422164 9.8451246563172 Other FALSE 1418 -Reddy_2240T ST2 0.5267484888336236 7.702938253054477 1071,999 0.185,0.206 ST2,N1 4.85204594792436,5.40052764865222 7 41.59993101148157 10.252573596576584 5.400527648652225 Other FALSE 1419 -Reddy_2241T MCD 0.8134104419513191 1.0535469664492718 467,1420,647,1897,755,986 0.152,0.179,0.214,0.224,0.227,0.24 MCD,BN2,MCD,MCD,MCD,MCD 5.57248165183957,24.2924352819672 5 25.593221498981805 29.864916933806775 24.2924352819672 BN2 FALSE 1420 -Reddy_2242T BN2 0.5289633175660765 11.703335770383738 1807,1603 0.288,0.324 BN2,MCD 3.46682051563525,3.08716990394084 9 40.57336455013418 6.553990419576084 3.4668205156352467 Other FALSE 1421 -Reddy_2243T MCD 1 0.09058716055405133 1422,247,895,950,1491,837,471,1968,156,739 0.105,0.13,0.135,0.139,0.142,0.157,0.161,0.169,0.18,0.18 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 68.5001684218594 1 6.205235754810534 68.5001684218594 68.5001684218594 MCD TRUE 1422 -Reddy_2244T EZB 1 0.10233387474315155 654,2073,25,1423,1160,210,396,72,1539,390 0.148,0.192,0.209,0.259,0.327,0.329,0.34,0.341,0.353,0.354 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.2099544658206 1 3.9101726942468042 38.20995446582055 38.20995446582055 EZB TRUE 1423 -Reddy_2246T BN2 0.5479445823552977 7.450314350364821 894,1631 0.258,0.313 BN2,MCD 3.87544986806494,3.19725418424381 6 28.873319766163657 7.072704052308746 3.8754498680649374 Other FALSE 1424 -Reddy_2247T EZB 0.6494500703402869 1.3477132943908763 1425,1975,994,77 0.132,0.166,0.223,0.279 N1,EZB,EZB,EZB 14.0664677574906,7.59257640909223 4 18.9575656018907 21.65904416658283 14.066467757490601 N1 FALSE 1425 -Reddy_2248T Other 1 10 0 0 1 2 Other TRUE 1426 -Reddy_2249T ST2 0.559274584081481 1.5462612531891657 1847,1121,1822,1082,1863,2017 0.134,0.147,0.191,0.203,0.217,0.223 ST2,ST2,EZB,BN2,EZB,ST2 4.93116912784049,9.84365170817747,18.7490475463526 5 28.990925755126447 33.52386838237057 18.749047546352614 ST2 TRUE 1427 -Reddy_2250T N1 0.38465701128763163 7.000422941715112 1425,286,1975 0.217,0.267,0.277 N1,BN2,EZB 3.75029110768896,3.60994967744901,4.60095958628349 6 32.208663041722986 11.961200371421455 4.600959586283486 Other FALSE 1428 -Reddy_2253T BN2 1 0.836101722059719 70,1841,1148,1813,804 0.124,0.157,0.165,0.181,0.184 BN2,BN2,BN2,BN2,BN2 31.5003044005115 4 26.337458754673044 31.500304400511542 31.500304400511542 Other FALSE 1429 -Reddy_2254T ST2 1 12.090711463623208 828 0.162 ST2 6.16359864630166 10 74.52229281001202 6.163598646301664 6.163598646301664 Other FALSE 1430 -Reddy_2255T ST2 0.5428665596004738 4.35326368060358 1500,1099,1226 0.144,0.307,0.389 ST2,EZB,EZB 5.82809382383447,6.92112403854905 7 30.129477905967946 12.74921786238352 6.921124038549049 Other FALSE 1431 -Reddy_2259T BN2 1 1.2085846375332705 1803,20,1096 0.198,0.242,0.272 BN2,BN2,BN2 12.8827262075181 3 15.569864983953591 12.88272620751807 12.88272620751807 Other FALSE 1432 -Reddy_2260T BN2 0.6631675486067201 1.2091689000872852 537,1433,1799 0.231,0.233,0.236 ST2,BN2,BN2 8.52237792712526,4.32863980592022 2 10.304994344270206 12.851017733045477 8.522377927125259 BN2 TRUE 1433 -Reddy_2261T Other 1 10 0 0 1 2 Other TRUE 1434 -Reddy_2262T EZB 1 0.8138153932178984 1435,2151,1297,288,1120,413 0.138,0.169,0.221,0.24,0.281,0.283 EZB,EZB,EZB,EZB,EZB,EZB 28.95428594214 4 23.563443599346147 28.954285942140018 28.954285942140018 EZB TRUE 1435 -Reddy_2263T ST2 0.6779905590507023 5.5772338848434275 1436,1901 0.129,0.272 ST2,BN2 3.67509933007958,7.73791800023604 9 43.15617846905634 11.413017330315622 7.737918000236042 ST2 TRUE 1436 -Reddy_2269T EZB 1 0.1972684977631123 1437,1241,1066,2003,1814,958,408,1372,1914,331 0.146,0.149,0.189,0.211,0.238,0.253,0.254,0.257,0.28,0.283 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.7142857816463 1 9.215256980222074 46.71428578164626 46.71428578164626 EZB TRUE 1437 -Reddy_2270T MCD 0.4357055067954105 3.0703556298758303 150,1232,811,151,1278 0.226,0.287,0.298,0.313,0.331 MCD,BN2,BN2,MCD,EZB 6.83687156203137,3.01776021192802,7.60900094376504 6 23.362338885419483 17.463632717724433 7.609000943765036 Other FALSE 1438 -Reddy_2271T EZB 0.7388301828707027 1.4093083098481747 160,1944,1439,388,242 0.147,0.164,0.18,0.247,0.277 ST2,EZB,EZB,EZB,EZB 19.2778843813136,6.81455854841343 6 27.16848265487757 26.092442929727014 19.277884381313584 EZB TRUE 1439 -Reddy_2272T BN2 0.5413897234966527 10.172120628817899 1697,697 0.239,0.282 BN2,N1 4.18046594627739,3.54126530358302 9 42.52420389019896 7.721731249860408 4.180465946277389 Other FALSE 1440 -Reddy_2274T ST2 0.4708758859258911 3.971055152694913 1416,1441,879 0.136,0.225,0.264 ST2,EZB,MCD 4.44106838795731,3.79215970436892,7.32687940104173 6 29.095442198680992 15.560107493367957 7.326879401041733 EZB FALSE 1441 -Reddy_2278T ST2 0.7573270711574335 0.6876749829648888 271,1442,1955,1284,1077,1401,701 0.202,0.212,0.217,0.244,0.251,0.278,0.28 ST2,ST2,ST2,ST2,ST2,BN2,EZB 3.59508181459634,3.57085443570629,22.3632588044598 3 15.378653617396289 29.529195054762422 22.363258804459793 ST2 TRUE 1442 -Reddy_2279T ST2 1 5.685461791587608 828 0.14 ST2 7.16110049449274 5 40.71416324715758 7.161100494492736 7.161100494492736 Other FALSE 1443 -Reddy_2281T BN2 1 0 936,1549,964,865,973,2213,954,1213,872,454,903 0.138,0.159,0.19,0.204,0.204,0.206,0.207,0.244,0.251,0.258,0.266 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 54.02256322327 0 0 54.02256322327001 54.02256322327001 BN2 TRUE 1444 -Reddy_2286T ST2 1 4.763223607654686 451,1653 0.237,0.336 ST2,ST2 7.18374157299923 7 34.21776745180035 7.1837415729992316 7.1837415729992316 Other FALSE 1445 -Reddy_2290T EZB 0.8692530952551276 0.22513026210254955 1446,1292,1177,165,1959,1738,1527,109 0.143,0.162,0.198,0.21,0.216,0.245,0.251,0.338 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 33.555669898298,5.04720661886797 2 7.5543967592304595 38.60287651716596 33.55566989829799 EZB TRUE 1446 -Reddy_2291T Other 1 10 0 0 1 2 Other TRUE 1447 -Reddy_2293T Other 1 10 0 0 1 2 Other TRUE 1448 -Reddy_2297T MCD 0.913452063875619 0 759,1009,1449,1904,764,858,479,1376,1406,305 0.111,0.116,0.132,0.133,0.135,0.137,0.149,0.158,0.159,0.159 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.31775111979409,66.6793809055685 0 0 72.99713202536262 66.67938090556852 MCD TRUE 1449 -Reddy_2298T EZB 1 0.07424821260553922 1450,699,477,1542,1160,1539,520,72,1289,1964 0.117,0.126,0.162,0.188,0.194,0.196,0.21,0.216,0.279,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.5498728775652 1 4.050230559018599 54.549872877565214 54.549872877565214 EZB TRUE 1450 -Reddy_2300T ST2 1 7.998428374425588 1436 0.245 ST2 4.08984542699555 6 32.71233571029592 4.089845426995547 4.089845426995547 Other FALSE 1451 -Reddy_2302T N1 0.5029328863978856 2.452187876137287 175,2211 0.188,0.19 N1,BN2 5.25104014247307,5.31300643952703 2 13.02848997684751 10.564046582000099 5.313006439527027 Other FALSE 1452 -Reddy_2303T EZB 0.8789885460573211 0.8096415733557399 62,179,1053,2126,1453,993 0.148,0.149,0.167,0.194,0.198,0.245 EZB,EZB,EZB,EZB,EZB,BN2 4.08274221003877,29.6557352399776 4 24.01051613871676 33.73847745001641 29.65573523997764 EZB TRUE 1453 -Reddy_2306T MCD 0.5448904449759533 8.924634161593493 951,1664 0.258,0.309 MCD,ST2 3.87671337552375,3.23795235456596 6 34.59824862590566 7.11466573008971 3.8767133755237477 Other FALSE 1454 -Reddy_2307T Other 1 10 0 0 1 2 Other TRUE 1455 -Reddy_2449T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1456 -Reddy_2450T EZB 1 0.10464680234400632 1180,55,1305,1457,1517,1570,2003,1219,278,1853 0.191,0.195,0.207,0.221,0.234,0.246,0.256,0.268,0.276,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6872587761259 1 4.4670851317526985 42.68725877612592 42.68725877612592 EZB TRUE 1457 -Reddy_2451T ST2 1 0 1436 0.156 ST2 6.40881971552097 0 0 6.408819715520973 6.408819715520973 Other FALSE 1458 -Reddy_2453T MCD 1 0 880,1598,518,1459,771,2180,783,1239,1240,1647,803 0.138,0.139,0.144,0.163,0.165,0.17,0.174,0.177,0.18,0.182,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.2302973202112 0 0 67.230297320211207 67.230297320211207 MCD TRUE 1459 -Reddy_2455T ST2 0.5736626817548309 6.655072189198426 537,1433 0.198,0.267 ST2,BN2 3.74613687436559,5.04065403989198 7 33.54591651625581 8.786790914257569 5.0406540398919795 Other FALSE 1460 -Reddy_2459T BN2 0.5547325909063578 6.612489275076578 572,874 0.252,0.314 BN2,ST2 3.97117197583907,3.18754204410108 6 26.259332099720517 7.158714019940155 3.9711719758390704 ST2 FALSE 1461 -Reddy_2461T BN2 1 5.455628341398821 1971 0.25 BN2 3.99620773469909 4 21.801824175541547 3.996207734699092 3.996207734699092 Other FALSE 1462 -Reddy_2462T Other 1 10 0 0 1 2 Other TRUE 1463 -Reddy_2463T EZB 1 3.767997030195807 1300,2050 0.123,0.127 EZB,EZB 16.0260171571386 7 60.38598505396515 16.02601715713856 16.02601715713856 Other FALSE 1464 -Reddy_2464T ST2 0.67122509177991 4.1307163161040394 68,2134,629 0.25,0.303,0.365 ST2,MCD,ST2 3.30269040955504,6.74275505170303 7 27.852408307562637 10.045445461258073 6.742755051703029 Other FALSE 1465 -Reddy_2466T ST2 1 5.239935677999477 1436 0.217 ST2 4.60672732888584 4 24.13895488944415 4.6067273288858415 4.6067273288858415 Other FALSE 1466 -Reddy_2467T ST2 0.5235904460335199 3.761968820630553 2127,1131,65,2211 0.23,0.237,0.244,0.271 ST2,ST2,EZB,BN2 3.69498800495407,4.09466892085264,8.56110023460655 7 32.206592152882735 16.350757160413256 8.561100234606545 Other FALSE 1467 -Reddy_2468T EZB 1 0 1468,1483,385,102,112,448,5,1663,2061,1691,170 0.153,0.209,0.212,0.213,0.233,0.252,0.271,0.28,0.288,0.29,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.5398206297599 0 0 46.53982062975985 46.53982062975985 EZB TRUE 1468 -Reddy_2469T ST2 0.660776260695788 0.6476152991018171 1211,1018,701,618,1713,1130,1401,1789 0.169,0.19,0.201,0.213,0.233,0.242,0.262,0.272 ST2,ST2,EZB,ST2,ST2,ST2,BN2,EZB 3.82127193027385,8.65411595067203,24.3008940695284 3 15.737630781279194 36.77628195047425 24.300894069528375 Other FALSE 1469 -Reddy_2470T Other 1 10 0 0 1 2 Other TRUE 1470 -Reddy_2471T EZB 1 1.065967803375348 1471 0.208 EZB 4.81494126286587 1 5.1325723613584575 4.814941262865872 4.814941262865872 EZB TRUE 1471 -Reddy_2473T ST2 0.801928770100425 0.15343440526953883 1917,1939,1045,2082,1472,1487,1363,1394,1884 0.158,0.158,0.166,0.187,0.187,0.198,0.22,0.231,0.25 ST2,ST2,ST2,ST2,ST2,EZB,ST2,EZB,ST2 9.37814889326025,37.9692063890546 1 5.825782600860974 47.3473552823149 37.96920638905465 ST2 TRUE 1472 -Reddy_2474T Other 1 10 0 0 1 2 Other TRUE 1473 -Reddy_2475T EZB 1 0 1474,2201,173,1673,1903,153,1054,287,1277,1554,1545 0.127,0.15,0.178,0.18,0.19,0.197,0.226,0.24,0.271,0.298,0.317 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.874804079031 0 0 54.87480407903098 54.87480407903098 EZB TRUE 1474 -Reddy_2476T EZB 1 0.5080305177731291 360,1294 0.126,0.14 EZB,EZB 15.0711874804765 1 7.656623179162371 15.071187480476485 15.071187480476485 Other FALSE 1475 -Reddy_2477T ST2 1 10.725339803911899 828 0.172 ST2 5.82344557021004 9 62.45843257008821 5.823445570210044 5.823445570210044 Other FALSE 1476 -Reddy_2478T BN2 0.8465176273404732 0.37454425823513726 2041,1362,582,922,489,1320,88,594 0.204,0.212,0.228,0.257,0.26,0.288,0.294,0.305 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 26.9852492581583,4.89270388374918 2 10.107170166687201 31.877953141907515 26.985249258158333 BN2 TRUE 1477 -Reddy_2479T EZB 0.5307592069336269 5.920733347924786 2050,262 0.137,0.155 EZB,N1 7.29448477755627,6.44900696458221 6 43.188699278407135 13.743491742138483 7.294484777556272 Other FALSE 1478 -Reddy_2480T ST2 0.5016679047297471 2.153686701611328 1479,951 0.176,0.177 ST2,MCD 5.64586876606981,5.68366192170973 2 12.240827097240931 11.329530687779535 5.68366192170973 ST2 TRUE 1479 -Reddy_2481T EZB 0.600937339262563 3.846004732780894 1480,632,1513 0.177,0.233,0.236 MCD,EZB,EZB 8.53013184306248,5.66457912885048 6 32.806927439663326 14.194710971912961 8.530131843062485 MCD FALSE 1480 -Reddy_2482T MCD 1 0.33248941508664565 1224,788,1310,767,156,471,1491,837 0.107,0.12,0.121,0.128,0.129,0.149,0.163,0.164 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.4911250160653 3 20.1126587745247 60.49112501606527 60.49112501606527 Other FALSE 1481 -Reddy_2483T Other 1 10 0 0 1 2 ST2 FALSE 1482 -Reddy_2485T EZB 1 0.20943481210568002 336,337,1384,1663,430,1483,112,646,2176 0.125,0.138,0.162,0.176,0.186,0.221,0.223,0.242,0.245 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.7423660028022 2 10.417783077488846 49.74236600280221 49.74236600280221 EZB TRUE 1483 -Reddy_2487T ST2 0.5739515197943658 0.19873825477276166 619,1363,1484,1139,1487,1394,1147,235,1917,1045 0.137,0.154,0.158,0.163,0.173,0.186,0.19,0.199,0.199,0.201 BN2,ST2,ST2,BN2,EZB,EZB,ST2,ST2,ST2,ST2 13.4292182059548,11.1630306864605,33.1294659709865 1 6.584092248627443 57.72171486340172 33.12946597098645 ST2 TRUE 1484 -Reddy_2488T EZB 1 0.15960175941194046 276,1304,517,1085,375,142,1047,1237,372,1485 0.187,0.217,0.223,0.25,0.251,0.256,0.266,0.282,0.285,0.286 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.6393060860701 1 6.486104752617162 40.63930608607006 40.63930608607006 EZB TRUE 1485 -Reddy_2522T EZB 0.362472607596188 2.5672169389356485 781,816,2138,2134,1486,508 0.166,0.174,0.193,0.222,0.23,0.231 MCD,EZB,EZB,MCD,ST2,ST2 10.9250802822891,10.5390901745054,8.67625961526267 5 28.047051159924465 30.140430072057143 10.925080282289112 ST2 FALSE 1486 -Reddy_2523T ST2 0.7723428798009497 0.35004510303901276 675,1472,1987,1487,1939,1363,1917,2004,89 0.232,0.239,0.254,0.269,0.272,0.276,0.285,0.298,0.303 ST2,ST2,BN2,EZB,ST2,ST2,ST2,ST2,ST2 3.93458007142065,3.71703397852422,25.9586417736144 2 9.086695434397686 33.61025582355931 25.958641773614435 EZB FALSE 1487 -Reddy_2524T EZB 0.5892075008856733 4.049974746861808 901,1387,1488 0.171,0.233,0.242 N1,EZB,EZB 8.40974814405618,5.86323400809732 6 34.0592676108955 14.272982152153503 8.409748144056184 EZB TRUE 1488 -Reddy_2525T EZB 0.8950607229507885 0.2657880461826261 367,1991,1745,6,44,565,196,625,433 0.207,0.261,0.271,0.276,0.281,0.286,0.287,0.289,0.295 EZB,EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 3.50035342376207,29.8556360797731 2 7.9352711811824 33.35598950353513 29.855636079773063 Other FALSE 1489 -Reddy_2528T Other 1 10 0 0 1 2 Other TRUE 1490 -Reddy_2529T MCD 1 0.09517213760947686 739,481,1968,1422,643,837,247,1491,895,950 0.132,0.141,0.141,0.149,0.156,0.175,0.178,0.182,0.183,0.185 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 62.6076585624067 1 5.958504696108511 62.60765856240668 62.60765856240668 MCD TRUE 1491 -Reddy_2530T BN2 0.505859053517267 1.279408578369985 993,330,1356,207,1318,1053,1133 0.13,0.191,0.204,0.257,0.27,0.285,0.29 BN2,BN2,EZB,EZB,EZB,EZB,BN2 16.3959730430138,16.0161641501662 4 20.977148561954944 32.412137193180016 16.39597304301385 EZB FALSE 1492 -Reddy_2533T EZB 0.678191022440997 0.6996107649168282 1703,1493,1392,1227,397,1528,1122,696 0.133,0.177,0.268,0.285,0.301,0.306,0.31,0.314 ST2,EZB,EZB,EZB,EZB,EZB,BN2,EZB 3.22642433851507,22.6677609462375,7.52967347505518 3 15.858609574549014 33.42385875980774 22.66776094623749 EZB TRUE 1493 -Reddy_2534T EZB 0.5494525404749967 0.6729698867279226 1494,881,1036,466,1701,1509,1322,646 0.13,0.151,0.23,0.361,0.378,0.379,0.385,0.405 BN2,N1,EZB,EZB,EZB,EZB,EZB,EZB 7.72050060299465,17.4722795191303,6.60665337221408 3 11.758317968867694 31.79943349433899 17.47227951913026 BN2 FALSE 1494 -Reddy_2538T EZB 0.7475790098254507 1.904025235631564 1400,781,816,2138 0.238,0.261,0.275,0.284 EZB,MCD,EZB,EZB 11.3557566744305,3.83428548189211 5 21.6216472778073 15.19004215632264 11.355756674430532 Other FALSE 1495 -Reddy_2539T Other 1 10 0 0 1 2 BN2 FALSE 1496 -Reddy_2540T ST2 0.7804907161448323 0.11681637207150672 619,2004,1484,1176,235,1139,1363,1497,234,1147 0.154,0.155,0.158,0.174,0.186,0.19,0.212,0.223,0.224,0.235 BN2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2,ST2 11.7618512607155,41.8206262279176 1 4.885333833703835 53.582477488633096 41.8206262279176 ST2 TRUE 1497 -Reddy_2541T EZB 1 4.701005207337186 1099 0.234 EZB 4.28173750780703 4 20.128470320651807 4.281737507807033 4.281737507807033 Other FALSE 1498 -Reddy_2542T MCD 1 0.5407919301304858 185,296,246,465,643,481,739 0.15,0.162,0.162,0.179,0.187,0.212,0.218 MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.2639066701191 4 21.233603872596973 39.26390667011912 39.26390667011912 Other FALSE 1499 -Reddy_2543T EZB 0.5327654720570203 0 1099,1500 0.176,0.201 EZB,ST2 5.68400444781521,4.98486346111502 0 0 10.668867908930237 5.684004447815212 ST2 FALSE 1500 -Reddy_2544T MCD 0.8352023031675876 0.26496423285163195 1736,556,1640,188,792,708,1273,49 0.136,0.156,0.171,0.181,0.194,0.197,0.211,0.226 EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 7.33209806144271,37.1594100261912 2 9.845914570808983 44.49150808763388 37.15941002619117 EZB FALSE 1501 -Reddy_2545T EZB 0.41549813559883514 3.5402113117457934 1441,1416,879,894 0.124,0.226,0.261,0.323 EZB,ST2,MCD,BN2 3.09644139572352,8.07652447684547,3.83232359056915,4.43288314190908 6 28.59260331252012 19.43817260504722 8.076524476845472 Other FALSE 1502 -Reddy_2547T MCD 0.8033945678927452 0.20622654540055071 790,749,883,1732,2084,1127,1503,758,2014 0.134,0.16,0.222,0.223,0.225,0.268,0.271,0.28,0.306 MCD,MCD,MCD,MCD,BN2,MCD,BN2,MCD,MCD 8.13759232533389,33.2528831977156 2 6.857627226472902 41.39047552304947 33.252883197715576 BN2 FALSE 1503 -Reddy_2548T ST2 1 3.3184306995278345 1667,566 0.127,0.195 ST2,ST2 12.9906263574111 7 43.10849331052858 12.990626357411141 12.990626357411141 Other FALSE 1504 -Reddy_2549T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1505 -Reddy_2550T MCD 0.5322567344028504 8.14461352520648 1603,2096 0.246,0.279 MCD,BN2 3.57941115915339,4.07310128179795 6 33.1738357892674 7.652512440951339 4.073101281797945 Other FALSE 1506 -Reddy_2551T EZB 0.6672789212142667 5.056996852368858 292,2216,115 0.201,0.203,0.204 EZB,ST2,EZB 9.86824338149752,4.92053994098477 8 49.90367571864279 14.788783322482294 9.868243381497523 Other FALSE 1507 -Reddy_2552T EZB 1 4.176841839782816 1508 0.227 EZB 4.4122619459927 3 18.429320304103868 4.412261945992703 4.412261945992703 EZB TRUE 1508 -Reddy_2553T BN2 0.5691274593514318 0.48192366876754156 1133,1012,1509,1850,1356,330,993 0.187,0.198,0.205,0.23,0.271,0.289,0.312 BN2,BN2,EZB,EZB,EZB,BN2,BN2 17.0717705822421,12.9246217930209 2 8.227290311351918 29.996392375263074 17.071770582242134 EZB FALSE 1509 -Reddy_2554T EZB 1 0.21802908985041808 354,1034,333,1705,443,149,332,404,121 0.153,0.171,0.195,0.212,0.237,0.245,0.278,0.283,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.0566257427255 2 8.95153874301569 41.056625742725515 41.056625742725515 EZB TRUE 1510 -Reddy_2555T EZB 1 0 137,1511,1539,1450,2104,364,699,520,435,9,2006 0.187,0.215,0.244,0.259,0.259,0.276,0.276,0.279,0.287,0.292,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.8848343272181 0 0 42.88483432721813 42.88483432721813 EZB TRUE 1511 -Reddy_2556T EZB 1 14.931551488392413 65 0.252 EZB 3.97365214295476 10 59.3327915694898 3.9736521429547564 3.9736521429547564 Other FALSE 1512 -Reddy_2561T MCD 0.5643068444446601 1.5490362344349955 1513,847,194,1480,632 0.139,0.189,0.204,0.205,0.226 EZB,MCD,MCD,MCD,EZB 11.6279414464611,15.0604315476935 4 23.32915417360508 26.68837299415458 15.060431547693454 EZB FALSE 1513 -Reddy_2562T MCD 0.8994793232408971 0.3676194819787926 1224,1310,788,767,156,471,1694,1376 0.104,0.12,0.122,0.127,0.13,0.151,0.162,0.165 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 6.07847485899243,54.391421036749 3 19.995346025620073 60.46989589574144 54.39142103674901 MCD TRUE 1514 -Reddy_2565T ST2 0.682165852133179 4.758607340425692 1188,1638,198 0.365,0.405,0.412 ST2,ST2,N1 2.42759120630316,5.2103269425056 8 24.79390003462491 7.637918148808762 5.210326942505603 Other FALSE 1515 -Reddy_2566T EZB 1 0 375,1560,1081,1046,2063,1048,2047,1281,1163,1889,76 0.19,0.196,0.206,0.225,0.235,0.254,0.265,0.267,0.272,0.275,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0438272705843 0 0 46.04382727058427 46.04382727058427 EZB TRUE 1516 -Reddy_2567T EZB 1 0.3326032603535593 1457,55,1180,1517,1305,278,1570,1853 0.113,0.115,0.119,0.15,0.171,0.178,0.186,0.209 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.2092477994132 3 18.030172559398853 54.209247799413234 54.209247799413234 EZB TRUE 1517 -Reddy_2570T EZB 0.5091294098174697 0.7933443297707798 940,2214,1093,24,384,826,132,972 0.217,0.247,0.272,0.297,0.298,0.302,0.33,0.338 ST2,EZB,EZB,EZB,EZB,BN2,BN2,BN2 9.30848117206121,14.4327414576072,4.60666138430276 3 11.450133598440372 28.34788401397122 14.43274145760725 Other FALSE 1518 -Reddy_2571T BN2 1 2.62891542367165 1108 0.199 BN2 5.03099839211949 2 13.226069269510194 5.030998392119488 5.030998392119488 Other FALSE 1519 -Reddy_2573T EZB 1 0 360,1294 0.113,0.142 EZB,EZB 15.8478603800918 0 0 15.847860380091774 15.847860380091774 Other FALSE 1520 -Reddy_2575T EZB 0.7880190644895925 1.0555346401452337 1002,1521,1260,628,1322 0.132,0.174,0.191,0.21,0.268 EZB,BN2,EZB,EZB,EZB 5.73309448085581,21.3122361148021 5 22.49580347812786 27.04533059565788 21.312236114802076 BN2 FALSE 1521 -Reddy_2576T Other 1 10 0 0 1 2 Other TRUE 1522 -Reddy_2577T MCD 0.7734120105217432 2.1868523218726046 1907,717,632,1480 0.155,0.16,0.202,0.241 MCD,MCD,EZB,MCD 4.94089891136615,16.8647533773677 7 36.880725081105346 21.805652288733818 16.864753377367673 Other FALSE 1523 -Reddy_2578T Other 1 10 0 0 1 2 Other TRUE 1524 -Reddy_2580T BN2 0.5773604226896474 0.22829799524140845 1112,34,274,1407,1191,897,273 0.115,0.145,0.156,0.165,0.17,0.184,0.203 BN2,EZB,MCD,BN2,BN2,MCD,BN2 25.6320595254365,6.90058829509507,11.862600828534 1 5.851747803565593 44.39524864906552 25.632059525436468 BN2 TRUE 1525 -Reddy_2581T MCD 1 0.18789415579200247 465,1526,2053,704,347,765,900,296,799 0.164,0.18,0.216,0.24,0.24,0.25,0.258,0.268,0.27 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.919261397135 2 7.500595920054945 39.91926139713495 39.91926139713495 MCD TRUE 1526 -Reddy_2582T EZB 1 0.19805919505062122 109,1959,165,1527,1292,1750,818,1446,1671 0.148,0.178,0.188,0.213,0.275,0.276,0.283,0.321,0.377 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.9507428733464 2 7.714552780118715 38.95074287334643 38.95074287334643 EZB TRUE 1527 -Reddy_2585T EZB 1 0.08358025248781577 1528,2005,1554,695,1162,1493,173,1277,2201,2093 0.206,0.208,0.257,0.262,0.264,0.277,0.277,0.289,0.296,0.319 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.3390320183747 1 3.2043859762342137 38.33903201837474 38.33903201837474 EZB TRUE 1528 -Reddy_2588T ST2 1 2.6759382653812582 530,702 0.201,0.23 ST2,ST2 9.32991457132684 5 24.966275414151657 9.329914571326835 9.329914571326835 Other FALSE 1529 -Reddy_2591T EZB 1 2.1501193493461117 2050 0.117 EZB 8.55129274427453 2 18.38629999138767 8.551292744274527 8.551292744274527 Other FALSE 1530 -Reddy_2594T MCD 0.8902279058542917 0.28769842077416374 789,1531,997,1501,738,154,1826,1728,214 0.117,0.149,0.152,0.188,0.199,0.199,0.248,0.272,0.29 MCD,MCD,MCD,EZB,MCD,MCD,MCD,MCD,MCD 5.30832584954795,43.0493728065589 2 12.385236571765233 48.357698656106884 43.04937280655893 MCD TRUE 1531 -Reddy_2596T BN2 1 4.747619172432946 1980,804 0.174,0.222 BN2,BN2 10.2425043707233 9 48.62751012417404 10.242504370723267 10.242504370723267 BN2 TRUE 1532 -Reddy_2597T EZB 0.8767118711074726 0.19621133813253286 42,16,1533,155,308,1933,343,639 0.14,0.161,0.17,0.174,0.178,0.193,0.194,0.213 EZB,EZB,EZB,EZB,BN2,EZB,EZB,EZB 5.62847545386481,40.0245448687265 1 7.853269506838434 45.653020322591345 40.02454486872653 EZB TRUE 1533 -Reddy_2599T EZB 1 4.679889215388363 199,532 0.281,0.293 EZB,EZB 6.97943647342468 7 32.662989481468344 6.979436473424679 6.979436473424679 Other FALSE 1534 -Reddy_2600T ST2 0.6415155287959219 3.678813912326177 198,1638,1188 0.351,0.391,0.394 N1,ST2,ST2 2.84782625681855,5.09624520394311 6 18.748137756891463 7.944071460761661 5.096245203943108 EZB FALSE 1535 -Reddy_2602T EZB 0.5206327138745972 10.177487180820986 292,2080 0.199,0.216 EZB,ST2 5.02814654550866,4.62961488882136 8 51.17389701020368 9.657761434330013 5.028146545508657 Other FALSE 1536 -Reddy_2603T EZB 1 0.26618320998914613 639,1537,17,121,80,155,42,1933,233 0.149,0.179,0.181,0.184,0.186,0.195,0.196,0.205,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4464877182067 2 12.895641613531993 48.44648771820666 48.44648771820666 EZB TRUE 1537 -Reddy_2604T BN2 0.5395728318232095 5.636123248021563 1538,31 0.128,0.15 BN2,EZB 7.8320244670596,6.68320685138038 6 44.14225517786828 14.515231318439977 7.832024467059596 BN2 TRUE 1538 -Reddy_2605T EZB 1 0.09715820024879344 1539,1450,1160,699,477,137,72,1542,520,1511 0.128,0.208,0.214,0.219,0.255,0.256,0.262,0.28,0.284,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.9615922578198 1 4.271229183841067 43.96159225781983 43.96159225781983 EZB TRUE 1539 -Reddy_2606T EZB 1 0.28890671210341623 1926,1219,1726,1590,2072,1305,1853,56,2013 0.145,0.206,0.216,0.244,0.253,0.258,0.271,0.284,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 38.913394315458 2 11.242340808462743 38.913394315458014 38.913394315458014 EZB TRUE 1540 -Reddy_2608T EZB 0.8999669834942996 0.36515173743309165 178,1551,320,1218,259,56,1088,1541 0.151,0.163,0.178,0.188,0.189,0.22,0.222,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 38.0350311786474,4.22766498268163 3 13.888557718204904 42.26269616132901 38.03503117864739 ST2 FALSE 1541 -Reddy_2609T EZB 1 0.14159047646879844 477,1542,520,699,1450,1289,119,72,1668,1160 0.154,0.156,0.171,0.19,0.199,0.23,0.256,0.268,0.274,0.279 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.261472389571 1 6.833364870725124 48.26147238957104 48.26147238957104 EZB TRUE 1542 -Reddy_2611T MCD 0.8935923155588671 0.4200843404459202 1897,986,1184,1420,1543,1911,467,755 0.109,0.133,0.183,0.204,0.205,0.205,0.21,0.219 MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD 4.91015362163268,41.2345740587172 3 17.32199884702466 46.14472768034987 41.234574058717186 MCD TRUE 1543 -Reddy_2612T ST2 1 5.379710440432992 318,90 0.28,0.325 ST2,ST2 6.64791763472669 9 35.7638719066778 6.647917634726695 6.647917634726695 Other FALSE 1544 -Reddy_2613T EZB 1 0.08505731166740922 1545,1042,1032,340,1581,1244,1277,1579,1243,1245 0.123,0.214,0.236,0.249,0.266,0.266,0.272,0.287,0.294,0.305 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.3952568897911 1 3.6060265784948404 42.395256889791106 42.395256889791106 EZB TRUE 1545 -Reddy_2614T ST2 1 6.297719657656408 1436 0.216 ST2 4.63737022287487 5 29.20485761242954 4.6373702228748686 4.6373702228748686 Other FALSE 1546 -Reddy_2616T ST2 0.6586052527510067 1.8690605130054305 1123,293,234,1176,1776,722 0.211,0.221,0.231,0.238,0.244,0.251 EZB,ST2,ST2,ST2,BN2,ST2 4.09691855135075,4.72940030724461,17.0273854813779 5 31.82521384296534 25.853704339973234 17.02738548137787 Other FALSE 1547 -Reddy_2617T ST2 1 2.917278578329435 1667,451 0.25,0.256 ST2,ST2 7.91212911379731 5 23.08188477265756 7.912129113797313 7.912129113797313 Other FALSE 1548 -Reddy_2619T BN2 1 0.5238099567614671 2213,973,1210,284,936,903,1549 0.145,0.162,0.189,0.218,0.225,0.239,0.256 BN2,BN2,BN2,BN2,BN2,BN2,BN2 35.4822897960438 4 18.58597668386357 35.482289796043844 35.482289796043844 BN2 TRUE 1549 -Reddy_2620T EZB 0.9008634343333632 0.326051389084142 809,1031,1864,1011,1701,473,1550,466 0.139,0.178,0.181,0.197,0.207,0.229,0.248,0.252 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.02562172710331,36.5812088609174 2 11.927353963479238 40.60683058802069 36.581208860917386 BN2 FALSE 1550 -Reddy_2621T EZB 0.8967590963805577 0.3126640408903956 320,2059,319,1551,1088,1541,259,178,149 0.156,0.157,0.163,0.18,0.191,0.198,0.204,0.213,0.222 EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 43.7595767948282,5.03789509193362 2 13.682046108324569 48.79747188676181 43.75957679482819 EZB TRUE 1551 -Reddy_2622T MCD 1 2.278927037283621 311 0.18 MCD 5.56291998975018 2 12.677488770887205 5.562919989750178 5.562919989750178 Other FALSE 1552 -Reddy_2624T EZB 1 9.076329961550293 1649 0.282 EZB 3.54428587789012 7 32.16910810579369 3.5442858778901214 3.5442858778901214 Other FALSE 1553 -Reddy_2626T EZB 1 0 1554,2005,1162,1277,173,1528,2201,2093,695,1474,1903 0.177,0.187,0.189,0.206,0.217,0.225,0.251,0.269,0.317,0.317,0.324 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2785127514732 0 0 47.278512751473194 47.278512751473194 EZB TRUE 1554 -Reddy_2628T ST2 0.729531331572305 0.45876603479375616 1555,722,1776,293,1644,1497,1341 0.154,0.166,0.175,0.188,0.21,0.212,0.226 ST2,ST2,BN2,ST2,ST2,ST2,BN2 10.1300208030882,27.3235624972499 2 12.535122423302742 37.45358330033817 27.32356249724995 ST2 TRUE 1555 -Reddy_2629T MCD 0.6637441989987397 1.5824129659766908 952,1398,1890,2030,285,1278 0.199,0.218,0.235,0.235,0.254,0.278 BN2,MCD,MCD,MCD,MCD,EZB 5.02726496766117,3.5991659076219,17.0279395462721 5 26.94523232188821 25.654370421555164 17.027939546272094 Other FALSE 1556 -Reddy_2630T BN2 1 13.690255154049348 1803 0.36 BN2 2.77673796172127 10 38.014251191899135 2.7767379617212726 2.7767379617212726 Other FALSE 1557 -Reddy_2632T ST2 0.6042170814523433 1.0696658900586657 1417,1687,649,545,1147,1394,2082 0.169,0.201,0.241,0.25,0.269,0.272,0.29 ST2,ST2,EZB,BN2,ST2,EZB,ST2 3.99846909965137,7.81539681103687,18.0354917979258 4 19.291950386674024 29.849357708614008 18.03549179792576 Other FALSE 1558 -Reddy_2634T MCD 0.7499589378043606 2.342609739822955 81,82,401 0.107,0.175,0.199 MCD,MCD,EZB 5.01506598356745,15.0419036178635 6 35.23730992068519 20.05696960143095 15.041903617863506 Other FALSE 1559 -Reddy_2636T EZB 1 0.08310371879914118 1081,1560,375,2063,1889,39,93,1485,1048,1046 0.16,0.166,0.209,0.226,0.238,0.249,0.256,0.264,0.269,0.275 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7733266826071 1 3.720829950333466 44.77332668260711 44.77332668260711 EZB TRUE 1560 -Reddy_2637T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1561 -Reddy_2639T MCD 0.7872076479954544 2.238163224136804 717,864,1907,632 0.152,0.196,0.215,0.226 MCD,MCD,MCD,EZB 4.41861283506361,16.346291511542 6 36.58566851215281 20.76490434660556 16.346291511541953 Other FALSE 1562 -Reddy_2640T EZB 1 0.2122376412539014 332,1083,589,333,122,231,1034,192,1705 0.148,0.174,0.179,0.202,0.208,0.227,0.255,0.257,0.266 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.8166452739923 2 9.299541440611032 43.8166452739923 43.8166452739923 EZB TRUE 1563 -Reddy_2642T MCD 1 0.20527239112514423 2053,1526,465,765,704,799,900,347 0.174,0.18,0.204,0.227,0.228,0.247,0.254,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 36.589308771467 2 7.510774901135239 36.589308771466975 36.589308771466975 Other FALSE 1564 -Reddy_2643T MCD 1 0.0906426671462638 1459,1647,518,803,1598,880,783,535,534,771 0.14,0.141,0.142,0.143,0.17,0.183,0.184,0.2,0.2,0.203 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.9786602498678 1 5.436625736907607 59.97866024986776 59.97866024986776 MCD TRUE 1565 -Reddy_2644T ST2 0.40766490186615534 2.8521806703605916 1082,2017,1863,1121,1983 0.15,0.162,0.173,0.192,0.244 BN2,ST2,EZB,ST2,EZB 6.6450354531488,9.87887293936333,11.372308537011 6 32.43587858663964 27.89621692952317 11.372308537011044 Other FALSE 1566 -Reddy_2646T MCD 1 0 1331,1699,305,798,21,23,709,479,2008,2034,1689 0.129,0.135,0.144,0.144,0.145,0.15,0.152,0.153,0.157,0.16,0.166 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.3456346006028 0 0 74.34563460060285 74.34563460060285 MCD TRUE 1567 -Reddy_2647T BN2 0.7186943152325345 1.259642143531561 1433,537,1799 0.115,0.184,0.191 BN2,ST2,BN2 13.9178057902567,5.44759824212884 3 17.53145471889486 19.36540403238549 13.917805790256653 Other FALSE 1568 -Reddy_2649T Other 1 10 0 0 1 2 Other TRUE 1569 -Reddy_2650T EZB 1 0.10466067224352066 2003,1180,1570,55,1517,1457,1172,278,1305,1066 0.193,0.194,0.197,0.203,0.205,0.216,0.241,0.246,0.248,0.27 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.8049412183837 1 4.793975939990991 45.804941218383746 45.804941218383746 EZB TRUE 1570 -Reddy_2651T ST2 1 11.992345527325512 35 0.263 ST2 3.79651437756603 8 45.52911221523103 3.796514377566034 3.796514377566034 Other FALSE 1571 -Reddy_2652T MCD 1 0 798,23,2008,1331,21,2034,1699,59,846,905,709 0.112,0.115,0.118,0.119,0.135,0.139,0.143,0.151,0.173,0.178,0.178 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 79.7641773992319 0 0 79.7641773992319 79.7641773992319 MCD TRUE 1572 -Reddy_2654T EZB 1 0.09182068869879492 108,1573,117,172,2063,1048,76,2047,1138,2089 0.131,0.177,0.178,0.192,0.211,0.223,0.273,0.28,0.299,0.3 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.2593228713454 1 4.339383573485643 47.259322871345375 47.259322871345375 EZB TRUE 1573 -Reddy_2655T BN2 1 5.349514031203395 2058,1793 0.254,0.293 BN2,BN2 7.34520774971009 8 39.293291919178024 7.345207749710087 7.345207749710087 Other FALSE 1574 -Reddy_2656T BN2 1 10.049853006443614 1697 0.263 BN2 3.79598875179198 8 38.149128969622765 3.7959887517919793 3.7959887517919793 Other FALSE 1575 -Reddy_2657T EZB 1 0 31 0.108 EZB 9.26325067277845 0 0 9.263250672778446 9.263250672778446 Other FALSE 1576 -Reddy_2659T EZB 1 4.949145569350474 1601,2166 0.187,0.345 EZB,EZB 8.25671264066838 9 40.86367278296397 8.25671264066838 8.25671264066838 Other FALSE 1577 -Reddy_2660T ST2 0.5888874432480815 2.614606940964133 1131,52 0.137,0.196 ST2,BN2 5.08931634696927,7.29005826318248 3 19.060636934949834 12.379374610151743 7.290058263182476 Other FALSE 1578 -Reddy_2662T EZB 1 0.12853560123499003 1579,1032,474,1054,1545,1079,1236,227,71,1474 0.149,0.151,0.213,0.231,0.239,0.248,0.281,0.291,0.317,0.336 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7083187198284 1 5.6180750256237095 43.70831871982837 43.70831871982837 EZB TRUE 1579 -Reddy_2663T ST2 0.33777527429436 5.165160893572753 1074,147,282,1425 0.186,0.269,0.273,0.314 ST2,MCD,BN2,N1 3.65733160054663,3.71840276196271,3.18959610520017,5.38896730703987 7 27.8348831910644 15.954297774749376 5.388967307039868 Other FALSE 1580 -Reddy_2664T EZB 1 0.10690180611234974 1243,105,1245,505,1581,341,1782,166,1244,1668 0.149,0.162,0.163,0.173,0.179,0.202,0.221,0.223,0.253,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.0655414307726 1 5.565900415166963 52.06554143077258 52.06554143077258 EZB TRUE 1581 -Reddy_2666T N1 0.5306670674802307 4.884252520449958 999,1071 0.206,0.233 N1,ST2 4.85621792550818,4.29493959509506 5 23.718994642517607 9.151157520603245 4.856217925508183 Other FALSE 1582 -Reddy_2667T BN2 0.5143500500683403 8.11282487052428 101,140 0.506,0.536 BN2,EZB 1.97452797887226,1.86435174634583 7 16.01899969454093 3.838879725218092 1.9745279788722623 Other FALSE 1583 -Reddy_2669T EZB 1 0.1342706709153777 1032,1579,474,1545,1054,1079,227,1236,1042,1017 0.136,0.166,0.208,0.224,0.249,0.271,0.295,0.304,0.332,0.337 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.0122300016137 1 5.775280979883214 43.012230001613744 43.012230001613744 Other FALSE 1584 -Reddy_2671T BN2 1 4.395274646953124 1538 0.157 BN2 6.35192419210641 4 27.91845136093351 6.35192419210641 6.35192419210641 Other FALSE 1585 -Reddy_2673T Other 1 10 0 0 1 2 Other TRUE 1586 -Reddy_2675T EZB 0.8893239549096854 0.12738173209796255 11,343,308,16,143,1533,139,575,218,1058 0.128,0.166,0.172,0.182,0.189,0.198,0.226,0.229,0.23,0.253 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 5.82168829210606,46.7794711309237 1 5.958850059283695 52.601159423029756 46.77947113092369 EZB TRUE 1587 -Reddy_2676T Other 1 10 0 0 1 2 Other TRUE 1588 -Reddy_2677T EZB 1 0 427,1020,366,689,161,645,78,1090,498,128,48 0.198,0.208,0.212,0.235,0.247,0.291,0.293,0.318,0.332,0.342,0.348 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6532134400376 0 0 41.65321344003758 41.65321344003758 EZB TRUE 1589 -Reddy_2678T EZB 1 0.10056136867352942 1305,1219,1926,55,1180,1726,1590,1853,1457 0.153,0.171,0.193,0.21,0.216,0.217,0.218,0.233,0.233 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7299395711652 1 4.498103943960637 44.72993957116521 44.72993957116521 EZB TRUE 1590 -Reddy_2680T ST2 0.5907706172975848 0.8878336485312514 1417,1687,545,649,1394,1147,2082 0.186,0.224,0.233,0.265,0.285,0.287,0.298 ST2,ST2,BN2,EZB,EZB,ST2,ST2 4.28994931935572,7.27263247296563,16.6919431295352 3 14.819668769771368 28.25452492185653 16.691943129535176 Other FALSE 1591 -Reddy_2681T Other 1 10 0 0 1 2 Other TRUE 1592 -Reddy_2684T ST2 0.39462092414400113 6.716429274097206 2216,1666,610 0.153,0.178,0.227 ST2,EZB,BN2 4.4097464488493,5.6058438748727,6.5287382187868 7 43.84980849557688 16.544328542508794 6.5287382187867955 Other FALSE 1593 -Reddy_2685T EZB 0.5502790977595933 8.788798448868697 31,1538 0.123,0.15 EZB,BN2 6.64547277593197,8.13140938103637 9 71.46531815516884 14.776882156968346 8.131409381036374 Other FALSE 1594 -Reddy_2687T MCD 1 0.2393934389149432 788,1224,156,1310,471,767,1491,950 0.108,0.123,0.128,0.13,0.134,0.141,0.15,0.158 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.474390479223 2 14.477172303106299 60.474390479223025 60.474390479223025 Other FALSE 1595 -Reddy_2688T MCD 0.5066868004818145 3.212910997462534 921,1835 0.146,0.15 MCD,EZB 6.66374324232442,6.84439570233151 3 21.9904342230062 13.508138944655927 6.844395702331506 Other FALSE 1596 -Reddy_2689T BN2 0.823957776263066 1.3846854047068649 523,596,2096,1603,2069 0.161,0.165,0.209,0.222,0.252 BN2,BN2,BN2,MCD,BN2 21.0532757922503,4.4981255037411 6 29.152163710797332 25.551401295991393 21.05327579225029 Other FALSE 1597 -Reddy_2690T MCD 1 0.23449752210180733 2180,1250,880,1239,771,929,647,1598,1240 0.159,0.175,0.191,0.193,0.196,0.205,0.212,0.213,0.22 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 46.3918376587509 2 10.878770976726388 46.391837658750866 46.391837658750866 MCD TRUE 1598 -Reddy_2691T EZB 1 12.269588240166984 1182 0.295 EZB 3.39002513756866 10 41.594212561782896 3.3900251375686605 3.3900251375686605 Other FALSE 1599 -Reddy_2692T EZB 0.8585612017961811 0.4044416920830485 1600,595,2083,1272,1318,1293,331,651 0.172,0.194,0.194,0.212,0.235,0.249,0.26,0.288 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.15800773576512,31.3101170027683 3 12.663116699917834 36.46812473853342 31.310117002768294 EZB TRUE 1600 -Reddy_2696T EZB 1 2.455421944244491 1601,1395 0.112,0.291 EZB,EZB 12.3350749400087 7 30.287813691597773 12.335074940008747 12.335074940008747 EZB TRUE 1601 -Reddy_2697T ST2 0.6950568658748894 2.576795800403351 702,530,1697 0.192,0.22,0.233 ST2,ST2,BN2 4.28543502115927,9.76779176636716 4 25.16960480278933 14.05322678752643 9.76779176636716 Other FALSE 1602 -Reddy_2698T Other 1 10 0 0 1 2 MCD FALSE 1603 -Reddy_2699T MCD 1 6.46149564720824 904 0.223 MCD 4.49302330720873 5 29.031650542334383 4.4930233072087304 4.4930233072087304 Other FALSE 1604 -Reddy_2700T Other 1 10 0 0 1 2 Other TRUE 1605 -Reddy_2704T EZB 0.9177480368194685 0.09377468707456524 404,2059,149,187,121,80,319,17,1541,1705 0.137,0.142,0.162,0.168,0.174,0.176,0.185,0.188,0.209,0.211 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB 53.3538309849841,4.78176707075917 1 5.0032388048461245 58.135598055743245 53.35383098498407 EZB TRUE 1606 -Reddy_2705T ST2 1 0.4338125267388648 1946,90 0.112,0.149 ST2,ST2 15.6706762103353 1 6.798135642512174 15.6706762103353 15.6706762103353 Other FALSE 1607 -Reddy_2706T EZB 1 3.378054033367146 31 0.178 EZB 5.61925054539168 3 18.982131969360903 5.619250545391681 5.619250545391681 N1 FALSE 1608 -Reddy_2707T N1 0.4097569615441078 1.5006658744871442 1609,464,176,659,1008,1064 0.135,0.151,0.183,0.242,0.276,0.284 MCD,BN2,N1,MCD,N1,N1 6.61980212384529,11.5388820353255,12.6060737051064 4 18.917504620522962 30.764757864277268 12.606073705106448 MCD FALSE 1609 -Reddy_2709T ST2 0.6578496185079283 1.5173935453172025 572,718,419,1302,1461 0.122,0.233,0.248,0.258,0.278 BN2,ST2,ST2,ST2,ST2 8.21848753184048,15.8016158390831 4 23.977269879806695 24.020103370923536 15.801615839083052 Other FALSE 1610 -Reddy_2713T EZB 0.7853998336439457 1.801664739742062 696,1122,1392,1471,1227 0.225,0.266,0.292,0.327,0.352 EZB,BN2,EZB,EZB,EZB 3.7660132042874,13.7829629602465 5 24.832278374646975 17.548976164533897 13.782962960246492 Other FALSE 1611 -Reddy_2714T EZB 1 1.079726257691078 1294,360 0.143,0.166 EZB,EZB 13.0365361453503 2 14.07589038547356 13.03653614535031 13.03653614535031 Other FALSE 1612 -Reddy_2715T MCD 0.3560204941568707 6.9263229656692324 147,1074,1425 0.229,0.232,0.279 MCD,ST2,N1 4.36199293014096,3.58459395808733,4.30549691518042 7 30.212571808122163 12.25208380340871 4.361992930140961 Other FALSE 1613 -Reddy_2716T BN2 0.8256804075127272 1.319432644421487 1816,591,610,1666,1614 0.201,0.236,0.276,0.296,0.314 BN2,BN2,BN2,EZB,BN2 16.0280339804346,3.38387628726487 5 21.14791125968223 19.411910267699437 16.028033980434564 BN2 TRUE 1614 -Reddy_2718T BN2 1 11.755533336412224 2170 0.324 BN2 3.08190522870411 9 36.2294396556943 3.0819052287041098 3.0819052287041098 Other FALSE 1615 -Reddy_2719T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1616 -Reddy_2720T ST2 1 12.348920276119966 2107 0.268 ST2 3.73276379567117 9 46.09560252233029 3.7327637956711746 3.7327637956711746 Other FALSE 1617 -Reddy_2725T MCD 0.5429514395634154 4.033000247253929 768,140 0.195,0.232 MCD,EZB 4.31147421570963,5.12182147521476 4 20.656307275931603 9.433295690924384 5.121821475214758 Other FALSE 1618 -Reddy_2727T MCD 1 0.1039821355114704 846,707,798,2008,2018,1947,23,1699,1331,905 0.131,0.148,0.156,0.167,0.169,0.172,0.173,0.174,0.182,0.183 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 60.9475299674279 1 6.337454320162492 60.94752996742791 60.94752996742791 Other FALSE 1619 -Reddy_2728T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1620 -Reddy_2758T ST2 1 0.6024476223461437 1188,1638 0.27,0.28 ST2,ST2 7.27628921935511 1 4.383583139703363 7.2762892193551085 7.2762892193551085 Other FALSE 1621 -Reddy_2761T ST2 1 3.435494915738986 1667,566 0.216,0.245 ST2,ST2 8.71825574779564 6 29.951523295664103 8.718255747795638 8.718255747795638 Other FALSE 1622 -Reddy_2762T ST2 1 1.1605818816944742 271,2198,1955,1077 0.163,0.202,0.241,0.244 ST2,ST2,ST2,ST2 19.3395869134925 5 22.445174171254916 19.339586913492468 19.339586913492468 Other FALSE 1623 -Reddy_2763T EZB 0.37334294234849835 3.403102823869741 1123,854,234,293,1341,649 0.173,0.229,0.254,0.258,0.272,0.274 EZB,BN2,ST2,ST2,BN2,EZB 8.04049378000831,9.44757700441686,7.81728772854756 5 32.15107598245784 25.305358512972727 9.447577004416859 Other FALSE 1624 -Reddy_2764T ST2 0.9087841737961099 0 1442,1284,1955,2076,1942,1998,1401,1661,1625,726 0.144,0.166,0.184,0.192,0.201,0.216,0.217,0.22,0.239,0.252 ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.60985592324989,45.9280399123446 0 0 50.53789583559446 45.92803991234457 ST2 TRUE 1625 -Reddy_2765T N1 1 1.3604232589713492 870,262 0.154,0.229 N1,N1 10.8453985418376 3 14.754332429129875 10.845398541837636 10.845398541837636 Other FALSE 1626 -Reddy_2767T MCD 0.8206372820234564 1.7355800484168311 1994,801,768,850 0.137,0.159,0.184,0.24 MCD,MCD,MCD,BN2 4.16191200626741,19.0419736909358 6 33.04886962046634 23.203885697203184 19.041973690935777 Other FALSE 1627 -Reddy_2768T ST2 0.7547185562884801 0.7359008789019338 1130,1789,1211,486,1018,1943,1713,701 0.196,0.218,0.231,0.237,0.245,0.263,0.265,0.275 ST2,EZB,ST2,ST2,ST2,ST2,ST2,EZB 8.22328911142449,25.3026433317001 3 18.620237466340267 33.5259324431246 25.302643331700114 Other FALSE 1628 -Reddy_2769T EZB 1 0.4278339027212356 651,1814,331,1241,1437,1600,408 0.157,0.197,0.202,0.255,0.348,0.362,0.369 EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.6676751627659 4 12.265003346830776 28.667675162765917 28.667675162765917 Other FALSE 1629 -Reddy_2770T ST2 1 2.2513131679494287 90,1946 0.2,0.238 ST2,ST2 9.21257389739265 4 20.74038892590727 9.212573897392653 9.212573897392653 Other FALSE 1630 -Reddy_2772T MCD 0.38889512607823035 7.816101811927579 1631,175,2211 0.254,0.301,0.348 MCD,N1,BN2 2.87176142262161,3.94085141630902,3.32084256342963 8 30.802095895550302 10.133455402360267 3.9408514163090205 MCD TRUE 1631 -Reddy_2773T BN2 1 1.6562123467136949 1113,2064,57,754,106 0.258,0.267,0.276,0.324,0.334 BN2,BN2,BN2,BN2,BN2 17.3227979831944 6 28.690231899393602 17.322797983194366 17.322797983194366 BN2 TRUE 1632 -Reddy_2775T EZB 0.4418704820795233 3.0880799133201076 1983,1863,1082,2017,1121 0.198,0.227,0.238,0.241,0.279 EZB,EZB,BN2,ST2,ST2 4.19895223192613,9.44748513496515,7.73422850267203 6 29.17458907667619 21.380665869563316 9.447485134965152 Other FALSE 1633 -Reddy_2776T EZB 1 2.9800149285383015 1535 0.225 EZB 4.4517726521168 2 13.2663489617666 4.451772652116796 4.451772652116796 Other FALSE 1634 -Reddy_2777T EZB 0.791005842500117 0.7027794310820751 1087,2126,1453,1059,179,62 0.156,0.181,0.188,0.208,0.229,0.239 BN2,EZB,EZB,EZB,EZB,EZB 6.39638352969326,24.2091779185824 3 17.013712284586042 30.60556144827562 24.209177918582355 Other FALSE 1635 -Reddy_2778T BN2 0.4827521434148294 2.3094539311683278 402,737,793,581 0.106,0.133,0.159,0.186 EZB,BN2,BN2,ST2 13.8486623784677,9.44897609992412,5.38926134665472 5 31.98284777137521 28.686899825046563 13.848662378467724 Other FALSE 1636 -Reddy_2779T EZB 1 1.034363076131765 1649 0.129 EZB 7.75122368385489 1 8.01757957341754 7.751223683854894 7.751223683854894 Other FALSE 1637 -Reddy_2781T Other 1 10 0 0 1 2 ST2 FALSE 1638 -Reddy_2783T ST2 0.6026927475184138 4.79780247451278 1436,1901 0.161,0.244 ST2,BN2 4.10448853661449,6.22627817095957 6 29.872452815634738 10.330766707574066 6.226278170959572 Other FALSE 1639 -Reddy_2784T MCD 1 0.2641436564595905 792,1640,1647,586,534,1811,803,1459,708 0.147,0.148,0.163,0.166,0.168,0.17,0.177,0.187,0.188 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.8809777755642 2 14.232318483255458 53.88097777556419 53.88097777556419 MCD TRUE 1640 -Reddy_2785T EZB 1 1.0069293066985767 2162 0.163 EZB 6.15375037972859 1 6.196391603456207 6.153750379728585 6.153750379728585 Other FALSE 1641 -Reddy_2786T ST2 0.7212399084181357 3.365747526257225 1653,1535,451 0.144,0.237,0.251 ST2,EZB,ST2 4.22247611099327,10.9248718721137 7 36.770360478243866 15.147347983106982 10.924871872113712 Other FALSE 1642 -Reddy_2788T EZB 0.7544291670164479 2.2419480358963724 1488,1087,1387,1453 0.139,0.197,0.207,0.282 EZB,BN2,EZB,EZB 5.06497646174225,15.5603412936505 7 34.885476601176975 20.625317755392754 15.560341293650508 Other FALSE 1643 -Reddy_2789T ST2 0.6895006161669308 0.6072839722923535 649,234,1687,235,1147,1139,1176 0.133,0.152,0.175,0.184,0.191,0.204,0.216 EZB,ST2,ST2,ST2,ST2,BN2,ST2 4.90001803544506,7.52364603845578,27.5882158871237 3 16.753881332391494 40.01187996102455 27.588215887123695 ST2 TRUE 1644 -Reddy_2790T BN2 1 5.683194195600907 804,1980 0.179,0.186 BN2,BN2 10.9488294759925 9 62.22432412658487 10.948829475992529 10.948829475992529 Other FALSE 1645 -Reddy_2791T ST2 1 9.045939856512717 670 0.219 ST2 4.57644722025014 8 41.398266310887536 4.576447220250135 4.576447220250135 Other FALSE 1646 -Reddy_2792T MCD 0.8374322022534004 0.4776743542828774 647,1420,1184,986,1897,1543,467,1977 0.136,0.146,0.189,0.19,0.198,0.225,0.253,0.256 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.84306402019837,35.2505370191963 3 16.83827750876927 42.09360103939469 35.25053701919632 MCD TRUE 1647 -Reddy_2793T MCD 0.4613538147525473 2.373303086353738 847,194,1071,1513,999 0.195,0.221,0.251,0.264,0.286 MCD,MCD,ST2,EZB,N1 3.78545200298732,9.65346226518396,3.49563834801203,3.9896533977232 5 22.91059178796043 20.924206013906506 9.653462265183958 Other FALSE 1648 -Reddy_2794T ST2 0.5169079267545914 8.229788417663073 511,1649 0.279,0.299 ST2,EZB 3.34791363934738,3.58226349408419 7 29.48127061263132 6.93017713343157 3.5822634940841906 EZB FALSE 1649 -Reddy_2805T ST2 1 4.36076136873364 451,1653 0.256,0.318 ST2,ST2 7.04946438671377 6 30.74103196784499 7.04946438671377 7.04946438671377 Other FALSE 1650 -Reddy_2806T EZB 0.5082149061630302 11.96838523696239 1601,1901 0.27,0.279 EZB,BN2 3.58000598407284,3.69960868692334 9 44.27834199111111 7.279614670996176 3.6996086869233396 Other FALSE 1651 -Reddy_2808T MCD 0.6065376323818631 5.572109030761872 904,1733 0.209,0.322 MCD,EZB 3.10281871034226,4.7831164278146 5 26.652046242611203 7.8859351381568565 4.7831164278146 Other FALSE 1652 -Reddy_2809T ST2 0.7673576017134041 2.38887217980688 1653,451,1535 0.121,0.219,0.256 ST2,ST2,EZB 3.89937788467083,12.8618742060471 6 30.725373471001543 16.7612520907179 12.861874206047068 ST2 TRUE 1653 -Reddy_2810T ST2 1 5.647429359160402 530,702 0.293,0.322 ST2,ST2 6.52529301295635 8 36.85113133849395 6.525293012956354 6.525293012956354 Other FALSE 1654 -Reddy_2812T EZB 1 3.1817979157041347 2050,1300 0.127,0.139 EZB,EZB 15.0669927807216 6 47.940126225629385 15.066992780721648 15.066992780721648 Other FALSE 1655 -Reddy_2813T ST2 1 14.634004361793213 670 0.287 ST2 3.48589395981691 10 51.012587412709266 3.4858939598169094 3.4858939598169094 Other FALSE 1656 -Reddy_2815T BN2 0.49751821314280875 0.9026816798876667 1719,1848,2170,1657,114,511 0.156,0.178,0.237,0.254,0.263,0.269 EZB,BN2,BN2,BN2,EZB,ST2 13.7680861371244,10.1902431667101,3.71520258588623 3 12.428199123097546 27.673531889720735 13.768086137124396 BN2 TRUE 1657 -Reddy_2816T ST2 0.6018184833195189 0.9503111777578093 1687,649,1417,1147,234,1394,1139 0.133,0.147,0.168,0.207,0.224,0.24,0.244 ST2,EZB,ST2,ST2,ST2,EZB,BN2 4.10311887118668,10.9625615111487,22.7705318756655 4 21.639090964935413 37.836212258000906 22.770531875665494 Other FALSE 1658 -Reddy_2818T Other 1 10 0 0 1 2 Other TRUE 1659 -Reddy_2819T MCD 1 0 540,947,1572,587,483,888,516,515,387,1567,2094 0.125,0.131,0.136,0.138,0.141,0.142,0.143,0.164,0.173,0.175,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.7089354503145 0 0 74.70893545031447 74.70893545031447 MCD TRUE 1660 -Reddy_2820T ST2 0.8093596064673173 1.138882562724699 1249,215,1396,606,1057 0.183,0.254,0.269,0.3,0.329 ST2,ST2,EZB,ST2,ST2 3.71576689945033,15.7752068160089 5 17.9661079661284 19.49097371545927 15.77520681600894 ST2 TRUE 1661 -Reddy_2821T EZB 0.6465339657312897 1.949001153122374 1944,160,1439 0.224,0.229,0.283 EZB,ST2,EZB 7.99071397054456,4.36859643553182 3 15.573910742862417 12.359310406076386 7.9907139705445625 Other FALSE 1662 -Reddy_2823T EZB 1 0.2381622748595342 1663,430,1483,337,336,1468,112,1384,170 0.108,0.152,0.165,0.176,0.177,0.222,0.233,0.244,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.0186431789981 2 11.912553844897522 50.01864317899815 50.01864317899815 EZB TRUE 1663 -Reddy_2824T BN2 0.635964971486329 4.510877077924508 833,286,834 0.251,0.278,0.297 ST2,BN2,BN2 6.96211898188142,3.98521191530634 7 31.405262929152023 10.94733089718776 6.962118981881423 ST2 FALSE 1664 -Reddy_2826T EZB 1 4.180538435438839 1395 0.208 EZB 4.80356846065454 4 20.08150257702808 4.803568460654539 4.803568460654539 Other FALSE 1665 -Reddy_2827T BN2 0.4545223976186366 2.0446031436970915 1666,115,1816,610,2216,591 0.178,0.186,0.218,0.24,0.245,0.262 EZB,EZB,BN2,BN2,ST2,BN2 12.5620712194674,10.9980394964404,4.07784790897721 5 25.68445030666979 27.63795862488497 12.562071219467393 EZB FALSE 1666 -Reddy_2828T ST2 0.5521682365889561 3.6924037508283263 1971,1667,566 0.119,0.191,0.193 BN2,ST2,ST2 8.43851868584952,10.4045366203999 6 38.41775004279539 18.843055306249454 10.404536620399933 ST2 TRUE 1667 -Reddy_2829T EZB 1 0.09403619661356857 1782,505,1668,119,105,1243,1245,166,341,1581 0.114,0.182,0.203,0.222,0.233,0.263,0.263,0.267,0.277,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.414148268417 1 4.364609972220181 46.414148268416966 46.414148268416966 EZB TRUE 1668 -Reddy_2830T BN2 0.4355315556625092 3.095197634503239 1763,834,1377,597 0.193,0.25,0.319,0.411 EZB,BN2,MCD,BN2 6.42584339965454,5.18982914568968,3.13835231487483 5 19.889255290298976 14.754024860219051 6.425843399654537 Other FALSE 1669 -Reddy_2831T EZB 1 0.5185698600740776 1289,1542,1964,477,520,699,72,119 0.201,0.254,0.26,0.284,0.313,0.317,0.327,0.331 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 28.7028097970705 3 14.884412060199718 28.702809797070515 28.702809797070515 Other FALSE 1670 -Reddy_2832T EZB 1 0.08302020726792979 1752,1671,398,1972,1750,397,172,818,1573 0.121,0.196,0.217,0.218,0.242,0.244,0.31,0.317,0.329 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.2736091896302 1 3.3435233823507042 40.273609189630236 40.273609189630236 EZB TRUE 1671 -Reddy_2833T EZB 0.7222289817173747 0.33337618684384734 1494,881,646,1036,625,466,1492,430,1181 0.24,0.28,0.289,0.309,0.349,0.364,0.376,0.387,0.389 BN2,N1,EZB,EZB,EZB,EZB,EZB,EZB,EZB 4.16146444785951,20.1188650429424,3.5762998500785 2 6.707150511642119 27.856629340880428 20.118865042942414 Other FALSE 1672 -Reddy_2834T EZB 1 0 287,676,382,1903,558,1138,2089,2016,174,93,153 0.144,0.163,0.184,0.19,0.195,0.213,0.22,0.226,0.233,0.242,0.247 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 55.0347511670775 0 0 55.034751167077545 55.034751167077545 EZB TRUE 1673 -Reddy_2836T MCD 1 0.09498527115875009 1422,895,950,247,1491,471,837,156,788,1968 0.113,0.124,0.124,0.125,0.127,0.145,0.149,0.166,0.17,0.175 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 72.127259314003 1 6.851027283878058 72.127259314003 72.127259314003 Other FALSE 1674 -Reddy_2837T ST2 1 3.894696927234366 1057,874 0.206,0.319 ST2,ST2 7.99684996894071 7 31.145307001587632 7.9968499689407135 7.9968499689407135 Other FALSE 1675 -Reddy_2838T BN2 0.36525665787604533 1.5614042882939494 826,1190,24,636,476,1183,15 0.194,0.195,0.242,0.253,0.283,0.303,0.335 BN2,BN2,EZB,ST2,MCD,ST2,EZB 10.3009629790861,7.11725064970369,3.53481956958813,7.24895232869186 4 16.083967769102266 28.20198552706978 10.300962979086108 Other FALSE 1676 -Reddy_2839T EZB 0.3563671022839069 4.148747907391088 1266,1052,1608 0.146,0.15,0.175 EZB,ST2,N1 6.85682215551517,5.72029403477285,6.66377925592673 4 28.4472265690464 19.240895446214743 6.856822155515168 Other FALSE 1677 -Reddy_2840T Other 1 10 0 0 1 2 Other TRUE 1678 -Reddy_2841T ST2 0.4749898482935771 3.87344013663434 1388,938,2196,1797 0.194,0.215,0.32,0.352 EZB,ST2,BN2,ST2 3.12848554737313,5.16205329061835,7.50065836275792 7 29.053351153488524 15.791197200749398 7.500658362757916 Other FALSE 1679 -Reddy_2843T BN2 1 1.4731988985216309 2069,523,1957,596,1765 0.184,0.197,0.229,0.258,0.283 BN2,BN2,BN2,BN2,BN2 22.2874718151158 6 32.83387892886048 22.287471815115794 22.287471815115794 Other FALSE 1680 -Reddy_2844T BN2 1 8.090678402055051 915 0.275 BN2 3.64261388852699 6 29.47121751493107 3.6426138885269883 3.6426138885269883 Other FALSE 1681 -Reddy_2865T Other 1 10 0 0 1 2 Other TRUE 1682 -Reddy_2867T N1 0.6822202024497086 3.782891712875407 697,1697,2077 0.207,0.242,0.246 N1,BN2,N1 4.13757358823498,8.88268012245023 7 33.60221702334009 13.020253710685207 8.882680122450232 Other FALSE 1683 -Reddy_2871T ST2 1 4.627153467137141 670 0.193 ST2 5.19181997581784 4 24.023347801857383 5.191819975817839 5.191819975817839 Other FALSE 1684 -Reddy_2872T EZB 0.700703490714783 2.788652353536351 1521,628,1002,402 0.225,0.27,0.293,0.305 BN2,EZB,EZB,EZB 4.44107752907384,10.3973097935183 6 28.99448242614132 14.838387322592126 10.397309793518286 Other FALSE 1685 -Reddy_2873T BN2 1 0.5670930315822685 2058,1793 0.151,0.153 BN2,BN2 13.1561237702862 1 7.460746112763142 13.156123770286195 13.156123770286195 Other FALSE 1686 -Reddy_2874T ST2 0.7838323148075419 1.8022686607958118 1461,572,2060,1057,1302 0.258,0.261,0.295,0.299,0.307 ST2,BN2,ST2,ST2,ST2 3.82564723705955,13.8719435645143 6 25.000969150652217 17.697590801573824 13.871943564514272 ST2 TRUE 1687 -Reddy_2877T MCD 0.7989426364249776 1.7816304987856433 1890,254,952,1398,285 0.214,0.215,0.24,0.267,0.286 MCD,MCD,BN2,MCD,MCD 4.1716612818791,16.5769509932565 6 29.534001466460673 20.748612275135564 16.57695099325646 Other FALSE 1688 -Reddy_2879T MCD 1 0 479,305,764,1689,1449,310,1009,1699,709,1904,1331 0.12,0.121,0.136,0.141,0.147,0.149,0.155,0.159,0.165,0.17,0.172 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.0527960013992 0 0 75.05279600139924 75.05279600139924 MCD TRUE 1689 -Reddy_2881T EZB 1 1.049122495647007 2050 0.111 EZB 8.98354655319312 1 9.424840779647036 8.983546553193122 8.983546553193122 Other FALSE 1690 -Reddy_2882T EZB 1 0 47,1220,1691,2061,448,385,43,102,83,435,1796 0.109,0.148,0.166,0.205,0.24,0.25,0.271,0.306,0.31,0.33,0.354 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 51.0649468953344 0 0 51.06494689533435 51.06494689533435 EZB TRUE 1691 -Reddy_2884T EZB 1 15.867027351533762 1508 0.392 EZB 2.55023021703118 10 40.46457260634155 2.5502302170311757 2.5502302170311757 Other FALSE 1692 -Reddy_2887T EZB 0.6343100350054522 1.4833323127466416 1392,1122,1703,696,1493,1528 0.196,0.217,0.225,0.266,0.274,0.314 EZB,BN2,ST2,EZB,EZB,EZB 4.61382645685843,15.7024102985149,4.43886632033306 5 23.29189258379279 24.755103075706394 15.702410298514902 Other FALSE 1693 -Reddy_2888T MCD 0.9180153501564746 0.10358687312834765 767,156,1224,788,837,1310,1694,471,1491,1376 0.11,0.118,0.126,0.132,0.145,0.147,0.15,0.155,0.16,0.169 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 5.90565320603732,66.127992327715 1 6.8499919514833625 72.03364553375233 66.12799232771502 MCD TRUE 1694 -Reddy_2889T EZB 0.7836008392778712 1.0502340584785088 1002,1521,1260,628,1322 0.124,0.172,0.202,0.22,0.286 EZB,BN2,EZB,EZB,EZB 5.81705629176935,21.064084431502 5 22.122218880630278 26.88114072327131 21.06408443150196 Other FALSE 1695 -Reddy_2891T ST2 1 8.93668740691894 318 0.237 ST2 4.21364573123317 7 37.65603474352925 4.213645731233173 4.213645731233173 Other FALSE 1696 -Reddy_2894T BN2 0.5471445698727676 9.929000885986547 1697,697 0.224,0.27 BN2,N1 4.47126560167524,3.70073472122842 9 44.39520012051465 8.172000322903664 4.471265601675243 BN2 TRUE 1697 -Reddy_2895T BN2 0.579337735605437 3.993175577144363 1538,31 0.113,0.156 BN2,EZB 8.85547744294063,6.4300406559041 5 35.36147624910333 15.285518098844728 8.855477442940629 Other FALSE 1698 -Reddy_2897T MCD 1 0 905,910,1699,798,305,479,823,764,1331,1904,23 0.113,0.117,0.128,0.155,0.155,0.164,0.167,0.167,0.171,0.175,0.181 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.3281692549772 0 0 73.32816925497723 73.32816925497723 MCD TRUE 1699 -Reddy_2898T ST2 1 2.5315231692589166 1057,874 0.271,0.272 ST2,ST2 7.35943036551187 4 18.63056848284091 7.359430365511868 7.359430365511868 Other FALSE 1700 -Reddy_2899T EZB 0.7021419394868738 0.3459685822134832 1550,2176,1031,1384,466,1701,1864,809,1049 0.106,0.158,0.217,0.241,0.245,0.257,0.276,0.28,0.294 BN2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 9.4321893973258,30.2624780304445,3.40556107098647 2 10.46986661845957 43.10022849875677 30.26247803044451 EZB TRUE 1701 -Reddy_2900T EZB 0.6649675200963695 0.925012689190717 491,282,77,994,1074,1975 0.146,0.162,0.2,0.318,0.363,0.367 EZB,BN2,EZB,EZB,ST2,EZB 6.16437708179719,17.7011043419929,2.75402074291362 5 16.37374612903234 26.61950216670373 17.701104341992913 Other FALSE 1702 -Reddy_2901T EZB 0.8113963859599898 0.5830878866881399 1703,1493,397,1227,398,1392,1972,1528 0.165,0.181,0.269,0.283,0.285,0.298,0.312,0.316 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB 26.0255628357552,6.04946644227579 3 15.17519043376991 32.07502927803102 26.025562835755228 ST2 FALSE 1703 -Reddy_2902T Other 1 10 0 0 1 2 Other TRUE 1704 -Reddy_2903T EZB 1 0.10145119805546597 121,1705,80,17,333,404,1537,187,639,149 0.155,0.162,0.172,0.179,0.184,0.185,0.204,0.205,0.205,0.206 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.4202961713613 1 5.521004245117893 54.42029617136131 54.42029617136131 EZB TRUE 1705 -Reddy_2904T EZB 0.4001690349620823 3.0057133895483674 2017,1082,1983,1863,1121 0.19,0.191,0.2,0.209,0.239 ST2,BN2,EZB,EZB,ST2 5.24405324590105,9.79594209296847,9.43951516072893 6 29.443794312075788 24.479510499598444 9.79594209296847 Other FALSE 1706 -Reddy_2905T EZB 0.5408518542236888 5.621222356837649 1471,724 0.26,0.306 EZB,ST2 3.8468370252431,3.26571513705771 4 21.623926289407354 7.112552162300813 3.8468370252431012 Other FALSE 1707 -Reddy_2908T BN2 0.5440147446472959 5.914921809189048 674,1664 0.233,0.278 BN2,ST2 4.29394238540273,3.59912012375159 6 25.39833346281987 7.893062509154321 4.293942385402733 Other FALSE 1708 -Reddy_2909T Other 1 10 0 0 1 2 Other TRUE 1709 -Reddy_2910T ST2 1 8.933605737266294 1710 0.141 ST2 7.11059967350489 8 63.52329403862715 7.110599673504892 7.110599673504892 ST2 TRUE 1710 -Reddy_2912T EZB 1 3.499326765134442 2166,1601,1395 0.242,0.257,0.297 EZB,EZB,EZB 11.3856596671 8 39.84214361179458 11.385659667099958 11.385659667099958 Other FALSE 1711 -Reddy_2915T N1 1 4.887798039971989 913 0.226 N1 4.43251666953964 4 21.665246289519043 4.432516669539644 4.432516669539644 Other FALSE 1712 -Reddy_2917T ST2 0.7425681476905392 0.14144209006833883 701,1211,1018,1401,1284,1713,618,1442,2076 0.156,0.16,0.161,0.174,0.204,0.214,0.224,0.229,0.236 EZB,ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.73844175989872,6.42585046307247,35.088183000446 1 4.962945940283441 47.25247522341721 35.088183000446016 ST2 TRUE 1713 -Reddy_2918T ST2 1 3.3519944714199936 1714,35 0.181,0.308 ST2,ST2 8.78472031506366 7 29.446333929064277 8.784720315063655 8.784720315063655 ST2 TRUE 1714 -Reddy_2919T BN2 1 1.00168145271497 1148,804,1980,1841,70 0.109,0.158,0.186,0.195,0.196 BN2,BN2,BN2,BN2,BN2 31.0982449629422 5 31.150535191365933 31.098244962942193 31.098244962942193 Other FALSE 1715 -Reddy_2921T BN2 1 2.8582223326304224 19,894,2211 0.224,0.263,0.281 BN2,BN2,BN2 11.8204578924924 6 33.78549673023925 11.820457892492378 11.820457892492378 Other FALSE 1716 -Reddy_2922T ST2 1 4.682945546042907 1028 0.246 ST2 4.05728695721394 4 19.000053885302982 4.057286957213936 4.057286957213936 Other FALSE 1717 -Reddy_2923T EZB 1 0.20934970630803418 149,1705,404,319,121,2059,80,333,187 0.124,0.173,0.175,0.177,0.209,0.214,0.225,0.231,0.237 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 47.6636389651018 2 9.97836881891623 47.663638965101775 47.663638965101775 Other FALSE 1718 -Reddy_2925T EZB 0.4808154899640812 1.045721842588662 1725,1909,353,2183 0.241,0.254,0.276,0.307 ST2,EZB,BN2,EZB 3.61788132282793,7.194917833437,4.15119011532548 2 7.523882734055764 14.963989271590414 7.194917833437 EZB TRUE 1719 -Reddy_2928T EZB 1 9.868566832038194 1300 0.173 EZB 5.77438543630711 9 56.98490859214478 5.774385436307114 5.774385436307114 Other FALSE 1720 -Reddy_2931T BN2 1 2.28395452278329 1381,1108,52 0.219,0.269,0.305 BN2,BN2,BN2 11.5508097328653 6 26.381524131186925 11.55080973286529 11.55080973286529 Other FALSE 1721 -Reddy_2933T BN2 0.627777655019822 0.30087244428254567 960,1722,590,1076,1086,863,684,838,13 0.119,0.13,0.171,0.19,0.232,0.254,0.256,0.259,0.262 ST2,BN2,BN2,EZB,BN2,BN2,BN2,ST2,BN2 29.4964930581523,5.25631622848259,12.2327640645239 2 8.874681964169415 46.98557335115875 29.496493058152275 BN2 TRUE 1722 -Reddy_2934T EZB 0.6055207940138612 4.944795937033077 1480,1513,632 0.19,0.245,0.249 MCD,EZB,EZB 8.09808612462307,5.27566784828788 8 40.043383366780105 13.373753972910958 8.098086124623073 Other FALSE 1723 -Reddy_2935T BN2 1 0 1724,976,1001,1409,796,98,519,41,991,857,100 0.167,0.192,0.199,0.202,0.207,0.219,0.257,0.264,0.268,0.286,0.294 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 48.9087035473229 0 0 48.908703547322936 48.908703547322936 BN2 TRUE 1724 -Reddy_2936T EZB 0.4813877373861316 2.9777618668164703 353,1725,1738,1527,2183 0.175,0.214,0.265,0.328,0.356 BN2,ST2,EZB,EZB,EZB 5.70776920469082,9.62633837859005,4.66295015412584 5 28.664943340837347 19.99705773740671 9.626338378590052 ST2 FALSE 1725 -Reddy_2939T EZB 1 0.08386364933106205 958,1066,1914,1437,1372,468,1172,1242,2132,54 0.133,0.143,0.161,0.182,0.205,0.209,0.21,0.215,0.22,0.221 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 54.3336825939969 1 4.556620903928184 54.33368259399688 54.33368259399688 EZB TRUE 1726 -Reddy_2944T ST2 0.7043277783856913 3.09698534143694 566,1667,1971 0.142,0.147,0.172 ST2,ST2,BN2 5.81363661704658,13.8488010150899 6 42.88953374021045 19.662437632136488 13.848801015089904 Other FALSE 1727 -Reddy_2945T MCD 1 0 1565,455,777,2010,783,1240,929,535,1239,771,534 0.144,0.15,0.159,0.161,0.171,0.173,0.182,0.182,0.197,0.2,0.209 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 63.6027131103941 0 0 63.60271311039409 63.60271311039409 MCD TRUE 1728 -Reddy_2946T ST2 1 5.91141195947365 1028 0.295 ST2 3.39458903893193 4 20.066814242240365 3.394589038931928 3.394589038931928 Other FALSE 1729 -Reddy_2947T EZB 1 7.498531278471929 2162 0.22 EZB 4.55021302856708 6 34.119914718420745 4.550213028567081 4.550213028567081 Other FALSE 1730 -Reddy_2948T MCD 1 0.1023393815148286 895,950,471,247,1491,1802,1422,788,156,837 0.125,0.126,0.133,0.135,0.139,0.158,0.159,0.159,0.17,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 68.5664633144784 1 7.0170494482629016 68.56646331447837 68.56646331447837 MCD TRUE 1731 -Reddy_2949T MCD 1 0.2495584861391479 1250,2180,1977,1239,929,771,880,1240,777 0.146,0.163,0.17,0.17,0.174,0.179,0.193,0.193,0.201 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 51.4263416431414 2 12.833879968136998 51.42634164314144 51.42634164314144 MCD TRUE 1732 -Reddy_2950T MCD 1 4.530298620641546 904 0.206 MCD 4.8648752693004 4 22.039337722104793 4.864875269300405 4.864875269300405 EZB FALSE 1733 -Reddy_2951T EZB 0.5548594003860352 4.172379176028997 1983,1863,1082,2017 0.219,0.283,0.307,0.309 EZB,EZB,BN2,ST2 3.26113982208539,8.09551726002396,3.23355668085767 7 33.77756763490728 14.590213762967016 8.095517260023957 Other FALSE 1734 -Reddy_2952T BN2 1 1.601666852067858 158,594,1735,1414,903 0.164,0.227,0.232,0.266,0.269 BN2,BN2,BN2,BN2,BN2 22.2851157730587 6 35.693331228202766 22.28511577305874 22.28511577305874 BN2 TRUE 1735 -Reddy_2953T MCD 0.8700105215675332 0.23728971780420766 556,1736,1640,792,188,708,621,1273,1169 0.236,0.242,0.264,0.264,0.298,0.313,0.32,0.326,0.326 MCD,EZB,MCD,MCD,MCD,MCD,MCD,MCD,MCD 4.12708773582726,27.6223106431454 2 6.554490297612135 31.74939837897267 27.622310643145408 EZB FALSE 1736 -Reddy_2954T Other 1 10 0 0 1 2 Other TRUE 1737 -Reddy_2955T EZB 0.6825929595995339 1.7521603374336148 1738,1446,1177,353,1527 0.123,0.273,0.283,0.289,0.31 EZB,EZB,ST2,BN2,EZB 3.4555233553478,15.0366121617193,3.53653024963087 6 26.34655543913651 22.028665766697983 15.036612161719313 EZB TRUE 1738 -Reddy_2956T ST2 0.5502852717352847 1.122861096263296 1152,2066,1174,629,1198,268,171 0.16,0.194,0.205,0.205,0.228,0.235,0.274 BN2,BN2,ST2,ST2,ST2,ST2,BN2 15.0455546287933,18.4102200727572 4 20.672119893344654 33.45577470155048 18.41022007275717 Other FALSE 1739 -Reddy_2957T MCD 1 0.9339556957720057 1063,650,753,2053,1194,864 0.166,0.189,0.196,0.208,0.23,0.241 MCD,MCD,MCD,MCD,MCD,MCD 29.7031036248309 4 27.741382812516942 29.703103624830916 29.703103624830916 Other FALSE 1740 -Reddy_2995T EZB 1 8.239088760509306 199,1182 0.344,0.35 EZB,EZB 5.76339504832738 9 47.48512336504911 5.76339504832738 5.76339504832738 Other FALSE 1741 -Reddy_2996T EZB 0.5397668333631275 2.8411749547249934 1266,2042,1608,1052 0.244,0.249,0.263,0.321 EZB,EZB,N1,ST2 8.12089342180921,3.80932063329392,3.11497382946978 5 23.072879000035275 15.045187884572906 8.120893421809209 Other FALSE 1742 -Reddy_2998T EZB 0.5383335625881247 4.9024641482587334 1037,724 0.218,0.255 EZB,ST2 4.57684876832217,3.9250338680088 5 22.43783699870159 8.501882636330976 4.576848768322172 Other FALSE 1743 -Reddy_2999T BN2 0.5977692322833882 3.419943969191406 1232,811,150,1283 0.169,0.275,0.306,0.316 BN2,BN2,MCD,ST2 9.55714604703704,3.26699231094532,3.16388098274246 7 32.684903986245814 15.988019340724824 9.55714604703704 Other FALSE 1744 -Reddy_3382T EZB 1 0 1511,137,364,9,2104,2006,435,138,168,1041,1295 0.192,0.194,0.217,0.227,0.24,0.26,0.272,0.273,0.274,0.283,0.284 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.436701207514 0 0 45.43670120751402 45.43670120751402 EZB TRUE 1745 -Reddy_3384T ST2 0.501728693769021 4.323375478829048 1436,1901 0.226,0.228 ST2,BN2 4.39201760252347,4.42249278087698 3 19.12009684414201 8.814510383400446 4.422492780876977 Other FALSE 1746 -Reddy_3385T ST2 1 1.0251107417856036 1884,1747,2052,339,959,1472 0.14,0.177,0.23,0.253,0.267,0.269 ST2,ST2,ST2,ST2,ST2,ST2 28.5260780592981 5 29.242389039601072 28.526078059298065 28.526078059298065 ST2 TRUE 1747 -Reddy_3386T BN2 0.6259078764198323 1.2797865851521564 1816,1666,115,591,610 0.15,0.219,0.242,0.253,0.254 BN2,EZB,EZB,BN2,BN2 14.5604902039542,8.70250224669146 4 18.634320036260014 23.262992450645694 14.560490203954235 Other FALSE 1748 -Reddy_3387T MCD 0.5434242509630629 8.363846674268062 1603,2096 0.224,0.266 MCD,BN2 3.7554513537642,4.46980231265509 7 37.38474120733597 8.22525366641929 4.46980231265509 Other FALSE 1749 -Reddy_3388T EZB 1 0.2211886508565042 1671,1752,398,1750,1972,397,109,818,1227 0.146,0.184,0.236,0.257,0.26,0.262,0.305,0.335,0.38 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.9496691884915 2 8.172847477396576 36.9496691884915 36.9496691884915 EZB TRUE 1750 -Reddy_3389T EZB 1 5.210867297793099 2166,1601 0.192,0.323 EZB,EZB 8.30721602046627 9 43.287800296750596 8.307216020466267 8.307216020466267 Other FALSE 1751 -Reddy_3390T EZB 1 0 1752,1750,1972,1671,398,172,1573,397,818,108,117 0.173,0.235,0.244,0.252,0.256,0.268,0.274,0.28,0.297,0.306,0.33 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6246821953859 0 0 42.62468219538594 42.62468219538594 EZB TRUE 1752 -Reddy_3394T Other 1 10 0 0 1 2 Other TRUE 1753 -Reddy_3396T Other 1 10 0 0 1 2 Other TRUE 1754 -Reddy_3398T BN2 0.4801010146149372 3.015886069891635 1835,921,70,1813 0.202,0.213,0.22,0.23 EZB,MCD,BN2,BN2 8.91007719661954,4.95525490920034,4.6934230839933 5 26.871777698943973 18.558755189813173 8.910077196619536 Other FALSE 1755 -Reddy_3399T Other 1 10 0 0 1 2 Other TRUE 1756 -Reddy_3400T Other 1 10 0 0 1 2 Other TRUE 1757 -Reddy_3401T BN2 0.652963901181276 0.33788621773464045 1722,960,590,1076,684,1317,863,1086,838 0.133,0.141,0.204,0.22,0.231,0.238,0.243,0.265,0.268 BN2,ST2,BN2,EZB,BN2,BN2,BN2,BN2,ST2 28.8710145259245,4.53890965872755,10.8054060559539 2 9.755117900326503 44.21533024060598 28.87101452592453 BN2 TRUE 1758 -Reddy_3403T Other 1 10 0 0 1 2 Other TRUE 1759 -Reddy_3404T Other 1 10 0 0 1 2 Other TRUE 1760 -Reddy_3405T EZB 0.5105706397666298 9.568847509367924 1099,1500 0.341,0.356 EZB,ST2 2.93131422116171,2.80993682786365 7 28.04929878433802 5.741251049025358 2.9313142211617116 Other FALSE 1761 -Reddy_3406T Other 1 10 0 0 1 2 Other TRUE 1762 -Reddy_3407T BN2 0.39948909227543883 1.7988811535150435 1763,1377,597,834 0.149,0.278,0.289,0.294 EZB,MCD,BN2,BN2 6.86163869769704,6.71505394415153,3.59934252500422 3 12.343272535516713 17.1760351668528 6.86163869769704 EZB FALSE 1763 -Reddy_3408T MCD 0.6647648570179695 2.9527014287423388 311,1278,2030 0.28,0.286,0.298 MCD,EZB,MCD 3.49561546363027,6.93173840069189 5 20.467353879391087 10.42735386432216 6.931738400691893 Other FALSE 1764 -Reddy_3409T BN2 1 0.39929691111425397 687,1957,1765,41,1973,519,2069,99 0.138,0.173,0.175,0.202,0.221,0.223,0.229,0.242 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 41.1747146118128 3 16.440936360507774 41.17471461181276 41.17471461181276 BN2 TRUE 1765 -Reddy_3411T Other 1 10 0 0 1 2 Other TRUE 1766 -Reddy_3412T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 1767 -Reddy_3417T EZB 0.8694942865169117 0.43028052064451977 1493,397,1703,1528,398,1972,2005,695 0.165,0.229,0.232,0.252,0.255,0.263,0.299,0.314 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB 28.6622051131032,4.30201967544659 3 12.332788538886055 32.96422478854978 28.662205113103184 Other FALSE 1768 -Reddy_3418T BN2 1 5.147197936145942 1980,804 0.173,0.191 BN2,BN2 11.0267977492679 9 56.75711061733031 11.026797749267871 11.026797749267871 Other FALSE 1769 -Reddy_3419T BN2 0.6806928796406567 4.255624494661825 52,1131,1381 0.198,0.244,0.271 BN2,ST2,BN2 8.74771034882729,4.10347498081106 8 37.22697043267616 12.851185329638351 8.747710348827292 Other FALSE 1770 -Reddy_3422T BN2 0.5648284276222134 6.0280893580196695 793,402 0.138,0.179 BN2,EZB 7.24135412495611,5.57909500760685 6 43.65152973829975 12.820449132562961 7.241354124956108 Other FALSE 1771 -Reddy_3423T BN2 0.6705915115894011 0.3896943901903497 1722,960,1317,684,590,1076,915,863,838 0.149,0.165,0.208,0.224,0.232,0.244,0.249,0.253,0.268 BN2,ST2,BN2,BN2,BN2,EZB,BN2,BN2,ST2 28.2752774744421,4.10456738788488,9.78483659542889 2 11.018717012865647 42.164681457755876 28.275277474442106 Other FALSE 1772 -Reddy_3429T EZB 1 10.56860305144766 2162 0.392 EZB 2.55059522295502 8 26.956228456330216 2.550595222955017 2.550595222955017 Other FALSE 1773 -Reddy_3430T BN2 0.6030418370232846 3.0937091887354953 1603,2096,596 0.177,0.217,0.25 MCD,BN2,BN2 8.60626595859229,5.66515839410977 5 26.62528407679847 14.271424352702061 8.606265958592292 Other FALSE 1774 -Reddy_3431T ST2 1 4.436233154188841 90,1946 0.18,0.22 ST2,ST2 10.1197905054313 8 44.89375015363964 10.119790505431268 10.119790505431268 Other FALSE 1775 -Reddy_3432T ST2 0.5179541203152043 0.8181657185378559 1497,1987,2004,608,722,619,1776,1176 0.187,0.19,0.201,0.279,0.28,0.28,0.28,0.304 ST2,BN2,ST2,BN2,ST2,BN2,BN2,ST2 15.9886896962126,17.1797085207111 3 14.05584856611851 33.16839821692366 17.179708520711085 BN2 FALSE 1776 -Reddy_3433T BN2 0.5821264846024939 1.0533187534215587 2170,1719,1657,114,1848 0.171,0.172,0.173,0.179,0.234 BN2,EZB,BN2,EZB,BN2 15.8906272252087,11.4069234712858 3 16.737895659943465 27.29755069649442 15.890627225208657 Other FALSE 1777 -Reddy_3434T EZB 0.5164350459746586 8.966462287468586 1535,1653 0.202,0.216 EZB,ST2 4.94054299531643,4.62608698811592 8 44.2991924471219 9.566629983432351 4.940542995316434 Other FALSE 1778 -Reddy_3438T EZB 1 0.09343740137144997 1726,1590,499,2072,1853,2013,1219,1540,1926 0.141,0.156,0.163,0.169,0.174,0.175,0.187,0.187,0.203 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.6432005703111 1 4.918843861165898 52.64320057031105 52.64320057031105 EZB TRUE 1779 -Reddy_3439T MCD 1 0 839,947,2039,416,483,587,1572,540,888,1947,707 0.144,0.146,0.155,0.165,0.167,0.174,0.178,0.178,0.19,0.196,0.196 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 64.7195270760112 0 0 64.71952707601118 64.71952707601118 Other FALSE 1780 -Reddy_3440T EZB 1 0 31 0.131 EZB 7.63894160747693 0 0 7.638941607476932 7.638941607476932 Other FALSE 1781 -Reddy_3441T EZB 0.9042572852695018 0.25356874159432297 1782,166,96,105,1967,341,505,1245,365 0.195,0.243,0.245,0.252,0.262,0.279,0.281,0.293,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,BN2 3.35178127606702,31.656430946678 2 8.027081358516718 35.008212222744994 31.65643094667798 EZB TRUE 1782 -Reddy_3444T Other 1 10 0 0 1 2 Other TRUE 1783 -Reddy_3446T EZB 1 12.711929835479935 1535 0.237 EZB 4.22380668385626 10 53.69273420381199 4.223806683856262 4.223806683856262 Other FALSE 1784 -Reddy_3447T MCD 1 4.82522514726196 751 0.204 MCD 4.90580540217528 4 23.671615594149745 4.905805402175282 4.905805402175282 Other FALSE 1785 -Reddy_3448T EZB 1 4.364856369419139 1397,2183 0.28,0.455 EZB,EZB 5.76655306611064 9 25.17017588020648 5.766553066110637 5.766553066110637 Other FALSE 1786 -Reddy_3449T EZB 0.7151193141451134 3.4924779790384464 1738,1177,1446 0.162,0.256,0.274 EZB,ST2,EZB 9.81585672719201,3.91032369198229 7 34.28166346511449 13.726180419174298 9.81585672719201 Other FALSE 1787 -Reddy_3450T EZB 1 0.1255902529728379 1788,1118,6,1142,196,438,367,1872,44,1991 0.135,0.145,0.148,0.151,0.168,0.196,0.206,0.208,0.227,0.234 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 57.1105839999725 1 7.172532691983061 57.11058399997255 57.11058399997255 EZB TRUE 1788 -Reddy_3451T ST2 0.4926452712758102 0.8407625315010147 486,1789,1130,1943,1341,1123,854 0.138,0.146,0.18,0.224,0.243,0.277,0.307 ST2,EZB,ST2,ST2,BN2,EZB,BN2 7.36632141288011,10.4488126878806,17.2986296864799 4 14.544039686703433 35.11376378724064 17.298629686479888 EZB FALSE 1789 -Reddy_3453T EZB 0.5966161046015607 5.1255564414380395 1500,1226,1099 0.245,0.288,0.389 ST2,EZB,EZB 6.03523190092938,4.08053911896272 7 30.933921745380914 10.115771019892094 6.035231900929378 Other FALSE 1790 -Reddy_3454T BN2 0.4690616022673212 0.7003624529486885 1719,1848,2170,114,1657,511 0.108,0.168,0.187,0.24,0.241,0.248 EZB,BN2,BN2,EZB,BN2,ST2 15.4487233793519,13.46140010921,4.02525920888514 2 10.819705800888652 32.93538269744702 15.448723379351906 BN2 TRUE 1791 -Reddy_3455T N1 1 1.3525106916740341 63,2175,849,2189,745 0.142,0.161,0.199,0.217,0.242 N1,N1,N1,N1,N1 27.0029158632595 6 36.521732411432794 27.002915863259453 27.002915863259453 Other FALSE 1792 -Reddy_3456T ST2 0.5240945775249176 5.7458147371639425 874,1793 0.235,0.259 ST2,BN2 3.85881958430136,4.24955531975513 5 24.41715758264247 8.108374904056493 4.249555319755133 BN2 FALSE 1793 -Reddy_3457T BN2 1 1.911229720839522 1096,20 0.176,0.249 BN2,BN2 9.69421694761379 3 18.52787555054566 9.694216947613786 9.694216947613786 Other FALSE 1794 -Reddy_3458T ST2 0.6552778286262717 1.1849524874882384 1847,1121,1822,1082,2017 0.115,0.172,0.191,0.208,0.218 ST2,ST2,EZB,BN2,ST2 4.8091968033561,5.23137283064757,19.0859863806318 4 22.61598703789631 29.126556014635486 19.085986380631816 Other FALSE 1795 -Reddy_3459T EZB 1 0.08789586499027306 78,164,326,366,1020,1094,329,1796,1041,86 0.175,0.196,0.202,0.203,0.206,0.221,0.235,0.244,0.26,0.261 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.0978538551811 1 4.051810738796336 46.09785385518109 46.09785385518109 EZB TRUE 1796 -Reddy_3460T Other 1 10 0 0 1 2 ST2 FALSE 1797 -Reddy_3461T MCD 1 0.5440544229754252 761,2130,2014,755,713,569,1911 0.207,0.254,0.266,0.283,0.29,0.302,0.31 MCD,MCD,MCD,MCD,MCD,MCD,MCD 26.0511705520176 4 14.17325456251232 26.05117055201759 26.05117055201759 Other FALSE 1798 -Reddy_3462T BN2 1 2.8069707491059015 1791,1799,1657 0.207,0.234,0.271 BN2,BN2,BN2 12.7971644447723 8 35.92126626797404 12.797164444772346 12.797164444772346 BN2 TRUE 1799 -Reddy_3463T BN2 1 2.4047250763090244 1980,1148,804 0.187,0.233,0.236 BN2,BN2,BN2 13.8692128641728 6 33.351643963144 13.869212864172782 13.869212864172782 Other FALSE 1800 -Reddy_3464T BN2 1 12.753867398548735 1807 0.32 BN2 3.12094190174053 9 39.80407917337329 3.1209419017405344 3.1209419017405344 Other FALSE 1801 -Reddy_3465T MCD 1 0.10324385995988875 156,788,767,1224,837,471,1491,1310,950,1694 0.103,0.121,0.125,0.129,0.136,0.14,0.144,0.148,0.158,0.166 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.247479058248 1 7.665596330264529 74.247479058248 74.247479058248 MCD TRUE 1802 -Reddy_3467T BN2 1 1.4483300980495688 20,1803,1096,523 0.16,0.209,0.23,0.35 BN2,BN2,BN2,BN2 18.2571970783188 7 26.442448034551703 18.257197078318754 18.257197078318754 BN2 TRUE 1803 -Reddy_3468T ST2 1 1.9931388527574783 1188,1638,828 0.193,0.236,0.38 ST2,ST2,ST2 12.0743251860937 7 24.06580664923154 12.07432518609371 12.07432518609371 Other FALSE 1804 -Reddy_3470T N1 1 3.380826041869617 262 0.149 N1 6.71030884901758 3 22.686386905746772 6.71030884901758 6.71030884901758 Other FALSE 1805 -Reddy_3471T BN2 1 0.08324418057570444 945,954,865,964,1996,638,1549,812,418,1213 0.148,0.166,0.185,0.185,0.187,0.188,0.21,0.221,0.244,0.247 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 51.711524316062 1 4.3046834680111985 51.711524316062025 51.711524316062025 BN2 TRUE 1806 -Reddy_3472T BN2 0.7886701804227201 2.610328530907274 1807,1854,1714,1003 0.203,0.251,0.309,0.315 BN2,BN2,ST2,BN2 12.0961675802608,3.24125467878462 7 31.57497134939025 15.33742225904537 12.096167580260754 BN2 TRUE 1807 -Reddy_3473T ST2 1 6.8253565072217555 90 0.238 ST2 4.20936908195414 5 28.730444654813752 4.209369081954139 4.209369081954139 Other FALSE 1808 -Reddy_3474T EZB 1 0.11722006976401672 227,474,1809,1579,1032,9,1017,364,168,1079 0.136,0.163,0.204,0.221,0.232,0.264,0.274,0.285,0.287,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 45.0045855410792 1 5.2754406568259595 45.00458554107918 45.00458554107918 EZB TRUE 1809 -Reddy_3475T Other 1 10 0 0 1 2 Other TRUE 1810 -Reddy_3476T MCD 0.8982305138735263 0.24219196373762825 1640,188,708,792,49,586,1736,1870,1273 0.123,0.15,0.153,0.158,0.176,0.177,0.178,0.187,0.19 MCD,MCD,MCD,MCD,MCD,MCD,EZB,MCD,MCD 5.62541223962583,49.650608635972 2 12.024978406314496 55.276020875597794 49.65060863597197 MCD TRUE 1811 -Reddy_3478T N1 1 1.368980322580875 891,814,745,2189,2175 0.2,0.236,0.246,0.259,0.32 N1,N1,N1,N1,N1 20.2839477996301 6 27.76832540195129 20.283947799630134 20.283947799630134 Other FALSE 1812 -Reddy_3479T EZB 1 10.041181541847445 2050 0.266 EZB 3.75288752583551 9 37.68342495304906 3.7528875258355114 3.7528875258355114 BN2 FALSE 1813 -Reddy_3480T EZB 1 0.2013864099479361 1814,651,1241,331,1437,2003,1066,408 0.147,0.178,0.217,0.218,0.308,0.332,0.352,0.353 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.5733959805581 2 6.761225686285062 33.57339598055809 33.57339598055809 EZB TRUE 1814 -Reddy_3481T BN2 0.46121382021977475 2.199314010555821 1123,854,1341,293,486,1776 0.144,0.207,0.25,0.266,0.287,0.3 EZB,BN2,BN2,ST2,ST2,BN2 12.161766137262,6.9577208504758,7.24955401685905 5 26.74754265878365 26.369041004596838 12.161766137261994 Other FALSE 1815 -Reddy_3482T EZB 1 0 1471 0.183 EZB 5.45348509672235 0 0 5.453485096722345 5.453485096722345 BN2 FALSE 1816 -Reddy_3483T N1 0.5453759236187435 9.398512378707471 999,1071 0.217,0.261 N1,ST2 4.60393505383718,3.83782934105689 8 43.27014059425395 8.441764394894069 4.603935053837176 Other FALSE 1817 -Reddy_3484T ST2 0.571221742696048 3.6668107647804113 537,1433 0.161,0.215 ST2,BN2 4.65187261880413,6.19726102909563 4 22.724183453642002 10.849133647899762 6.1972610290956345 Other FALSE 1818 -Reddy_3485T EZB 0.8277382907872816 1.6911767694186275 1221,2114,1879,202,727 0.176,0.214,0.223,0.236,0.252 EZB,EZB,EZB,EZB,ST2 19.0807123614448,3.97091225688315 6 32.26885748963426 23.051624618327928 19.080712361444782 Other FALSE 1819 -Reddy_3486T BN2 0.5681870116463736 10.228832884428286 1807,1603 0.247,0.325 BN2,MCD 4.05281713794147,3.08007582664437 9 41.45558921515025 7.13289296458584 4.052817137941471 Other FALSE 1820 -Reddy_3487T Other 1 10 0 0 1 2 Other TRUE 1821 -Reddy_3488T ST2 0.6431489230586498 2.136916457681326 2198,1822,1121,1847,1863 0.14,0.21,0.22,0.222,0.237 ST2,EZB,ST2,ST2,EZB 8.97794956321389,16.1808635757736 5 34.577153674566986 25.15881313898752 16.180863575773632 EZB FALSE 1822 -Reddy_3489T ST2 1 12.958413690394229 90 0.247 ST2 4.04505224971663 9 52.41746045108789 4.045052249716625 4.045052249716625 Other FALSE 1823 -Reddy_3490T Other 1 10 0 0 1 2 Other TRUE 1824 -Reddy_3495T ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 1825 -Reddy_3496T MCD 1 0.15621014511657066 918,1399,1905,1826,2036,738,786,1225,789 0.125,0.231,0.251,0.255,0.322,0.331,0.336,0.342,0.383 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 34.853079733927 2 5.444404642996144 34.853079733927 34.853079733927 MCD TRUE 1826 -Reddy_3498T N1 0.2834905986825798 3.1580946121045703 175,2127,2211,65 0.187,0.192,0.237,0.245 N1,ST2,BN2,EZB 4.22241357569006,4.08393788667711,5.34231652074967,5.19610692503235 3 16.871541020336778 18.844774908149198 5.342316520749674 Other FALSE 1827 -Reddy_3500T EZB 0.5312128087370616 1.686885887884803 844,383,561,141,2042 0.117,0.158,0.178,0.194,0.279 BN2,EZB,EZB,BN2,EZB 13.6933301328226,15.516789913188 4 26.17505392983014 29.21012004601058 15.516789913188026 Other FALSE 1828 -Reddy_3502T N1 0.4304125367668938 1.841427440913497 1008,464,1609,1064,659,176,743 0.192,0.205,0.208,0.245,0.251,0.258,0.266 N1,BN2,MCD,N1,MCD,N1,MCD 4.87729609423079,12.5569343297047,13.1742916193252 4 24.25950210242219 30.608522043260713 13.174291619325231 Other FALSE 1829 -Reddy_3503T BN2 0.5488782697091588 3.368169044552817 1813,70,1835,921 0.252,0.291,0.323,0.334 BN2,BN2,EZB,MCD 7.40466437419966,3.09419537700584,2.99168047025975 7 24.940161330482347 13.490540221465253 7.404664374199659 Other FALSE 1830 -Reddy_3504T EZB 1 9.489375977983654 2162 0.201 EZB 4.97554329717075 8 47.21480104158974 4.975543297170754 4.975543297170754 Other FALSE 1831 -Reddy_3505T N1 1 0.5367504201710752 2189,63,745,2175,891,814 0.156,0.16,0.166,0.207,0.215,0.218 N1,N1,N1,N1,N1,N1 32.7376847926325 3 17.571966067873706 32.737684792632486 32.737684792632486 Other FALSE 1832 -Reddy_3506T EZB 0.6376145055125348 2.039564772044877 816,2138,781,1400,911 0.182,0.193,0.206,0.23,0.271 EZB,EZB,MCD,EZB,ST2 15.0264020443461,4.84784529976042,3.69234644325939 6 30.647320260231393 23.566593787365903 15.026402044346085 Other FALSE 1833 -Reddy_3507T BN2 0.8069262618708926 2.4420962190060744 1799,1433,537,1791 0.135,0.187,0.253,0.269 BN2,BN2,ST2,BN2 16.5099890976539,3.95035514665759 7 40.31898195121215 20.460344244311514 16.50998909765392 Other FALSE 1834 -Reddy_3508T EZB 0.47063159843347335 3.6456159914904585 1532,921,1835,1294 0.192,0.207,0.216,0.233 BN2,MCD,EZB,EZB 5.19746390115178,8.92209760406105,4.83814901350034 7 32.526541703003666 18.957710518713167 8.92209760406105 EZB TRUE 1835 -Reddy_3510T BN2 0.6386408058348632 4.0360123220050905 1901,2166 0.154,0.272 BN2,EZB 6.49573785530804,3.67545351856054 6 26.216878024538158 10.171191373868579 6.495737855308038 Other FALSE 1836 -Reddy_3511T Other 1 10 0 0 1 2 Other TRUE 1837 -Reddy_3512T ST2 1 11.085423855047718 1028 0.318 ST2 3.14540109677373 8 34.86810435186871 3.145401096773725 3.145401096773725 Other FALSE 1838 -Reddy_3513T Other 1 10 0 0 1 2 Other TRUE 1839 -Reddy_3514T EZB 0.5388034137612049 5.3179359906210095 1500,1226,1099 0.195,0.333,0.334 ST2,EZB,EZB 5.99889222844036,5.1348386931292 6 31.901724885479684 11.133730921569558 5.998892228440364 Other FALSE 1840 -Reddy_3515T BN2 1 1.8405392451780023 1841,1813,70,1148 0.188,0.252,0.252,0.261 BN2,BN2,BN2,BN2 17.071100249764 7 31.420029968058635 17.071100249763997 17.071100249763997 BN2 TRUE 1841 -Reddy_3517T EZB 0.507899318486165 12.808888014266365 1099,1500 0.364,0.375 EZB,ST2 2.74949508707294,2.66396972179515 9 35.21797466609288 5.4134648088680954 2.749495087072943 Other FALSE 1842 -Reddy_3519T MCD 0.6854500822305918 4.876192474540915 81,751,401 0.19,0.219,0.222 MCD,MCD,EZB 4.50577751192874,9.81874542510111 8 47.87809255131105 14.324522937029846 9.818745425101106 Other FALSE 1843 -Reddy_3520T Other 1 10 0 0 1 2 Other TRUE 1844 -Reddy_3521T EZB 1 8.359832319780965 1395 0.245 EZB 4.08843099873702 7 34.178597600436106 4.088430998737019 4.088430998737019 Other FALSE 1845 -Reddy_3522T BN2 0.5917839373952027 1.9873410484502416 834,1763,286,147 0.138,0.239,0.343,0.355 BN2,EZB,BN2,MCD 10.1452729214723,4.18345312313437,2.81481618488974 5 20.162117324572694 17.143542229496454 10.145272921472344 Other FALSE 1846 -Reddy_3523T ST2 0.5715445818318989 1.2635014521167374 1121,1847,1082,2017,1863,1822 0.118,0.145,0.171,0.192,0.199,0.221 ST2,ST2,BN2,ST2,EZB,EZB 5.84535648776268,9.54642316095806,20.5320971329749 4 25.942334542515685 35.92387678169564 20.532097132974897 ST2 TRUE 1847 -Reddy_3525T BN2 0.5018797194703165 1.7819853784508366 1848,511,1719,2170 0.139,0.179,0.201,0.291 BN2,ST2,EZB,BN2 10.6265923403532,4.96528722057496,5.58170432400846 5 18.93643217326708 21.17358388493663 10.626592340353211 BN2 TRUE 1848 -Reddy_3527T EZB 0.6652119983455392 4.474184783061411 1395,724,1037 0.251,0.265,0.285 EZB,ST2,EZB 7.48396734721593,3.76653229174043 7 33.484652821841976 11.25049963895636 7.483967347215927 Other FALSE 1849 -Reddy_3528T EZB 0.7620750708540039 1.9114183997508813 1387,1488,901 0.14,0.154,0.235 EZB,EZB,N1 13.6345124674107,4.25678589527088 5 26.06125800184153 17.891298362681542 13.634512467410662 EZB TRUE 1850 -Reddy_3529T BN2 0.5882890001232716 1.8066821654336094 1657,114,2170,1719,1848 0.148,0.158,0.175,0.197,0.259 BN2,EZB,BN2,EZB,BN2 16.3191045424467,11.4208405169156 6 29.483435132685084 27.73994505936227 16.319104542446716 Other FALSE 1851 -Reddy_3531T ST2 1 0 1028 0.187 ST2 5.35353677087316 0 0 5.353536770873159 5.353536770873159 Other FALSE 1852 -Reddy_3532T EZB 1 0 1305,1219,1926,55,1180,1590,1726,1853,1457 0.141,0.163,0.193,0.2,0.206,0.207,0.208,0.22,0.222 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.9567483521824 0 0 46.95674835218238 46.95674835218238 EZB TRUE 1853 -Reddy_3535T BN2 0.7479166791683285 2.0981160877189637 1854,1807,1714,1003 0.237,0.25,0.25,0.275 BN2,BN2,ST2,BN2 11.8505860574022,3.99420840630713 5 24.863905255933584 15.84479446370932 11.850586057402191 BN2 TRUE 1854 -Reddy_3536T BN2 0.6970953667880958 3.398044640029846 1793,2058,874 0.199,0.223,0.242 BN2,BN2,ST2 9.507616651958,4.13128715508472 6 32.3073058036444 13.63890380704272 9.507616651958001 Other FALSE 1855 -Reddy_3537T EZB 0.8788006651178047 0.6782993368605525 1879,2114,1221,1268,136,1132,727 0.142,0.146,0.181,0.186,0.191,0.195,0.206 EZB,EZB,EZB,EZB,EZB,EZB,ST2 35.1933711796022,4.85367541074656 3 23.871640333011484 40.047046590348806 35.193371179602245 Other FALSE 1856 -Reddy_3538T MCD 0.8952757531819948 0.44446386295225276 1184,1543,986,1897,1977,647,485,1420 0.129,0.158,0.171,0.194,0.207,0.208,0.211,0.217 MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2 4.6167480750623,39.4680576441472 3 17.542125363739842 44.08480571920947 39.46805764414717 Other FALSE 1857 -Reddy_3540T Other 1 10 0 0 1 2 Other TRUE 1858 -Reddy_3542T EZB 0.5559474585072289 7.445875082030368 1535,1653 0.233,0.292 EZB,ST2 4.29014930455404,3.42667579987848 8 31.943915804968853 7.716825104432521 4.290149304554042 Other FALSE 1859 -Reddy_3545T MCD 1 2.5945875873626414 768,801,1994 0.165,0.182,0.198 MCD,MCD,MCD 16.6237870783757 7 43.13187160851316 16.623787078375738 16.623787078375738 Other FALSE 1860 -Reddy_3546T EZB 1 0.1767609058362126 654,25,1160,1539,2073,72,1423,1450,699,47 0.162,0.256,0.261,0.262,0.281,0.293,0.35,0.355,0.355,0.385 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 35.7562782669588 1 6.320312135799317 35.75627826695879 35.75627826695879 Other FALSE 1861 -Reddy_3547T ST2 0.8082020612309335 1.0409414269323842 2052,1884,1970,1128,339 0.172,0.182,0.199,0.201,0.216 ST2,ST2,ST2,EZB,ST2 4.97521390015758,20.9646576755634 4 21.822980675949914 25.939871575720964 20.964657675563387 Other FALSE 1862 -Reddy_3548T EZB 0.41796463993914135 2.787476542508923 1863,1082,2017,1983,1121 0.188,0.208,0.217,0.226,0.242 EZB,BN2,ST2,EZB,ST2 4.80853433735013,9.72743530178664,8.73737545341226 5 27.11499772250346 23.273345092549032 9.727435301786638 EZB TRUE 1863 -Reddy_3549T EZB 0.8853978303134309 0.30641681192932374 1864,809,1031,1550,473,1701,1011,2176,466 0.165,0.195,0.207,0.216,0.22,0.247,0.255,0.269,0.277 EZB,EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB 4.63967163233938,35.8453527352502 2 10.98361870761743 40.48502436758958 35.845352735250195 EZB TRUE 1864 -Reddy_3551T EZB 0.5551557933471828 0.16888061315703431 1865,371,381,213,216,1049,400,380,1107 0.202,0.204,0.225,0.231,0.255,0.283,0.289,0.296,0.302 BN2,EZB,EZB,BN2,EZB,ST2,EZB,EZB,ST2 9.27370777038507,20.1129514161345,6.84272301062574 1 3.396687567554438 36.22938219714533 20.112951416134514 BN2 FALSE 1865 -Reddy_3552T MCD 0.8151225084834663 1.7086658324716437 2030,285,1398,1194,952 0.247,0.283,0.283,0.306,0.307 MCD,MCD,MCD,MCD,BN2 3.2615797872919,14.3802637953825 6 24.571065409099116 17.641843582674426 14.380263795382522 Other FALSE 1866 -Reddy_3555T EZB 1 0 135,679,513,617,103,1080,1867,162,1040,1287,468 0.114,0.122,0.149,0.167,0.171,0.181,0.215,0.221,0.231,0.249,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 62.4562763092819 0 0 62.45627630928195 62.45627630928195 EZB TRUE 1867 -Reddy_3556T EZB 1 3.318394760797893 2050 0.125 EZB 8.00292817653142 3 26.556874932043687 8.002928176531416 8.002928176531416 Other FALSE 1868 -Reddy_3558T Other 1 10 0 0 1 2 Other TRUE 1869 -Reddy_3559T MCD 1 0.11361571288216786 1647,803,1459,518,534,792,535,783,1811,1640 0.113,0.121,0.144,0.162,0.181,0.186,0.192,0.198,0.206,0.208 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 61.0420977866528 1 6.9353414558535595 61.04209778665281 61.04209778665281 MCD TRUE 1870 -Reddy_3560T ST2 1 13.35873775598508 833 0.289 ST2 3.46318563666242 10 46.26378872046751 3.4631856366624207 3.4631856366624207 Other FALSE 1871 -Reddy_3561T EZB 1 0.1874668053281026 1142,1118,6,1872,79,1788,1991,196,367 0.134,0.146,0.167,0.172,0.176,0.197,0.203,0.239,0.239 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1286830309182 2 9.3974640631113 50.1286830309182 50.1286830309182 EZB TRUE 1872 -Reddy_3562T MCD 0.4199125665363848 1.4722456665836505 1008,464,1609,659,176,869,1064 0.171,0.178,0.189,0.216,0.257,0.258,0.27 N1,BN2,MCD,MCD,N1,MCD,N1 5.61331750635051,13.7974450266916,13.4471372447585 4 20.31322865047279 32.857899777800604 13.79744502669156 Other FALSE 1873 -Reddy_3563T BN2 1 3.9961997444805237 1148,1841 0.239,0.254 BN2,BN2 8.11307605169048 6 32.421472444716535 8.113076051690475 8.113076051690475 Other FALSE 1874 -Reddy_3565T ST2 1 2.9910757620445962 1710,1479 0.238,0.269 ST2,ST2 7.91280567993489 6 23.667801279022072 7.912805679934894 7.912805679934894 BN2 FALSE 1875 -Reddy_3566T BN2 1 1.1834162322318615 1148,804,1980,70,1841 0.111,0.147,0.177,0.195,0.202 BN2,BN2,BN2,BN2,BN2 31.5271583085902 6 37.30975089852928 31.52715830859023 31.52715830859023 Other FALSE 1876 -Reddy_3580T ST2 0.675509184959186 0.9627413489827835 629,2066,1152,1198,1174,1101,268 0.135,0.167,0.209,0.215,0.283,0.291,0.296 ST2,BN2,BN2,ST2,ST2,ST2,ST2 10.7670254785886,22.4142695828274 4 21.57914413463505 33.181295061416066 22.41426958282743 Other FALSE 1877 -Reddy_3582T ST2 1 13.119001165221853 1667 0.304 ST2 3.29142945097823 10 43.180266802628864 3.2914294509782254 3.2914294509782254 Other FALSE 1878 -Reddy_3586T EZB 0.8786813784259708 0.19670357065637847 202,1221,727,1132,2114,2087,1879,1896,351 0.128,0.187,0.191,0.22,0.23,0.234,0.248,0.263,0.271 EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 37.8340249873452,5.22370437425721 2 7.442087807313449 43.057729361602426 37.83402498734522 EZB TRUE 1879 -Reddy_3587T EZB 0.7039360332270582 5.858659300743166 1471,696,1122 0.314,0.342,0.389 EZB,EZB,BN2 2.57064706633212,6.11209502603337 8 35.80868237129645 8.68274209236549 6.112095026033371 Other FALSE 1880 -Reddy_3589T ST2 0.666760278165212 0.6064393276738677 701,618,1211,1018,1401,1284,1442 0.143,0.186,0.21,0.221,0.237,0.25,0.26 EZB,ST2,ST2,ST2,BN2,ST2,ST2 4.2257995100583,7.01283725482387,22.4867447802807 3 13.63684638612729 33.72538154516289 22.486744780280716 Other FALSE 1881 -Reddy_3590T EZB 0.5386540123283826 2.2305250173062015 1266,2042,1608,1052 0.165,0.192,0.202,0.213 EZB,EZB,N1,ST2 11.2634899061119,4.95506237825257,4.69188307547697 4 25.123496017758367 20.910435359841394 11.263489906111854 Other FALSE 1882 -Reddy_3592T BN2 0.6053772778656177 3.5424169127613623 1108,65 0.152,0.232 BN2,EZB 6.60023343512834,4.30244440966759 4 23.38077854877164 10.902677844795924 6.600233435128336 Other FALSE 1883 -Reddy_3593T ST2 0.8176928287459795 0.8608854486624752 1970,2052,1128,339,444,1884 0.156,0.165,0.168,0.203,0.206,0.222 ST2,ST2,EZB,ST2,ST2,ST2 5.96029378965802,26.7333942789981 4 23.014390128146125 32.693688068656115 26.733394278998098 ST2 TRUE 1884 -Reddy_3594T MCD 1 2.880664849275111 864,650,2030 0.227,0.242,0.266 MCD,MCD,MCD 12.3014137296049 7 35.43625012726297 12.301413729604862 12.301413729604862 Other FALSE 1885 -Reddy_3595T EZB 1 0.08827211228029339 349,2015,1886,413,1222,348,463,316,288,2001 0.119,0.139,0.154,0.158,0.159,0.178,0.206,0.211,0.211,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.120950724096 1 5.218731200435094 59.120950724096 59.120950724096 EZB TRUE 1886 -Reddy_3596T ST2 1 16.45731262983338 318 0.295 ST2 3.38706553465923 9 55.74199640162076 3.387065534659234 3.387065534659234 Other FALSE 1887 -Reddy_3597T BN2 0.7228770789152266 3.6123012675200195 2032,318 0.128,0.335 BN2,ST2 7.78707417562905,2.98526098723791 8 28.129257914897224 10.772335162866959 7.787074175629049 Other FALSE 1888 -Reddy_3600T EZB 1 0 2016,240,174,558,39,93,1138,382,153,1903,1673 0.112,0.128,0.149,0.18,0.191,0.21,0.227,0.229,0.241,0.259,0.26 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 59.5480543804017 0 0 59.54805438040171 59.54805438040171 EZB TRUE 1889 -Reddy_3601T MCD 1 1.0597773207335293 1063,753,650,1194,2053 0.145,0.177,0.203,0.214,0.221 MCD,MCD,MCD,MCD,MCD 26.7034850554977 4 28.29974784636316 26.70348505549767 26.70348505549767 MCD TRUE 1890 -Reddy_3602T MCD 1 0.6178677440746084 704,347,2053,717,465,864 0.148,0.244,0.268,0.269,0.29,0.3 MCD,MCD,MCD,MCD,MCD,MCD 25.0793403451142 3 15.495715441915035 25.07934034511422 25.07934034511422 Other FALSE 1891 -Reddy_3604T BN2 0.7774963695821668 1.1765252362064424 459,1003,1714,1854 0.177,0.178,0.234,0.273 BN2,BN2,ST2,BN2 14.9211961174052,4.27014251922919 4 17.55516378651286 19.19133863663444 14.921196117405248 Other FALSE 1892 -Reddy_3606T BN2 1 2.059048778465043 1803,20,1096 0.191,0.277,0.309 BN2,BN2,BN2 12.0724304871561 5 24.857723247683005 12.072430487156145 12.072430487156145 Other FALSE 1893 -Reddy_3607T BN2 0.579347624373424 9.835770330596299 1697,697 0.202,0.278 BN2,N1 4.96099016607975,3.6020727643071 9 48.79515988590724 8.563062930386852 4.960990166079753 Other FALSE 1894 -Reddy_3608T EZB 1 11.976788510795991 292 0.216 EZB 4.63367906823512 9 55.496594227154226 4.633679068235117 4.633679068235117 Other FALSE 1895 -Reddy_3609T EZB 0.9126129408660896 0 351,1896,527,1132,727,2001,1268,1516,348,2114,1879 0.145,0.159,0.165,0.208,0.215,0.225,0.232,0.241,0.25,0.258,0.259 EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB,EZB,EZB,EZB 48.6771507713303,4.66107027683347 0 0 53.33822104816373 48.67715077133026 EZB TRUE 1896 -Reddy_3610T MCD 1 0.5594109070752007 761,1911,755,1897,986,1543 0.145,0.163,0.218,0.228,0.239,0.27 MCD,MCD,MCD,MCD,MCD,MCD 29.9014711250353 4 16.7272090849389 29.90147112503526 29.90147112503526 MCD TRUE 1897 -Reddy_3612T MCD 1 5.522252714089882 879,1631 0.29,0.315 MCD,MCD 6.62635024290413 7 36.592380613387476 6.626350242904129 6.626350242904129 Other FALSE 1898 -Reddy_3613T EZB 0.6100818283601454 5.55170991161664 1471,724 0.21,0.328 EZB,ST2 4.76330546199823,3.04434466061179 6 26.444490145433264 7.807650122610018 4.7633054619982325 Other FALSE 1899 -Reddy_3614T Other 1 10 0 0 1 2 Other TRUE 1900 -Reddy_3616T BN2 0.546216492830402 2.2224569957537583 1901,2166 0.193,0.232 BN2,EZB 5.18000568408344,4.30342396711864 2 11.51233987063548 9.48342965120208 5.180005684083443 BN2 TRUE 1901 -Reddy_3617T Other 1 10 0 0 1 2 Other TRUE 1902 -Reddy_3618T EZB 1 0.13855175519850285 2063,108,172,1048,1138,117,1573,382,93,676 0.184,0.197,0.221,0.233,0.235,0.238,0.243,0.246,0.255,0.257 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.7945617774797 1 6.067813402419071 43.79456177747965 43.79456177747965 EZB TRUE 1903 -Reddy_3620T MCD 0.9204254335524926 0 1009,759,1449,764,1904,479,858,305,1376,1406,1689 0.115,0.116,0.126,0.131,0.135,0.144,0.145,0.154,0.16,0.166,0.179 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,BN2,MCD,MCD 6.24802841727307,72.2698787006336 0 0 78.51790711790663 72.26987870063356 MCD TRUE 1904 -Reddy_3622T MCD 1 0 1171,896,2207,455,1728,2010,485,214,1977,1565,777 0.147,0.218,0.221,0.227,0.253,0.254,0.259,0.272,0.297,0.312,0.32 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 45.4626641358264 0 0 45.46266413582643 45.46266413582643 MCD TRUE 1905 -Reddy_3623T Other 1 10 0 0 1 2 Other TRUE 1906 -Reddy_3624T MCD 1 0.8997891988249243 717,1907,704,864 0.149,0.191,0.225,0.227 MCD,MCD,MCD,MCD 20.7897101670169 4 18.706356654982525 20.789710167016906 20.789710167016906 MCD TRUE 1907 -Reddy_3625T ST2 1 7.691733336522279 1667 0.273 ST2 3.66394561111704 6 28.182092600233457 3.6639456111170436 3.6639456111170436 Other FALSE 1908 -Reddy_3627T EZB 0.5019183978492316 0.539364779184855 1909,1725,353,2183 0.252,0.268,0.303,0.32 EZB,ST2,BN2,EZB 3.30222317932801,7.09054718899012,3.73412200975327 1 3.824391418889452 14.126892378071405 7.090547188990122 EZB TRUE 1909 -Reddy_3628T BN2 0.634034431537761 0.7234544977864404 1722,960,1317,915,838,590,1076,684 0.159,0.178,0.18,0.234,0.237,0.244,0.245,0.258 BN2,ST2,BN2,BN2,ST2,BN2,EZB,BN2 24.1150374495502,4.082166315296,9.837067325231 3 17.44613230716552 38.03427109007716 24.11503744955017 Other FALSE 1910 -Reddy_3629T MCD 1 0.8543271553798658 755,467,761,713,2028,1897 0.108,0.185,0.202,0.207,0.209,0.229 MCD,MCD,MCD,MCD,MCD,MCD 33.5572260702027 4 28.668849490995306 33.55722607020265 33.55722607020265 MCD TRUE 1911 -Reddy_3630T ST2 0.5025709964804748 6.750394278411023 724,1471 0.27,0.273 ST2,EZB 3.66638444578616,3.70428437296969 4 25.00538003690197 7.37066881875585 3.704284372969691 Other FALSE 1912 -Reddy_3631T BN2 0.5224084189762277 8.484853282196651 19,641 0.247,0.271 BN2,MCD 4.0409273105209,3.69426064536996 7 34.28667535379136 7.735187955890858 4.040927310520901 Other FALSE 1913 -Reddy_3632T EZB 1 0.1976065171841884 1779,278,2132,1172,1570,1517,733,162,1914 0.148,0.151,0.152,0.16,0.179,0.185,0.194,0.205,0.205 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.1132296703206 2 10.297913814371771 52.113229670320635 52.113229670320635 EZB TRUE 1914 -Reddy_3634T Other 1 10 0 0 1 2 Other TRUE 1915 -Reddy_3635T EZB 1 2.719496554400507 1601,1395 0.177,0.294 EZB,EZB 9.05335100424728 6 24.620556861828845 9.05335100424728 9.05335100424728 Other FALSE 1916 -Reddy_3636T ST2 0.7178320048071831 0.2545992177095862 1045,1917,1939,2082,1487,1472,1363,1394,619 0.14,0.15,0.159,0.162,0.186,0.194,0.204,0.204,0.25 ST2,ST2,ST2,ST2,EZB,ST2,ST2,EZB,BN2 3.99964167427697,10.2810912775626,36.3300138200648 2 9.249593097966962 50.61074677190443 36.33001382006483 ST2 TRUE 1917 -Reddy_3637T EZB 0.3500157342474907 8.242879621322917 1733,1178,904 0.25,0.266,0.272 EZB,ST2,MCD 4.00667659399264,3.68119275930839,3.7592634523333 7 33.02655284585343 11.447132805634329 4.006676593992638 Other FALSE 1918 -Reddy_3638T MCD 1 2.5392616536040484 311,194 0.149,0.192 MCD,MCD 11.9440635049664 5 30.329102446372758 11.944063504966405 11.944063504966405 Other FALSE 1919 -Reddy_3639T EZB 1 0.11865389809301656 520,477,1450,699,1542,119,1668,1295,1539,1160 0.12,0.191,0.196,0.202,0.224,0.234,0.253,0.264,0.293,0.304 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 46.805271748772 1 5.553627944294737 46.80527174877198 46.80527174877198 Other FALSE 1920 -Reddy_3640T BN2 0.7131477208760549 3.867401354768396 1793,2058,874 0.188,0.229,0.257 BN2,BN2,ST2 9.67636592888127,3.89216362765225 7 37.42239070259016 13.568529556533516 9.676365928881266 Other FALSE 1921 -Reddy_3642T ST2 1 5.421524608805046 511 0.216 ST2 4.61921143570973 4 25.043168471973978 4.619211435709728 4.619211435709728 Other FALSE 1922 -Reddy_3643T Other 1 10 0 0 1 2 Other TRUE 1923 -Reddy_3644T ST2 0.748094092058749 2.9947509113455006 566,1667,1971 0.122,0.188,0.219 ST2,ST2,BN2 4.55939021922083,13.5401861523034 6 40.54948481939833 18.099576371524222 13.540186152303399 Other FALSE 1924 -Reddy_3645T EZB 0.6782179667063387 3.484515732458005 342,793,1257 0.193,0.217,0.22 EZB,BN2,EZB 4.61345984839668,9.7237602915059 6 33.88259571440273 14.33722013990258 9.723760291505897 Other FALSE 1925 -Reddy_3647T EZB 1 0.1432912890182315 1926,1219,1726,1590,1853,1305,2072,499,2013,1540 0.121,0.153,0.157,0.185,0.212,0.215,0.222,0.238,0.252,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.5319706019267 1 7.527373782217918 52.531970601926695 52.531970601926695 EZB TRUE 1926 -Reddy_3648T BN2 1 0.40345520109556426 1927,519,687,1765,41,1001,1006,1409 0.165,0.178,0.198,0.198,0.21,0.24,0.246,0.262 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 38.5848390228326 3 15.567253987196889 38.58483902283257 38.58483902283257 BN2 TRUE 1927 -Reddy_3649T MCD 1 0.5313583690284062 1514,1731,1802,471,895,950,247 0.17,0.174,0.184,0.24,0.24,0.244,0.245 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.5665261914417 4 17.835854611033724 33.566526191441675 33.566526191441675 Other FALSE 1928 -Reddy_3651T BN2 1 0.23121366808099333 1014,889,106,1935,111,1982,1632,943,579 0.16,0.175,0.178,0.204,0.225,0.225,0.237,0.244,0.253 BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 43.655747286719 2 10.093805462979162 43.655747286718956 43.655747286718956 BN2 TRUE 1929 -Reddy_3652T ST2 0.5582562300889267 10.149856753449912 1436,2166 0.245,0.309 ST2,EZB 3.23279480625978,4.08546303110727 8 41.46686453725406 7.318257837367047 4.085463031107269 Other FALSE 1930 -Reddy_3653T EZB 0.5591968776405299 9.227394132111657 1863,2198 0.212,0.269 EZB,ST2 4.71063609532383,3.71329523133136 9 43.46689586450444 8.423931326655183 4.710636095323825 Other FALSE 1931 -Reddy_3654T Other 1 10 0 0 1 2 Other TRUE 1932 -Reddy_3655T EZB 1 0.25112907340176294 639,42,121,17,80,333,1705,1537,155 0.18,0.193,0.194,0.203,0.203,0.204,0.209,0.209,0.219 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.7705495068777 2 11.243186613349947 44.77054950687768 44.77054950687768 EZB TRUE 1933 -Reddy_3656T ST2 1 3.7037580637313154 35,566 0.244,0.304 ST2,ST2 7.39789147666651 7 27.40000021131275 7.3978914766665085 7.3978914766665085 Other FALSE 1934 -Reddy_3659T BN2 0.9101477863898948 0.08905047208115915 579,961,1935,692,260,1982,892,933,232,667 0.161,0.169,0.187,0.188,0.198,0.209,0.212,0.223,0.248,0.248 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 45.4484019262146,4.48678728793037 1 4.04720164686367 49.93518921414493 45.44840192621456 BN2 TRUE 1935 -Reddy_3660T Other 1 10 0 0 1 2 Other TRUE 1936 -Reddy_3661T EZB 1 0 1809,78,168,86,645,1041,227,1094,329,1020,138 0.2,0.204,0.228,0.23,0.234,0.248,0.263,0.266,0.285,0.29,0.296 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.8421757373637 0 0 44.842175737363654 44.842175737363654 EZB TRUE 1937 -Reddy_3663T EZB 0.4699458248546141 4.807346469638401 1649,1848,511 0.183,0.322,0.327 EZB,BN2,ST2 3.10213797369911,5.46566527040595,3.06261239390353 6 26.275346641911277 11.630415638008596 5.465665270405953 Other FALSE 1938 -Reddy_3664T ST2 0.6264604777940087 0.1379647188286141 1917,1487,1363,1045,1939,2082,1394,619,1472,1139 0.141,0.142,0.15,0.154,0.161,0.175,0.185,0.191,0.192,0.215 ST2,EZB,ST2,ST2,ST2,ST2,EZB,BN2,ST2,BN2 9.8750826555099,12.4485577531196,37.4388186660998 1 5.165236090543932 59.76245907472932 37.43881866609982 ST2 TRUE 1939 -Reddy_3665T EZB 0.7416270271149038 2.6388338801240816 1178,2138,816,1733 0.261,0.27,0.272,0.275 ST2,EZB,EZB,EZB 11.0140989781048,3.83716530355456 6 29.064377542462847 14.851264281659313 11.014098978104753 Other FALSE 1940 -Reddy_3666T ST2 0.6093698926426129 0.19975117737644646 1139,1147,235,1484,234,1394,1176,619,1687,649 0.115,0.137,0.14,0.144,0.171,0.182,0.189,0.194,0.204,0.213 BN2,ST2,ST2,ST2,ST2,EZB,ST2,BN2,ST2,EZB 13.8311890171872,10.1934030776756,37.4775595375066 1 7.486186642812805 61.50215163236926 37.47755953750656 Other FALSE 1941 -Reddy_3667T ST2 0.9169468160855078 0.10664877189478816 1955,1442,1284,1998,1625,1942,2076,1661,1401,271 0.144,0.163,0.2,0.225,0.226,0.231,0.233,0.233,0.253,0.262 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,BN2,ST2 3.95621452468199,43.6784978152462 1 4.6582581502051985 47.63471233992822 43.67849781524623 ST2 TRUE 1942 -Reddy_3669T ST2 0.7786525602164894 0.5435441953860158 1130,1713,1943,1018,1211,1789,486,1401 0.165,0.19,0.19,0.198,0.198,0.199,0.21,0.256 ST2,ST2,ST2,ST2,ST2,EZB,ST2,BN2 3.90911287781137,5.03573833717399,31.4660576427538 3 17.103192983400625 40.41090885773919 31.466057642753828 ST2 TRUE 1943 -Reddy_3671T EZB 0.808511125339633 0.8649684107012838 160,388,1944,1439,1037,242 0.17,0.181,0.185,0.199,0.209,0.245 ST2,EZB,EZB,EZB,EZB,EZB 24.8400617323619,5.88315400808606 4 21.485868718362866 30.723215740447973 24.840061732361917 EZB TRUE 1944 -Reddy_3672T MCD 1 1.1219309992045825 755,467,2028,713,1897,761 0.132,0.146,0.201,0.212,0.223,0.241 MCD,MCD,MCD,MCD,MCD,MCD 32.7608623234953 5 36.75542700140282 32.76086232349528 32.76086232349528 Other FALSE 1945 -Reddy_3673T ST2 1 1.5326077420306168 1946,90 0.153,0.193 ST2,ST2 11.7466197453786 3 18.002960364456953 11.746619745378599 11.746619745378599 ST2 TRUE 1946 -Reddy_3674T MCD 1 0 798,2008,23,1331,1699,2034,21,846,59,707,905 0.116,0.126,0.13,0.139,0.148,0.154,0.154,0.154,0.16,0.176,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 75.3608957443108 0 0 75.36089574431075 75.36089574431075 MCD TRUE 1947 -Reddy_3675T Other 1 10 0 0 1 2 Other TRUE 1948 -Reddy_3677T ST2 1 5.106368790825767 2107 0.223 ST2 4.47838507032524 4 22.86828575640884 4.478385070325236 4.478385070325236 Other FALSE 1949 -Reddy_3679T Other 1 10 0 0 1 2 Other TRUE 1950 -Reddy_3680T N1 1 4.1916599646626675 901,262 0.216,0.231 N1,N1 8.94820584311807 7 37.507836188158585 8.948205843118075 8.948205843118075 Other FALSE 1951 -Reddy_3681T BN2 0.5718334639495825 4.123298535311059 1971,1667 0.13,0.174 BN2,ST2 7.68760481897778,5.75617786187557 4 31.69828969014131 13.443782680853353 7.6876048189777775 Other FALSE 1952 -Reddy_3682T ST2 0.5586431870017705 1.5624015688905857 1082,1121,2017,1847,1863,1822 0.157,0.159,0.16,0.165,0.231,0.25 BN2,ST2,ST2,ST2,EZB,EZB 6.38167104405022,8.32414331963691,18.6137446203182 5 29.082143797713883 33.31955898400534 18.61374462031822 Other FALSE 1953 -Reddy_3683T EZB 1 2.0470475273823494 1221,2087,202,2114 0.194,0.229,0.233,0.245 EZB,EZB,EZB,EZB 17.8971424365732 7 36.63630117199679 17.897142436573155 17.897142436573155 Other FALSE 1954 -Reddy_3684T ST2 1 0.3730553410793455 1955,271,1442,1625,1284,1998,606,1661 0.13,0.2,0.219,0.251,0.263,0.279,0.29,0.291 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 35.5209267872738 3 13.25127145808087 35.52092678727375 35.52092678727375 ST2 TRUE 1955 -Reddy_3685T EZB 0.5569145528153134 1.3105538211883916 1909,353,1725,2183 0.159,0.285,0.286,0.398 EZB,BN2,ST2,EZB 3.50905977600141,8.80626138501381,3.49726865280166 4 11.541079508513622 15.812589813816876 8.806261385013807 Other FALSE 1956 -Reddy_3686T BN2 1 1.28713954340516 523,2069,596,1096,20 0.197,0.274,0.282,0.29,0.298 BN2,BN2,BN2,BN2,BN2 19.0823537450307 6 24.56165208647458 19.082353745030716 19.082353745030716 BN2 TRUE 1957 -Reddy_3687T Other 1 10 0 0 1 2 Other TRUE 1958 -Reddy_3688T EZB 0.909035518975261 0.32676885008190487 1527,1959,165,1292,1446,109,1738,1177 0.144,0.197,0.207,0.246,0.257,0.268,0.279,0.311 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 32.1286777988413,3.21502124061908 3 10.498651098979396 35.343699039460375 32.128677798841295 EZB TRUE 1959 -Reddy_3689T EZB 0.7113274112417116 5.745369881448987 1471,696,1122 0.29,0.344,0.388 EZB,EZB,BN2 2.57881710547196,6.35454617839446 8 36.50921822362429 8.933363283866424 6.354546178394459 Other FALSE 1960 -Reddy_3691T EZB 1 3.2166968534257223 31 0.126 EZB 7.93225696065116 3 25.51566600589088 7.932256960651163 7.932256960651163 Other FALSE 1961 -Reddy_3692T EZB 1 0 1962,1033,1264,142,463,2215,1937,372,1163,316,1304 0.114,0.116,0.141,0.15,0.151,0.161,0.179,0.188,0.192,0.192,0.192 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 70.5698282736951 0 0 70.56982827369511 70.56982827369511 EZB TRUE 1962 -Reddy_3693T ST2 1 1.054964882659469 271,2198,1955,1077,1249 0.194,0.226,0.233,0.284,0.286 ST2,ST2,ST2,ST2,ST2 20.8859436114731 5 22.033937051309962 20.885943611473056 20.885943611473056 Other FALSE 1963 -Reddy_3694T EZB 1 0 72,1160,1964,1542,1289,699,477,1450,1539,520 0.126,0.172,0.19,0.194,0.205,0.21,0.216,0.224,0.26,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.1220133408542 0 0 50.1220133408542 50.1220133408542 EZB TRUE 1964 -Reddy_3696T BN2 1 2.039219086253074 1089 0.174 BN2 5.74950966553007 2 11.724509846545454 5.7495096655300735 5.7495096655300735 Other FALSE 1965 -Reddy_3697T EZB 1 16.330712458768197 1535 0.326 EZB 3.07139462108422 10 50.15806240433362 3.0713946210842153 3.0713946210842153 Other FALSE 1966 -Reddy_3698T EZB 1 1.2434423774906191 1967,532,1782,1182,96 0.21,0.278,0.338,0.358,0.367 EZB,EZB,EZB,EZB,EZB 16.8323935676635 5 20.930111476633247 16.832393567663452 16.832393567663452 EZB TRUE 1967 -Reddy_3699T MCD 1 0.09814576498778148 1422,837,1491,950,895,247,471,1968,156,788 0.126,0.128,0.131,0.14,0.147,0.151,0.151,0.153,0.156,0.167 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 69.5728986530488 1 6.828285360720867 69.57289865304881 69.57289865304881 MCD TRUE 1968 -Reddy_3700T BN2 0.6944038158730992 4.294147870948459 1799,1791,1719 0.233,0.272,0.285 BN2,BN2,EZB 7.96738608973568,3.50632115036351 8 34.21313401426284 11.473707240099188 7.967386089735681 Other FALSE 1969 -Reddy_3701T ST2 0.7308948055427646 0.871754885009075 444,629,1970,2066,339,1128,1198 0.246,0.26,0.304,0.305,0.322,0.324,0.336 ST2,ST2,ST2,BN2,ST2,EZB,ST2 3.27614508691657,3.0854343121294,17.2781701489952 4 15.062329231404563 23.639749548041188 17.278170148995223 ST2 TRUE 1970 -Reddy_3703T ST2 0.4737998128267772 3.4801002388401545 1863,1121,1082,1847 0.139,0.155,0.189,0.21 EZB,ST2,BN2,ST2 5.29797286207813,7.18276536134954,11.2378740607581 7 39.10892820289996 23.71861228418581 11.237874060758134 BN2 FALSE 1971 -Reddy_3704T EZB 1 0.08409978036861997 2089,695,172,676,1972,108,397,1573,398,382 0.181,0.188,0.192,0.253,0.254,0.278,0.288,0.292,0.292,0.298 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.1983734965811 1 3.4647741626068442 41.1983734965811 41.1983734965811 EZB TRUE 1972 -Reddy_3706T BN2 1 1.620440397572186 1957,2069,99,1973 0.181,0.208,0.233,0.257 BN2,BN2,BN2,BN2 18.5111659758562 6 29.996241153441208 18.51116597585624 18.51116597585624 BN2 TRUE 1973 -Reddy_3707T EZB 1 0.6393752332215447 1601,1395 0.176,0.229 EZB,EZB 10.0353175014784 1 6.416333467959972 10.035317501478355 10.035317501478355 Other FALSE 1974 -Reddy_3708T EZB 0.6602101781733728 1.8174192207697926 561,141,2042 0.168,0.175,0.194 EZB,BN2,EZB 5.71306698164546,11.1004648390942 3 20.174198158049077 16.813531820739666 11.100464839094208 EZB TRUE 1975 -Reddy_3709T ST2 0.6960737903534137 0.4871034354070237 1987,675,608,457,89,1411,2004,1472 0.199,0.219,0.232,0.266,0.297,0.305,0.311,0.313 BN2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 9.33121713422224,21.371028470304 2 10.409901386066412 30.70224560452629 21.371028470304047 Other FALSE 1976 -Reddy_3711T MCD 1 0 880,1598,2180,771,1239,518,1250,1240,929,783,1459 0.125,0.127,0.146,0.161,0.171,0.175,0.18,0.184,0.189,0.195,0.195 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.0510023257962 0 0 67.05100232579622 67.05100232579622 MCD TRUE 1977 -Reddy_3712T EZB 1 0.1393136390810201 172,108,2063,1138,676,382,2089,1573,93,117 0.199,0.206,0.222,0.226,0.228,0.229,0.235,0.249,0.255,0.258 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.6490366958355 1 6.080906144477835 43.64903669583554 43.64903669583554 Other FALSE 1978 -Reddy_3713T Other 1 10 0 0 1 2 Other TRUE 1979 -Reddy_3715T BN2 0.47756716559512885 4.291178056949658 1835,921,70,804 0.187,0.197,0.206,0.214 EZB,MCD,BN2,BN2 9.51101029242081,5.34019480006958,5.06433973054721 7 40.81343866625851 19.915544823037596 9.511010292420806 BN2 TRUE 1980 -Reddy_3716T MCD 1 0.5033064705444877 1286,2108,301,300,686,1325,223 0.105,0.193,0.247,0.25,0.272,0.28,0.281 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.5291538233315 4 16.87544007116421 33.529153823331534 33.529153823331534 Other FALSE 1981 -Reddy_3717T BN2 0.9250368615000537 0.0875572412805069 579,1935,1982,961,692,889,260,933,892,1014 0.135,0.149,0.161,0.203,0.206,0.212,0.248,0.267,0.269,0.27 BN2,BN2,BN2,BN2,BN2,BN2,BN2,MCD,BN2,BN2 46.2944189908856,3.75160719212929 1 4.053411613525852 50.046026183014895 46.29441899088561 BN2 TRUE 1982 -Reddy_3718T EZB 0.5584237776503348 2.0370013588074394 1983,2017,1082,1863 0.165,0.254,0.257,0.262 EZB,ST2,BN2,EZB 3.88697230586889,9.89596899047737,3.9383116060935 4 20.158102280318683 17.721252902439755 9.895968990477368 EZB TRUE 1983 -Reddy_3719T Other 1 10 0 0 1 2 Other TRUE 1984 -Reddy_3721T ST2 0.6769893575746951 1.4316783095114276 618,701,1211,1130,1018,1789 0.297,0.351,0.364,0.377,0.388,0.39 ST2,EZB,ST2,ST2,ST2,EZB 5.40907252206665,11.3367302832309 5 16.230550847283073 16.74580280529758 11.336730283230935 Other FALSE 1985 -Reddy_3724T Other 1 10 0 0 1 2 Other TRUE 1986 -Reddy_3725T ST2 0.5649700573742674 0.7872273609992654 1987,608,457,675,2004,1497,1411 0.131,0.183,0.266,0.286,0.29,0.307,0.327 BN2,BN2,ST2,ST2,ST2,ST2,ST2 13.0980324264527,17.0103144781219 3 13.390984976379478 30.108346904574624 17.010314478121874 BN2 FALSE 1987 -Reddy_3726T ST2 0.46326622273488227 0.5321864479856246 1789,486,1130,1943,1123,1341,854 0.177,0.177,0.208,0.266,0.267,0.281,0.284 EZB,ST2,ST2,ST2,EZB,BN2,BN2 7.07148013088333,9.38824885627481,14.2067386069391 2 7.5606337566871655 30.666467594097256 14.206738606939108 Other FALSE 1988 -Reddy_3727T ST2 1 13.341393549924467 1028 0.316 ST2 3.16916243018431 10 42.28104320472386 3.169162430184307 3.169162430184307 Other FALSE 1989 -Reddy_3728T EZB 0.7000711265710431 2.2809570032475452 1488,1387,901 0.184,0.201,0.224 EZB,EZB,N1 10.4137598957812,4.46152848639778 4 23.753338564420474 14.875288382178947 10.413759895781165 Other FALSE 1990 -Reddy_3729T EZB 0.8468368793471144 0 1272,2083,595,1600,1318,1293,207 0.152,0.172,0.179,0.179,0.2,0.207,0.222 EZB,BN2,EZB,EZB,EZB,EZB,EZB 5.80003638282943,32.0683248656599 0 0 37.868361248489336 32.0683248656599 EZB TRUE 1991 -Reddy_3730T EZB 1 1.0383006714722411 1649 0.159 EZB 6.28523311064902 1 6.525961759146445 6.285233110649025 6.285233110649025 Other FALSE 1992 -Reddy_3731T BN2 1 2.418537382974467 871,19 0.191,0.3 BN2,BN2 8.56957688048072 5 20.725842041716326 8.569576880480716 8.569576880480716 Other FALSE 1993 -Reddy_3732T MCD 0.8243629222113816 2.0051383473347064 1994,801,768,850 0.14,0.148,0.185,0.243 MCD,MCD,MCD,BN2 4.11348702837051,19.3068925416036 7 38.71299060303981 23.420379569974113 19.306892541603602 MCD TRUE 1994 -Reddy_3733T MCD 1 8.75522903198767 904 0.234 MCD 4.26621639480479 7 37.3517016365367 4.266216394804794 4.266216394804794 Other FALSE 1995 -Reddy_3734T BN2 0.8818328739938658 0 1212,836,1477,294,872,1213,669,88,454,560,1193 0.141,0.145,0.16,0.169,0.178,0.193,0.193,0.197,0.229,0.235,0.239 ST2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2,BN2 52.910239200485,7.09006330678245 0 0 60.00030250726746 52.910239200485016 BN2 TRUE 1996 -Reddy_3736T MCD 1 2.6373178084416176 1480,864,717 0.2,0.223,0.225 MCD,MCD,MCD 13.9195159514473 6 36.71018730363919 13.919515951447321 13.919515951447321 Other FALSE 1997 -Reddy_3737T ST2 1 0.09874233166295932 1625,1998,1661,1955,1942,606,1442,1284,2076,726 0.163,0.171,0.183,0.189,0.21,0.216,0.219,0.242,0.249,0.27 ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2,ST2 48.5747151620265 1 4.796380634962604 48.57471516202655 48.57471516202655 ST2 TRUE 1998 -Reddy_3739T ST2 0.37736637427566616 6.62003132474622 938,1388,2196 0.213,0.231,0.292 ST2,EZB,BN2 3.42747206662328,4.33037629395983,4.7018840407277 7 31.126619634941676 12.459732401310799 4.701884040727696 Other FALSE 1999 -Reddy_3740T N1 1 1.4444251571952305 814,891,745,2189,3 0.267,0.279,0.322,0.347,0.355 N1,N1,N1,N1,N1 16.1213352908538 5 23.286062261688574 16.12133529085384 16.12133529085384 Other FALSE 2000 -Reddy_3741T EZB 1 0.12869289843010714 2001,349,2015,413,527,348,1886,1222,1268,288 0.147,0.163,0.173,0.179,0.212,0.217,0.227,0.234,0.247,0.255 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.2574137190742 1 6.4677722391086885 50.257413719074194 50.257413719074194 EZB TRUE 2001 -Reddy_3742T Other 1 10 0 0 1 2 Other TRUE 2002 -Reddy_3743T EZB 1 0.1045143717222697 2003,1437,1066,1241,1570,1814,1180,1517,1172,55 0.133,0.238,0.25,0.261,0.268,0.288,0.288,0.29,0.291,0.297 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.474684769417 1 4.230186249332531 40.47468476941695 40.47468476941695 EZB TRUE 2003 -Reddy_3745T ST2 0.6244725775671172 0.7102068760587007 1987,608,457,675,1411,2004,1482,1497 0.161,0.164,0.235,0.267,0.296,0.321,0.335,0.338 BN2,BN2,ST2,ST2,ST2,ST2,ST2,ST2 12.29407084584,20.4440731921956 3 14.5195213557447 32.738144038035614 20.44407319219565 ST2 TRUE 2004 -Reddy_3746T EZB 1 0.080828250679757 2005,1528,1554,1162,1277,2093,1493,173,2201 0.148,0.163,0.219,0.22,0.258,0.26,0.276,0.281,0.313 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0856995149384 1 3.2400569690668553 40.085699514938405 40.085699514938405 EZB TRUE 2005 -Reddy_3747T EZB 1 0 691,780,250,368,116,118,275,28,433,44,5 0.149,0.179,0.193,0.194,0.214,0.217,0.217,0.234,0.235,0.24,0.267 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 52.9484857348115 0 0 52.94848573481151 52.94848573481151 EZB TRUE 2006 -Reddy_3748T ST2 0.5946471574745994 7.570664645537175 2216,292 0.137,0.202 ST2,EZB 4.96062391254049,7.27716843063184 9 55.09300175740374 12.23779234317233 7.277168430631844 Other FALSE 2007 -Reddy_3749T MCD 0.9096757616274521 0 1449,759,1009,764,1376,479,1904,858,305,310,1689 0.112,0.128,0.138,0.146,0.148,0.153,0.159,0.162,0.164,0.165,0.167 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 6.75252221968647,68.0061731355434 0 0 74.75869535522986 68.00617313554339 MCD TRUE 2008 -Reddy_3750T ST2 0.5185863937999452 11.661752299215479 1714,1971 0.317,0.342 ST2,BN2 2.92457683274495,3.15040068155825 9 36.73919239161199 6.074977514303206 3.1504006815582546 Other FALSE 2009 -Reddy_3753T MCD 1 0.2049532161899637 1728,214,997,1905,896,789,154,1531,738 0.139,0.228,0.251,0.251,0.258,0.258,0.259,0.271,0.316 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 38.0245257712155 2 7.793248850908769 38.024525771215465 38.024525771215465 MCD TRUE 2010 -Reddy_3754T ST2 1 4.0193769655082185 1653 0.222 ST2 4.50757968007316 3 18.117661936278953 4.507579680073158 4.507579680073158 Other FALSE 2011 -Reddy_3755T ST2 0.35286998421664073 6.737619590508876 1725,2183,353 0.237,0.243,0.276 ST2,EZB,BN2 3.61668401661341,4.11665399415288,4.21686955518162 6 28.411662925612163 11.950207565947922 4.216869555181624 Other FALSE 2012 -Reddy_3756T EZB 1 0.12630335128454967 354,1034,1060,443,205,662,660,149,319,548 0.188,0.214,0.227,0.24,0.269,0.287,0.288,0.3,0.303,0.308 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 39.1426544039348 1 4.9438484293899005 39.142654403934785 39.142654403934785 EZB TRUE 2013 -Reddy_3757T MCD 0.7695995475438903 0.5144387188982464 2014,291,761,2130,267,850,790,569 0.172,0.294,0.3,0.301,0.317,0.327,0.33,0.34 MCD,BN2,MCD,MCD,MCD,BN2,MCD,MCD 6.46714015848493,21.6019894354202 3 11.11289977081104 28.069129593905167 21.601989435420236 MCD TRUE 2014 -Reddy_3758T EZB 1 0 1516,1281,1046,1163,351,2215,1896,348,1264,463,2047 0.151,0.184,0.208,0.212,0.225,0.227,0.228,0.234,0.24,0.26,0.265 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.8502225347506 0 0 50.85022253475059 50.85022253475059 EZB TRUE 2015 -Reddy_3759T EZB 1 0 463,1264,1886,316,348,1962,1033,1222,1937,2215,142 0.113,0.142,0.148,0.168,0.169,0.169,0.171,0.186,0.188,0.194,0.204 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.1159237030782 0 0 67.11592370307821 67.11592370307821 EZB TRUE 2016 -Reddy_3760T ST2 0.6418584330957545 1.5812801844649582 1847,2017,1121,1082,1822 0.164,0.178,0.178,0.179,0.245 ST2,ST2,ST2,BN2,EZB 5.57714908096992,4.08755893588807,17.321011905013 5 27.3893729002787 26.985719921870995 17.321011905013005 ST2 TRUE 2017 -Reddy_3761T MCD 1 0.09172911799212197 253,823,905,910,2203,2018,1699,1904,764,305 0.132,0.138,0.155,0.155,0.162,0.175,0.188,0.194,0.207,0.207 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 59.7700483503636 1 5.4826538175253345 59.77004835036357 59.77004835036357 MCD TRUE 2018 -Reddy_3762T ST2 1 10.924863719683197 537 0.259 ST2 3.86661737681705 8 42.242267897885235 3.866617376817053 3.866617376817053 Other FALSE 2019 -Reddy_3763T BN2 0.4211002033327609 3.2244541547150756 1763,834,1377,597 0.192,0.27,0.303,0.404 EZB,BN2,MCD,BN2 6.17895925171209,5.19520863831705,3.29920249262387 5 19.923770830998208 14.673370382653005 6.178959251712092 Other FALSE 2020 -Reddy_3764T EZB 1 0.7773679546734579 1967,532,1037,1182,388,1269,242 0.221,0.238,0.245,0.266,0.268,0.286,0.299 EZB,EZB,EZB,EZB,EZB,EZB,EZB 27.1260718777164 4 21.086939013905607 27.126071877716406 27.126071877716406 Other FALSE 2021 -Reddy_3765T EZB 1 0.5499403168418484 1322,1011,1260,1701,1036,809,1031 0.134,0.187,0.229,0.238,0.249,0.251,0.272 EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0387925789297 4 18.16936405892869 33.038792578929666 33.038792578929666 Other FALSE 2022 -Reddy_3766T EZB 0.5828447784460639 1.4262753161449115 1480,1513,632 0.127,0.167,0.2 MCD,EZB,EZB 10.9742589338997,7.85452591540172 2 15.652314630403879 18.828784849301393 10.974258933899675 Other FALSE 2023 -Reddy_3767T ST2 1 13.842707473384138 90 0.289 ST2 3.46037304352841 10 47.90093179034772 3.460373043528409 3.460373043528409 Other FALSE 2024 -Reddy_3770T Other 1 10 0 0 1 2 Other TRUE 2025 -Reddy_3771T Other 1 10 0 0 1 2 Other TRUE 2026 -Reddy_3775T Other 1 10 0 0 1 2 Other TRUE 2027 -Reddy_3776T MCD 0.8600974355676998 1.1230925149004276 467,755,2028,713,1420,1897 0.133,0.195,0.204,0.231,0.238,0.251 MCD,MCD,MCD,MCD,BN2,MCD 4.20648824452881,25.8607822275886 5 29.0440509492748 30.067270472117443 25.860782227588636 MCD TRUE 2028 -Reddy_3777T MCD 0.7556785713863908 3.702380418756605 311,194,1513 0.148,0.219,0.274 MCD,MCD,EZB 3.65551602180337,11.3063972354432 8 41.86058373118855 14.961913257246522 11.306397235443153 Other FALSE 2029 -Reddy_3778T MCD 0.7356975478320327 0.8990237003267655 2030,1398,285,952,1194,1890,1278 0.19,0.22,0.233,0.238,0.27,0.285,0.29 MCD,MCD,MCD,BN2,MCD,MCD,EZB 4.20187015228481,3.45118600569777,21.3026197928367 4 19.15156007281026 28.955675950819295 21.302619792836715 MCD TRUE 2030 -Reddy_3779T MCD 0.8799859539513479 0.45337115396694433 2030,285,1398,1194,952,1890,1063,650 0.148,0.171,0.183,0.194,0.211,0.243,0.269,0.274 MCD,MCD,MCD,MCD,BN2,MCD,MCD,MCD 4.72939841054999,34.677642400596 3 15.72184275201126 39.40704081114602 34.67764240059603 Other FALSE 2031 -Reddy_3780T BN2 1 1.695535013368328 2064,1113,57,2032,106 0.3,0.301,0.309,0.339,0.35 BN2,BN2,BN2,BN2,BN2 15.6898825806378 6 26.602745271109264 15.689882580637832 15.689882580637832 BN2 TRUE 2032 -Reddy_3781T EZB 1 12.711929835479935 1535 0.237 EZB 4.22380668385626 10 53.69273420381199 4.223806683856262 4.223806683856262 Other FALSE 2033 -Reddy_3782T MCD 1 0 479,1689,305,310,764,1449,1009,709,1699,759,1904 0.128,0.131,0.131,0.139,0.143,0.144,0.159,0.162,0.169,0.174,0.176 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.1053767037571 0 0 74.10537670375706 74.10537670375706 MCD TRUE 2034 -Reddy_3783T ST2 1 3.897396336812171 451,1653 0.239,0.298 ST2,ST2 7.53208047378096 6 29.355502847088413 7.532080473780964 7.532080473780964 Other FALSE 2035 -Reddy_3784T MCD 1 0.10688699605329603 296,643,481,185,739,1968,465,900,246,1526 0.127,0.151,0.16,0.169,0.169,0.212,0.231,0.239,0.244,0.247 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 53.9637130460121 1 5.768019183370295 53.963713046012096 53.963713046012096 MCD TRUE 2036 -Reddy_3785T MCD 1 0.2340830453565609 185,643,296,481,739,246,1968,1422,465 0.121,0.126,0.135,0.151,0.157,0.203,0.207,0.221,0.23 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 55.1883172134067 2 12.918649361418144 55.18831721340666 55.18831721340666 Other FALSE 2037 -Reddy_3786T EZB 0.523554648672809 8.740176291507833 1535,1653 0.194,0.214 EZB,ST2 5.1434505000305,4.6806442970818 8 44.95466411691068 9.824094797112297 5.143450500030499 Other FALSE 2038 -Reddy_3787T MCD 1 0 839,416,1947,947,707,483,587,846,1572,2039,888 0.112,0.138,0.146,0.148,0.151,0.155,0.166,0.171,0.173,0.175,0.186 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 71.5483508810204 0 0 71.54835088102041 71.54835088102041 MCD TRUE 2039 -Reddy_3811T ST2 0.6984889860131895 4.604050353063143 874,1793,35 0.245,0.33,0.34 ST2,BN2,ST2 3.02829530785288,7.01543499509835 8 32.29941596607409 10.043730302951232 7.015434995098351 Other FALSE 2040 -Reddy_3812T BN2 0.6336376618908068 0.27439748467993624 666,1038,489,648,1320,1362,922,2041 0.134,0.165,0.213,0.274,0.286,0.298,0.312,0.319 BN2,ST2,BN2,ST2,BN2,BN2,BN2,ST2 22.2122023889683,12.8428515083742 1 6.0949724647345676 35.05505389734245 22.212202388968283 ST2 FALSE 2041 -Reddy_3814T EZB 0.7676844995166651 2.6255404257008723 2042,561,141 0.115,0.235,0.255 EZB,EZB,BN2 3.92545168884171,12.9716200979085 7 34.05751295389264 16.8970717867502 12.971620097908488 EZB TRUE 2042 -Reddy_3815T BN2 0.5887760295901914 0.6830508261573515 1356,330,1133,993,1012,1492,1509 0.163,0.179,0.213,0.216,0.246,0.268,0.297 EZB,BN2,BN2,BN2,BN2,EZB,EZB 18.9736101505745,13.2519037919357 3 12.95994008853742 32.225513942510176 18.973610150574498 Other FALSE 2043 -Reddy_3816T ST2 0.6917262119901566 0.13217062031806592 619,2004,1484,1176,1139,235,1363,1487,1497,234 0.145,0.151,0.16,0.185,0.191,0.193,0.201,0.226,0.228,0.233 BN2,ST2,ST2,ST2,BN2,ST2,ST2,EZB,ST2,ST2 12.1266198651347,4.42391342522315,37.1372401567618 1 4.908452068420195 53.687773447119675 37.137240156761806 Other FALSE 2044 -Reddy_3817T ST2 0.6799391482106795 1.6721809542903316 1427,1077,1822,744,271 0.13,0.172,0.227,0.273,0.279 ST2,ST2,EZB,BN2,ST2 3.65661070646129,4.40147001024855,17.118633872596 6 28.625453525224398 25.176714589305863 17.118633872596014 Other FALSE 2045 -Reddy_3819T BN2 0.6627676118740473 4.0073229790182285 793,402,737 0.206,0.218,0.241 BN2,EZB,BN2 9.00751667883378,4.58324502625928 7 36.09602857098055 13.590761705093053 9.007516678833777 Other FALSE 2046 -Reddy_3820T EZB 1 0 1281,2047,76,1048,1896,351,1516,2063,117,1046,1163 0.143,0.156,0.167,0.219,0.236,0.247,0.275,0.282,0.289,0.3,0.325 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.3256623339448 0 0 49.325662333944784 49.325662333944784 EZB TRUE 2047 -Reddy_3821T ST2 1 12.320296927630936 530 0.321 ST2 3.11466795360282 9 38.37363401936338 3.1146679536028214 3.1146679536028214 Other FALSE 2048 -Reddy_3822T EZB 0.7775103958891271 0.3537562863713823 2148,29,2091,4,1297,1103,1022,220,1563 0.106,0.206,0.224,0.23,0.232,0.248,0.265,0.274,0.276 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.0666951279721,9.46224763961426 2 11.697551271046079 42.52894276758632 33.06669512797207 Other FALSE 2049 -Reddy_3823T Other 1 10 0 0 1 2 EZB FALSE 2050 -Reddy_3824T MCD 0.8734416895482868 0.6236090819031643 465,246,185,296,2185,347,643 0.15,0.168,0.187,0.19,0.213,0.217,0.223 MCD,MCD,MCD,MCD,ST2,MCD,MCD 32.3471157226408,4.68697150918069 4 20.171955138011448 37.0340872318215 32.34711572264081 Other FALSE 2051 -Reddy_3825T ST2 1 0.3648517478978073 339,959,2052,1404,1101,1884,1198 0.125,0.139,0.168,0.168,0.208,0.243,0.244 ST2,ST2,ST2,ST2,ST2,ST2,ST2 40.0882806635767 3 14.626279270323824 40.08828066357669 40.08828066357669 ST2 TRUE 2052 -Reddy_3826T MCD 1 0.5694150439719238 2053,765,799,753,1526,1063,900 0.151,0.2,0.21,0.222,0.233,0.236,0.267 MCD,MCD,MCD,MCD,MCD,MCD,MCD 33.1581954268722 4 18.880775307022077 33.158195426872204 33.158195426872204 MCD TRUE 2053 -Reddy_3828T EZB 1 9.811210712042895 1294 0.16 EZB 6.26897266649864 8 61.50621177905561 6.2689726664986445 6.2689726664986445 Other FALSE 2054 -Reddy_3829T Other 1 10 0 0 1 2 Other TRUE 2055 -Reddy_3830T Other 1 10 0 0 1 2 Other TRUE 2056 -Reddy_3831T ST2 0.7478223082518661 2.775664169807983 2198,1863,1121,1847 0.173,0.2,0.21,0.235 ST2,EZB,ST2,ST2 4.99761428809763,14.8202143764976 7 41.13593803371758 19.817828664595243 14.820214376497612 Other FALSE 2057 -Reddy_3832T ST2 1 12.067462989262273 1057 0.34 ST2 2.94182824866223 9 35.500403511497694 2.9418282486622287 2.9418282486622287 BN2 FALSE 2058 -Reddy_3833T EZB 1 0.0910028492426778 149,404,1705,319,121,80,2059,187,17,333 0.106,0.154,0.161,0.186,0.187,0.204,0.205,0.217,0.219,0.223 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.3054859561426 1 5.1239596500025595 56.305485956142626 56.305485956142626 EZB TRUE 2059 -Reddy_3834T ST2 0.7024816133801862 0.42765981724665214 701,1211,1018,618,1401,1713,1284,1442 0.127,0.153,0.166,0.194,0.203,0.227,0.236,0.261 EZB,ST2,ST2,ST2,BN2,ST2,ST2,ST2 4.91577689147235,7.86124935525195,30.1683069539814 3 12.901772638580608 42.94533320070574 30.168306953981443 ST2 TRUE 2060 -Reddy_3835T EZB 1 0.14203691126126566 654,47,25,1691,1220,1539,2061,385,1160,137 0.24,0.263,0.27,0.275,0.284,0.3,0.342,0.344,0.349,0.349 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 33.7212296602757 1 4.789659304877343 33.721229660275725 33.721229660275725 EZB TRUE 2061 -Reddy_3837T ST2 0.5100885034909277 0.7441534229226979 1152,268,1174,1198,2066,531,171,629 0.143,0.144,0.166,0.212,0.215,0.218,0.223,0.256 BN2,ST2,ST2,ST2,BN2,BN2,BN2,ST2 20.7040115583701,21.5567063588439 3 16.04149682387318 42.26071791721397 21.556706358843908 Other FALSE 2062 -Reddy_3838T EZB 1 0.16850063290653075 2063,1138,382,108,93,676,172,558,2089,1048 0.2,0.204,0.215,0.225,0.227,0.228,0.232,0.253,0.253,0.259 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.8411996637193 1 7.387269890718285 43.841199663719294 43.841199663719294 EZB TRUE 2063 -Reddy_3839T BN2 1 0.7230467193665451 2064,57,106,817,1113,1014 0.132,0.136,0.236,0.263,0.279,0.311 BN2,BN2,BN2,BN2,BN2,BN2 29.7508693125288 5 21.511268454726753 29.750869312528778 29.750869312528778 BN2 TRUE 2064 -Reddy_3840T BN2 1 4.625293239751589 1841,1148 0.233,0.248 BN2,BN2 8.32699185295415 8 38.51477912493538 8.326991852954148 8.326991852954148 Other FALSE 2065 -Reddy_3842T ST2 0.5147442186052977 0.6838674892414122 268,531,1152,1198,1101,2066,1174,171 0.17,0.188,0.191,0.196,0.222,0.226,0.23,0.268 ST2,BN2,BN2,ST2,ST2,BN2,ST2,BN2 18.7022052578452,19.8387168185329 3 13.567053460461498 38.54092207637815 19.83871681853294 BN2 FALSE 2066 -Reddy_3843T EZB 0.39152512696945546 2.4241001269933213 353,1725,1909,1738,2183 0.119,0.165,0.309,0.324,0.334 BN2,ST2,EZB,EZB,EZB 8.40734163000481,9.31882361687694,6.0751771829336 5 22.58976151309976 23.80134242981535 9.318823616876942 Other FALSE 2067 -Reddy_3845T EZB 0.8632165054867962 0.8460787045751229 1053,62,1453,2126,179,1087 0.174,0.185,0.192,0.224,0.226,0.25 EZB,EZB,EZB,EZB,EZB,BN2 4.00345129165954,25.2650748993648 4 21.376241841848028 29.268526191024346 25.265074899364805 Other FALSE 2068 -Reddy_3846T BN2 0.8643926213901894 1.6025995275045042 523,596,2069,2096,1603 0.123,0.188,0.189,0.25,0.28 BN2,BN2,BN2,BN2,MCD 22.7412292832006,3.56768257056584 6 36.44508330412882 26.308911853766404 22.741229283200568 BN2 TRUE 2069 -Reddy_3847T Other 1 10 0 0 1 2 Other TRUE 2070 -Reddy_3848T EZB 0.5850133457006871 2.002581333648861 1816,1909,115,1666,591 0.367,0.395,0.484,0.525,0.529 BN2,EZB,EZB,EZB,BN2 4.61720500352681,6.50894798402592 6 13.034697734501695 11.126152987552734 6.50894798402592 Other FALSE 2071 -Reddy_3849T EZB 1 0.21378634664867666 2072,2013,56,75,1726,499,1926,1590,178 0.13,0.181,0.188,0.215,0.217,0.234,0.234,0.241,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.4767079105922 2 9.5085128951658 44.47670791059219 44.47670791059219 EZB TRUE 2072 -Reddy_3851T EZB 1 0.07499392508992421 2073,1423,25,654,210,693,390,396,877,216 0.161,0.19,0.218,0.22,0.279,0.292,0.302,0.302,0.317,0.383 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 40.0150638541714 1 3.0008867011482625 40.01506385417138 40.01506385417138 EZB TRUE 2073 -Reddy_3852T ST2 0.5237129885175699 9.859182935281646 1653,1535 0.217,0.239 ST2,EZB 4.19195920717422,4.60937088605436 9 45.44463078217121 8.801330093228582 4.6093708860543625 Other FALSE 2074 -Reddy_3854T EZB 0.8937323848497506 0.35679208764364745 207,1272,1745,1293,1318,595,2083,1600 0.137,0.187,0.205,0.209,0.237,0.242,0.245,0.272 EZB,EZB,EZB,EZB,EZB,EZB,BN2,EZB 4.08559048895775,34.3606518886587 2 12.259608720151164 38.446242377616414 34.360651888658666 Other FALSE 2075 -Reddy_3856T ST2 0.8746538067685165 0.3262916625965437 1713,1018,1401,2076,1943,1211,1284,726 0.119,0.181,0.185,0.196,0.198,0.202,0.232,0.234 ST2,ST2,BN2,ST2,ST2,ST2,ST2,ST2 5.39514415796127,37.646802461014 2 12.283837766447903 43.041946618975246 37.646802461013976 ST2 TRUE 2076 -Reddy_3859T N1 0.8458551560391431 1.653892697999731 2077,697,3,1697 0.149,0.15,0.183,0.291 N1,N1,N1,BN2 3.43280301751237,18.8371797422359 6 31.154674026592357 22.269982759748235 18.837179742235868 N1 TRUE 2077 -Reddy_3861T ST2 0.5350772389993116 9.551288370939043 2107,674 0.29,0.334 ST2,BN2 2.99374953658493,3.44549110231444 8 32.90887909770986 6.439240638899374 3.44549110231444 Other FALSE 2078 -Reddy_3865T N1 1 2.076967633341159 901 0.177 N1 5.6640931559225 2 11.764138157080218 5.664093155922504 5.664093155922504 Other FALSE 2079 -Reddy_3867T ST2 0.5937631884332601 3.624033326431361 1500,1099,1226,2080 0.165,0.326,0.371,0.422 ST2,EZB,EZB,ST2 5.76207382821553,8.42195298607746 7 30.52143829518284 14.184026814292988 8.421952986077462 ST2 TRUE 2080 -Reddy_3871T EZB 1 1.7280710161971964 2087,1221,202,2114 0.203,0.209,0.229,0.268 EZB,EZB,EZB,EZB 17.796949319512 6 30.754392295779073 17.79694931951198 17.79694931951198 Other FALSE 2081 -Reddy_3872T ST2 0.8408352331482096 0.9968576487857301 1045,2082,1128,1884,1917,1939 0.199,0.199,0.232,0.234,0.236,0.239 ST2,ST2,EZB,ST2,ST2,ST2 4.30607421568748,22.7481181213463 5 22.676635544745356 27.0541923370338 22.748118121346323 ST2 TRUE 2082 -Reddy_3873T EZB 0.7957379076644695 0.5616503312837695 2083,1318,1600,1272,207,595,1293 0.126,0.133,0.147,0.176,0.246,0.275,0.302 BN2,EZB,EZB,EZB,EZB,EZB,EZB 7.9552963175933,30.991217088945 4 17.40622734489319 38.94651340653832 30.991217088945017 BN2 FALSE 2083 -Reddy_3875T MCD 0.7696189712027001 0.1307718184078722 790,749,2084,1503,1732,883,758,1127,1271 0.175,0.179,0.2,0.233,0.238,0.239,0.248,0.27,0.273 MCD,MCD,BN2,BN2,MCD,MCD,MCD,MCD,MCD 9.31024348139475,31.1021269728858 1 4.067281700596802 40.412370454280534 31.102126972885777 BN2 FALSE 2084 -Reddy_3876T BN2 1 0.5768061123683302 2085,423,1496,1632,100,99,943 0.14,0.244,0.244,0.254,0.266,0.27,0.295 BN2,BN2,BN2,BN2,BN2,BN2,BN2 30.1259904090527 4 17.376855409091313 30.12599040905274 30.12599040905274 BN2 TRUE 2085 -Reddy_3879T MCD 0.78200755096723 1.642304946514413 246,2185,185,643,296 0.118,0.158,0.185,0.225,0.227 MCD,ST2,MCD,MCD,MCD 22.6874529975698,6.32435509751664 6 37.25971628172214 29.011808095086444 22.687452997569807 Other FALSE 2086 -Reddy_3880T EZB 1 1.3887944252813162 2087,202,2183,1221,110 0.167,0.268,0.273,0.32,0.32 EZB,EZB,EZB,EZB,EZB 19.6378850079833 5 27.272985223402774 19.63788500798332 19.63788500798332 EZB TRUE 2087 -Reddy_3881T EZB 1 0.2327269277453198 2015,413,349,2001,1222,1886,288,348,527 0.136,0.139,0.153,0.18,0.204,0.21,0.215,0.221,0.252 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 49.4469317146641 2 11.507632504386391 49.44693171466409 49.44693171466409 Other FALSE 2088 -Reddy_3882T EZB 1 0.09903500855490226 2089,676,172,695,382,1138,108,287,558,93 0.149,0.195,0.199,0.206,0.234,0.251,0.265,0.274,0.284,0.292 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 44.3787663688231 1 4.395051506992403 44.378766368823086 44.378766368823086 EZB TRUE 2089 -Reddy_3883T BN2 0.6464910058905083 4.953757382900154 175,2211,894 0.264,0.275,0.304 N1,BN2,BN2 6.92205341803465,3.78506138319893 6 34.29017322441838 10.70711480123358 6.922053418034648 Other FALSE 2090 -Reddy_3885T EZB 0.9219512054213446 0 29,1103,2091,1022,1563,1279,1,2148,4,1110,2217 0.108,0.165,0.165,0.171,0.177,0.206,0.209,0.211,0.216,0.224,0.227 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2,EZB,EZB,EZB 56.0447899040595,4.74453340773796 0 0 60.78932331179748 56.04478990405953 EZB TRUE 2091 -Reddy_3886T EZB 1 1.2481004710715147 2087,202,2183,1221,110 0.151,0.248,0.293,0.303,0.313 EZB,EZB,EZB,EZB,EZB 20.5335784904097 5 25.627968986664328 20.53357849040975 20.53357849040975 Other FALSE 2092 -Reddy_3887T EZB 1 0.2691915671587876 2093,2005,1162,1528,1554,1277,399,1042 0.13,0.195,0.211,0.225,0.232,0.266,0.318,0.327 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 36.2801407886664 3 9.766307955642548 36.28014078866635 36.28014078866635 EZB TRUE 2093 -Reddy_3888T MCD 1 0 540,2094,621,705,516,947,1169,1572,888,587,515 0.132,0.138,0.15,0.161,0.163,0.174,0.175,0.18,0.183,0.184,0.185 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 67.177139986775 0 0 67.17713998677503 67.17713998677503 MCD TRUE 2094 -Reddy_3889T EZB 1 1.1727551274641788 2087,202,110,1221,2183 0.158,0.238,0.293,0.301,0.316 EZB,EZB,EZB,EZB,EZB 20.43071267414 5 23.96022304634504 20.430712674139976 20.430712674139976 Other FALSE 2095 -Reddy_3892T BN2 0.6542733643457493 2.843389827301343 2096,1603,596 0.23,0.239,0.281 BN2,MCD,BN2 7.91212161624164,4.1808689400123 5 22.497246115992546 12.092990556253943 7.912121616241642 BN2 TRUE 2096 -Reddy_3893T EZB 1 0 1485,1889,1085,644,39,1081,1560,240,174,2016,1109 0.15,0.165,0.178,0.208,0.23,0.238,0.243,0.269,0.271,0.285,0.294 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 50.154232372604 0 0 50.154232372604014 50.154232372604014 EZB TRUE 2097 -Reddy_3894T MCD 0.8213000161913456 0.7619769924187101 1994,801,850,569,768,2130 0.131,0.156,0.175,0.227,0.251,0.259 MCD,MCD,BN2,MCD,MCD,MCD 5.72904208416154,26.3305136139296 4 20.06324557238199 32.05955569809116 26.330513613929618 Other FALSE 2098 -Reddy_3895T ST2 0.5069083357223804 2.3974616745825363 2216,115 0.165,0.169 ST2,EZB 5.9073913110664,6.07291932695816 2 14.55959133921376 11.980310638024562 6.07291932695816 Other FALSE 2099 -Reddy_3896T EZB 1 0.10368298776641943 660,289,443,192,1034,548,354,498,930,2100 0.149,0.151,0.198,0.2,0.257,0.26,0.294,0.316,0.354,0.355 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.3619291605728 1 4.4958943706840175 43.36192916057282 43.36192916057282 EZB TRUE 2100 -Reddy_3898T EZB 0.6432412657386634 1.7891985035583582 1392,1122,1703,1493,696,1528 0.236,0.247,0.262,0.297,0.303,0.306 EZB,BN2,ST2,EZB,EZB,EZB 4.04270596485868,14.1678989128371,3.81518803620791 5 25.349183533414188 22.025792913903665 14.167898912837076 Other FALSE 2101 -Reddy_3899T ST2 1 14.016899216734158 1028 0.257 ST2 3.88925809517028 10 54.51533874786924 3.889258095170277 3.889258095170277 Other FALSE 2102 -Reddy_3900T Other 1 10 0 0 1 2 Other TRUE 2103 -Reddy_3901T EZB 1 0.08399829957132808 43,83,435,2104,47,137,1511,2006,1220,138 0.186,0.2,0.204,0.225,0.233,0.247,0.257,0.277,0.28,0.29 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 42.6023385724786 1 3.5785239978502057 42.60233857247863 42.60233857247863 EZB TRUE 2104 -Reddy_3902T EZB 1 0 1649 0.12 EZB 8.34708690234558 0 0 8.347086902345577 8.347086902345577 Other FALSE 2105 -Reddy_3903T Other 1 10 0 0 1 2 Other TRUE 2106 -Reddy_3904T ST2 1 12.517929401145567 2107 0.348 ST2 2.87482977861706 9 35.986916209039315 2.8748297786170616 2.8748297786170616 ST2 TRUE 2107 -Reddy_3905T MCD 1 0.5183825607303053 1286,2108,301,300,223,686,1325 0.137,0.169,0.232,0.24,0.302,0.304,0.309 MCD,MCD,MCD,MCD,MCD,MCD,MCD 31.5114247885973 4 16.33497307417347 31.51142478859726 31.51142478859726 MCD TRUE 2108 -Reddy_3906T EZB 0.7910405699515841 0.9353420781409004 160,1439,1944,388,242,1037 0.153,0.161,0.175,0.206,0.243,0.262 ST2,EZB,EZB,EZB,EZB,EZB 24.6945286687276,6.52324903670543 5 23.097831763717732 31.21777770543305 24.694528668727617 Other FALSE 2109 -Reddy_3907T Other 1 10 0 0 1 2 Other TRUE 2110 -Reddy_3908T ST2 1 11.303636107032387 1710 0.141 ST2 7.07152247181114 10 79.93391674405528 7.071522471811137 7.071522471811137 Other FALSE 2111 -Reddy_3909T ST2 0.7259532558056461 3.2098197821623047 35,1971,1714 0.167,0.278,0.283 ST2,BN2,ST2 3.60148565790489,9.5403805901054 7 30.622902347477606 13.141866248010295 9.540380590105404 Other FALSE 2112 -Reddy_3910T EZB 1 3.2790067298217553 1099 0.278 EZB 3.59719503638313 2 11.795226732781707 3.597195036383133 3.597195036383133 Other FALSE 2113 -Reddy_3911T EZB 0.9003651192367199 0.39232640703814087 1268,136,1879,2114,2001,527,1132,727 0.156,0.163,0.184,0.202,0.206,0.215,0.216,0.244 EZB,EZB,EZB,EZB,EZB,EZB,EZB,ST2 37.0511067880796,4.10009509265241 3 14.536127602953755 41.15120188073204 37.051106788079636 EZB TRUE 2114 -Reddy_3912T N1 1 1.2817275503178773 63,2175,2189,745,849 0.148,0.195,0.205,0.224,0.242 N1,N1,N1,N1,N1 25.3467730251452 6 32.48765729798267 25.346773025145247 25.346773025145247 Other FALSE 2115 -Reddy_3913T Other 1 10 0 0 1 2 Other TRUE 2116 -Reddy_3914T EZB 0.6115935189150578 1.7745877170711999 628,1521,2196,1002,1260 0.213,0.28,0.336,0.344,0.369 EZB,BN2,BN2,EZB,EZB 6.55293849054757,10.3184032858387 6 18.31091173083648 16.87134177638628 10.318403285838707 Other FALSE 2117 -Reddy_3915T EZB 1 2.0787542309613727 2050 0.123 EZB 8.12902987177093 2 16.8982552395552 8.129029871770928 8.129029871770928 Other FALSE 2118 -Reddy_3916T MCD 1 0.10610230098041583 1911,1543,986,1897,1184,761,485,755 0.129,0.189,0.198,0.204,0.21,0.236,0.267,0.277 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 39.3561217794167 1 4.1757750784615695 39.3561217794167 39.3561217794167 Other FALSE 2119 -Reddy_3917T BN2 0.6171037559920607 1.775963846057334 52,1131 0.13,0.21 BN2,ST2 7.69107573073125,4.77210514614904 2 13.659072435067692 12.46318087688029 7.691075730731251 Other FALSE 2120 -Reddy_3918T EZB 0.37399782594101133 2.0952165041828446 597,1763,1185,1377,15 0.233,0.286,0.318,0.343,0.371 BN2,EZB,ST2,MCD,EZB 4.29147746280812,6.18731967504973,2.91945381713723,3.14548070836926 4 12.963774299819436 16.543731663364337 6.187319675049733 BN2 FALSE 2121 -Reddy_3919T Other 1 10 0 0 1 2 Other TRUE 2122 -Reddy_3920T Other 1 10 0 0 1 2 Other TRUE 2123 -Reddy_3922T EZB 0.8623736246640167 0.993062939302499 1967,96,166,1782,365,532 0.161,0.244,0.289,0.301,0.309,0.316 EZB,EZB,EZB,EZB,BN2,EZB 3.23538620555872,20.2730888063005 5 20.132453158725347 23.5084750118592 20.273088806300482 Other FALSE 2124 -Reddy_3923T EZB 0.7008764611318922 2.810864156889439 1521,628,1002,402 0.226,0.27,0.294,0.306 BN2,EZB,EZB,EZB 4.4268114000635,10.3724632301267 6 29.155585112216823 14.799274630190215 10.37246323012671 Other FALSE 2125 -Reddy_3924T EZB 0.8027642124665704 1.1350803563506915 1453,1087,2126,62,179,1053 0.16,0.162,0.189,0.203,0.224,0.24 EZB,BN2,EZB,EZB,EZB,EZB 6.16626577490018,25.0971568119113 5 28.48728969745347 31.263422586811483 25.097156811911308 EZB TRUE 2126 -Reddy_3925T BN2 1 3.565877763048783 1381,52 0.172,0.313 BN2,BN2 9.01359406846772 8 32.141374653897444 9.013594068467718 9.013594068467718 ST2 FALSE 2127 -Reddy_3926T N1 1 1.4303073254452472 3,2077,697 0.175,0.282,0.295 N1,N1,N1 12.6534011791262 4 18.098252398301778 12.65340117912623 12.65340117912623 Other FALSE 2128 -Reddy_3927T EZB 0.7228505035864103 2.322156988109942 1983,292,2080 0.181,0.254,0.276 EZB,EZB,ST2 9.46417272079986,3.62867659429853 5 21.977294820284882 13.092849315098395 9.464172720799862 Other FALSE 2129 -Reddy_3928T MCD 0.8717643597753499 1.1578708815959649 713,2028,2130,569,755,850 0.153,0.181,0.187,0.202,0.255,0.259 MCD,MCD,MCD,MCD,MCD,BN2 3.86459773837394,26.272092277005 5 30.419690646146325 30.136690015378942 26.272092277005004 MCD TRUE 2130 -Reddy_3929T EZB 1 1.203805831974895 1508 0.163 EZB 6.1522618556993 1 7.4061287017275035 6.152261855699297 6.152261855699297 Other FALSE 2131 -Reddy_3930T EZB 1 0.49574195257114145 1540,1853,1779,278,1457,499,1590,733 0.164,0.168,0.181,0.184,0.189,0.19,0.196,0.204 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 43.5450469633972 3 21.587106606436596 43.545046963397226 43.545046963397226 EZB TRUE 2132 -Reddy_3931T ST2 0.5442257531388032 7.683818320494888 537,1433 0.245,0.292 ST2,BN2 3.4227246398789,4.08696829132629 7 31.403521832174622 7.509692931205185 4.086968291326288 Other FALSE 2133 -Reddy_3932T ST2 0.5921833296154126 2.9846536177165763 68,2134,1174,781 0.121,0.206,0.288,0.308 ST2,MCD,ST2,MCD 8.09201312909702,11.7502682603958 7 35.07048067253055 19.84228138949281 11.750268260395789 MCD FALSE 2134 -Reddy_3933T MCD 1 1.8481783388855781 717,864,650,1907 0.141,0.164,0.217,0.233 MCD,MCD,MCD,MCD 22.0806852425917 7 40.80904417310841 22.08068524259169 22.08068524259169 MCD TRUE 2135 -Reddy_3935T ST2 1 4.382196582478197 874,1057 0.214,0.313 ST2,ST2 7.87136773744597 8 34.49388079846485 7.871367737445967 7.871367737445967 Other FALSE 2136 -Reddy_3936T ST2 0.5322043486476992 9.739232916807579 1667,1971 0.239,0.272 ST2,BN2 3.67884496709224,4.18536872633877 9 40.76228086853551 7.864213693431006 4.1853687263387656 Other FALSE 2137 -Reddy_3937T EZB 0.5225508428941588 2.600025198297264 2138,816,1178,911 0.204,0.209,0.212,0.241 EZB,EZB,ST2,ST2 9.69514152632229,8.85834787697777 5 25.20761226949616 18.55348940330007 9.695141526322294 EZB TRUE 2138 -Reddy_3938T N1 0.5218505415626262 2.0973344947464163 999,1071 0.15,0.163 N1,ST2 6.68546337440957,6.12560577652138 2 14.021652948512962 12.811069150930944 6.685463374409568 Other FALSE 2139 -Reddy_3939T BN2 0.48041457590977493 0.56531687181669 24,132,1093,972,2140,133,826,384 0.195,0.198,0.203,0.206,0.209,0.282,0.295,0.297 EZB,BN2,EZB,BN2,ST2,BN2,BN2,EZB 16.8353748543467,13.4201489220648,4.7879068323811 2 9.517321448520653 35.043430608792605 16.835374854346725 ST2 FALSE 2140 -Reddy_3940T N1 1 1.4178570790120801 3,2077,697 0.165,0.281,0.288 N1,N1,N1 13.096077155336 4 18.568365701981545 13.096077155336008 13.096077155336008 Other FALSE 2141 -Reddy_3941T EZB 0.6015125760391237 2.184315438246913 1471,1122,696,1944,160 0.284,0.29,0.308,0.311,0.316 EZB,BN2,EZB,EZB,ST2 3.4494355547915,9.98237346941173,3.1636436181077 6 21.80465247958244 16.59545264231093 9.98237346941173 Other FALSE 2142 -Reddy_3942T Other 1 10 0 0 1 2 Other TRUE 2143 -Reddy_3943T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2144 -Reddy_3944T MCD 0.9261977735343647 0.08022218059277471 1197,786,815,2145,1399,1251,1826,1225,738,1358 0.153,0.19,0.213,0.24,0.259,0.295,0.297,0.313,0.325,0.334 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,EZB 2.98969590888138,37.5198666349191 1 3.009925517003304 40.509562543800506 37.519866634919126 MCD TRUE 2145 -Reddy_3945T Other 1 10 0 0 1 2 Other TRUE 2146 -Reddy_3946T EZB 1 0.26953411845778785 1926,2072,1726,1219,56,1590,2013,1853,499 0.175,0.187,0.198,0.217,0.223,0.229,0.234,0.255,0.256 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 41.6472080425218 2 11.225343505969215 41.64720804252183 41.64720804252183 Other FALSE 2147 -Reddy_3948T EZB 0.8413978943988127 0.2807701252031316 2148,29,2091,1103,4,1297,1022,1563,2217 0.142,0.167,0.196,0.216,0.219,0.219,0.231,0.24,0.255 ST2,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 37.272762764275,7.02585387405967 2 10.465078267992109 44.29861663833465 37.27276276427499 ST2 FALSE 2148 -Reddy_3949T Other 1 10 0 0 1 2 Other TRUE 2149 -Reddy_3951T ST2 1 1.101408660842422 1653,451 0.213,0.222 ST2,ST2 9.201499772977 2 10.134611542696451 9.201499772977002 9.201499772977002 Other FALSE 2150 -Reddy_3952T EZB 1 0 287,2089,695,676,1903,2201,382,173,558,1138,1474 0.174,0.18,0.183,0.199,0.224,0.25,0.251,0.277,0.279,0.281,0.288 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 48.4543524647701 0 0 48.45435246477009 48.45435246477009 EZB TRUE 2151 -Reddy_3953T Other 1 10 0 0 1 2 Other TRUE 2152 -Reddy_3956T BN2 0.8139352579173745 1.5022640304358752 523,596,2096,1603,20 0.188,0.204,0.238,0.24,0.261 BN2,BN2,BN2,MCD,BN2 18.2512036345549,4.17220591432552 6 27.418126732352366 22.42340954888044 18.25120363455492 Other FALSE 2153 -Reddy_3957T BN2 1 10.189072367543012 1096 0.317 BN2 3.15312332000267 8 32.12740169129468 3.153123320002669 3.153123320002669 Other FALSE 2154 -Reddy_3958T Other 1 10 0 0 1 2 Other TRUE 2155 -Reddy_3959T Other 1 10 0 0 1 2 Other TRUE 2156 -Reddy_3960T Other 1 10 0 0 1 2 Other TRUE 2157 -Reddy_3963T BN2 0.5035616081813236 12.725270988573078 1108,65 0.24,0.244 BN2,EZB 4.16542173048048,4.10649904903333 9 53.00612030205511 8.271920779513804 4.165421730480479 Other FALSE 2158 -Reddy_3964T BN2 0.5170618704320097 5.556200029620968 1131,2211,52 0.139,0.257,0.263 ST2,BN2,BN2 7.6918920742496,7.18426204599708 8 42.7376909707869 14.87615412024668 7.691892074249596 Other FALSE 2159 -Reddy_3965T BN2 0.5570823643073782 3.3412789332361097 811,150,1232,151 0.177,0.214,0.241,0.323 BN2,MCD,BN2,MCD 9.78626430544217,7.78073284337164 7 32.69863875885444 17.56699714881382 9.786264305442174 Other FALSE 2160 -Reddy_3966T ST2 1 4.09365025163088 833 0.208 ST2 4.81007093057937 3 19.690748075328614 4.810070930579369 4.810070930579369 Other FALSE 2161 -Reddy_3967T EZB 1 2.3061255259322126 2162 0.176 EZB 5.67828708430472 2 13.094842788686307 5.678287084304718 5.678287084304718 EZB TRUE 2162 -Reddy_3968T Other 1 10 0 0 1 2 Other TRUE 2163 -Reddy_3969T ST2 1 1.171508296925573 724 0.146 ST2 6.82858648551738 1 7.999745724057449 6.82858648551738 6.82858648551738 Other FALSE 2164 -Reddy_3971T BN2 0.6254863639530983 3.744660948629725 874,915,1317 0.263,0.307,0.324 ST2,BN2,BN2 6.35321424088118,3.80402436097837 6 23.79063326610599 10.157238601859543 6.353214240881178 Other FALSE 2165 -Reddy_3972T EZB 1 5.006947366885208 2166,1395 0.209,0.334 EZB,EZB 7.77890365343529 9 38.94856116482153 7.778903653435285 7.778903653435285 EZB TRUE 2166 -Reddy_3973T ST2 0.6950568658748894 2.576795800403351 702,530,1697 0.192,0.22,0.233 ST2,ST2,BN2 4.28543502115927,9.76779176636716 4 25.16960480278933 14.05322678752643 9.76779176636716 Other FALSE 2167 -Reddy_3974T ST2 0.5998279985637407 3.7211270438027024 1863,1121,2198 0.164,0.214,0.224 EZB,ST2,ST2 6.09321864116067,9.13328051243196 6 33.98609711344677 15.22649915359263 9.13328051243196 Other FALSE 2168 -Reddy_3975T Other 1 10 0 0 1 2 Other TRUE 2169 -Reddy_3977T ST2 1 5.747036865390891 511 0.286 ST2 3.49221455559903 5 20.069885792882317 3.4922145555990345 3.4922145555990345 BN2 FALSE 2170 -Reddy_3978T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2171 -Reddy_3979T Other 1 10 0 0 1 2 Other TRUE 2172 -Reddy_3980T Other 1 10 0 0 1 2 Other TRUE 2173 -Reddy_3981T EZB 1 11.61186420181836 1300 0.141 EZB 7.07900177501649 10 82.20040729592264 7.079001775016493 7.079001775016493 Other FALSE 2174 -Reddy_3982T N1 1 0.8393233091659625 63,2189,745,2175,891,814 0.146,0.153,0.17,0.192,0.218,0.227 N1,N1,N1,N1,N1,N1 33.4693516504191 5 28.091606982869006 33.46935165041907 33.46935165041907 N1 TRUE 2175 -Reddy_3983T EZB 0.8802000235278724 0.11116713382236527 466,2176,1550,1031,1384,1701,646,336,337 0.178,0.193,0.206,0.214,0.222,0.227,0.255,0.273,0.273 EZB,EZB,BN2,EZB,EZB,EZB,EZB,EZB,EZB 4.85081379699174,35.6401273520687 1 3.9620108067935624 40.49094114906045 35.6401273520687 EZB TRUE 2176 -Reddy_3984T MCD 1 4.677792690100579 311,1480 0.211,0.285 MCD,MCD 8.24756692483167 9 38.5804082720929 8.247566924831672 8.247566924831672 Other FALSE 2177 -Reddy_3985T ST2 1 5.680303051713426 90,1946 0.212,0.228 ST2,ST2 9.09975724267867 8 51.68937883543899 9.099757242678669 9.099757242678669 Other FALSE 2178 -Reddy_3986T ST2 1 3.540455026856559 702,530 0.251,0.259 ST2,ST2 7.8374313788033 6 27.748073322727482 7.837431378803302 7.837431378803302 Other FALSE 2179 -Reddy_3987T MCD 0.8580498593792176 0.45526561114242214 647,1420,2180,1250,880,1239,771,1184 0.161,0.197,0.22,0.235,0.25,0.254,0.258,0.259 MCD,BN2,MCD,MCD,MCD,MCD,MCD,MCD 5.07589452709854,30.6823971160123 3 13.968640274335849 35.75829164311087 30.68239711601234 MCD TRUE 2180 -Reddy_3989T Other 1 10 0 0 1 2 Other TRUE 2181 -Reddy_3990T ST2 1 6.070797277611823 1946,90 0.249,0.285 ST2,ST2 7.52842329730477 9 45.70353165798725 7.528423297304775 7.528423297304775 Other FALSE 2182 -Reddy_3991T EZB 1 2.89742120606839 2183,2087,202 0.215,0.221,0.327 EZB,EZB,EZB 12.2508467092708 8 35.49586304773432 12.250846709270784 12.250846709270784 EZB TRUE 2183 -Reddy_3992T MCD 1 0.10752452644635288 1911,1543,986,1897,1184,761,755,485 0.124,0.188,0.19,0.195,0.206,0.232,0.268,0.27 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 40.367903613789 1 4.340539719704674 40.367903613788954 40.367903613788954 Other FALSE 2184 -Reddy_3993T MCD 0.8775597838624773 0.4478332892077526 465,347,246,1526,2185,296,185 0.134,0.193,0.212,0.216,0.237,0.237,0.244 MCD,MCD,MCD,MCD,ST2,MCD,MCD 30.2832422606694,4.2252240769529 3 13.561843989470795 34.508466337622295 30.283242260669397 ST2 FALSE 2185 -Reddy_3994T Other 1 10 0 0 1 2 Other TRUE 2186 -Reddy_3995T EZB 1 14.19381110526913 1738 0.255 EZB 3.91484950613711 9 55.566634395666256 3.914849506137108 3.914849506137108 Other FALSE 2187 -Reddy_3996T EZB 1 5.093090754441603 1471,696 0.267,0.37 EZB,EZB 6.44587551612189 7 32.829428995441916 6.445875516121895 6.445875516121895 Other FALSE 2188 -Reddy_3997T N1 1 0.4944415035299157 2189,745,63,891,814,2175 0.142,0.149,0.168,0.197,0.202,0.21 N1,N1,N1,N1,N1,N1 34.4686786267454 3 17.04274528489746 34.46867862674539 34.46867862674539 N1 TRUE 2189 -Reddy_3998T EZB 0.5980627290377105 8.96754987374837 1471,451 0.26,0.387 EZB,ST2 3.84758882659645,2.58583134788468 9 34.503444696180644 6.433420174481135 3.8475888265964513 Other FALSE 2190 -Reddy_4000T ST2 1 1.0120285044049695 1710 0.108 ST2 9.29083287316596 1 9.40258769730667 9.290832873165957 9.290832873165957 Other FALSE 2191 -Reddy_648T ST2 0.7625883532961555 2.050494505224302 2198,1822,1847,1121 0.144,0.198,0.214,0.218 ST2,EZB,ST2,ST2 5.04226008609761,16.196209702992 5 33.210239001445565 21.23846978908958 16.196209702991972 Other FALSE 2192 -Reddy_658T BN2 0.4768324944489882 3.589031506620371 1835,921,70,1813 0.197,0.208,0.214,0.229 EZB,MCD,BN2,BN2 9.02148372867564,5.08487268608788,4.81325127493592 6 32.37838933867988 18.91960768969943 9.021483728675637 Other FALSE 2193 -Reddy_683T BN2 1 4.224959356239236 1799,1433 0.24,0.302 BN2,BN2 7.48564017555709 7 31.62652549716025 7.4856401755570925 7.4856401755570925 Other FALSE 2194 -Reddy_684T MCD 0.8154425909986589 0.2754773545575484 2195,2203,823,1406,858,1904,253,1514 0.121,0.156,0.173,0.192,0.199,0.205,0.216,0.22 BN2,MCD,MCD,MCD,MCD,MCD,MCD,MCD 8.25121591100807,36.45691017104 2 10.043053169260284 44.708126082048096 36.45691017104002 BN2 FALSE 2195 -Reddy_689T EZB 0.38450464801078765 7.089700596491321 1388,938,2196 0.216,0.236,0.315 EZB,ST2,BN2 3.17205508559572,4.62861504885835,4.23719487655222 7 32.815494872819755 12.037865011006293 4.628615048858351 BN2 FALSE 2196 -Reddy_690T BN2 1 1.9387103750022947 1096,20,1803 0.15,0.222,0.362 BN2,BN2,BN2 13.9527118377507 6 27.050267199264557 13.95271183775067 13.95271183775067 Other FALSE 2197 -Reddy_695T ST2 0.8062325511483714 1.090784560699278 1822,1077,1427,2198,271,1847 0.167,0.174,0.181,0.187,0.24,0.241 EZB,ST2,ST2,ST2,ST2,ST2 5.9937942000712,24.9391320246053 5 27.203220169680378 30.932926224676493 24.93913202460529 ST2 TRUE 2198 -Reddy_702T EZB 1 0 2050 0.104 EZB 9.57867225560371 0 0 9.578672255603708 9.578672255603708 Other FALSE 2199 -Reddy_704T N1 0.4183428332643767 5.117714935883117 175,2211,2127 0.144,0.153,0.319 N1,BN2,ST2 6.52106281320116,6.94632060418925,3.13698996218041 8 35.549288705491975 16.604373379570823 6.9463206041892525 Other FALSE 2200 -Reddy_705T EZB 1 0 2201,173,1903,1474,287,1673,153,1277,1554,1054,676 0.122,0.164,0.171,0.175,0.198,0.216,0.226,0.27,0.284,0.291,0.295 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 53.9660442126533 0 0 53.966044212653344 53.966044212653344 EZB TRUE 2201 -Reddy_707T ST2 0.39064380794049275 1.8164261147449845 781,816,2134,2138,508,1486,955 0.167,0.193,0.205,0.21,0.222,0.224,0.227 MCD,EZB,MCD,EZB,ST2,ST2,ST2 9.94622470650091,10.8852754777466,13.354580889026 4 24.257609478301053 34.18608107327349 13.354580889025964 Other FALSE 2202 -Reddy_759T MCD 1 0 709,21,480,1331,2034,1233,23,1689,2008,59,798 0.117,0.134,0.139,0.143,0.147,0.151,0.156,0.163,0.165,0.165,0.17 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 74.2042672258608 0 0 74.20426722586076 74.20426722586076 MCD TRUE 2203 -Reddy_787T EZB 1 6.280889999832026 1182,532 0.303,0.326 EZB,EZB 6.37498404782281 9 40.040573555059 6.374984047822813 6.374984047822813 Other FALSE 2204 -Reddy_790T BN2 0.5203270020046952 1.9526121426261727 1275,869,871,1008 0.205,0.217,0.25,0.28 BN2,MCD,BN2,N1 8.87102629068039,4.60036172055561,3.57755670334243 4 17.321673652738536 17.048944714578425 8.871026290680385 Other FALSE 2205 -Reddy_793T EZB 0.597915783680905 6.684718393361021 2166,1901 0.183,0.272 EZB,BN2 3.68214061233606,5.47549468617042 8 36.602140041393916 9.157635298506474 5.475494686170417 Other FALSE 2206 -Reddy_799T MCD 1 0 1565,777,1240,783,929,1239,771,535,518,1250,455 0.104,0.122,0.135,0.138,0.149,0.161,0.162,0.172,0.179,0.18,0.19 MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD,MCD 73.738507111808 0 0 73.73850711180805 73.73850711180805 MCD TRUE 2207 -Reddy_800T Other 1 10 0 0 1 2 Other TRUE 2208 -Reddy_813T N1 1 2.3335292892514774 901,870,262 0.158,0.206,0.233 N1,N1,N1 15.4848262899365 6 36.13429568653818 15.48482628993653 15.48482628993653 Other FALSE 2209 -Reddy_816T BN2 1 3.5503051112518813 2210,459,1003 0.278,0.281,0.291 BN2,BN2,BN2 10.590374578741 8 37.599060996976064 10.590374578740972 10.590374578740972 BN2 TRUE 2210 -Reddy_823T ST2 0.6633975443396457 4.1390047533383125 1131,52 0.12,0.237 ST2,BN2 4.21904245280681,8.31515740776698 6 34.41647603550383 12.534199860573793 8.315157407766984 BN2 FALSE 2211 -Reddy_829T MCD 1 0.7952109721596647 1063,753,1194,285,650 0.101,0.149,0.182,0.229,0.234 MCD,MCD,MCD,MCD,MCD 30.7993101945123 4 24.491949401625174 30.79931019451227 30.79931019451227 Other FALSE 2212 -Reddy_830T BN2 1 0.5473385290095821 2213,973,284,1210,936,1549,903 0.121,0.136,0.189,0.218,0.251,0.266,0.278 BN2,BN2,BN2,BN2,BN2,BN2,BN2 36.8455969133522 4 20.167014815034214 36.84559691335224 36.84559691335224 BN2 TRUE 2213 -SU-DHL-10 EZB 0.4334712893288 2.4020609860668145 2214,940,826,1190,1964,636 0.171,0.326,0.364,0.368,0.388,0.399 EZB,ST2,BN2,BN2,EZB,ST2 5.46270585573423,8.44305137615619,5.572006541645 5 20.28072431402252 19.477763773535422 8.443051376156193 EZB TRUE 2214 -SU-DHL-4 EZB 1 0 2215,1264,1163,1033,1962,463,142,1516,348,1046,1886 0.125,0.128,0.152,0.152,0.154,0.158,0.169,0.189,0.195,0.198,0.214 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 67.7598864327769 0 0 67.7598864327769 67.7598864327769 EZB TRUE 2215 -SU-DHL-5 ST2 0.5257855907234787 4.784629922395934 292,2080,2216 0.112,0.193,0.212 EZB,ST2,ST2 8.93986429609053,9.91208140865832 8 47.42564130109104 18.85194570474885 9.912081408658322 ST2 TRUE 2216 -SU-DHL-6 EZB 1 0.12302174542890365 4,2217,2091,144,1587,1103,1022,220,1110,1563 0.113,0.149,0.165,0.18,0.187,0.189,0.21,0.211,0.213,0.224 EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB,EZB 56.4685273359641 1 6.946856794670066 56.46852733596413 56.46852733596413 EZB TRUE 2217 -SU-DHL-9 ST2 0.648110905416895 3.5175107249960416 2218,190,581 0.179,0.186,0.23 ST2,BN2,ST2 5.38930150125847,9.92603956562898 6 34.91495062883499 15.31534106688745 9.92603956562898 ST2 TRUE 2218 -Toledo EZB 0.8580747539032058 0.7012936620260001 179,62,1053,2126,1453,993 0.167,0.169,0.174,0.213,0.219,0.225 EZB,EZB,EZB,EZB,EZB,BN2 4.44941124102979,26.9009747078891 3 18.865483064964334 31.35038594891886 26.90097470788907 Other FALSE 2219 -WSU-NHL EZB 1 0.3085673171661916 729,1867,135,679,2100,513,1040 0.183,0.202,0.234,0.253,0.254,0.257,0.271 EZB,EZB,EZB,EZB,EZB,EZB,EZB 30.1237837902919 2 9.295215147064788 30.123783790291917 30.123783790291917 Other FALSE 2220 From 487fd3f81d37f416a1876cdf74cc79c0881d07f1 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 3 Jun 2025 15:17:57 -0700 Subject: [PATCH 26/79] missing metadata message fix --- R/umap.R | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/R/umap.R b/R/umap.R index 2924043..9cf656a 100644 --- a/R/umap.R +++ b/R/umap.R @@ -487,11 +487,24 @@ make_and_annotate_umap = function(df, if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } - if(!missing(metadata)){ - keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] - df= df[keep_rows,] - metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) - message(paste(nrow(metadata),"rows of the data have atleast 1 feature")) + + if (!missing(metadata)) { + df_sample_ids <- rownames(df) + metadata_sample_ids <- metadata[[join_column]] + + # overlaps and mismatches + shared_ids <- intersect(df_sample_ids, metadata_sample_ids) + missing_from_metadata <- setdiff(df_sample_ids, metadata_sample_ids) + missing_from_df <- setdiff(metadata_sample_ids, df_sample_ids) + + # shared rows + df <- df[shared_ids, , drop = FALSE] + metadata <- metadata %>% filter((!!sym(join_column)) %in% shared_ids) + + # otherwise message + message(length(missing_from_metadata), " samples were missing from metadata.") + message(length(missing_from_df), " samples in metadata were dropped as missing from the feature matrix.") + message("using ", length(shared_ids), " samples provided in metadata and having features.") } if(missing(umap_out)){ From dfaa54747fc2dd3148fe086f9a7ae815148b1417 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 4 Jun 2025 09:38:31 -0700 Subject: [PATCH 27/79] optimize other addn to predict_single_sample --- R/umap.R | 34 +++++++++++++++++++++++++++------- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/R/umap.R b/R/umap.R index 9cf656a..6252c5c 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1420,6 +1420,16 @@ predict_single_sample_DLBCLone <- function( prefix <- paste0("k_", best_params$k, ".") colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) train_pred = rownames_to_column(train_pred, var = "sample_id") + + if(best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params + train_pred = mutate(train_pred,predicted_label_optimized = ifelse( + other_score > best_params$threshold_outgroup, + "Other", + predicted_label + )) + }else{ # optimize_for_other = F + train_pred = mutate(train_pred,predicted_label_optimized = predicted_label) + } } test_pred = weighted_knn_predict_with_conf( @@ -1437,6 +1447,16 @@ predict_single_sample_DLBCLone <- function( colnames(test_pred) <- sub(prefix, "", colnames(test_pred)) test_pred = rownames_to_column(test_pred, var = "sample_id") + if(best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params + test_pred = mutate(test_pred,predicted_label_optimized = ifelse( + other_score > best_params$threshold_outgroup, + "Other", + predicted_label + )) + }else{ # optimize_for_other = F + test_pred = mutate(test_pred,predicted_label_optimized = predicted_label) + } + anno_umap = select(projection$df, sample_id, V1, V2) anno_out = left_join(test_pred,anno_umap,by="sample_id") @@ -1445,7 +1465,7 @@ predict_single_sample_DLBCLone <- function( mutate( label = as.character(paste( sample_id, - predicted_label, + predicted_label_optimized, round(confidence,3) )), V1 = as.numeric(V1), @@ -1508,12 +1528,12 @@ predict_single_sample_DLBCLone <- function( # Add the predicted labels for Other (unclassified) cases, if provided if(!missing(other_df)){ in_df = bind_rows( - mutate(predictions_df,dataset=title2,lymphgen=predicted_label), - mutate(other_df,dataset=title3,lymphgen=predicted_label) + mutate(predictions_df,dataset=title2,lymphgen=predicted_label_optimized), + mutate(other_df,dataset=title3,lymphgen=predicted_label_optimized) ) }else{ in_df = bind_rows( - mutate(predictions_df,dataset=title2,lymphgen=predicted_label) + mutate(predictions_df,dataset=title2,lymphgen=predicted_label_optimized) ) } @@ -1525,11 +1545,11 @@ predict_single_sample_DLBCLone <- function( if(annotate_accuracy){ #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label %in% truth_classes) %>% - group_by(predicted_label) %>% + centroids = filter(predictions_df,predicted_label_optimized %in% truth_classes) %>% + group_by(predicted_label_optimized) %>% summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% - mutate(lymphgen=predicted_label) + mutate(lymphgen=predicted_label_optimized) centroids = left_join(centroids,acc_df) %>% mutate(label=paste(lymphgen,":",round(accuracy,3))) From c61e82b94e3adabd873fad4ef0bc9d273b796ddb Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 4 Jun 2025 20:27:18 -0700 Subject: [PATCH 28/79] updates --- NAMESPACE | 2 + R/umap.R | 192 ++++++++++++++++++++------ man/make_and_annotate_umap.Rd | 3 +- man/make_neighborhood_plot.Rd | 18 ++- man/make_umap_scatterplot.Rd | 34 +++++ man/predict_single_sample_DLBCLone.Rd | 2 +- 6 files changed, 206 insertions(+), 45 deletions(-) create mode 100644 man/make_umap_scatterplot.Rd diff --git a/NAMESPACE b/NAMESPACE index 66e8f5f..31c5963 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,6 +9,8 @@ export(classify_fl) export(complete_missing_from_matrix) export(construct_reduced_winning_version) export(make_and_annotate_umap) +export(make_neighborhood_plot) +export(make_umap_scatterplot) export(massage_matrix_for_clustering) export(optimize_outgroup) export(predict_single_sample_DLBCLone) diff --git a/R/umap.R b/R/umap.R index 6252c5c..6fa69ed 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,3 +1,5 @@ + + #' Assemble genetic features for UMAP input #' #' This function assembles a matrix of genetic features for each sample, including mutation status, @@ -465,7 +467,8 @@ make_and_annotate_umap = function(df, target_column, target_metric="euclidean", target_weight=0.5, - calc_dispersion = FALSE){ + calc_dispersion = FALSE, + algorithm = "tumap"){ # Function to compute mean (or median) pairwise distance within a group pairwise_dispersion <- function(df_group) { @@ -487,7 +490,6 @@ make_and_annotate_umap = function(df, if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } - if (!missing(metadata)) { df_sample_ids <- rownames(df) metadata_sample_ids <- metadata[[join_column]] @@ -509,16 +511,38 @@ make_and_annotate_umap = function(df, if(missing(umap_out)){ if(missing(target_column)){ - umap_out = umap2(df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1) # possibly add rng_type = "deterministic" - #IMPORTANT: n_threads must not be changed because it will break reproducibility + if(algorithm == "umap"){ + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + a=1.8956, + b = 0.806, + approx_pow=TRUE, + init=init, + seed = seed, + n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") # possibly add rng_type = "deterministic" + + }else if(algorithm == "tumap"){ + umap_out = tumap(df %>% as.matrix(), + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") + }else{ + stop("unsupported algorithm option") + } }else{ #supervised if(missing(metadata)){ @@ -534,23 +558,24 @@ make_and_annotate_umap = function(df, n_epochs=n_epochs, init=init, seed = seed, - n_threads = 1, + n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility y = metadata[[target_column]], target_metric = target_metric, target_weight = target_weight ) # possibly add rng_type = "deterministic" - #IMPORTANT: n_threads must not be changed because it will break reproducibility } - }else{ umap_out = umap_transform(X=df, - model=umap_out$model,seed=seed) - ret_model = FALSE + model=umap_out$model, + seed=seed, + batch = TRUE, + n_threads = 1, + n_sgd_threads = 1) + } - - if(ret_model){ + if(!is.null(names(umap_out))){ umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) }else{ umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) @@ -570,8 +595,7 @@ make_and_annotate_umap = function(df, arrange(mean_pairwise_distance) results[["dispersion"]] = dispersion_df } - - + results[["df"]]=umap_df results[["features"]] = df @@ -961,8 +985,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, return(to_ret) } - - #' Weighted k-nearest neighbor with confidence estimate #' #' @param train_coords Data frame containing coordinates for samples with known @@ -1044,7 +1066,7 @@ weighted_knn_predict_with_conf <- function( } } - distances = distances + epsilon + distances = distances + epsilon weights <- 1 / distances if(use_weights){ weights <- 1 / distances @@ -1095,7 +1117,7 @@ weighted_knn_predict_with_conf <- function( } neighbors_other = length(others_closer) other_weighted_votes = sum(others_weights) - + mean_other_dist = mean(others_distances) if (length(neighbor_labels) == 0) { preds[i] <- "Other" confs[i] <- 1 @@ -1103,12 +1125,14 @@ weighted_knn_predict_with_conf <- function( rel_other = 10 neighbor_info <- data.frame( other_score = rel_other, + neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), neighbor = paste(neighbors,collapse=","), distance = paste(round(distances, 3),collapse=","), label = paste(neighbor_labels,collapse=","), weighted_votes = "", neighbors_other = neighbors_other, other_weighted_votes = 0, + mean_other_dist = mean_other_dist, total_w = 1, pred_w = 2 ) @@ -1150,9 +1174,9 @@ weighted_knn_predict_with_conf <- function( #rel_other = ifelse(rel_other==0,0,log(rel_other)) neighbor_info <- data.frame( other_score = rel_other, + neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), neighbor = paste(neighbors,collapse=","), distance = paste(round(distances, 3),collapse=","), - #weight = paste(round(weights, 3),collapse=","), label = paste(neighbor_labels,collapse=","), weighted_votes = paste(weighted_votes,collapse=","), neighbors_other = neighbors_other, @@ -1191,8 +1215,11 @@ weighted_knn_predict_with_conf <- function( #' #' @param single_sample_prediction_output A list containing prediction results and annotation data frames. #' Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations). +#' @param training_predictions The equivalent data frame of prediction results for all training samples (e.g. optimized_model$df) #' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. #' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. +#' @param add_circle Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable. +#' @param label_column Does nothing, i.e. this is not currently working. #' #' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. #' @@ -1203,26 +1230,48 @@ weighted_knn_predict_with_conf <- function( #' @import ggplot2 #' @importFrom rlang sym #' +#' @export #' @examples -#' # Assuming 'output' is the result of DLBCLone_predict_single_sample on sample_id "SAMPLE123": -#' make_neighborhood_plot(output, "SAMPLE123") +#' +#' # Assuming 'optimization_result' is the output of DLBCLone_optimize_params +#' # and 'output' is the result of DLBCLone_predict_single_sample +#' # on sample_id "SAMPLE123": +#' make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") make_neighborhood_plot <- function(single_sample_prediction_output, + training_predictions, this_sample_id, - prediction_in_title = TRUE){ + prediction_in_title = TRUE, + add_circle = TRUE, + label_column = "predicted_label_optimized"){ + + circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ + r = diameter / 2 + tt <- seq(0,2*pi,length.out = npoints) + xx <- center[1] + r * cos(tt) + yy <- center[2] + r * sin(tt) + return(data.frame(x = xx, y = yy)) + } + if(missing(training_predictions)){ + training_predictions = single_sample_prediction_output$anno_df + } + single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction,sample_id==this_sample_id) #extract the sample_id for all the nearest neighbors with non-Other labels my_neighbours = filter(single_sample_prediction_output$prediction, sample_id == this_sample_id) %>% pull(neighbor_id) %>% strsplit(.,",") %>% unlist() #set up links connecting each neighbor to the sample's point - links_df = filter(single_sample_prediction_output$anno_df,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) - my_x = filter(single_sample_prediction_output$anno_df, - sample_id==this_sample_id) %>% pull(V1) - my_y = filter(single_sample_prediction_output$anno_df, - sample_id==this_sample_id) %>% pull(V2) + links_df = filter(training_predictions,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) + + sample_row <- single_sample_prediction_output$anno_df %>% + filter(sample_id == this_sample_id) %>% + slice_head(n = 1) # ensures 1 sample_id selected. Duplicates may occur when ignore_top = TRUE. + my_x <- sample_row$V1 + my_y <- sample_row$V2 + if(prediction_in_title){ title = paste(this_sample_id, pull(single_sample_prediction_output$prediction, - !!sym("predicted_label"))) + !!sym(label_column))) if(single_sample_prediction_output$prediction$predicted_label_optimized == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") } @@ -1230,20 +1279,30 @@ make_neighborhood_plot <- function(single_sample_prediction_output, }else{ title = this_sample_id } + links_df = mutate(links_df,my_x=my_x,my_y=my_y) + links_df = links_df %>% select(V1,V2,my_x,my_y,group) %>% mutate(length = abs(V1-my_x)+abs(V2-my_y)) - - pp=ggplot(mutate(single_sample_prediction_output$anno_df,group=lymphgen), + pp=ggplot(mutate(training_predictions,group=lymphgen), aes(x=V1,y=V2,colour=group)) + geom_point(alpha=0.8,size=0.5) + geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=0.5)+ scale_colour_manual(values=get_gambl_colours()) + ggtitle(title)+ theme_minimal() + if(add_circle){ + #add a circle around the sample + max_d = 1 + d = max(links_df$length)*2 + if(d>max_d){ + d = max_d + } + circle = circleFun(c(my_x,my_y),diameter=d,npoints=100) + pp = pp + geom_path(data=circle,aes(x=x,y=y),colour="black",alpha=1,inherit.aes=FALSE) + } return(pp) } - #' Predict class for a single sample without using umap_transform and plot result of classification #' #' @param seed Random seed for reproducibility @@ -1269,7 +1328,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' @param title2 additional argument #' @param title3 additional argument #' -#' @returns a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +#' @returns a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object #' @export #' #' @examples @@ -1601,6 +1660,59 @@ predict_single_sample_DLBCLone <- function( model=umap_out$model, plot = pp, df = predictions_df, - anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id") + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), + projection = projection$df )) } + +#' Make UMAP scatterplot +#' +#' @param df +#' @param drop_composite +#' @param colour_by +#' @param drop_other +#' @param high_confidence +#' @param custom_colours +#' @param add_labels +#' +#' @returns +#' @export +#' +#' @examples +make_umap_scatterplot = function(df, + drop_composite = TRUE, + colour_by="lymphgen", + drop_other = FALSE, + high_confidence = FALSE, + custom_colours, + add_labels = FALSE){ + + if(!missing(custom_colours)){ + cols = custom_colours + }else{ + cols = get_gambl_colours() + } + if(drop_composite){ + df = filter(df,!is.na(lymphgen),!grepl("COMP",lymphgen)) + } + if(drop_other){ + df = filter(df,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") + } + if(high_confidence){ + df = filter(df,Confidence > 0.7) + } + if(add_labels){ + labels = group_by(df,!!sym(colour_by)) %>% + summarise(median_x = median(V1),median_y = median(V2)) + } + + p = ggplot(df, + aes(x=V1,y=V2,colour=!!sym(colour_by),label=cohort)) + geom_point(alpha=0.8) + + scale_colour_manual(values=cols) + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + if(add_labels){ + p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + ggMarginal(p,groupColour = TRUE,groupFill=TRUE) + +} diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 2eaaaea..03ad886 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -20,7 +20,8 @@ make_and_annotate_umap( target_column, target_metric = "euclidean", target_weight = 0.5, - calc_dispersion = FALSE + calc_dispersion = FALSE, + algorithm = "tumap" ) } \arguments{ diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index 08fc3d7..5fa1e2c 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -6,17 +6,26 @@ \usage{ make_neighborhood_plot( single_sample_prediction_output, + training_predictions, this_sample_id, - prediction_in_title = TRUE + prediction_in_title = TRUE, + add_circle = TRUE, + label_column = "predicted_label_optimized" ) } \arguments{ \item{single_sample_prediction_output}{A list containing prediction results and annotation data frames. Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations).} +\item{training_predictions}{The equivalent data frame of prediction results for all training samples (e.g. optimized_model$df)} + \item{this_sample_id}{Character. The sample ID for which the neighborhood plot will be generated.} \item{prediction_in_title}{Logical. If \code{TRUE}, includes the predicted label in the plot title.} + +\item{add_circle}{Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable.} + +\item{label_column}{Does nothing, i.e. this is not currently working.} } \value{ A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. @@ -28,6 +37,9 @@ Generates a UMAP plot highlighting the neighborhood of a given sample, showing i The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. } \examples{ -# Assuming 'output' is the result of DLBCLone_predict_single_sample on sample_id "SAMPLE123": -make_neighborhood_plot(output, "SAMPLE123") + +# Assuming 'optimization_result' is the output of DLBCLone_optimize_params +# and 'output' is the result of DLBCLone_predict_single_sample +# on sample_id "SAMPLE123": +make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") } diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd new file mode 100644 index 0000000..9d70c39 --- /dev/null +++ b/man/make_umap_scatterplot.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{make_umap_scatterplot} +\alias{make_umap_scatterplot} +\title{Make UMAP scatterplot} +\usage{ +make_umap_scatterplot( + df, + drop_composite = TRUE, + colour_by = "lymphgen", + drop_other = FALSE, + high_confidence = FALSE, + custom_colours, + add_labels = FALSE +) +} +\arguments{ +\item{df}{} + +\item{drop_composite}{} + +\item{colour_by}{} + +\item{drop_other}{} + +\item{high_confidence}{} + +\item{custom_colours}{} + +\item{add_labels}{} +} +\description{ +Make UMAP scatterplot +} diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 7860ab6..b163902 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -67,7 +67,7 @@ samples to estimate overall accuracy} \item{seed}{Random seed for reproducibility} } \value{ -a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object } \description{ Predict class for a single sample without using umap_transform and plot result of classification From 2a8fb1d61fd2a081e43ebb523f64a85564e7b613 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 5 Jun 2025 13:50:57 -0700 Subject: [PATCH 29/79] predict_single_sample anno_df fix for smooth nearest neighbor plot runs --- R/umap.R | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/R/umap.R b/R/umap.R index 6fa69ed..10f8c70 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1659,8 +1659,7 @@ predict_single_sample_DLBCLone <- function( umap_input = umap_out$features, model=umap_out$model, plot = pp, - df = predictions_df, - anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), + anno_df = predictions_df, projection = projection$df )) } From b040c8563fc1cc0bc399359bc1c60091122bc802 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 17 Jun 2025 21:42:29 -0700 Subject: [PATCH 30/79] neighborhood plot circle fix --- R/umap.R | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/R/umap.R b/R/umap.R index 10f8c70..0715e69 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1280,7 +1280,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, title = this_sample_id } links_df = mutate(links_df,my_x=my_x,my_y=my_y) - links_df = links_df %>% select(V1,V2,my_x,my_y,group) %>% mutate(length = abs(V1-my_x)+abs(V2-my_y)) + links_df = links_df %>% select(V1, V2, my_x, my_y, group) %>% mutate(length = sqrt((V1 - my_x)^2 + (V2 - my_y)^2)) # Euclidean distance pp=ggplot(mutate(training_predictions,group=lymphgen), @@ -1292,11 +1292,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, theme_minimal() if(add_circle){ #add a circle around the sample - max_d = 1 - d = max(links_df$length)*2 - if(d>max_d){ - d = max_d - } + d = quantile(links_df$length, 1)*2.1 # Euclidean distances and adding a 10% spacer circle = circleFun(c(my_x,my_y),diameter=d,npoints=100) pp = pp + geom_path(data=circle,aes(x=x,y=y),colour="black",alpha=1,inherit.aes=FALSE) } From 673c6bda00b1c52b187686e347ac7d6c26aafa9b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 17 Jun 2025 21:52:28 -0700 Subject: [PATCH 31/79] quick fix --- R/umap.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/umap.R b/R/umap.R index 0715e69..0eb536d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1292,7 +1292,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, theme_minimal() if(add_circle){ #add a circle around the sample - d = quantile(links_df$length, 1)*2.1 # Euclidean distances and adding a 10% spacer + d = max(links_df$length)*2.1 # adding a 10% spacer circle = circleFun(c(my_x,my_y),diameter=d,npoints=100) pp = pp + geom_path(data=circle,aes(x=x,y=y),colour="black",alpha=1,inherit.aes=FALSE) } From 3e7faf35e8950117e25bf7637d84f8727a08c3ea Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 18 Jun 2025 11:23:02 -0700 Subject: [PATCH 32/79] quick predict_single_sample fix --- R/umap.R | 6 +++--- man/predict_single_sample_DLBCLone.Rd | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/umap.R b/R/umap.R index 0eb536d..326eac7 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1334,7 +1334,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, #' train_df = train_df, #' train_metadata = train_metadata, #' umap_out = umap_out, -#' best_params = best_params +#' best_params = best_params, #' predictions_df = predictions_df, #' annotate_accuracy = TRUE #' ) @@ -1364,7 +1364,7 @@ predict_single_sample_DLBCLone <- function( message("Warning: you have supplied more than one sample to test with. Will proceed with all") } - trained_features = colnames(umap_out$features) + trained_features = colnames(train_df[-1]) # Exclude sample_id column train_df = train_df %>% column_to_rownames("sample_id") %>% @@ -1652,7 +1652,7 @@ predict_single_sample_DLBCLone <- function( return(list( prediction = test_pred, train_prediction = train_pred, - umap_input = umap_out$features, + umap_input_features = trained_features, model=umap_out$model, plot = pp, anno_df = predictions_df, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index b163902..c08bbe0 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -79,7 +79,7 @@ predict_single_sample_DLBCLone( train_df = train_df, train_metadata = train_metadata, umap_out = umap_out, - best_params = best_params + best_params = best_params, predictions_df = predictions_df, annotate_accuracy = TRUE ) From da4ff14cff8e2cddc55591bea29892885acf998f Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 19 Jun 2025 11:17:02 -0700 Subject: [PATCH 33/79] changes so stored umaps work --- R/umap.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/umap.R b/R/umap.R index 326eac7..b4cbe10 100644 --- a/R/umap.R +++ b/R/umap.R @@ -567,7 +567,7 @@ make_and_annotate_umap = function(df, }else{ umap_out = umap_transform(X=df, - model=umap_out$model, + model=umap_out, seed=seed, batch = TRUE, n_threads = 1, @@ -1364,7 +1364,7 @@ predict_single_sample_DLBCLone <- function( message("Warning: you have supplied more than one sample to test with. Will proceed with all") } - trained_features = colnames(train_df[-1]) # Exclude sample_id column + trained_features <- train_df %>% column_to_rownames("sample_id") %>% select(where(is.numeric)) %>% colnames() train_df = train_df %>% column_to_rownames("sample_id") %>% @@ -1653,7 +1653,7 @@ predict_single_sample_DLBCLone <- function( prediction = test_pred, train_prediction = train_pred, umap_input_features = trained_features, - model=umap_out$model, + model=umap_out, plot = pp, anno_df = predictions_df, projection = projection$df From 3e4dff874b33902f1e36bb53dcecc8d468f500dd Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 19 Jun 2025 14:33:11 -0700 Subject: [PATCH 34/79] adding function DLBCL_save_optimized --- NAMESPACE | 1 + R/umap.R | 81 ++++++++++++++++++++++++++++++++++ man/DLBCLone_save_optimized.Rd | 53 ++++++++++++++++++++++ 3 files changed, 135 insertions(+) create mode 100644 man/DLBCLone_save_optimized.Rd diff --git a/NAMESPACE b/NAMESPACE index 31c5963..9961d07 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,7 @@ # Generated by roxygen2: do not edit by hand export(DLBCLone_optimize_params) +export(DLBCLone_save_optimized) export(DLBCLone_train_test_plot) export(assemble_genetic_features) export(classify_bl) diff --git a/R/umap.R b/R/umap.R index b4cbe10..ad0416f 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1711,3 +1711,84 @@ make_umap_scatterplot = function(df, ggMarginal(p,groupColour = TRUE,groupFill=TRUE) } + + +#' modal storage for DLBCLone outputs +#' +#' @param combined_mutation_df Data frame containing the mutation status of the samples used +#' @param metadata Metadata with truth labels in lymphgen column +#' @param truth_classes Vector of classes to used for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other") +#' @param optimized_out output of DLBCLone_optimize_params(), if set to NULL values will not be saved +#' @param predict_single output of predict_single_sample_DLBCLone(), if set to NULL values will not be saved +#' @param neighborhood_plot output of make_neighborhood_plot(), if set to NULL values will not be saved +#' @param path Path to save the files +#' @param name_prefix Prefix for the saved files, all files will be in path and start with name_prefix +#' +#' @returns saves the files to the specified path +#' @export +#' +#' @examples +#' DLBCLone_save_optimized( +#' combined_mutation_df = status_df, +#' metadata = status_metadata, +#' truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), +#' optimized_out = lymphgen_DLBCLone, +#' predict_single=NULL, +#' neighborhood_plot=NULL, +#' path="/save_optimized/trial_folder", +#' name_prefix="test_A" +#' ) +#' +DLBCLone_save_optimized = function( + combined_mutation_df, + metadata, + truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), + optimized_out=NULL, + predict_single=NULL, + neighborhood_plot=NULL, + path="models/", + name_prefix="test" +){ + #all files will be in path and start with name_prefix + prefix = paste0(path,"/",name_prefix) + + out_mut = paste0(prefix,"_mutation_status_df.tsv") + write_tsv(combined_mutation_df,file=out_mut) + + out_meta = paste0(prefix,"_metadata.tsv") + write_tsv(metadata,file=out_meta) + + out_classes = paste0(prefix,"_classes.txt") + write.table(truth_classes,file=out_classes,quote=F,row.names=F) + + if(!is.null(optimized_out)){ # DLBCLone_optimize_params() + out_param = paste0(prefix,"_optimized_best_params.tsv") + write_tsv(optimized_out$best_params,file=out_param) + + out_model = paste0(prefix,"_optimized_uwot.rds") + save_uwot(optimized_out$model,file=out_model) + } + + if(!is.null(predict_single)){ # predict_single_sample_DLBCLone() + out_prediction = paste0(prefix,"_predict_single_prediction.tsv") + write_tsv(predict_single$prediction,file=out_prediction) + + out_projection = paste0(prefix,"_predict_single_projection.tsv") + write_tsv(predict_single$projection,file=out_projection) + + out_anno_df = paste0(prefix, "_predict_single_anno_df.tsv") + write_tsv(predict_single$anno_df,file=out_anno_df) + + out_plot = paste0(prefix,"_predict_single_plot.pdf") + pdf(out_plot, width = 10, height = 14) + print(predict_single$plot) + dev.off() + } + + if(!is.null(neighborhood_plot)){ # make_neighborhood_plot() + out_neighbor = paste0(prefix,"_neighborhood_plot.pdf") + pdf(out_neighbor, width = 10, height = 14) + print(neighborhood_plot) + dev.off() + } +} diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd new file mode 100644 index 0000000..5cc7e4e --- /dev/null +++ b/man/DLBCLone_save_optimized.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_save_optimized} +\alias{DLBCLone_save_optimized} +\title{modal storage for DLBCLone outputs} +\usage{ +DLBCLone_save_optimized( + combined_mutation_df, + metadata, + truth_classes = c("MCD", "EZB", "BN2", "ST2", "N1", "Other"), + optimized_out = NULL, + predict_single = NULL, + neighborhood_plot = NULL, + path = "models/", + name_prefix = "test" +) +} +\arguments{ +\item{combined_mutation_df}{Data frame containing the mutation status of the samples used} + +\item{metadata}{Metadata with truth labels in lymphgen column} + +\item{truth_classes}{Vector of classes to used for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other")} + +\item{optimized_out}{output of DLBCLone_optimize_params(), if set to NULL values will not be saved} + +\item{predict_single}{output of predict_single_sample_DLBCLone(), if set to NULL values will not be saved} + +\item{neighborhood_plot}{output of make_neighborhood_plot(), if set to NULL values will not be saved} + +\item{path}{Path to save the files} + +\item{name_prefix}{Prefix for the saved files, all files will be in path and start with name_prefix} +} +\value{ +saves the files to the specified path +} +\description{ +modal storage for DLBCLone outputs +} +\examples{ +DLBCLone_save_optimized( + combined_mutation_df = status_df, + metadata = status_metadata, + truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), + optimized_out = lymphgen_DLBCLone, + predict_single=NULL, + neighborhood_plot=NULL, + path="/save_optimized/trial_folder", + name_prefix="test_A" +) + +} From 31ea9af697edda148fb2307948fdb19a8215f205 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 19 Jun 2025 14:40:27 -0700 Subject: [PATCH 35/79] forgot to add @import's --- R/umap.R | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/R/umap.R b/R/umap.R index ad0416f..f20d828 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1725,6 +1725,10 @@ make_umap_scatterplot = function(df, #' @param name_prefix Prefix for the saved files, all files will be in path and start with name_prefix #' #' @returns saves the files to the specified path +#' +#' @import uwot +#' @import readr +#' #' @export #' #' @examples @@ -1781,14 +1785,14 @@ DLBCLone_save_optimized = function( out_plot = paste0(prefix,"_predict_single_plot.pdf") pdf(out_plot, width = 10, height = 14) - print(predict_single$plot) + predict_single$plot dev.off() } if(!is.null(neighborhood_plot)){ # make_neighborhood_plot() out_neighbor = paste0(prefix,"_neighborhood_plot.pdf") pdf(out_neighbor, width = 10, height = 14) - print(neighborhood_plot) + neighborhood_plot dev.off() } } From 540997de15d1e2c500706f035affd9721ab55d53 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 20 Jun 2025 11:20:40 -0700 Subject: [PATCH 36/79] DLBCLone_load_optimized function, and best_params rds update to DLBCLone_save_optimized --- NAMESPACE | 1 + R/umap.R | 62 ++++++++++++++++++++++++++++++++-- man/DLBCLone_load_optimized.Rd | 26 ++++++++++++++ 3 files changed, 87 insertions(+), 2 deletions(-) create mode 100644 man/DLBCLone_load_optimized.Rd diff --git a/NAMESPACE b/NAMESPACE index 9961d07..835b8c8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export(DLBCLone_load_optimized) export(DLBCLone_optimize_params) export(DLBCLone_save_optimized) export(DLBCLone_train_test_plot) diff --git a/R/umap.R b/R/umap.R index f20d828..d7a4a39 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1766,8 +1766,8 @@ DLBCLone_save_optimized = function( write.table(truth_classes,file=out_classes,quote=F,row.names=F) if(!is.null(optimized_out)){ # DLBCLone_optimize_params() - out_param = paste0(prefix,"_optimized_best_params.tsv") - write_tsv(optimized_out$best_params,file=out_param) + out_param = paste0(prefix,"_optimized_best_params.rds") + saveRDS(optimized_out$best_params,file=out_param) out_model = paste0(prefix,"_optimized_uwot.rds") save_uwot(optimized_out$model,file=out_model) @@ -1796,3 +1796,61 @@ DLBCLone_save_optimized = function( dev.off() } } + + +#' load DLBCLone_save_optimized's DLBCLone_optimize_params outputs +#' +#' @param path Path to open saved the files +#' @param name_prefix Prefix of the saved files, all files will be in path and start with name_prefix +#' +#' @returns saves the files to the specified path +#' +#' @import uwot +#' @import readr +#' @import dplyr +#' +#' @export +#' +#' @examples +#' load_optimized <- DLBCLone_load_optimized( +#' path="/save_optimized/trial_folder", +#' name_prefix="test_A" +#' ) +#' + +DLBCLone_load_optimized <- function( + path="models/", + name_prefix="test" +){ + #all files will be in path and start with name_prefix + prefix = paste0(path,"/",name_prefix) + + load_mut = paste0(prefix,"_mutation_status_df.tsv") + load_meta = paste0(prefix,"_metadata.tsv") + load_classes = paste0(prefix,"_classes.txt") + load_param = paste0(prefix,"_optimized_best_params.rds") + load_model = paste0(prefix,"_optimized_uwot.rds") + + required_files <- c(load_mut, load_meta, load_classes, load_param, load_model) + + # Check existence + missing_files <- required_files[!file.exists(required_files)] + if (length(missing_files) > 0) { + stop("The following required files are missing:\n", paste(missing_files, collapse = "\n")) + } + + mut_df <- read_tsv(load_mut) + metadata <- read_tsv(load_meta) %>% + mutate(lymphgen = as.factor(lymphgen)) + classes <- read.table(load_classes) + best_params <- readRDS(load_param) + uwot_model <- load_uwot(load_model) + + return(list( + df = mut_df, + metadata = metadata, + truth_classes = classes, + best_params = best_params, + model = uwot_model + )) +} diff --git a/man/DLBCLone_load_optimized.Rd b/man/DLBCLone_load_optimized.Rd new file mode 100644 index 0000000..f5a8d7e --- /dev/null +++ b/man/DLBCLone_load_optimized.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_load_optimized} +\alias{DLBCLone_load_optimized} +\title{load DLBCLone_save_optimized's DLBCLone_optimize_params outputs} +\usage{ +DLBCLone_load_optimized(path = "models/", name_prefix = "test") +} +\arguments{ +\item{path}{Path to open saved the files} + +\item{name_prefix}{Prefix of the saved files, all files will be in path and start with name_prefix} +} +\value{ +saves the files to the specified path +} +\description{ +load DLBCLone_save_optimized's DLBCLone_optimize_params outputs +} +\examples{ +load_optimized <- DLBCLone_load_optimized( + path="/save_optimized/trial_folder", + name_prefix="test_A" +) + +} From 6f76b222a2a49e4f6c844c262957945a670ccd2d Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 24 Jun 2025 11:11:22 -0700 Subject: [PATCH 37/79] predict_single_sample update so that lymphgen truth label is used and not predicted label for training --- R/umap.R | 58 +++++++++++++++++--------------------------------------- 1 file changed, 17 insertions(+), 41 deletions(-) diff --git a/R/umap.R b/R/umap.R index d7a4a39..048ee72 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1457,35 +1457,6 @@ predict_single_sample_DLBCLone <- function( ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - - if(predict_training && !is.null(stored_train_prediction)){ - train_pred = stored_train_prediction - }else{ - train_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = train_coords, # <- predicitng training on self - k = best_params$k, - conf_threshold = best_params$threshold, - na_label = "Other", - use_weights = best_params$use_w, - ignore_top = ignore_top - ) - train_pred <- as.data.frame(train_pred) - prefix <- paste0("k_", best_params$k, ".") - colnames(train_pred) <- sub(prefix, "", colnames(train_pred)) - train_pred = rownames_to_column(train_pred, var = "sample_id") - - if(best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params - train_pred = mutate(train_pred,predicted_label_optimized = ifelse( - other_score > best_params$threshold_outgroup, - "Other", - predicted_label - )) - }else{ # optimize_for_other = F - train_pred = mutate(train_pred,predicted_label_optimized = predicted_label) - } - } test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -1527,9 +1498,15 @@ predict_single_sample_DLBCLone <- function( V2 = as.numeric(V2) ) - predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") - predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") - predictions_df = bind_rows(predictions_train_df, predictions_test_df) + predictions_test_df <- left_join(test_pred, projection$df, by = "sample_id") + + missing_cols <- setdiff(names(predictions_test_df), names(projection$df)) + na_cols <- setNames(rep(list(NA), length(missing_cols)), missing_cols) + projection_filled <- projection$df %>% + filter(sample_id %in% train_id) %>% + mutate(!!!na_cols) + + predictions_df <- bind_rows(predictions_test_df, projection_filled) if(make_plot){ title = paste0( @@ -1583,12 +1560,12 @@ predict_single_sample_DLBCLone <- function( # Add the predicted labels for Other (unclassified) cases, if provided if(!missing(other_df)){ in_df = bind_rows( - mutate(predictions_df,dataset=title2,lymphgen=predicted_label_optimized), - mutate(other_df,dataset=title3,lymphgen=predicted_label_optimized) + mutate(predictions_df,dataset=title2), + mutate(other_df,dataset=title3) ) }else{ in_df = bind_rows( - mutate(predictions_df,dataset=title2,lymphgen=predicted_label_optimized) + mutate(predictions_df,dataset=title2) ) } @@ -1600,11 +1577,11 @@ predict_single_sample_DLBCLone <- function( if(annotate_accuracy){ #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,predicted_label_optimized %in% truth_classes) %>% - group_by(predicted_label_optimized) %>% + centroids = filter(predictions_df,lymphgen %in% truth_classes) %>% + group_by(lymphgen) %>% summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% - mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) %>% - mutate(lymphgen=predicted_label_optimized) + mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) + centroids = left_join(centroids,acc_df) %>% mutate(label=paste(lymphgen,":",round(accuracy,3))) @@ -1651,12 +1628,11 @@ predict_single_sample_DLBCLone <- function( return(list( prediction = test_pred, - train_prediction = train_pred, umap_input_features = trained_features, model=umap_out, plot = pp, anno_df = predictions_df, - projection = projection$df + projection = projection$df )) } From e2db5397a5ce82693f02ba83294c013f52169d0d Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 24 Jun 2025 16:09:44 -0700 Subject: [PATCH 38/79] no_other option added to neighborhood plot --- R/umap.R | 7 ++++++- man/make_neighborhood_plot.Rd | 3 +++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/R/umap.R b/R/umap.R index 048ee72..686390c 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1219,6 +1219,7 @@ weighted_knn_predict_with_conf <- function( #' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. #' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. #' @param add_circle Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable. +#' @param drop_other Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot. #' @param label_column Does nothing, i.e. this is not currently working. #' #' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. @@ -1242,6 +1243,7 @@ make_neighborhood_plot <- function(single_sample_prediction_output, this_sample_id, prediction_in_title = TRUE, add_circle = TRUE, + drop_other = FALSE, label_column = "predicted_label_optimized"){ circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ @@ -1281,7 +1283,10 @@ make_neighborhood_plot <- function(single_sample_prediction_output, } links_df = mutate(links_df,my_x=my_x,my_y=my_y) links_df = links_df %>% select(V1, V2, my_x, my_y, group) %>% mutate(length = sqrt((V1 - my_x)^2 + (V2 - my_y)^2)) # Euclidean distance - + + if(drop_other){ + training_predictions = filter(training_predictions,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") + } pp=ggplot(mutate(training_predictions,group=lymphgen), aes(x=V1,y=V2,colour=group)) + diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index 5fa1e2c..8703be2 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -10,6 +10,7 @@ make_neighborhood_plot( this_sample_id, prediction_in_title = TRUE, add_circle = TRUE, + drop_other = FALSE, label_column = "predicted_label_optimized" ) } @@ -25,6 +26,8 @@ Must include elements \code{prediction} (data frame with prediction results) and \item{add_circle}{Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable.} +\item{drop_other}{Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot.} + \item{label_column}{Does nothing, i.e. this is not currently working.} } \value{ From 303c2345505971dfb6e28805047cdce7cb88ff1a Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 27 Jun 2025 10:46:09 -0700 Subject: [PATCH 39/79] knn update for vectorized k --- R/umap.R | 23 ++++++++++++++++++++--- man/weighted_knn_predict_with_conf.Rd | 3 ++- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/R/umap.R b/R/umap.R index 686390c..6a65d0f 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1023,7 +1023,8 @@ weighted_knn_predict_with_conf <- function( use_weights = TRUE, ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE #big change here. Other is considered separately for optimization + separate_other = TRUE, #big change here. Other is considered separately for optimization + max_neighbors = 500 ) { if (nrow(train_coords)==0 || nrow(test_coords) == 0) { print("train_coords:") @@ -1034,8 +1035,7 @@ weighted_knn_predict_with_conf <- function( } train_labels = as.character(train_labels) - max_k <- max(k) - nn <- get.knnx(train_coords, test_coords, max_k) + nn <- get.knnx(train_coords, test_coords, max_neighbors) results_list <- list() @@ -1099,6 +1099,13 @@ weighted_knn_predict_with_conf <- function( weights <- weights[valid] distances <- distances[valid] neighbors <- neighbors[valid] + #number of neighbours should be, at least, k - 1. If less than that, warn the user + if(length(neighbors) < k-1){ + print(paste("Warning: number of neighbors is less than k-1.")) + print(paste("i:", i,"k:",k)) + print(paste("num_neighbors:",length(neighbors))) + print(table(valid)) + } #now take the first k neighbors if(length(neighbor_labels) > curr_k){ @@ -1109,6 +1116,7 @@ weighted_knn_predict_with_conf <- function( } others_closer = which(other_dists < max(distances)) + others_distances = other_dists[others_closer] if(use_weights){ others_weights = 1 / others_distances @@ -1118,6 +1126,7 @@ weighted_knn_predict_with_conf <- function( neighbors_other = length(others_closer) other_weighted_votes = sum(others_weights) mean_other_dist = mean(others_distances) + if (length(neighbor_labels) == 0) { preds[i] <- "Other" confs[i] <- 1 @@ -1203,6 +1212,14 @@ weighted_knn_predict_with_conf <- function( stop("") } to_return = bind_cols(to_return,all_neighbors) + if(nrow(to_return ) != nrow(test_coords)){ + print("mismatch in row number for to_return and test_coords") + print(nrow(to_return)) + print(nrow(test_coords)) + print(head(to_return)) + print(head(test_coords)) + stop("") + } } results_list[[paste0("k_", curr_k)]] <- to_return } diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index e4eaf13..74d7971 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -16,7 +16,8 @@ weighted_knn_predict_with_conf( use_weights = TRUE, ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE + separate_other = TRUE, + max_neighbors = 500 ) } \arguments{ From e05754f7638ed463eea3e9ae874e331547207015 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 3 Jul 2025 15:26:17 -0700 Subject: [PATCH 40/79] summarize_all_ssm_status added, assemble_genetic_features updated to ryans code, make_neighborhood_plot updated to ryans code except this has the circle fix, make_umap_scatterplot now has facet option --- NAMESPACE | 1 + R/umap.R | 355 ++++++++++++++++++++++++++----- man/DLBCLone_optimize_params.Rd | 3 +- man/assemble_genetic_features.Rd | 6 +- man/make_neighborhood_plot.Rd | 10 +- man/make_umap_scatterplot.Rd | 5 +- man/summarize_all_ssm_status.Rd | 83 ++++++++ 7 files changed, 408 insertions(+), 55 deletions(-) create mode 100644 man/summarize_all_ssm_status.Rd diff --git a/NAMESPACE b/NAMESPACE index 835b8c8..850f7bc 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,6 +16,7 @@ export(make_umap_scatterplot) export(massage_matrix_for_clustering) export(optimize_outgroup) export(predict_single_sample_DLBCLone) +export(summarize_all_ssm_status) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) import(GAMBLR.data) diff --git a/R/umap.R b/R/umap.R index 6a65d0f..91657da 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,4 +1,137 @@ +#' Summarize SSM (Somatic Single Nucleotide Mutation) Status Across Samples +#' +#' This function summarizes the mutation status for a set of genes +#' across multiple samples, separating mutations by class for genes specified +#' by \code{separate_by_class_genes} and counting the number of hits +#' in each sample per mutation category. +#' +#' @param maf_df A data frame containing mutation annotation format (MAF) data, +#' with at least the following columns: +#' \code{Hugo_Symbol}, \code{Variant_Classification}, and \code{Tumor_Sample_Barcode}. +#' @param silent_maf_df (Optional) A separate data frame containing silent mutation data if +#' the user doesn't want to pull silent mutation status from \code{maf_df}. +#' This argument is useful when you want to combine mutations from +#' the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene +#' @param these_samples_metadata A data frame containing metadata for the samples, +#' with at least a \code{sample_id} column. +#' Any sample that does not have a matching sample_id in these_samples_metadata will be dropped. +#' @param genes_of_interest A character vector of gene symbols to include +#' in the summary. If missing, defaults to all Tier 1 B-cell lymphoma genes. +#' @param synon_genes (Optional) A character vector of gene symbols for which +#' synonymous mutations should be included. +#' @param separate_by_class_genes (Optional) A character vector of +#' gene symbols for which mutations should be separated by class +#' (e.g., "Nonsense_Mutation", "Missense_Mutation"). +#' @param count_hits Logical; if \code{TRUE}, counts the number of mutations +#' per gene per sample. If \code{FALSE} (default), only presence/absence is recorded. +#' +#' @return A wide-format data frame (matrix) with samples as rows and +#' mutation types as columns. Each cell contains either the count of +#' mutations (if \code{count_hits = TRUE}) or a binary indicator (0/1) +#' for mutation presence. +#' +#' @details +#' - Mutations are grouped and optionally separated by mutation +#' class for genes specified in \code{separate_by_class_genes} +#' - Synonymous mutations can be counted as another separate feature for genes specified by \code{synon_genes} genes. +#' - The function simplifies mutation annotations and pivots the data to a wide format suitable for downstream analysis. +#' +#' @examples +#' # A basic example, using only the output of get_all_coding_ssm +#' # Since the only non-coding class this function handles is Silent, +#' # we will be missing most non-coding types such as Intron, UTR, Flank +#' \dontrun{ +#' sample_metadata = get_sample_metadata() %>% filter(seq_type!= "mrna") +#' +#' maf_data = get_all_coding_ssm(sample_metadata) +#' +#' mutation_matrix <- summarize_all_ssm_status( +#' maf_df = maf_data, +#' these_samples_metadata = sample_metadata, +#' genes_of_interest = c("TP53", "SGK1", "BCL2"), +#' synon_genes = c("BCL2"), +#' separate_by_class_genes = c("TP53","SGK1"), +#' count_hits = FALSE +#' ) +#' } +#' +#' @import dplyr tidyr tibble +#' @export +summarize_all_ssm_status <- function(maf_df, + these_samples_metadata, + genes_of_interest, + synon_genes, + silent_maf_df, + separate_by_class_genes = NULL, + count_hits = FALSE){ + if(missing(genes_of_interest)){ + message("defaulting to all Tier 1 B-cell lymphoma genes") + genes_of_interest = filter(lymphoma_genes, + DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 ) %>% + pull(Gene) %>% unique() + } + if(missing(silent_maf_df)){ + silent_maf_df = maf_df + } + maf_df = filter(maf_df, + Hugo_Symbol %in% genes_of_interest) + if(!missing(these_samples_metadata)){ + maf_df = filter(maf_df, + Tumor_Sample_Barcode %in% these_samples_metadata$sample_id) + } + if(!missing(synon_genes)){ + if(any(!synon_genes %in% silent_maf_df$Hugo_Symbol)){ + missing = synon_genes[!synon_genes %in% silent_maf_df$Hugo_Symbol] + not_missing = synon_genes[synon_genes %in% silent_maf_df$Hugo_Symbol] + + message("Warning: Some synonymous genes have no mutations in silent_maf_df") + message(paste(missing,collapse=", ")) + } + maf_nonsilent = filter(maf_df, Variant_Classification %in% vc_nonSynonymous) + maf_silent = filter(silent_maf_df, ! Variant_Classification %in% vc_nonSynonymous, Hugo_Symbol %in% synon_genes) + maf_df = bind_rows(maf_silent,maf_nonsilent) + } + #Simplify annotations + silent_types = c("Silent","Intron","5'UTR","3'UTR","5'Flank","3'Flank") + nonsense_types = c("Nonsense_Mutation","Frame_Shift_Del","Frame_Shift_Ins") + missense_types = c("In_Frame_Del", + "In_Frame_Ins", + "Missense_Mutation", + "Splice_Region") + gene_mutations = mutate(maf_df, + mutation_type=case_when( + Variant_Classification %in% nonsense_types ~ "Nonsense_Mutation", + Variant_Classification %in% missense_types ~ "Missense_Mutation", + Variant_Classification %in% silent_types ~ "Silent", + TRUE ~ Variant_Classification) + ) %>% + mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) + separated_coding_maf = filter(gene_mutations, + Hugo_Symbol %in% genes_of_interest, + Hugo_Symbol %in% separate_by_class_genes, + mutation_type != "Silent") + + unseparated_coding_maf = filter(gene_mutations, + Hugo_Symbol %in% genes_of_interest, !Hugo_Symbol %in% separate_by_class_genes, mutation_type != "Silent") %>% + mutate(mutation = paste(Hugo_Symbol,"Coding",sep=":")) + separated_silent_maf = filter(gene_mutations,Hugo_Symbol %in% synon_genes, mutation_type == "Silent") %>% + mutate(mutation = paste(Hugo_Symbol,"Silent",sep=":")) + gene_mutations = bind_rows(separated_coding_maf, unseparated_coding_maf,separated_silent_maf) + + mutation_distinct = select(gene_mutations,mutation,Tumor_Sample_Barcode) %>% + mutate(mutated = 1) %>% + group_by(Tumor_Sample_Barcode,mutation,mutated) %>% + count() %>% ungroup() + if(count_hits){ + mutation_distinct = mutation_distinct %>% select(-mutated) %>% rename(mutated=n) %>% distinct() + } else{ + mutation_distinct = mutation_distinct %>% select(-n) %>% distinct() + } + mutation_wide = pivot_wider(mutation_distinct, names_from = "mutation",values_from = "mutated",values_fill = 0) %>% + column_to_rownames("Tumor_Sample_Barcode") + return(mutation_wide) +} #' Assemble genetic features for UMAP input #' @@ -14,6 +147,7 @@ #' @param sv_value Value to assign for SV presence (default: 3). #' @param synon_value Value to assign for synonymous mutations (default: 1). #' @param coding_value Value to assign for coding mutations (default: 2). +#' @param verbose Defaults to FALSE #' #' @return Matrix of assembled features for each sample. #' @export @@ -27,7 +161,9 @@ assemble_genetic_features <- function(these_samples_metadata, sv_value = 3, synon_value = 1, coding_value = 2, - include_ashm = TRUE){ + include_ashm = TRUE, + annotated_sv, + verbose = FALSE){ if(include_ashm){ #TODO: ensure this supports both genome builds correctly some_regions = GAMBLR.utils::create_bed_data( @@ -59,17 +195,20 @@ assemble_genetic_features <- function(these_samples_metadata, ashm_matrix = ashm_matrix_genome }else if("capture" %in% these_samples_metadata$seq_type){ ashm_matrix = ashm_matrix_cap + }else{ stop("no eligible seq_type provided in these_samples_metadata") } -} - + } + + status_with_silent = get_coding_ssm_status( these_samples_metadata = these_samples_metadata, + # drop all coding variants from this one maf_data = maf_with_synon, include_hotspots = TRUE, - genes_of_interest = "MYD88", + genes_of_interest = hotspot_genes, include_silent_genes = synon_genes[synon_genes %in% genes], gene_symbols = genes ) @@ -79,29 +218,40 @@ assemble_genetic_features <- function(these_samples_metadata, these_samples_metadata = these_samples_metadata, maf_data = maf_with_synon, include_hotspots = TRUE, - genes_of_interest = "MYD88", + genes_of_interest = hotspot_genes, include_silent = FALSE, gene_symbols = genes ) - status_without_silent = status_without_silent %>% column_to_rownames("sample_id") + status_without_silent = status_without_silent %>% + column_to_rownames("sample_id") - if(any(! colnames(status_with_silent) %in% colnames(status_without_silent))){ + if (any(! colnames(status_with_silent) %in% colnames(status_without_silent))){ print(colnames(status_with_silent)[!colnames(status_with_silent) %in% colnames(status_without_silent)]) stop("some columns are missing from the status_without_silent matrix") } - if(include_ashm){ + # Instead of just relying on the MAF(s) supplied by + if (include_ashm){ ashm_matrix = select(ashm_matrix, any_of(colnames(status_with_silent))) %>% select(any_of(synon_genes)) - + ashm_matrix[ashm_matrix>1]= 1 + + if(verbose){ + print(head(colSums(ashm_matrix))) + print(head(ashm_matrix[,c(1:10)])) + } #fill in gaps from aSHM (other non-coding variants in the genes) missing = status_with_silent[rownames(ashm_matrix), colnames(ashm_matrix)]==0 & ashm_matrix[rownames(ashm_matrix), - colnames(ashm_matrix)] == synon_value - - status_with_silent[rownames(missing), - colnames(missing)] = synon_value + colnames(ashm_matrix)] > 0 + + + fill = missing + fill[]=0 + fill[missing] = synon_value + status_with_silent[rownames(fill), + colnames(fill)] = fill } @@ -114,6 +264,22 @@ assemble_genetic_features <- function(these_samples_metadata, bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) + + # include SV from provided annotated SV data frame + if(!missing(annotated_sv)){ + annotated_sv = filter(annotated_sv,tumour_sample_id %in% these_samples_metadata$sample_id) + bcl2_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL2") %>% pull(tumour_sample_id) + bcl6_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL6") %>% pull(tumour_sample_id) + myc_sv_id = filter(annotated_sv,!is.na(partner),gene=="MYC") %>% pull(tumour_sample_id) + n_b = length(bcl2_id) + print(paste(n_b,"BCL2 FISH")) + bcl2_id = unique(c(bcl2_id,bcl2_sv_id)) + n_b = length(bcl2_id) + print(paste(n_b,"BCL2 FISH or SV")) + bcl6_id = unique(c(bcl6_id,bcl6_sv_id)) + myc_id = unique(c(myc_id,myc_sv_id)) + } + status_combined[,"BCL2_SV"] = 0 status_combined[bcl2_id,"BCL2_SV"] = sv_value @@ -529,7 +695,19 @@ make_and_annotate_umap = function(df, rng_type = "deterministic") # possibly add rng_type = "deterministic" }else if(algorithm == "tumap"){ - umap_out = tumap(df %>% as.matrix(), + X = df %>% as.matrix() + umap_args = list(X=X, + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") + umap_out = tumap(X, n_neighbors = n_neighbors, metric = metric, ret_model = ret_model, @@ -684,7 +862,8 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, verbose = FALSE, seed = 12345, maximize = "balanced_accuracy", - exclude_other_for_accuracy = FALSE + exclude_other_for_accuracy = FALSE, + weights_opt = c(TRUE) ) { if(optimize_for_other){ exclude_other_for_accuracy = FALSE @@ -693,7 +872,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, } na_opt = c("drop") num_class = length(truth_classes) - weights_opt = c(TRUE,FALSE) + threshs = seq(0,0.9,0.1) ks = seq(min_k,max_k,2) results <- data.frame() @@ -1255,13 +1434,17 @@ weighted_knn_predict_with_conf <- function( #' # and 'output' is the result of DLBCLone_predict_single_sample #' # on sample_id "SAMPLE123": #' make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") -make_neighborhood_plot <- function(single_sample_prediction_output, - training_predictions, - this_sample_id, - prediction_in_title = TRUE, - add_circle = TRUE, - drop_other = FALSE, - label_column = "predicted_label_optimized"){ +make_neighborhood_plot <- function( + single_sample_prediction_output, + training_predictions, + this_sample_id, + prediction_in_title = TRUE, + add_circle = TRUE, + label_column = "predicted_label_optimized", + point_size = 0.5, + point_alpha = 0.9, + line_alpha = 0.9 +){ circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ r = diameter / 2 @@ -1272,12 +1455,24 @@ make_neighborhood_plot <- function(single_sample_prediction_output, } if(missing(training_predictions)){ training_predictions = single_sample_prediction_output$anno_df + }else if(missing(single_sample_prediction_output)){ + #Just plot the single sample in the context of the rest based on the optimization + single_sample_prediction_output = list() + single_sample_prediction_output[["prediction"]] = filter(training_predictions, sample_id==this_sample_id) + single_sample_prediction_output[["anno_df"]] = training_predictions + }else{ + single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction, sample_id==this_sample_id) } - single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction,sample_id==this_sample_id) + #extract the sample_id for all the nearest neighbors with non-Other labels - my_neighbours = filter(single_sample_prediction_output$prediction, - sample_id == this_sample_id) %>% - pull(neighbor_id) %>% strsplit(.,",") %>% unlist() + my_neighbours = filter( + single_sample_prediction_output$prediction, + sample_id == this_sample_id + ) %>% + pull(neighbor_id) %>% + strsplit(.,",") %>% + unlist() + #set up links connecting each neighbor to the sample's point links_df = filter(training_predictions,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) @@ -1305,10 +1500,11 @@ make_neighborhood_plot <- function(single_sample_prediction_output, training_predictions = filter(training_predictions,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") } - pp=ggplot(mutate(training_predictions,group=lymphgen), - aes(x=V1,y=V2,colour=group)) + - geom_point(alpha=0.8,size=0.5) + - geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=0.5)+ + pp=ggplot( + mutate(training_predictions,group=lymphgen), + aes(x=V1,y=V2,colour=group)) + + geom_point(alpha=point_alpha,size=point_size) + + geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=line_alpha)+ scale_colour_manual(values=get_gambl_colours()) + ggtitle(title)+ theme_minimal() @@ -1667,19 +1863,23 @@ predict_single_sample_DLBCLone <- function( #' @param high_confidence #' @param custom_colours #' @param add_labels +#' @param facet If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely #' #' @returns #' @export #' #' @examples -make_umap_scatterplot = function(df, - drop_composite = TRUE, - colour_by="lymphgen", - drop_other = FALSE, - high_confidence = FALSE, - custom_colours, - add_labels = FALSE){ - +make_umap_scatterplot = function( + df, + drop_composite = TRUE, + colour_by="lymphgen", + drop_other = FALSE, + high_confidence = FALSE, + custom_colours, + add_labels = FALSE, + facet = FALSE # if TRUE: truth, predicted, predicted_optimized +){ + if(!missing(custom_colours)){ cols = custom_colours }else{ @@ -1698,16 +1898,75 @@ make_umap_scatterplot = function(df, labels = group_by(df,!!sym(colour_by)) %>% summarise(median_x = median(V1),median_y = median(V2)) } - - p = ggplot(df, - aes(x=V1,y=V2,colour=!!sym(colour_by),label=cohort)) + geom_point(alpha=0.8) + - scale_colour_manual(values=cols) + theme_Morons() + - guides(colour = guide_legend(nrow = 1)) - if(add_labels){ - p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + + if(facet){ + truth <- df + p_truth = ggplot(truth,aes( + x=V1, + y=V2, + colour=!!sym(colour_by), + label=cohort + )) + + geom_point(alpha=0.8) + + labs(title = "Truth") + + scale_colour_manual(values=cols) + + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + if(add_labels){ + p_truth = p_truth + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + + predicted <- df %>% mutate(lymphgen=predicted_label) + p_predicted = ggplot(predicted,aes( + x=V1, + y=V2, + colour=!!sym(colour_by), + label=cohort + )) + + geom_point(alpha=0.8) + + labs(title = "Predicted") + + scale_colour_manual(values=cols) + + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + if(add_labels){ + p_predicted = p_predicted + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + + predicted_optimized <- df %>% mutate(lymphgen=predicted_label_optimized) + p_predicted_optimized = ggplot(predicted_optimized,aes( + x=V1, + y=V2, + colour=!!sym(colour_by), + label=cohort + )) + + geom_point(alpha=0.8) + + labs(title = "Predicted_Optimized") + + scale_colour_manual(values=cols) + + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + if(add_labels){ + p_predicted_optimized = p_predicted_optimized + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + + ppp = ggarrange(p_truth,p_predicted,p_predicted_optimized, ncol = 2, nrow = 2) + return(ppp) + + }else{ + p = ggplot(df,aes( + x=V1, + y=V2, + colour=!!sym(colour_by), + label=cohort + )) + + geom_point(alpha=0.8) + + scale_colour_manual(values=cols) + + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + if(add_labels){ + p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + ggMarginal(p,groupColour = TRUE,groupFill=TRUE) } - ggMarginal(p,groupColour = TRUE,groupFill=TRUE) - } diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 76ea427..c85a7b0 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -16,7 +16,8 @@ DLBCLone_optimize_params( verbose = FALSE, seed = 12345, maximize = "balanced_accuracy", - exclude_other_for_accuracy = FALSE + exclude_other_for_accuracy = FALSE, + weights_opt = c(TRUE) ) } \arguments{ diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd index a494ea4..dff771b 100644 --- a/man/assemble_genetic_features.Rd +++ b/man/assemble_genetic_features.Rd @@ -15,7 +15,9 @@ assemble_genetic_features( sv_value = 3, synon_value = 1, coding_value = 2, - include_ashm = TRUE + include_ashm = TRUE, + annotated_sv, + verbose = FALSE ) } \arguments{ @@ -36,6 +38,8 @@ assemble_genetic_features( \item{synon_value}{Value to assign for synonymous mutations (default: 1).} \item{coding_value}{Value to assign for coding mutations (default: 2).} + +\item{verbose}{Defaults to FALSE} } \value{ Matrix of assembled features for each sample. diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index 8703be2..ce4db34 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -10,8 +10,10 @@ make_neighborhood_plot( this_sample_id, prediction_in_title = TRUE, add_circle = TRUE, - drop_other = FALSE, - label_column = "predicted_label_optimized" + label_column = "predicted_label_optimized", + point_size = 0.5, + point_alpha = 0.9, + line_alpha = 0.9 ) } \arguments{ @@ -26,9 +28,9 @@ Must include elements \code{prediction} (data frame with prediction results) and \item{add_circle}{Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable.} -\item{drop_other}{Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot.} - \item{label_column}{Does nothing, i.e. this is not currently working.} + +\item{drop_other}{Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot.} } \value{ A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index 9d70c39..a9a467a 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -11,7 +11,8 @@ make_umap_scatterplot( drop_other = FALSE, high_confidence = FALSE, custom_colours, - add_labels = FALSE + add_labels = FALSE, + facet = FALSE ) } \arguments{ @@ -28,6 +29,8 @@ make_umap_scatterplot( \item{custom_colours}{} \item{add_labels}{} + +\item{facet}{If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely} } \description{ Make UMAP scatterplot diff --git a/man/summarize_all_ssm_status.Rd b/man/summarize_all_ssm_status.Rd new file mode 100644 index 0000000..ec7b844 --- /dev/null +++ b/man/summarize_all_ssm_status.Rd @@ -0,0 +1,83 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{summarize_all_ssm_status} +\alias{summarize_all_ssm_status} +\title{Summarize SSM (Somatic Single Nucleotide Mutation) Status Across Samples} +\usage{ +summarize_all_ssm_status( + maf_df, + these_samples_metadata, + genes_of_interest, + synon_genes, + silent_maf_df, + separate_by_class_genes = NULL, + count_hits = FALSE +) +} +\arguments{ +\item{maf_df}{A data frame containing mutation annotation format (MAF) data, +with at least the following columns: +\code{Hugo_Symbol}, \code{Variant_Classification}, and \code{Tumor_Sample_Barcode}.} + +\item{these_samples_metadata}{A data frame containing metadata for the samples, +with at least a \code{sample_id} column. +Any sample that does not have a matching sample_id in these_samples_metadata will be dropped.} + +\item{genes_of_interest}{A character vector of gene symbols to include +in the summary. If missing, defaults to all Tier 1 B-cell lymphoma genes.} + +\item{synon_genes}{(Optional) A character vector of gene symbols for which +synonymous mutations should be included.} + +\item{silent_maf_df}{(Optional) A separate data frame containing silent mutation data if +the user doesn't want to pull silent mutation status from \code{maf_df}. +This argument is useful when you want to combine mutations from +the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene} + +\item{separate_by_class_genes}{(Optional) A character vector of +gene symbols for which mutations should be separated by class +(e.g., "Nonsense_Mutation", "Missense_Mutation").} + +\item{count_hits}{Logical; if \code{TRUE}, counts the number of mutations +per gene per sample. If \code{FALSE} (default), only presence/absence is recorded.} +} +\value{ +A wide-format data frame (matrix) with samples as rows and +mutation types as columns. Each cell contains either the count of +mutations (if \code{count_hits = TRUE}) or a binary indicator (0/1) +for mutation presence. +} +\description{ +This function summarizes the mutation status for a set of genes +across multiple samples, separating mutations by class for genes specified +by \code{separate_by_class_genes} and counting the number of hits +in each sample per mutation category. +} +\details{ +\itemize{ +\item Mutations are grouped and optionally separated by mutation +class for genes specified in \code{separate_by_class_genes} +\item Synonymous mutations can be counted as another separate feature for genes specified by \code{synon_genes} genes. +\item The function simplifies mutation annotations and pivots the data to a wide format suitable for downstream analysis. +} +} +\examples{ +# A basic example, using only the output of get_all_coding_ssm +# Since the only non-coding class this function handles is Silent, +# we will be missing most non-coding types such as Intron, UTR, Flank +\dontrun{ +sample_metadata = get_sample_metadata() \%>\% filter(seq_type!= "mrna") + +maf_data = get_all_coding_ssm(sample_metadata) + +mutation_matrix <- summarize_all_ssm_status( + maf_df = maf_data, + these_samples_metadata = sample_metadata, + genes_of_interest = c("TP53", "SGK1", "BCL2"), + synon_genes = c("BCL2"), + separate_by_class_genes = c("TP53","SGK1"), + count_hits = FALSE +) +} + +} From 4f5802de6d8f29a0e2a5b0216bd4da93f84782e4 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 3 Jul 2025 15:28:23 -0700 Subject: [PATCH 41/79] summarize_all_ssm_status formatted better, the only thing added was 'Splice_Site' to 'nonsense_types' --- R/umap.R | 129 +++++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 93 insertions(+), 36 deletions(-) diff --git a/R/umap.R b/R/umap.R index 91657da..0649a44 100644 --- a/R/umap.R +++ b/R/umap.R @@ -58,28 +58,36 @@ #' #' @import dplyr tidyr tibble #' @export -summarize_all_ssm_status <- function(maf_df, - these_samples_metadata, - genes_of_interest, - synon_genes, - silent_maf_df, - separate_by_class_genes = NULL, - count_hits = FALSE){ +summarize_all_ssm_status <- function( + maf_df, + these_samples_metadata, + genes_of_interest, + synon_genes, + silent_maf_df, + separate_by_class_genes = NULL, + count_hits = FALSE +){ if(missing(genes_of_interest)){ message("defaulting to all Tier 1 B-cell lymphoma genes") genes_of_interest = filter(lymphoma_genes, DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 ) %>% pull(Gene) %>% unique() } + if(missing(silent_maf_df)){ silent_maf_df = maf_df } - maf_df = filter(maf_df, - Hugo_Symbol %in% genes_of_interest) + + maf_df = filter( + maf_df, + Hugo_Symbol %in% genes_of_interest + ) + if(!missing(these_samples_metadata)){ maf_df = filter(maf_df, Tumor_Sample_Barcode %in% these_samples_metadata$sample_id) } + if(!missing(synon_genes)){ if(any(!synon_genes %in% silent_maf_df$Hugo_Symbol)){ missing = synon_genes[!synon_genes %in% silent_maf_df$Hugo_Symbol] @@ -92,45 +100,94 @@ summarize_all_ssm_status <- function(maf_df, maf_silent = filter(silent_maf_df, ! Variant_Classification %in% vc_nonSynonymous, Hugo_Symbol %in% synon_genes) maf_df = bind_rows(maf_silent,maf_nonsilent) } + #Simplify annotations - silent_types = c("Silent","Intron","5'UTR","3'UTR","5'Flank","3'Flank") - nonsense_types = c("Nonsense_Mutation","Frame_Shift_Del","Frame_Shift_Ins") - missense_types = c("In_Frame_Del", - "In_Frame_Ins", - "Missense_Mutation", - "Splice_Region") - gene_mutations = mutate(maf_df, - mutation_type=case_when( - Variant_Classification %in% nonsense_types ~ "Nonsense_Mutation", - Variant_Classification %in% missense_types ~ "Missense_Mutation", - Variant_Classification %in% silent_types ~ "Silent", - TRUE ~ Variant_Classification) - ) %>% - mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) - separated_coding_maf = filter(gene_mutations, - Hugo_Symbol %in% genes_of_interest, - Hugo_Symbol %in% separate_by_class_genes, - mutation_type != "Silent") + silent_types = c( + "Silent", + "Intron", + "5'UTR", + "3'UTR", + "5'Flank", + "3'Flank" + ) + nonsense_types = c( + "Nonsense_Mutation", + "Translation_Start_Site", + "Frame_Shift_Del", + "Frame_Shift_Ins", + "Nonstop_Mutation", + "Splice_Site" + ) + missense_types = c( + "In_Frame_Del", + "In_Frame_Ins", + "Missense_Mutation", + "Splice_Region" + ) + + gene_mutations = mutate( + maf_df, + mutation_type=case_when( + Variant_Classification %in% nonsense_types ~ "Nonsense_Mutation", + Variant_Classification %in% missense_types ~ "Missense_Mutation", + Variant_Classification %in% silent_types ~ "Silent", + TRUE ~ Variant_Classification + ) + ) %>% + mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) + + separated_coding_maf = filter( + gene_mutations, + Hugo_Symbol %in% genes_of_interest, + Hugo_Symbol %in% separate_by_class_genes, + mutation_type != "Silent" + ) - unseparated_coding_maf = filter(gene_mutations, - Hugo_Symbol %in% genes_of_interest, !Hugo_Symbol %in% separate_by_class_genes, mutation_type != "Silent") %>% + unseparated_coding_maf = filter( + gene_mutations, + Hugo_Symbol %in% genes_of_interest, + !Hugo_Symbol %in% separate_by_class_genes, + mutation_type != "Silent" + ) %>% mutate(mutation = paste(Hugo_Symbol,"Coding",sep=":")) - separated_silent_maf = filter(gene_mutations,Hugo_Symbol %in% synon_genes, mutation_type == "Silent") %>% + + separated_silent_maf = filter( + gene_mutations, + Hugo_Symbol %in% synon_genes, + mutation_type == "Silent" + ) %>% mutate(mutation = paste(Hugo_Symbol,"Silent",sep=":")) - gene_mutations = bind_rows(separated_coding_maf, unseparated_coding_maf,separated_silent_maf) + + gene_mutations = bind_rows( + separated_coding_maf, + unseparated_coding_maf, + separated_silent_maf + ) - mutation_distinct = select(gene_mutations,mutation,Tumor_Sample_Barcode) %>% + mutation_distinct = select( + gene_mutations, + mutation, + Tumor_Sample_Barcode + ) %>% mutate(mutated = 1) %>% - group_by(Tumor_Sample_Barcode,mutation,mutated) %>% - count() %>% ungroup() + group_by( + Tumor_Sample_Barcode, + mutation, + mutated + ) %>% + count() %>% + ungroup() + if(count_hits){ mutation_distinct = mutation_distinct %>% select(-mutated) %>% rename(mutated=n) %>% distinct() } else{ mutation_distinct = mutation_distinct %>% select(-n) %>% distinct() } + mutation_wide = pivot_wider(mutation_distinct, names_from = "mutation",values_from = "mutated",values_fill = 0) %>% - column_to_rownames("Tumor_Sample_Barcode") - return(mutation_wide) + column_to_rownames("Tumor_Sample_Barcode") + + return(mutation_wide) } #' Assemble genetic features for UMAP input From 9c874f49da33b6628f6d8f3a5ea71ade20621120 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 3 Jul 2025 17:05:28 -0700 Subject: [PATCH 42/79] make_and_annotate_umap ryans update, vectorized k version of knn and optimize params with identical outputs to ryans update --- R/umap.R | 189 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 107 insertions(+), 82 deletions(-) diff --git a/R/umap.R b/R/umap.R index 0649a44..3416ccf 100644 --- a/R/umap.R +++ b/R/umap.R @@ -675,24 +675,25 @@ DLBCLone_train_test_plot = function(test_df, #' metric="cosine") #' #' -make_and_annotate_umap = function(df, - metadata, - umap_out, - n_neighbors=55, - min_dist=0, - metric="cosine", - n_epochs=1500, - init="spca", - ret_model=TRUE, - na_vals = "drop", - join_column="sample_id", - seed=12345, - target_column, - target_metric="euclidean", - target_weight=0.5, - calc_dispersion = FALSE, - algorithm = "tumap"){ - +make_and_annotate_umap = function( + df, + metadata, + umap_out, + n_neighbors=55, + min_dist=0, + metric="cosine", + n_epochs=1500, + init="spca", + ret_model=TRUE, + na_vals = "drop", + join_column="sample_id", + seed=12345, + target_column, + target_metric="euclidean", + target_weight=0.5, + calc_dispersion = FALSE, + algorithm = "tumap" +){ # Function to compute mean (or median) pairwise distance within a group pairwise_dispersion <- function(df_group) { coords <- as.matrix(df_group[, c("V1", "V2")]) @@ -706,10 +707,18 @@ make_and_annotate_umap = function(df, if(na_vals == "to_zero"){ df[is.na(df)] = 0 }else if(na_vals == "drop"){ - df <- df[, colSums(is.na(df)) == 0] + if(any(sapply(df, is.factor))){ + numeric_cols = names(df)[!sapply(df, is.factor)] + df <- df[, colSums(is.na(df[,numeric_cols])) == 0] + rs = rowSums(df[,numeric_cols],na.rm=TRUE) + df = df[rs>0,] + } else{ + df <- df[, colSums(is.na(df)) == 0] + rs = rowSums(df,na.rm=TRUE) + df = df[rs>0,] + } } - rs = rowSums(df,na.rm=TRUE) - df = df[rs>0,] + if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } @@ -735,46 +744,52 @@ make_and_annotate_umap = function(df, if(missing(umap_out)){ if(missing(target_column)){ if(algorithm == "umap"){ - umap_out = umap2(df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - a=1.8956, - b = 0.806, - approx_pow=TRUE, - init=init, - seed = seed, - n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic") # possibly add rng_type = "deterministic" + umap_out = umap2( + df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + a=1.8956, + b = 0.806, + approx_pow=TRUE, + init=init, + seed = seed, + n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic" + ) # possibly add rng_type = "deterministic" }else if(algorithm == "tumap"){ X = df %>% as.matrix() - umap_args = list(X=X, - n_neighbors = n_neighbors, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic") - umap_out = tumap(X, - n_neighbors = n_neighbors, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic") + umap_args = list( + X=X, + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic" + ) + umap_out = tumap( + X, + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic" + ) }else{ stop("unsupported algorithm option") } @@ -785,29 +800,31 @@ make_and_annotate_umap = function(df, } metadata[[target_column]] = factor(metadata[[target_column]]) print(table(metadata[[target_column]])) - umap_out = umap2(df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility - y = metadata[[target_column]], - target_metric = target_metric, - target_weight = target_weight - ) # possibly add rng_type = "deterministic" + umap_out = umap2( + df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility + y = metadata[[target_column]], + target_metric = target_metric, + target_weight = target_weight + ) # possibly add rng_type = "deterministic" } }else{ - umap_out = umap_transform(X=df, - model=umap_out, - seed=seed, - batch = TRUE, - n_threads = 1, - n_sgd_threads = 1) - + umap_out = umap_transform( + X=df, + model=umap_out, + seed=seed, + batch = TRUE, + n_threads = 1, + n_sgd_threads = 1 + ) } if(!is.null(names(umap_out))){ @@ -976,10 +993,14 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids } pred_all = weighted_knn_predict_with_conf( @@ -1000,10 +1021,14 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids } n_other = nrow(test_coords) @@ -1079,8 +1104,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, pred_factor = pred_factor[keep_idx] } - - if(verbose){ print("true_factor") print(levels(true_factor )) @@ -1145,7 +1168,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, N1_sn = unname(sn["Class: N1"]), BN2_sn= unname(sn["Class: BN2"]), ST2_sn = unname(sn["Class: ST2"]), - N1_bacc = unname(bal_acc["Class: N1"]), BN2_bacc = unname(bal_acc["Class: BN2"]), MCD_bacc = unname(bal_acc["Class: MCD"]), EZB_bacc = unname(bal_acc["Class: EZB"]), @@ -1186,8 +1208,10 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = outs$df %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids pred = weighted_knn_predict_with_conf( - train_coords = train_coords, + train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, k=best_params$k, @@ -1423,6 +1447,7 @@ weighted_knn_predict_with_conf <- function( neighbor = paste(neighbors,collapse=","), distance = paste(round(distances, 3),collapse=","), label = paste(neighbor_labels,collapse=","), + vote_labels = paste(names(weighted_votes),collapse=","), weighted_votes = paste(weighted_votes,collapse=","), neighbors_other = neighbors_other, other_weighted_votes = other_weighted_votes, From 2bbb6038b43ae0861818d27684724169048c0cf5 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 4 Jul 2025 10:40:40 -0700 Subject: [PATCH 43/79] optimize_outgroup reformat, predict_single_sample max_neighbors added --- R/umap.R | 90 ++++++++++++++------------- man/predict_single_sample_DLBCLone.Rd | 10 +-- 2 files changed, 50 insertions(+), 50 deletions(-) diff --git a/R/umap.R b/R/umap.R index 3416ccf..2dd74ff 100644 --- a/R/umap.R +++ b/R/umap.R @@ -471,52 +471,61 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @returns a list of data frames with the predictions and the UMAP input #' @export #' -optimize_outgroup <- function(predicted_labels, - true_labels, - other_score, - all_classes = c("MCD", - "EZB", - "BN2", - "N1", - "ST2", - "Other"), - maximize ="balanced_accuracy", - exclude_other_for_accuracy = FALSE){ +optimize_outgroup <- function( + predicted_labels, + true_labels, + other_score, + all_classes = c( + "MCD", + "EZB", + "BN2", + "N1", + "ST2", + "Other" + ), + maximize ="balanced_accuracy", + exclude_other_for_accuracy = FALSE +){ rel_thresholds = seq(1,10,0.1) sens_df = data.frame() acc_df = data.frame() - predictions = data.frame(predicted_label=as.character(predicted_labels), - true_label=as.character(true_labels)) + predictions = data.frame( + predicted_label=as.character(predicted_labels), + true_label=as.character(true_labels) + ) for(threshold in rel_thresholds){ - predictions_new = mutate(predictions, - predicted_label = ifelse(other_score < threshold, - predicted_label, - "Other")) - - pred = factor(predictions_new[["predicted_label"]],levels=all_classes) - truth = factor(predictions_new[["true_label"]],levels=all_classes) - conf_matrix <- confusionMatrix(pred, truth) - - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - if(maximize == "balanced_accuracy"){ - bal_acc$average_accuracy = mean(bal_acc) - }else{ - bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] - } - bal_acc$threshold = threshold - acc_df = bind_rows(acc_df,bal_acc) - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity = mean(sn) - sn$threshold = threshold - sens_df = bind_rows(sens_df,sn) + predictions_new = mutate( + predictions, + predicted_label = ifelse( + other_score < threshold, + predicted_label, + "Other" + ) + ) + + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + } + bal_acc$threshold = threshold + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sens_df = bind_rows(sens_df,sn) } if(maximize %in% c("balanced_accuracy","accuracy")){ best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) }else{ best = slice_head(arrange(sens_df,desc(average_sensitivity)),n=1) - } return(best) @@ -1608,10 +1617,7 @@ make_neighborhood_plot <- function( #' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful #' for reproducibility and for using the same UMAP model on different datasets. #' @param best_params Data frame from DLBCLone_optimize_params with the best parameters -#' @param other_df Data frame containing the predictions for samples in the "Other" class -#' @param predict_training Set to TRUE to predict the projected training samples once stored as stored_train_prediction. Use -#' stored_train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run -#' @param stored_train_prediction Data frame containing the projected train predictions from train samples +#' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled #' samples to estimate overall accuracy @@ -1646,8 +1652,6 @@ predict_single_sample_DLBCLone <- function( umap_out, best_params, other_df, - predict_training = FALSE, - stored_train_prediction = NULL, ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","N1","BN2"), drop_unlabeled_from_training=TRUE, @@ -1657,7 +1661,8 @@ predict_single_sample_DLBCLone <- function( title1="GAMBL", title2="predicted_class_for_HighConf", title3 ="predicted_class_for_Other", - seed = 12345 + seed = 12345, + max_neighbors = 500 ){ if(nrow(test_df)>1){ @@ -1767,6 +1772,7 @@ predict_single_sample_DLBCLone <- function( na_label = "Other", use_weights = best_params$use_w, ignore_top = ignore_top, + max_neighbors = max_neighbors ) test_pred <- as.data.frame(test_pred) prefix <- paste0("k_", best_params$k, ".") diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index c08bbe0..02276df 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -11,8 +11,6 @@ predict_single_sample_DLBCLone( umap_out, best_params, other_df, - predict_training = FALSE, - stored_train_prediction = NULL, ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), drop_unlabeled_from_training = TRUE, @@ -22,7 +20,8 @@ predict_single_sample_DLBCLone( title1 = "GAMBL", title2 = "predicted_class_for_HighConf", title3 = "predicted_class_for_Other", - seed = 12345 + seed = 12345, + max_neighbors = 500 ) } \arguments{ @@ -39,11 +38,6 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} -\item{predict_training}{Set to TRUE to predict the projected training samples once stored as stored_train_prediction. Use -stored_train_prediction for subsequent use wehn using the same training set. Set to to FALSE to predict training samples every run} - -\item{stored_train_prediction}{Data frame containing the projected train predictions from train samples} - \item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with distance = 0. This is usually only relevant when re-classifying labeled samples to estimate overall accuracy} From b0c64c39104dc3ce7eb44d48ea75830f1079ff6c Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 4 Jul 2025 18:21:54 -0700 Subject: [PATCH 44/79] summarize_all_ssm_status hotspot and SV additions --- R/umap.R | 71 ++++++++++++++++++++++++++++++--- man/summarize_all_ssm_status.Rd | 6 +++ 2 files changed, 71 insertions(+), 6 deletions(-) diff --git a/R/umap.R b/R/umap.R index 2dd74ff..c7036ba 100644 --- a/R/umap.R +++ b/R/umap.R @@ -23,6 +23,8 @@ #' @param separate_by_class_genes (Optional) A character vector of #' gene symbols for which mutations should be separated by class #' (e.g., "Nonsense_Mutation", "Missense_Mutation"). +#' @param hotspot_genes Vector of hotspot genes. +#' @param annotated_sv SV from provided annotated SV data frame #' @param count_hits Logical; if \code{TRUE}, counts the number of mutations #' per gene per sample. If \code{FALSE} (default), only presence/absence is recorded. #' @@ -58,6 +60,7 @@ #' #' @import dplyr tidyr tibble #' @export +#' summarize_all_ssm_status <- function( maf_df, these_samples_metadata, @@ -65,6 +68,8 @@ summarize_all_ssm_status <- function( synon_genes, silent_maf_df, separate_by_class_genes = NULL, + hotspot_genes = NULL, + annotated_sv, count_hits = FALSE ){ if(missing(genes_of_interest)){ @@ -143,13 +148,12 @@ summarize_all_ssm_status <- function( mutation_type != "Silent" ) - unseparated_coding_maf = filter( + unseparated_coding_silent_maf = filter( gene_mutations, - Hugo_Symbol %in% genes_of_interest, - !Hugo_Symbol %in% separate_by_class_genes, - mutation_type != "Silent" + Hugo_Symbol %in% genes_of_interest, + !Hugo_Symbol %in% separate_by_class_genes ) %>% - mutate(mutation = paste(Hugo_Symbol,"Coding",sep=":")) + mutate(mutation = paste(Hugo_Symbol,sep=":")) separated_silent_maf = filter( gene_mutations, @@ -160,7 +164,7 @@ summarize_all_ssm_status <- function( gene_mutations = bind_rows( separated_coding_maf, - unseparated_coding_maf, + unseparated_coding_silent_maf, separated_silent_maf ) @@ -186,6 +190,61 @@ summarize_all_ssm_status <- function( mutation_wide = pivot_wider(mutation_distinct, names_from = "mutation",values_from = "mutated",values_fill = 0) %>% column_to_rownames("Tumor_Sample_Barcode") + + if(!is.null(hotspot_genes)){ + hotspot_features = get_coding_ssm_status( + these_samples_metadata = sample_metadata, + maf_data = maf_df, + include_hotspots = TRUE, + genes_of_interest = hotspot_genes + ) + + hotspot_features <- hotspot_features %>% + column_to_rownames("sample_id") %>% + select(contains("HOTSPOT")) + + mutation_wide <- mutation_wide %>% rownames_to_column(var = "sample_id") + hotspot_features <- hotspot_features %>% rownames_to_column(var = "sample_id") + + mutation_wide <- left_join(mutation_wide, hotspot_features, by = "sample_id") + + # Replace NA in hotspot columns with 0 (if necessary) + hotspot_cols <- colnames(hotspot_features)[-1] # exclude sample_id + mutation_wide[hotspot_cols][is.na(mutation_wide[hotspot_cols])] <- 0 + + mutation_wide <- mutation_wide %>% column_to_rownames("sample_id") + } + + #TODO: generalize this to use the column names provided in metadata_columns + bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) + bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) + myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) + + # include SV from provided annotated SV data frame + if(!missing(annotated_sv)){ + annotated_sv = filter(annotated_sv,tumour_sample_id %in% these_samples_metadata$sample_id) + bcl2_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL2") %>% pull(tumour_sample_id) + bcl6_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL6") %>% pull(tumour_sample_id) + myc_sv_id = filter(annotated_sv,!is.na(partner),gene=="MYC") %>% pull(tumour_sample_id) + n_b = length(bcl2_id) + print(paste(n_b,"BCL2 FISH")) + bcl2_id = unique(c(bcl2_id,bcl2_sv_id)) + n_b = length(bcl2_id) + print(paste(n_b,"BCL2 FISH or SV")) + bcl6_id = unique(c(bcl6_id,bcl6_sv_id)) + myc_id = unique(c(myc_id,myc_sv_id)) + } + + mutation_wide[,"BCL2_SV"] = 0 + mutation_wide[bcl2_id,"BCL2_SV"] = 1 + + mutation_wide[,"MYC_SV"] = 0 + mutation_wide[myc_id,"MYC_SV"] = 1 + + mutation_wide[,"BCL6_SV"] = 0 + mutation_wide[bcl6_id,"BCL6_SV"] = 1 + + mutation_wide[is.na(mutation_wide)] <- 0 return(mutation_wide) } diff --git a/man/summarize_all_ssm_status.Rd b/man/summarize_all_ssm_status.Rd index ec7b844..2203ca8 100644 --- a/man/summarize_all_ssm_status.Rd +++ b/man/summarize_all_ssm_status.Rd @@ -11,6 +11,8 @@ summarize_all_ssm_status( synon_genes, silent_maf_df, separate_by_class_genes = NULL, + hotspot_genes = NULL, + annotated_sv, count_hits = FALSE ) } @@ -38,6 +40,10 @@ the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene} gene symbols for which mutations should be separated by class (e.g., "Nonsense_Mutation", "Missense_Mutation").} +\item{hotspot_genes}{Vector of hotspot genes.} + +\item{annotated_sv}{SV from provided annotated SV data frame} + \item{count_hits}{Logical; if \code{TRUE}, counts the number of mutations per gene per sample. If \code{FALSE} (default), only presence/absence is recorded.} } From e2436093e8999d750034c4813f41b12ae061cec6 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 7 Jul 2025 10:32:20 -0700 Subject: [PATCH 45/79] updates to saving and loading optimize params --- R/umap.R | 13 +++++++++++-- man/make_umap_scatterplot.Rd | 1 + 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/R/umap.R b/R/umap.R index c7036ba..b67d18e 100644 --- a/R/umap.R +++ b/R/umap.R @@ -2004,6 +2004,7 @@ predict_single_sample_DLBCLone <- function( #' Make UMAP scatterplot #' #' @param df +#' @param title #' @param drop_composite #' @param colour_by #' @param drop_other @@ -2018,6 +2019,7 @@ predict_single_sample_DLBCLone <- function( #' @examples make_umap_scatterplot = function( df, + title, drop_composite = TRUE, colour_by="lymphgen", drop_other = FALSE, @@ -2106,6 +2108,7 @@ make_umap_scatterplot = function( label=cohort )) + geom_point(alpha=0.8) + + labs(title = title) + scale_colour_manual(values=cols) + theme_Morons() + guides(colour = guide_legend(nrow = 1)) @@ -2175,6 +2178,9 @@ DLBCLone_save_optimized = function( out_model = paste0(prefix,"_optimized_uwot.rds") save_uwot(optimized_out$model,file=out_model) + + out_pred = paste0(prefix,"_optimized_pred.tsv") + write_tsv(optimized_out$predictions,file=out_pred) } if(!is.null(predict_single)){ # predict_single_sample_DLBCLone() @@ -2234,8 +2240,9 @@ DLBCLone_load_optimized <- function( load_classes = paste0(prefix,"_classes.txt") load_param = paste0(prefix,"_optimized_best_params.rds") load_model = paste0(prefix,"_optimized_uwot.rds") + load_pred = paste0(prefix,"_optimized_pred.tsv") - required_files <- c(load_mut, load_meta, load_classes, load_param, load_model) + required_files <- c(load_mut, load_meta, load_classes, load_param, load_model, load_pred) # Check existence missing_files <- required_files[!file.exists(required_files)] @@ -2249,12 +2256,14 @@ DLBCLone_load_optimized <- function( classes <- read.table(load_classes) best_params <- readRDS(load_param) uwot_model <- load_uwot(load_model) + predictions <- read_tsv(load_pred) return(list( df = mut_df, metadata = metadata, truth_classes = classes, best_params = best_params, - model = uwot_model + model = uwot_model, + predictions = predictions )) } diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index a9a467a..e2e15bf 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -6,6 +6,7 @@ \usage{ make_umap_scatterplot( df, + title, drop_composite = TRUE, colour_by = "lymphgen", drop_other = FALSE, From d417d62a3677e625afc3262fba6e57b47f8a5540 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 8 Jul 2025 11:16:57 -0700 Subject: [PATCH 46/79] fixed vectorized k knn, k to curr_k fix --- R/umap.R | 18 +++++++++--------- man/predict_single_sample_DLBCLone.Rd | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/R/umap.R b/R/umap.R index b67d18e..6f3381d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1428,9 +1428,9 @@ weighted_knn_predict_with_conf <- function( distances <- distances[valid] neighbors <- neighbors[valid] #number of neighbours should be, at least, k - 1. If less than that, warn the user - if(length(neighbors) < k-1){ + if(length(neighbors) < curr_k-1){ print(paste("Warning: number of neighbors is less than k-1.")) - print(paste("i:", i,"k:",k)) + print(paste("i:", i,"k:",curr_k)) print(paste("num_neighbors:",length(neighbors))) print(table(valid)) } @@ -1646,9 +1646,9 @@ make_neighborhood_plot <- function( links_df = mutate(links_df,my_x=my_x,my_y=my_y) links_df = links_df %>% select(V1, V2, my_x, my_y, group) %>% mutate(length = sqrt((V1 - my_x)^2 + (V2 - my_y)^2)) # Euclidean distance - if(drop_other){ - training_predictions = filter(training_predictions,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") - } + # if(drop_other){ + # training_predictions = filter(training_predictions,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") + #} pp=ggplot( mutate(training_predictions,group=lymphgen), @@ -1712,7 +1712,7 @@ predict_single_sample_DLBCLone <- function( best_params, other_df, ignore_top = FALSE, - truth_classes = c("EZB","MCD","ST2","N1","BN2"), + truth_classes = c("EZB","MCD","ST2","BN2"), drop_unlabeled_from_training=TRUE, make_plot = TRUE, annotate_accuracy = FALSE, @@ -1880,10 +1880,10 @@ predict_single_sample_DLBCLone <- function( ) if("BN2" %in% truth_classes){ - print(best_params) + # print(best_params) acc_df = data.frame( lymphgen = c( - "N1", + # "N1", "BN2", "EZB", "MCD", @@ -1892,7 +1892,7 @@ predict_single_sample_DLBCLone <- function( "A53" ), accuracy = c( - best_params$N1_bacc, + # best_params$N1_bacc, best_params$BN2_bacc, best_params$EZB_bacc, best_params$MCD_bacc, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 02276df..e609714 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -12,7 +12,7 @@ predict_single_sample_DLBCLone( best_params, other_df, ignore_top = FALSE, - truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2"), + truth_classes = c("EZB", "MCD", "ST2", "BN2"), drop_unlabeled_from_training = TRUE, make_plot = TRUE, annotate_accuracy = FALSE, From 1d3b9b5173ca654ebfdf673769d378285b90740a Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 8 Jul 2025 13:51:41 -0700 Subject: [PATCH 47/79] make_and_annotate_umap reproducibility fix, make_umap_scatterplot title fix --- R/umap.R | 60 +++++++++++++++++------------------- man/make_umap_scatterplot.Rd | 2 +- 2 files changed, 29 insertions(+), 33 deletions(-) diff --git a/R/umap.R b/R/umap.R index 6f3381d..bd7346e 100644 --- a/R/umap.R +++ b/R/umap.R @@ -812,7 +812,7 @@ make_and_annotate_umap = function( if(missing(umap_out)){ if(missing(target_column)){ if(algorithm == "umap"){ - umap_out = umap2( + embedding = umap2( df %>% as.matrix(), n_neighbors = n_neighbors, min_dist = min_dist, @@ -831,7 +831,8 @@ make_and_annotate_umap = function( ) # possibly add rng_type = "deterministic" }else if(algorithm == "tumap"){ - X = df %>% as.matrix() + #X = df %>% as.matrix() + X = df umap_args = list( X=X, n_neighbors = n_neighbors, @@ -845,7 +846,7 @@ make_and_annotate_umap = function( n_sgd_threads = 1, rng_type = "deterministic" ) - umap_out = tumap( + embedding = tumap( X, n_neighbors = n_neighbors, metric = metric, @@ -861,6 +862,7 @@ make_and_annotate_umap = function( }else{ stop("unsupported algorithm option") } + umap_out = embedding }else{ #supervised if(missing(metadata)){ @@ -868,7 +870,7 @@ make_and_annotate_umap = function( } metadata[[target_column]] = factor(metadata[[target_column]]) print(table(metadata[[target_column]])) - umap_out = umap2( + embedding = umap2( df %>% as.matrix(), n_neighbors = n_neighbors, min_dist = min_dist, @@ -885,7 +887,7 @@ make_and_annotate_umap = function( } }else{ - umap_out = umap_transform( + embedding = umap_transform( X=df, model=umap_out, seed=seed, @@ -895,10 +897,10 @@ make_and_annotate_umap = function( ) } - if(!is.null(names(umap_out))){ - umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) + if(!is.null(names(embedding))){ + umap_df = as.data.frame(embedding$embedding) %>% rownames_to_column(join_column) }else{ - umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) + umap_df = as.data.frame(embedding) %>% rownames_to_column(join_column) } if(!missing(metadata)){ umap_df = left_join(umap_df,metadata,by=join_column) @@ -1028,30 +1030,24 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, other_pred = NULL for(na_option in na_opt){ if(missing(umap_out)){ - outs = make_and_annotate_umap(df=combined_mutation_status_df, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option) - }else{ - #project onto existing model instead of re-running UMAP - outs = make_and_annotate_umap(df=combined_mutation_status_df, - umap_out = umap_out, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option) + stop("umap_out must be provided by running make_and_annotate_umap first") } + + #project onto existing model instead of re-running UMAP + outs = make_and_annotate_umap( + df=combined_mutation_status_df, + umap_out = umap_out, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option + ) + ignore_top = FALSE if(is.null(eval_group)){ ignore_top = TRUE @@ -2019,7 +2015,7 @@ predict_single_sample_DLBCLone <- function( #' @examples make_umap_scatterplot = function( df, - title, + title = NULL, drop_composite = TRUE, colour_by="lymphgen", drop_other = FALSE, diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index e2e15bf..9938a51 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -6,7 +6,7 @@ \usage{ make_umap_scatterplot( df, - title, + title = NULL, drop_composite = TRUE, colour_by = "lymphgen", drop_other = FALSE, From 947db0706ce6390fa67acf950f00c4c6b1ab3450 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 10 Jul 2025 10:28:50 -0700 Subject: [PATCH 48/79] optimize_params list param fix, DLBCLone_save_optimized simplified --- R/umap.R | 119 ++++++++++---------------- man/DLBCLone_save_optimized.Rd | 15 ++-- man/predict_single_sample_DLBCLone.Rd | 12 +-- 3 files changed, 53 insertions(+), 93 deletions(-) diff --git a/R/umap.R b/R/umap.R index bd7346e..9d98e08 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1036,7 +1036,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, #project onto existing model instead of re-running UMAP outs = make_and_annotate_umap( df=combined_mutation_status_df, - umap_out = umap_out, + umap_out = umap_out$model, min_dist = 0, n_neighbors = 55, n_epochs = 1500, @@ -1301,6 +1301,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, model=best_fit$model, features=best_fit$features, df=outs$df, + truth_classes = truth_classes, predictions=xx_d) if(!"Other" %in% truth_classes && n_other > 0){ to_ret[["predictions_other"]] = xx_o @@ -1669,9 +1670,8 @@ make_neighborhood_plot <- function( #' @param test_df Data frame containing the mutation status of the test sample #' @param train_df Data frame containing the mutation status of the training samples #' @param train_metadata Metadata for training samples with truth labels in lymphgen column -#' @param umap_out UMAP output from a previous run. The function will use this model to project the data, useful -#' for reproducibility and for using the same UMAP model on different datasets. -#' @param best_params Data frame from DLBCLone_optimize_params with the best parameters +#' @param optimize_params list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +#' previous, Data frame with the best parameters. useful for reproducibility. #' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with #' distance = 0. This is usually only relevant when re-classifying labeled @@ -1694,8 +1694,7 @@ make_neighborhood_plot <- function( #' test_df = test_df, #' train_df = train_df, #' train_metadata = train_metadata, -#' umap_out = umap_out, -#' best_params = best_params, +#' optimize_params = optimize_params, #' predictions_df = predictions_df, #' annotate_accuracy = TRUE #' ) @@ -1704,8 +1703,7 @@ predict_single_sample_DLBCLone <- function( test_df, train_df, train_metadata, - umap_out, - best_params, + optimize_params, other_df, ignore_top = FALSE, truth_classes = c("EZB","MCD","ST2","BN2"), @@ -1783,11 +1781,11 @@ predict_single_sample_DLBCLone <- function( projection <- make_and_annotate_umap( df = combined_df, metadata = combined_metadata, - umap_out = umap_out, + umap_out = optimize_params$model, ret_model = FALSE, seed = seed, join_column = "sample_id", - na_vals = best_params$na_option + na_vals = optimize_params$best_params$na_option ) train_coords = dplyr::filter( @@ -1822,21 +1820,21 @@ predict_single_sample_DLBCLone <- function( train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, - k = best_params$k, - conf_threshold = best_params$threshold, + k = optimize_params$best_params$k, + conf_threshold = optimize_params$best_params$threshold, na_label = "Other", - use_weights = best_params$use_w, + use_weights = optimize_params$best_params$use_w, ignore_top = ignore_top, max_neighbors = max_neighbors ) test_pred <- as.data.frame(test_pred) - prefix <- paste0("k_", best_params$k, ".") + prefix <- paste0("k_", optimize_params$best_params$k, ".") colnames(test_pred) <- sub(prefix, "", colnames(test_pred)) test_pred = rownames_to_column(test_pred, var = "sample_id") - if(best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params + if(optimize_params$best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params test_pred = mutate(test_pred,predicted_label_optimized = ifelse( - other_score > best_params$threshold_outgroup, + other_score > optimize_params$best_params$threshold_outgroup, "Other", predicted_label )) @@ -1871,12 +1869,12 @@ predict_single_sample_DLBCLone <- function( if(make_plot){ title = paste0( - "N_class:", best_params$num_classes," N_feats:",best_params$num_features, - " k=",best_params$k," threshold=",best_params$threshold," bacc=",round(best_params$accuracy,3) + "N_class:", optimize_params$best_params$num_classes," N_feats:",optimize_params$best_params$num_features, + " k=",optimize_params$best_params$k," threshold=",optimize_params$best_params$threshold," bacc=",round(optimize_params$best_params$accuracy,3) ) if("BN2" %in% truth_classes){ - # print(best_params) + # print(optimize_params$best_params) acc_df = data.frame( lymphgen = c( # "N1", @@ -1888,13 +1886,13 @@ predict_single_sample_DLBCLone <- function( "A53" ), accuracy = c( - # best_params$N1_bacc, - best_params$BN2_bacc, - best_params$EZB_bacc, - best_params$MCD_bacc, - best_params$ST2_bacc, - best_params$Other_bacc, - best_params$A53_bacc + # optimize_params$best_params$N1_bacc, + optimize_params$best_params$BN2_bacc, + optimize_params$best_params$EZB_bacc, + optimize_params$best_params$MCD_bacc, + optimize_params$best_params$ST2_bacc, + optimize_params$best_params$Other_bacc, + optimize_params$best_params$A53_bacc ) ) }else if("C1" %in% truth_classes){ @@ -1907,11 +1905,11 @@ predict_single_sample_DLBCLone <- function( "C5" ), accuracy = c( - best_params$C1_bacc, - best_params$C2_bacc, - best_params$C3_bacc, - best_params$C4_bacc, - best_params$C5_bacc + optimize_params$best_params$C1_bacc, + optimize_params$best_params$C2_bacc, + optimize_params$best_params$C3_bacc, + optimize_params$best_params$C4_bacc, + optimize_params$best_params$C5_bacc ) ) }else{ @@ -1990,7 +1988,7 @@ predict_single_sample_DLBCLone <- function( return(list( prediction = test_pred, umap_input_features = trained_features, - model=umap_out, + model=optimize_params$model, plot = pp, anno_df = predictions_df, projection = projection$df @@ -2146,13 +2144,9 @@ make_umap_scatterplot = function( #' name_prefix="test_A" #' ) #' -DLBCLone_save_optimized = function( - combined_mutation_df, - metadata, - truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), - optimized_out=NULL, - predict_single=NULL, - neighborhood_plot=NULL, + +DLBCLone_save_optimized = function( # <- ensure rownmaes works, restore lsit option of umap_out not umap_out$model to all fxs + optimized_params=NULL, path="models/", name_prefix="test" ){ @@ -2160,47 +2154,22 @@ DLBCLone_save_optimized = function( prefix = paste0(path,"/",name_prefix) out_mut = paste0(prefix,"_mutation_status_df.tsv") - write_tsv(combined_mutation_df,file=out_mut) + write_tsv(optimized_params$features,file=out_mut) out_meta = paste0(prefix,"_metadata.tsv") - write_tsv(metadata,file=out_meta) - - out_classes = paste0(prefix,"_classes.txt") - write.table(truth_classes,file=out_classes,quote=F,row.names=F) + write_tsv(optimized_params$df,file=out_meta) - if(!is.null(optimized_out)){ # DLBCLone_optimize_params() - out_param = paste0(prefix,"_optimized_best_params.rds") - saveRDS(optimized_out$best_params,file=out_param) + out_param = paste0(prefix,"_optimized_best_params.rds") + saveRDS(optimized_params$best_params,file=out_param) - out_model = paste0(prefix,"_optimized_uwot.rds") - save_uwot(optimized_out$model,file=out_model) - - out_pred = paste0(prefix,"_optimized_pred.tsv") - write_tsv(optimized_out$predictions,file=out_pred) - } + out_model = paste0(prefix,"_optimized_uwot.rds") + save_uwot(optimized_params$model,file=out_model) - if(!is.null(predict_single)){ # predict_single_sample_DLBCLone() - out_prediction = paste0(prefix,"_predict_single_prediction.tsv") - write_tsv(predict_single$prediction,file=out_prediction) + out_pred = paste0(prefix,"_optimized_pred.tsv") + write_tsv(optimized_params$predictions,file=out_pred) - out_projection = paste0(prefix,"_predict_single_projection.tsv") - write_tsv(predict_single$projection,file=out_projection) - - out_anno_df = paste0(prefix, "_predict_single_anno_df.tsv") - write_tsv(predict_single$anno_df,file=out_anno_df) - - out_plot = paste0(prefix,"_predict_single_plot.pdf") - pdf(out_plot, width = 10, height = 14) - predict_single$plot - dev.off() - } - - if(!is.null(neighborhood_plot)){ # make_neighborhood_plot() - out_neighbor = paste0(prefix,"_neighborhood_plot.pdf") - pdf(out_neighbor, width = 10, height = 14) - neighborhood_plot - dev.off() - } + out_classes = paste0(prefix,"_classes.txt") + write.table(optimized_params$truth_classes,file=out_classes,quote=F,row.names=F) } @@ -2224,7 +2193,7 @@ DLBCLone_save_optimized = function( #' ) #' -DLBCLone_load_optimized <- function( +DLBCLone_load_optimized <- function( # set sample_id to rownames path="models/", name_prefix="test" ){ diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd index 5cc7e4e..ecfefb5 100644 --- a/man/DLBCLone_save_optimized.Rd +++ b/man/DLBCLone_save_optimized.Rd @@ -5,17 +5,16 @@ \title{modal storage for DLBCLone outputs} \usage{ DLBCLone_save_optimized( - combined_mutation_df, - metadata, - truth_classes = c("MCD", "EZB", "BN2", "ST2", "N1", "Other"), - optimized_out = NULL, - predict_single = NULL, - neighborhood_plot = NULL, + optimized_params = NULL, path = "models/", name_prefix = "test" ) } \arguments{ +\item{path}{Path to save the files} + +\item{name_prefix}{Prefix for the saved files, all files will be in path and start with name_prefix} + \item{combined_mutation_df}{Data frame containing the mutation status of the samples used} \item{metadata}{Metadata with truth labels in lymphgen column} @@ -27,10 +26,6 @@ DLBCLone_save_optimized( \item{predict_single}{output of predict_single_sample_DLBCLone(), if set to NULL values will not be saved} \item{neighborhood_plot}{output of make_neighborhood_plot(), if set to NULL values will not be saved} - -\item{path}{Path to save the files} - -\item{name_prefix}{Prefix for the saved files, all files will be in path and start with name_prefix} } \value{ saves the files to the specified path diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index e609714..8fb50ad 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -8,8 +8,7 @@ predict_single_sample_DLBCLone( test_df, train_df, train_metadata, - umap_out, - best_params, + optimize_params, other_df, ignore_top = FALSE, truth_classes = c("EZB", "MCD", "ST2", "BN2"), @@ -31,10 +30,8 @@ predict_single_sample_DLBCLone( \item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} -\item{umap_out}{UMAP output from a previous run. The function will use this model to project the data, useful -for reproducibility and for using the same UMAP model on different datasets.} - -\item{best_params}{Data frame from DLBCLone_optimize_params with the best parameters} +\item{optimize_params}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +previous, Data frame with the best parameters. useful for reproducibility.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} @@ -72,8 +69,7 @@ predict_single_sample_DLBCLone( test_df = test_df, train_df = train_df, train_metadata = train_metadata, - umap_out = umap_out, - best_params = best_params, + optimize_params = optimize_params, predictions_df = predictions_df, annotate_accuracy = TRUE ) From 043bf3608d63baef2ad953a8d4ee54c6eabd61c3 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 10 Jul 2025 10:33:09 -0700 Subject: [PATCH 49/79] for got to swap optimize in predict single sample --- R/umap.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/umap.R b/R/umap.R index 9d98e08..e72e2fb 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1722,7 +1722,7 @@ predict_single_sample_DLBCLone <- function( message("Warning: you have supplied more than one sample to test with. Will proceed with all") } - trained_features <- train_df %>% column_to_rownames("sample_id") %>% select(where(is.numeric)) %>% colnames() + trained_features = colnames(optimize_params$features) train_df = train_df %>% column_to_rownames("sample_id") %>% From ff3084a11d2cd57898109b6e9537601a93c7eacf Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 10 Jul 2025 22:43:34 -0700 Subject: [PATCH 50/79] sample_id colname_to_rownames update --- R/umap.R | 57 ++++++++++++++++++--------- man/predict_single_sample_DLBCLone.Rd | 2 +- 2 files changed, 39 insertions(+), 20 deletions(-) diff --git a/R/umap.R b/R/umap.R index e72e2fb..d19a541 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,4 +1,4 @@ - + #' Summarize SSM (Somatic Single Nucleotide Mutation) Status Across Samples #' #' This function summarizes the mutation status for a set of genes @@ -1028,6 +1028,11 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, this_accuracy = 0 best_pred = NULL other_pred = NULL + + if("sample_id" %in% colnames(combined_mutation_status_df)){ + combined_mutation_status_df = combined_mutation_status_df %>% column_to_rownames(var = "sample_id") + } + for(na_option in na_opt){ if(missing(umap_out)){ stop("umap_out must be provided by running make_and_annotate_umap first") @@ -1057,14 +1062,10 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids } pred_all = weighted_knn_predict_with_conf( @@ -1085,14 +1086,10 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids } n_other = nrow(test_coords) @@ -1272,8 +1269,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, test_coords = outs$df %>% select(V1,V2) train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, @@ -1605,26 +1600,32 @@ make_neighborhood_plot <- function( }else if(missing(single_sample_prediction_output)){ #Just plot the single sample in the context of the rest based on the optimization single_sample_prediction_output = list() - single_sample_prediction_output[["prediction"]] = filter(training_predictions, sample_id==this_sample_id) + single_sample_prediction_output[["prediction"]] = filter( + training_predictions, + rownames(training_predictions) == this_sample_id + ) single_sample_prediction_output[["anno_df"]] = training_predictions }else{ - single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction, sample_id==this_sample_id) + single_sample_prediction_output$prediction = filter( + single_sample_prediction_output$prediction, + rownames(single_sample_prediction_output$prediction) == this_sample_id + ) } #extract the sample_id for all the nearest neighbors with non-Other labels my_neighbours = filter( single_sample_prediction_output$prediction, - sample_id == this_sample_id + rownames(single_sample_prediction_output$prediction) == this_sample_id ) %>% pull(neighbor_id) %>% strsplit(.,",") %>% unlist() #set up links connecting each neighbor to the sample's point - links_df = filter(training_predictions,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) + links_df = filter(training_predictions,rownames(training_predictions) %in% my_neighbours) %>% mutate(group=lymphgen) sample_row <- single_sample_prediction_output$anno_df %>% - filter(sample_id == this_sample_id) %>% + filter(rownames(single_sample_prediction_output$anno_df) == this_sample_id) %>% slice_head(n = 1) # ensures 1 sample_id selected. Duplicates may occur when ignore_top = TRUE. my_x <- sample_row$V1 my_y <- sample_row$V2 @@ -1705,7 +1706,7 @@ predict_single_sample_DLBCLone <- function( train_metadata, optimize_params, other_df, - ignore_top = FALSE, + ignore_top = TRUE, truth_classes = c("EZB","MCD","ST2","BN2"), drop_unlabeled_from_training=TRUE, make_plot = TRUE, @@ -1722,6 +1723,15 @@ predict_single_sample_DLBCLone <- function( message("Warning: you have supplied more than one sample to test with. Will proceed with all") } + # cannot have duplicate rownames, sample_id needs to be a column in function, outputted as rownames + if(!"sample_id" %in% colnames(train_df)){ + train_df = train_df %>% rownames_to_column("sample_id") + } + + if(!"sample_id" %in% colnames(test_df)){ + test_df = test_df %>% rownames_to_column("sample_id") + } + trained_features = colnames(optimize_params$features) train_df = train_df %>% @@ -1858,14 +1868,23 @@ predict_single_sample_DLBCLone <- function( ) predictions_test_df <- left_join(test_pred, projection$df, by = "sample_id") + + test_pred <- test_pred %>% + column_to_rownames("sample_id") missing_cols <- setdiff(names(predictions_test_df), names(projection$df)) na_cols <- setNames(rep(list(NA), length(missing_cols)), missing_cols) projection_filled <- projection$df %>% filter(sample_id %in% train_id) %>% mutate(!!!na_cols) - - predictions_df <- bind_rows(predictions_test_df, projection_filled) + + predictions_df <- bind_rows( + predictions_test_df, + projection_filled %>% filter(!(sample_id %in% predictions_test_df$sample_id)) + ) + + predictions_df <- predictions_df %>% + column_to_rownames("sample_id") if(make_plot){ title = paste0( diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 8fb50ad..9ef8a93 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -10,7 +10,7 @@ predict_single_sample_DLBCLone( train_metadata, optimize_params, other_df, - ignore_top = FALSE, + ignore_top = TRUE, truth_classes = c("EZB", "MCD", "ST2", "BN2"), drop_unlabeled_from_training = TRUE, make_plot = TRUE, From de33958073c91f2a652140a39141cb37fbf6e055 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 17 Jul 2025 16:31:26 -0700 Subject: [PATCH 51/79] rowname ids added to optimize params --- R/umap.R | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/R/umap.R b/R/umap.R index d19a541..042c768 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1060,12 +1060,20 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, for(use_w in weights_opt){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + test_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) + test_ids = filter(outs$df,dataset == eval_group) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids } pred_all = weighted_knn_predict_with_conf( @@ -1084,12 +1092,20 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, if(!"Other" %in% truth_classes){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) + test_ids = filter(outs$df,lymphgen %in% "Other") %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) + test_ids = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(sample_id) + rownames(train_coords) = train_ids } n_other = nrow(test_coords) @@ -1267,8 +1283,13 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, best_params$seed = seed test_coords = outs$df %>% select(V1,V2) + test_ids = filter(outs$df) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids + pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, From b382ab95d071a57ad7de569fe025760474d7ca8a Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 17 Jul 2025 17:06:27 -0700 Subject: [PATCH 52/79] trying out dlbclass labeling when optimizing for other --- R/umap.R | 200 +++++++++++++++++++++++++++----- man/DLBCLone_optimize_params.Rd | 5 + man/optimize_outgroup.Rd | 9 ++ 3 files changed, 186 insertions(+), 28 deletions(-) diff --git a/R/umap.R b/R/umap.R index 042c768..8aca03b 100644 --- a/R/umap.R +++ b/R/umap.R @@ -513,10 +513,14 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' based on the specified metric (balanced accuracy or accuracy). #' This function is not generally meant to be called directly but rather is #' a helper function used by DLBCLone_optimize_params. -#' +#' @param metadata Metadata data frame with one row per sample and a column sample_id that +#' matches the row names of df. This data frame will be joined to the UMAP output. #' @param predicted_labels Vector of predicted labels for the samples #' @param true_labels Vector of true labels for the samples #' @param other_score Vector of scores for the "Other" class for each sample () +#' @param optimize_for_dlbclass Set to TRUE to optimize the optimized other thresholds into +#' assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: +#' FALSE #' @param all_classes Vector of classes to use for training and testing. #' Default: c("MCD","EZB","BN2","N1","ST2","Other") #' @param maximize Metric to use for optimization. @@ -531,9 +535,11 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @export #' optimize_outgroup <- function( + metadata, predicted_labels, true_labels, other_score, + optimize_for_dlbclass = FALSE, all_classes = c( "MCD", "EZB", @@ -554,33 +560,106 @@ optimize_outgroup <- function( true_label=as.character(true_labels) ) - for(threshold in rel_thresholds){ - predictions_new = mutate( - predictions, - predicted_label = ifelse( - other_score < threshold, - predicted_label, - "Other" + if (optimize_for_dlbclass) { + if ("sample_id" %in% colnames(metadata)) { + metadata <- metadata %>% column_to_rownames(var = "sample_id") + } + + dlbclass_labels <- metadata %>% select(Confidence_dlbclass, Predicted_dlbclass) + dlbclass_thresholds <- seq(0.90, 0.98, 0.04) + + for (threshold in rel_thresholds) { + for (conf_threshold in dlbclass_thresholds) { + # Initial Other assignment + predictions_new <- mutate( + predictions, + predicted_label = ifelse( + other_score < threshold, + predicted_label, + "Other" + ) + ) + + # Join with DLBCL mapping + preds_df <- predictions_new %>% rownames_to_column("sample_id") + labels_df <- dlbclass_labels %>% rownames_to_column("sample_id") + predictions_bound <- inner_join(preds_df, labels_df, by = "sample_id") + + # Apply per-row override + predictions_bound <- predictions_bound %>% + mutate( + predicted_label = ifelse( + predicted_label == "Other" & Confidence_dlbclass > conf_threshold, + case_when( + Predicted_dlbclass == "C1" ~ "BN2", + Predicted_dlbclass == "C3" ~ "EZB", + Predicted_dlbclass == "C4" ~ "ST2", + Predicted_dlbclass == "C5" ~ "MCD", + TRUE ~ "Other" + ), + predicted_label + ) + ) + + predictions_new <- predictions_bound %>% + select(predicted_label, true_label) + + pred <- factor(predictions_new[["predicted_label"]], levels = all_classes) + truth <- factor(predictions_new[["true_label"]], levels = all_classes) + + conf_matrix <- confusionMatrix(pred, truth) + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + + if (maximize == "balanced_accuracy") { + bal_acc$average_accuracy <- mean(bal_acc) + } else { + bal_acc$average_accuracy <- conf_matrix$overall[["Accuracy"]] + } + + bal_acc$threshold <- threshold + bal_acc$dlbclass_threshold <- conf_threshold + acc_df <- bind_rows(acc_df, bal_acc) + + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity <- mean(sn) + sn$threshold <- threshold + sn$dlbclass_threshold <- conf_threshold + sens_df <- bind_rows(sens_df, sn) + } + } + + }else{ + + for(threshold in rel_thresholds){ + predictions_new = mutate( + predictions, + predicted_label = ifelse( + other_score < threshold, + predicted_label, + "Other" + ) ) - ) - pred = factor(predictions_new[["predicted_label"]],levels=all_classes) - truth = factor(predictions_new[["true_label"]],levels=all_classes) - conf_matrix <- confusionMatrix(pred, truth) + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - if(maximize == "balanced_accuracy"){ - bal_acc$average_accuracy = mean(bal_acc) - }else{ - bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + } + bal_acc$threshold = threshold + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sens_df = bind_rows(sens_df,sn) } - bal_acc$threshold = threshold - acc_df = bind_rows(acc_df,bal_acc) - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity = mean(sn) - sn$threshold = threshold - sens_df = bind_rows(sens_df,sn) + } + if(maximize %in% c("balanced_accuracy","accuracy")){ best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) }else{ @@ -951,6 +1030,9 @@ make_and_annotate_umap = function( #' in the neighborhood of the sample in question. This parameter will NOT change #' the value in predicted_label. Instead, the predicted_label_optimized column #' will contain the optimized label. Default: FALSE +#' @param optimize_for_dlbclass Set to TRUE to optimize the optimized other thresholds into +#' assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: +#' FALSE #' #' @param verbose Whether to print verbose outputs to console #' @param seed Random seed to use for reproducibility (default: 12345) @@ -1000,6 +1082,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, "BN2", "Other"), optimize_for_other = FALSE, + optimize_for_dlbclass = FALSE, eval_group = NULL, min_k=3, max_k=33, @@ -1208,20 +1291,24 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, if(optimize_for_other){ optimized_accuracy_and_thresh = optimize_outgroup( + metadata = metadata_df, pred_factor, true_factor, xx_d$other_score, + optimize_for_dlbclass = optimize_for_dlbclass, all_classes = truth_classes, maximize = maximize, exclude_other_for_accuracy = exclude_other_for_accuracy ) out_opt_thresh = optimized_accuracy_and_thresh$threshold + out_opt_thresh_dlbclass = optimized_accuracy_and_thresh$dlbclass_threshold optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 out_opt_acc = optimized_accuracy_and_thresh$average_accuracy }else{ out_opt_acc = 0 out_opt_thresh = 0 + out_opt_thresh_dlbclass = 0 } if(maximize == "sensitivity"){ @@ -1257,6 +1344,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, C5_bacc = unname(bal_acc["Class: C5"]), ST2_bacc = unname(bal_acc["Class: ST2"]), threshold_outgroup = out_opt_thresh, + threshold_dlbclass = if(length(out_opt_thresh_dlbclass) == 0) NA else out_opt_thresh_dlbclass, accuracy_out = out_opt_acc, na_option= na_option) @@ -1304,14 +1392,70 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, ) pred = pred[[1]] - if(optimize_for_other){ - pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, - "Other", - predicted_label)) + if(optimize_for_other & optimize_for_dlbclass){ + + # Start from predicted_label + pred <- pred %>% + mutate( + predicted_label_optimized = ifelse( + other_score >= best_params$threshold, # mark as Other + "Other", + predicted_label + ) + ) + + # Ensure sample_id is present + if (!"sample_id" %in% colnames(pred)) { + pred <- pred %>% rownames_to_column(var = "sample_id") + } + + if (!"sample_id" %in% colnames(metadata_df)) { + metadata_df <- metadata_df %>% rownames_to_column(var = "sample_id") + } + + # Join DLBCL cluster predictions + dlbclass_labels <- metadata_df %>% + select(sample_id, Confidence_dlbclass, Predicted_dlbclass) + + pred <- inner_join(pred, dlbclass_labels, by = "sample_id") %>% + mutate( + predicted_label_optimized = ifelse( + predicted_label_optimized == "Other" & Confidence_dlbclass > best_params$threshold_dlbclass, + case_when( + Predicted_dlbclass == "C1" ~ "BN2", + Predicted_dlbclass == "C2" ~ "Other", + Predicted_dlbclass == "C3" ~ "EZB", + Predicted_dlbclass == "C4" ~ "ST2", + Predicted_dlbclass == "C5" ~ "MCD", + TRUE ~ predicted_label_optimized + ), + predicted_label_optimized + ) + ) + + pred <- rownames_to_column(pred) + + }else if(optimize_for_other){ + + pred = mutate( + pred, + predicted_label_optimized = ifelse( + other_score > best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) + }else{ + pred = mutate(pred,predicted_label_optimized = predicted_label) + } - xx_d = bind_cols(outs$df,pred) + + xx_d <- bind_cols( + outs$df, + pred %>% select(-any_of(colnames(outs$df))) + ) to_ret = list(params=results, best_params = best_params, model=best_fit$model, diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index c85a7b0..8d2fb69 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -10,6 +10,7 @@ DLBCLone_optimize_params( umap_out, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), optimize_for_other = FALSE, + optimize_for_dlbclass = FALSE, eval_group = NULL, min_k = 3, max_k = 33, @@ -43,6 +44,10 @@ in the neighborhood of the sample in question. This parameter will NOT change the value in predicted_label. Instead, the predicted_label_optimized column will contain the optimized label. Default: FALSE} +\item{optimize_for_dlbclass}{Set to TRUE to optimize the optimized other thresholds into +assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: +FALSE} + \item{eval_group}{If desired, use this to specify which rows will be evaluated and held out from training rather than using all samples. NOTE: this parameter will probably become deprecated!} diff --git a/man/optimize_outgroup.Rd b/man/optimize_outgroup.Rd index 3b2637a..96978db 100644 --- a/man/optimize_outgroup.Rd +++ b/man/optimize_outgroup.Rd @@ -5,21 +5,30 @@ \title{Optimize the threshold for classifying samples as "Other"} \usage{ optimize_outgroup( + metadata, predicted_labels, true_labels, other_score, + optimize_for_dlbclass = FALSE, all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), maximize = "balanced_accuracy", exclude_other_for_accuracy = FALSE ) } \arguments{ +\item{metadata}{Metadata data frame with one row per sample and a column sample_id that +matches the row names of df. This data frame will be joined to the UMAP output.} + \item{predicted_labels}{Vector of predicted labels for the samples} \item{true_labels}{Vector of true labels for the samples} \item{other_score}{Vector of scores for the "Other" class for each sample ()} +\item{optimize_for_dlbclass}{Set to TRUE to optimize the optimized other thresholds into +assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: +FALSE} + \item{all_classes}{Vector of classes to use for training and testing. Default: c("MCD","EZB","BN2","N1","ST2","Other")} From 5aeaff135ca262014eb243c83a39bb21de100297 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 18 Jul 2025 12:07:58 -0700 Subject: [PATCH 53/79] high conf dlbclass labeling to optimize other --- R/umap.R | 143 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 72 insertions(+), 71 deletions(-) diff --git a/R/umap.R b/R/umap.R index 8aca03b..c155c74 100644 --- a/R/umap.R +++ b/R/umap.R @@ -552,7 +552,9 @@ optimize_outgroup <- function( exclude_other_for_accuracy = FALSE ){ - rel_thresholds = seq(1,10,0.1) + rel_thresholds <- seq(1,10,0.1) + dlbclass_threshold <- 0.70 + sens_df = data.frame() acc_df = data.frame() predictions = data.frame( @@ -561,71 +563,71 @@ optimize_outgroup <- function( ) if (optimize_for_dlbclass) { - if ("sample_id" %in% colnames(metadata)) { - metadata <- metadata %>% column_to_rownames(var = "sample_id") + if (!"sample_id" %in% colnames(metadata)) { + metadata <- metadata %>% rownames_to_column(var = "sample_id") } - - dlbclass_labels <- metadata %>% select(Confidence_dlbclass, Predicted_dlbclass) - dlbclass_thresholds <- seq(0.90, 0.98, 0.04) + + dlbclass_data <- metadata %>% + select(sample_id, Confidence_dlbclass, Predicted_dlbclass) %>% + mutate( + Predicted_dlbclass = recode( + Predicted_dlbclass, + "C1" = "BN2", "C3" = "EZB", "C4" = "ST2", "C5" = "MCD", "C2" = "Other" + ) + ) for (threshold in rel_thresholds) { - for (conf_threshold in dlbclass_thresholds) { - # Initial Other assignment - predictions_new <- mutate( - predictions, - predicted_label = ifelse( - other_score < threshold, - predicted_label, - "Other" - ) + # Initial Other assignment + predictions_new <- mutate( + predictions, + predicted_label = ifelse( + other_score < threshold, + predicted_label, + "Other" ) + ) - # Join with DLBCL mapping - preds_df <- predictions_new %>% rownames_to_column("sample_id") - labels_df <- dlbclass_labels %>% rownames_to_column("sample_id") - predictions_bound <- inner_join(preds_df, labels_df, by = "sample_id") - - # Apply per-row override - predictions_bound <- predictions_bound %>% - mutate( - predicted_label = ifelse( - predicted_label == "Other" & Confidence_dlbclass > conf_threshold, - case_when( - Predicted_dlbclass == "C1" ~ "BN2", - Predicted_dlbclass == "C3" ~ "EZB", - Predicted_dlbclass == "C4" ~ "ST2", - Predicted_dlbclass == "C5" ~ "MCD", - TRUE ~ "Other" - ), - predicted_label - ) + predictions_new <- predictions_new %>% rownames_to_column("sample_id") + + # Apply per-row override + predictions_new <- predictions_new %>% + left_join(dlbclass_data, by = "sample_id") %>% + mutate( + predicted_label = case_when( + predicted_label == "Other" & Confidence_dlbclass > dlbclass_threshold ~ Predicted_dlbclass, + TRUE ~ predicted_label ) + ) - predictions_new <- predictions_bound %>% - select(predicted_label, true_label) - - pred <- factor(predictions_new[["predicted_label"]], levels = all_classes) - truth <- factor(predictions_new[["true_label"]], levels = all_classes) + # Join with DLBCL mapping + pred = factor(predictions_new$predicted_label, levels = all_classes) + truth = factor(predictions_new$true_label, levels = all_classes) + + if(exclude_other_for_accuracy){ + keep <- truth != "Other" + pred <- pred[keep] + truth <- truth[keep] + } - conf_matrix <- confusionMatrix(pred, truth) - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + conf_matrix <- confusionMatrix(pred, truth) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - if (maximize == "balanced_accuracy") { - bal_acc$average_accuracy <- mean(bal_acc) - } else { - bal_acc$average_accuracy <- conf_matrix$overall[["Accuracy"]] - } + if (maximize == "balanced_accuracy") { + bal_acc$average_accuracy <- mean(bal_acc) + } else { + bal_acc$average_accuracy <- conf_matrix$overall[["Accuracy"]] + } - bal_acc$threshold <- threshold - bal_acc$dlbclass_threshold <- conf_threshold - acc_df <- bind_rows(acc_df, bal_acc) + bal_acc$threshold <- threshold + bal_acc$dlbclass_threshold <- dlbclass_threshold + acc_df <- bind_rows(acc_df, bal_acc) - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity <- mean(sn) - sn$threshold <- threshold - sn$dlbclass_threshold <- conf_threshold - sens_df <- bind_rows(sens_df, sn) - } + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity <- mean(sn) + sn$threshold <- threshold + sn$dlbclass_threshold <- dlbclass_threshold + sens_df <- bind_rows(sens_df, sn) } }else{ @@ -669,7 +671,6 @@ optimize_outgroup <- function( return(best) } - #' Plot the result of a DLBCLone classification #' #' @param test_df Data frame containing the test data with UMAP coordinates @@ -1398,7 +1399,7 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, pred <- pred %>% mutate( predicted_label_optimized = ifelse( - other_score >= best_params$threshold, # mark as Other + other_score >= best_params$threshold_outgroup, # mark as Other "Other", predicted_label ) @@ -1415,25 +1416,22 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, # Join DLBCL cluster predictions dlbclass_labels <- metadata_df %>% - select(sample_id, Confidence_dlbclass, Predicted_dlbclass) + select(sample_id, Confidence_dlbclass, Predicted_dlbclass) %>% + mutate( + Predicted_dlbclass = recode( + Predicted_dlbclass, + "C1" = "BN2", "C3" = "EZB", "C4" = "ST2", "C5" = "MCD", "C2" = "Other") + ) - pred <- inner_join(pred, dlbclass_labels, by = "sample_id") %>% + pred <- pred %>% + left_join(dlbclass_labels, by = "sample_id") %>% mutate( predicted_label_optimized = ifelse( predicted_label_optimized == "Other" & Confidence_dlbclass > best_params$threshold_dlbclass, - case_when( - Predicted_dlbclass == "C1" ~ "BN2", - Predicted_dlbclass == "C2" ~ "Other", - Predicted_dlbclass == "C3" ~ "EZB", - Predicted_dlbclass == "C4" ~ "ST2", - Predicted_dlbclass == "C5" ~ "MCD", - TRUE ~ predicted_label_optimized - ), + Predicted_dlbclass, predicted_label_optimized ) - ) - - pred <- rownames_to_column(pred) + ) }else if(optimize_for_other){ @@ -1448,7 +1446,10 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, }else{ - pred = mutate(pred,predicted_label_optimized = predicted_label) + pred = mutate( + pred, + predicted_label_optimized = predicted_label + ) } From 9931edae6a22e7332c9295c6bb60b2e9efe7059d Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 21 Jul 2025 09:36:19 -0700 Subject: [PATCH 54/79] revert --- R/umap.R | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/R/umap.R b/R/umap.R index c155c74..8d5c587 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1144,20 +1144,12 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, for(use_w in weights_opt){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - test_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) - test_ids = filter(outs$df,dataset == eval_group) %>% pull(sample_id) - rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids } pred_all = weighted_knn_predict_with_conf( @@ -1176,20 +1168,12 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, if(!"Other" %in% truth_classes){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) - test_ids = filter(outs$df,lymphgen %in% "Other") %>% pull(sample_id) - rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) - test_ids = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% pull(sample_id) - rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(sample_id) - rownames(train_coords) = train_ids } n_other = nrow(test_coords) @@ -1372,13 +1356,8 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, best_params$seed = seed test_coords = outs$df %>% select(V1,V2) - test_ids = filter(outs$df) %>% pull(sample_id) - rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids - pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, From 0f70a8fa32d6c909e7e7e18b7ac8e99756463bf1 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 21 Jul 2025 09:44:30 -0700 Subject: [PATCH 55/79] final reversion --- R/umap.R | 226 +++++++------------------------- man/DLBCLone_optimize_params.Rd | 5 - man/optimize_outgroup.Rd | 9 -- 3 files changed, 50 insertions(+), 190 deletions(-) diff --git a/R/umap.R b/R/umap.R index 8d5c587..22d729e 100644 --- a/R/umap.R +++ b/R/umap.R @@ -513,14 +513,9 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' based on the specified metric (balanced accuracy or accuracy). #' This function is not generally meant to be called directly but rather is #' a helper function used by DLBCLone_optimize_params. -#' @param metadata Metadata data frame with one row per sample and a column sample_id that -#' matches the row names of df. This data frame will be joined to the UMAP output. #' @param predicted_labels Vector of predicted labels for the samples #' @param true_labels Vector of true labels for the samples #' @param other_score Vector of scores for the "Other" class for each sample () -#' @param optimize_for_dlbclass Set to TRUE to optimize the optimized other thresholds into -#' assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: -#' FALSE #' @param all_classes Vector of classes to use for training and testing. #' Default: c("MCD","EZB","BN2","N1","ST2","Other") #' @param maximize Metric to use for optimization. @@ -535,11 +530,9 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @export #' optimize_outgroup <- function( - metadata, predicted_labels, true_labels, other_score, - optimize_for_dlbclass = FALSE, all_classes = c( "MCD", "EZB", @@ -552,9 +545,7 @@ optimize_outgroup <- function( exclude_other_for_accuracy = FALSE ){ - rel_thresholds <- seq(1,10,0.1) - dlbclass_threshold <- 0.70 - + rel_thresholds = seq(1,10,0.1) sens_df = data.frame() acc_df = data.frame() predictions = data.frame( @@ -562,106 +553,33 @@ optimize_outgroup <- function( true_label=as.character(true_labels) ) - if (optimize_for_dlbclass) { - if (!"sample_id" %in% colnames(metadata)) { - metadata <- metadata %>% rownames_to_column(var = "sample_id") - } - - dlbclass_data <- metadata %>% - select(sample_id, Confidence_dlbclass, Predicted_dlbclass) %>% - mutate( - Predicted_dlbclass = recode( - Predicted_dlbclass, - "C1" = "BN2", "C3" = "EZB", "C4" = "ST2", "C5" = "MCD", "C2" = "Other" - ) - ) - - for (threshold in rel_thresholds) { - # Initial Other assignment - predictions_new <- mutate( - predictions, - predicted_label = ifelse( - other_score < threshold, - predicted_label, - "Other" - ) - ) - - predictions_new <- predictions_new %>% rownames_to_column("sample_id") - - # Apply per-row override - predictions_new <- predictions_new %>% - left_join(dlbclass_data, by = "sample_id") %>% - mutate( - predicted_label = case_when( - predicted_label == "Other" & Confidence_dlbclass > dlbclass_threshold ~ Predicted_dlbclass, - TRUE ~ predicted_label - ) - ) - - # Join with DLBCL mapping - pred = factor(predictions_new$predicted_label, levels = all_classes) - truth = factor(predictions_new$true_label, levels = all_classes) - - if(exclude_other_for_accuracy){ - keep <- truth != "Other" - pred <- pred[keep] - truth <- truth[keep] - } - - conf_matrix <- confusionMatrix(pred, truth) - - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - - if (maximize == "balanced_accuracy") { - bal_acc$average_accuracy <- mean(bal_acc) - } else { - bal_acc$average_accuracy <- conf_matrix$overall[["Accuracy"]] - } - - bal_acc$threshold <- threshold - bal_acc$dlbclass_threshold <- dlbclass_threshold - acc_df <- bind_rows(acc_df, bal_acc) - - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity <- mean(sn) - sn$threshold <- threshold - sn$dlbclass_threshold <- dlbclass_threshold - sens_df <- bind_rows(sens_df, sn) - } - - }else{ - - for(threshold in rel_thresholds){ - predictions_new = mutate( - predictions, - predicted_label = ifelse( - other_score < threshold, - predicted_label, - "Other" - ) + for(threshold in rel_thresholds){ + predictions_new = mutate( + predictions, + predicted_label = ifelse( + other_score < threshold, + predicted_label, + "Other" ) + ) - pred = factor(predictions_new[["predicted_label"]],levels=all_classes) - truth = factor(predictions_new[["true_label"]],levels=all_classes) - conf_matrix <- confusionMatrix(pred, truth) + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - if(maximize == "balanced_accuracy"){ - bal_acc$average_accuracy = mean(bal_acc) - }else{ - bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] - } - bal_acc$threshold = threshold - acc_df = bind_rows(acc_df,bal_acc) - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity = mean(sn) - sn$threshold = threshold - sens_df = bind_rows(sens_df,sn) + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] } - + bal_acc$threshold = threshold + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sens_df = bind_rows(sens_df,sn) } - if(maximize %in% c("balanced_accuracy","accuracy")){ best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) }else{ @@ -1031,9 +949,6 @@ make_and_annotate_umap = function( #' in the neighborhood of the sample in question. This parameter will NOT change #' the value in predicted_label. Instead, the predicted_label_optimized column #' will contain the optimized label. Default: FALSE -#' @param optimize_for_dlbclass Set to TRUE to optimize the optimized other thresholds into -#' assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: -#' FALSE #' #' @param verbose Whether to print verbose outputs to console #' @param seed Random seed to use for reproducibility (default: 12345) @@ -1083,7 +998,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, "BN2", "Other"), optimize_for_other = FALSE, - optimize_for_dlbclass = FALSE, eval_group = NULL, min_k=3, max_k=33, @@ -1144,12 +1058,20 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, for(use_w in weights_opt){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) + test_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) + test_ids = filter(outs$df,dataset == eval_group) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids } pred_all = weighted_knn_predict_with_conf( @@ -1168,12 +1090,20 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, if(!"Other" %in% truth_classes){ if(is.null(eval_group)){ test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) + test_ids = filter(outs$df,lymphgen %in% "Other") %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids }else{ test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) + test_ids = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) + train_ids = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(sample_id) + rownames(train_coords) = train_ids } n_other = nrow(test_coords) @@ -1276,24 +1206,20 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, if(optimize_for_other){ optimized_accuracy_and_thresh = optimize_outgroup( - metadata = metadata_df, pred_factor, true_factor, xx_d$other_score, - optimize_for_dlbclass = optimize_for_dlbclass, all_classes = truth_classes, maximize = maximize, exclude_other_for_accuracy = exclude_other_for_accuracy ) out_opt_thresh = optimized_accuracy_and_thresh$threshold - out_opt_thresh_dlbclass = optimized_accuracy_and_thresh$dlbclass_threshold optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 out_opt_acc = optimized_accuracy_and_thresh$average_accuracy }else{ out_opt_acc = 0 out_opt_thresh = 0 - out_opt_thresh_dlbclass = 0 } if(maximize == "sensitivity"){ @@ -1329,7 +1255,6 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, C5_bacc = unname(bal_acc["Class: C5"]), ST2_bacc = unname(bal_acc["Class: ST2"]), threshold_outgroup = out_opt_thresh, - threshold_dlbclass = if(length(out_opt_thresh_dlbclass) == 0) NA else out_opt_thresh_dlbclass, accuracy_out = out_opt_acc, na_option= na_option) @@ -1356,8 +1281,13 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, best_params$seed = seed test_coords = outs$df %>% select(V1,V2) + test_ids = filter(outs$df) %>% pull(sample_id) + rownames(test_coords) = test_ids train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) + train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + rownames(train_coords) = train_ids + pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, @@ -1372,70 +1302,14 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, ) pred = pred[[1]] - if(optimize_for_other & optimize_for_dlbclass){ - - # Start from predicted_label - pred <- pred %>% - mutate( - predicted_label_optimized = ifelse( - other_score >= best_params$threshold_outgroup, # mark as Other - "Other", - predicted_label - ) - ) - - # Ensure sample_id is present - if (!"sample_id" %in% colnames(pred)) { - pred <- pred %>% rownames_to_column(var = "sample_id") - } - - if (!"sample_id" %in% colnames(metadata_df)) { - metadata_df <- metadata_df %>% rownames_to_column(var = "sample_id") - } - - # Join DLBCL cluster predictions - dlbclass_labels <- metadata_df %>% - select(sample_id, Confidence_dlbclass, Predicted_dlbclass) %>% - mutate( - Predicted_dlbclass = recode( - Predicted_dlbclass, - "C1" = "BN2", "C3" = "EZB", "C4" = "ST2", "C5" = "MCD", "C2" = "Other") - ) - - pred <- pred %>% - left_join(dlbclass_labels, by = "sample_id") %>% - mutate( - predicted_label_optimized = ifelse( - predicted_label_optimized == "Other" & Confidence_dlbclass > best_params$threshold_dlbclass, - Predicted_dlbclass, - predicted_label_optimized - ) - ) - - }else if(optimize_for_other){ - - pred = mutate( - pred, - predicted_label_optimized = ifelse( - other_score > best_params$threshold_outgroup, - "Other", - predicted_label - ) - ) - + if(optimize_for_other){ + pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, + "Other", + predicted_label)) }else{ - - pred = mutate( - pred, - predicted_label_optimized = predicted_label - ) - + pred = mutate(pred,predicted_label_optimized = predicted_label) } - - xx_d <- bind_cols( - outs$df, - pred %>% select(-any_of(colnames(outs$df))) - ) + xx_d = bind_cols(outs$df,pred) to_ret = list(params=results, best_params = best_params, model=best_fit$model, diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 8d2fb69..c85a7b0 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -10,7 +10,6 @@ DLBCLone_optimize_params( umap_out, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), optimize_for_other = FALSE, - optimize_for_dlbclass = FALSE, eval_group = NULL, min_k = 3, max_k = 33, @@ -44,10 +43,6 @@ in the neighborhood of the sample in question. This parameter will NOT change the value in predicted_label. Instead, the predicted_label_optimized column will contain the optimized label. Default: FALSE} -\item{optimize_for_dlbclass}{Set to TRUE to optimize the optimized other thresholds into -assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: -FALSE} - \item{eval_group}{If desired, use this to specify which rows will be evaluated and held out from training rather than using all samples. NOTE: this parameter will probably become deprecated!} diff --git a/man/optimize_outgroup.Rd b/man/optimize_outgroup.Rd index 96978db..3b2637a 100644 --- a/man/optimize_outgroup.Rd +++ b/man/optimize_outgroup.Rd @@ -5,30 +5,21 @@ \title{Optimize the threshold for classifying samples as "Other"} \usage{ optimize_outgroup( - metadata, predicted_labels, true_labels, other_score, - optimize_for_dlbclass = FALSE, all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), maximize = "balanced_accuracy", exclude_other_for_accuracy = FALSE ) } \arguments{ -\item{metadata}{Metadata data frame with one row per sample and a column sample_id that -matches the row names of df. This data frame will be joined to the UMAP output.} - \item{predicted_labels}{Vector of predicted labels for the samples} \item{true_labels}{Vector of true labels for the samples} \item{other_score}{Vector of scores for the "Other" class for each sample ()} -\item{optimize_for_dlbclass}{Set to TRUE to optimize the optimized other thresholds into -assigning lymphgen labels only if scoring a high confidence assoicated dlbclass label. Default: -FALSE} - \item{all_classes}{Vector of classes to use for training and testing. Default: c("MCD","EZB","BN2","N1","ST2","Other")} From 67157cd37d15bcfc5736e4e13cbbb7080d1a162b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 21 Jul 2025 14:19:37 -0700 Subject: [PATCH 56/79] documentation update --- R/umap.R | 159 ++++++++++++++------------ man/DLBCLone_optimize_params.Rd | 7 ++ man/DLBCLone_save_optimized.Rd | 21 +--- man/assemble_genetic_features.Rd | 6 + man/make_and_annotate_umap.Rd | 13 ++- man/make_neighborhood_plot.Rd | 6 +- man/make_umap_scatterplot.Rd | 16 +-- man/predict_single_sample_DLBCLone.Rd | 2 + man/weighted_knn_predict_with_conf.Rd | 5 + 9 files changed, 136 insertions(+), 99 deletions(-) diff --git a/R/umap.R b/R/umap.R index 22d729e..d64b23d 100644 --- a/R/umap.R +++ b/R/umap.R @@ -9,17 +9,17 @@ #' @param maf_df A data frame containing mutation annotation format (MAF) data, #' with at least the following columns: #' \code{Hugo_Symbol}, \code{Variant_Classification}, and \code{Tumor_Sample_Barcode}. -#' @param silent_maf_df (Optional) A separate data frame containing silent mutation data if -#' the user doesn't want to pull silent mutation status from \code{maf_df}. -#' This argument is useful when you want to combine mutations from -#' the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene #' @param these_samples_metadata A data frame containing metadata for the samples, #' with at least a \code{sample_id} column. -#' Any sample that does not have a matching sample_id in these_samples_metadata will be dropped. +#' Any sample that does not have a matching sample_id in these_samples_metadata will be dropped. #' @param genes_of_interest A character vector of gene symbols to include #' in the summary. If missing, defaults to all Tier 1 B-cell lymphoma genes. #' @param synon_genes (Optional) A character vector of gene symbols for which -#' synonymous mutations should be included. +#' synonymous mutations should be included. +#' @param silent_maf_df (Optional) A separate data frame containing silent mutation data if +#' the user doesn't want to pull silent mutation status from \code{maf_df}. +#' This argument is useful when you want to combine mutations from +#' the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene #' @param separate_by_class_genes (Optional) A character vector of #' gene symbols for which mutations should be separated by class #' (e.g., "Nonsense_Mutation", "Missense_Mutation"). @@ -260,26 +260,31 @@ summarize_all_ssm_status <- function( #' @param synon_genes Vector of gene symbols for synonymous mutations. #' @param maf_with_synon MAF data frame including synonymous mutations. #' @param hotspot_genes Vector of hotspot genes. +#' @param genome_build Genome build to use, default: "grch37". #' @param sv_value Value to assign for SV presence (default: 3). #' @param synon_value Value to assign for synonymous mutations (default: 1). #' @param coding_value Value to assign for coding mutations (default: 2). +#' @param include_ashm Logical if TRUE, includes aSHM counts in the feature matrix. +#' @param annotated_sv Annotated SV data frame. #' @param verbose Defaults to FALSE #' #' @return Matrix of assembled features for each sample. #' @export -assemble_genetic_features <- function(these_samples_metadata, - metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), - genes, - synon_genes, - maf_with_synon, - hotspot_genes, - genome_build = "grch37", - sv_value = 3, - synon_value = 1, - coding_value = 2, - include_ashm = TRUE, - annotated_sv, - verbose = FALSE){ +assemble_genetic_features <- function( + these_samples_metadata, + metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), + genes, + synon_genes, + maf_with_synon, + hotspot_genes, + genome_build = "grch37", + sv_value = 3, + synon_value = 1, + coding_value = 2, + include_ashm = TRUE, + annotated_sv, + verbose = FALSE +){ if(include_ashm){ #TODO: ensure this supports both genome builds correctly some_regions = GAMBLR.utils::create_bed_data( @@ -713,17 +718,24 @@ DLBCLone_train_test_plot = function(test_df, #' @param umap_out Optional UMAP output from a previous run. If provided, the function #' will use this model to project the data instead of re-running UMAP. This is useful #' for reproducibility and for using the same UMAP model on different datasets. -#' @param join_column The column name in the metadata data frame that contains the sample IDs (default sample_id). #' @param n_neighbors Passed to UMAP2. The number of neighbors to consider when calculating the UMAP embedding. #' @param min_dist Passed to UMAP2. The minimum distance between points in the UMAP embedding. #' @param metric Passed to UMAP2. The distance metric to use for calculating distances between points. #' @param n_epochs Passed to UMAP2. The number of epochs to run the UMAP algorithm. #' @param init Passed to UMAP2. The initialization method for the UMAP algorithm. +#' @param ret_model If TRUE, the function will return the UMAP model object in addition to the UMAP coordinates. #' @param na_vals How to deal with NA values. Two options are "drop", which #' will remove all columns containing at least one NA or "to_zero", which sets #' all NA to zero and leaves the column intact. +#' @param join_column The column name in the metadata data frame that contains the sample IDs (default sample_id). #' @param seed Passed to UMAP2. The random seed for reproducibility. -#' @param ret_model additional argument +#' @param target_column If provided, the function will run a supervised UMAP +#' using the specified target column from the metadata. If not provided, the function will run an unsupervised UMAP. +#' @param target_metric The distance metric to use for the supervised UMAP. +#' Default: "euclidean". Passed to UMAP2. +#' @param target_weight The weight to assign to the target column in the supervised UMAP. +#' Default: 0.5. Passed to UMAP2. +#' @param calc_dispersion If TRUE, calculates the pairwise dispersion of the UMAP coordinates #' #' @import uwot #' @export @@ -931,16 +943,11 @@ make_and_annotate_umap = function( #' one column per mutation #' @param metadata_df Data frame of metadata with one row per sample and #' three required columns: sample_id, dataset and lymphgen -#' @param truth_classes Vector of classes to use for training and testing. -#' Default: c("EZB","MCD","ST2","N1","BN2","Other") -#' @param eval_group If desired, use this to specify which rows will be -#' evaluated and held out from training rather than using all samples. -#' NOTE: this parameter will probably become deprecated! #' @param umap_out The output of a previous run of make_and_annotate_umap. #' If provided, the function will use this model to project the data #' instead of re-running UMAP. -#' @param min_k Starting k for knn (Default: 3) -#' @param max_k Ending k for knn (Default: 33) +#' @param truth_classes Vector of classes to use for training and testing. +#' Default: c("EZB","MCD","ST2","N1","BN2","Other") #' @param optimize_for_other Set to TRUE to optimize the threshold for #' classifying samples as "Other" based on the relative proportion of #' samples near the sample in UMAP space with the "Other" label. Rather than @@ -949,13 +956,22 @@ make_and_annotate_umap = function( #' in the neighborhood of the sample in question. This parameter will NOT change #' the value in predicted_label. Instead, the predicted_label_optimized column #' will contain the optimized label. Default: FALSE -#' +#' @param eval_group If desired, use this to specify which rows will be +#' evaluated and held out from training rather than using all samples. +#' NOTE: this parameter will probably become deprecated! +#' @param min_k Starting k for knn (Default: 3) +#' @param max_k Ending k for knn (Default: 33) #' @param verbose Whether to print verbose outputs to console #' @param seed Random seed to use for reproducibility (default: 12345) #' @param maximize Metric to use for optimization. Either "sensitivity" #' (average sensitivity across all classes), "accuracy" #' (actual accuracy across all samples) or "balanced_accuracy" (the mean of the #' balanced accuracy values across all classes). Default: "balanced_accuracy" +#' @param exclude_other_for_accuracy Set to TRUE to exclude the +#' "Other" class from the 'lymphgen' column when calculating accuracy metrics +#' (passed to DLBCLone_optimize_params). Default: FALSE +#' @param weights_opt Vector of TRUE/FALSE values to indicate whether to use +#' weighted knn or not. Default: c(TRUE). #' #' @returns List of data frames with the results of the parameter optimization #' including the best model, the associated knn parameters and the annotated @@ -988,25 +1004,28 @@ make_and_annotate_umap = function( #' truth_classes = c("MCD","EZB","ST2","BN2","Other")) #' -DLBCLone_optimize_params = function(combined_mutation_status_df, - metadata_df, - umap_out, - truth_classes = c("EZB", - "MCD", - "ST2", - "N1", - "BN2", - "Other"), - optimize_for_other = FALSE, - eval_group = NULL, - min_k=3, - max_k=33, - verbose = FALSE, - seed = 12345, - maximize = "balanced_accuracy", - exclude_other_for_accuracy = FALSE, - weights_opt = c(TRUE) - ) { +DLBCLone_optimize_params = function( + combined_mutation_status_df, + metadata_df, + umap_out, + truth_classes = c( + "EZB", + "MCD", + "ST2", + "N1", + "BN2", + "Other" + ), + optimize_for_other = FALSE, + eval_group = NULL, + min_k=3, + max_k=33, + verbose = FALSE, + seed = 12345, + maximize = "balanced_accuracy", + exclude_other_for_accuracy = FALSE, + weights_opt = c(TRUE) +){ if(optimize_for_other){ exclude_other_for_accuracy = FALSE }else{ @@ -1346,6 +1365,9 @@ DLBCLone_optimize_params = function(combined_mutation_status_df, #' distance = 0. This is usually only relevant when re-classifying labeled #' samples to estimate overall accuracy #' @param track_neighbors Set to TRUE to include details for the nearest neighbors of each sample +#' @param separate_other Set to TRUE to treat the "Other" class separately +#' when calculating the confidence. +#' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' #' @returns data frame with labels and confidence values for rows in test_coords #' @export @@ -1576,8 +1598,10 @@ weighted_knn_predict_with_conf <- function( #' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. #' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. #' @param add_circle Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable. -#' @param drop_other Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot. #' @param label_column Does nothing, i.e. this is not currently working. +#' @param point_size Numeric. Size of the points in the plot. Default: 0.5. +#' @param point_alpha Numeric. Transparency of the points in the plot. Default: 0.9. +#' @param line_alpha Numeric. Transparency of the lines connecting the sample to its neighbors. Default: 0.9. #' #' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. #' @@ -1686,7 +1710,6 @@ make_neighborhood_plot <- function( #' Predict class for a single sample without using umap_transform and plot result of classification #' -#' @param seed Random seed for reproducibility #' @param test_df Data frame containing the mutation status of the test sample #' @param train_df Data frame containing the mutation status of the training samples #' @param train_metadata Metadata for training samples with truth labels in lymphgen column @@ -1704,6 +1727,8 @@ make_neighborhood_plot <- function( #' @param title1 additional argument #' @param title2 additional argument #' @param title3 additional argument +#' @param seed Random seed for reproducibility +#' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' #' @returns a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object #' @export @@ -2035,14 +2060,14 @@ predict_single_sample_DLBCLone <- function( #' Make UMAP scatterplot #' -#' @param df -#' @param title -#' @param drop_composite -#' @param colour_by -#' @param drop_other -#' @param high_confidence -#' @param custom_colours -#' @param add_labels +#' @param df Data frame containing the UMAP coordinates and annotations. +#' @param title Title for the plot. Default: NULL. +#' @param drop_composite If TRUE: removes composite labels from the lymphgen column. +#' @param colour_by Column name to color the points by. Default: "lymphgen". +#' @param drop_other If TRUE: removes "Other" and "NOS" labels from the lymphgen column. +#' @param high_confidence If TRUE: filters the data to include only samples with confidence > 0.7. +#' @param custom_colours Custom color palette for the plot. If not provided, uses default GAMBL colors. +#' @param add_labels If TRUE: adds labels to the points based on the median coordinates of each group. #' @param facet If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely #' #' @returns @@ -2154,12 +2179,7 @@ make_umap_scatterplot = function( #' modal storage for DLBCLone outputs #' -#' @param combined_mutation_df Data frame containing the mutation status of the samples used -#' @param metadata Metadata with truth labels in lymphgen column -#' @param truth_classes Vector of classes to used for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other") -#' @param optimized_out output of DLBCLone_optimize_params(), if set to NULL values will not be saved -#' @param predict_single output of predict_single_sample_DLBCLone(), if set to NULL values will not be saved -#' @param neighborhood_plot output of make_neighborhood_plot(), if set to NULL values will not be saved +#' @param optimized_params List containing the optimized parameters from DLBCLone_optimize_params #' @param path Path to save the files #' @param name_prefix Prefix for the saved files, all files will be in path and start with name_prefix #' @@ -2172,23 +2192,18 @@ make_umap_scatterplot = function( #' #' @examples #' DLBCLone_save_optimized( -#' combined_mutation_df = status_df, -#' metadata = status_metadata, -#' truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), -#' optimized_out = lymphgen_DLBCLone, -#' predict_single=NULL, -#' neighborhood_plot=NULL, +#' optimzied_params = optimized_params, #' path="/save_optimized/trial_folder", #' name_prefix="test_A" #' ) #' -DLBCLone_save_optimized = function( # <- ensure rownmaes works, restore lsit option of umap_out not umap_out$model to all fxs +DLBCLone_save_optimized = function( optimized_params=NULL, path="models/", name_prefix="test" ){ - #all files will be in path and start with name_prefix + # all files will be in path and start with name_prefix prefix = paste0(path,"/",name_prefix) out_mut = paste0(prefix,"_mutation_status_df.tsv") diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index c85a7b0..674dcab 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -59,6 +59,13 @@ NOTE: this parameter will probably become deprecated!} (average sensitivity across all classes), "accuracy" (actual accuracy across all samples) or "balanced_accuracy" (the mean of the balanced accuracy values across all classes). Default: "balanced_accuracy"} + +\item{exclude_other_for_accuracy}{Set to TRUE to exclude the +"Other" class from the 'lymphgen' column when calculating accuracy metrics +(passed to DLBCLone_optimize_params). Default: FALSE} + +\item{weights_opt}{Vector of TRUE/FALSE values to indicate whether to use +weighted knn or not. Default: c(TRUE).} } \value{ List of data frames with the results of the parameter optimization diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd index ecfefb5..57fcde6 100644 --- a/man/DLBCLone_save_optimized.Rd +++ b/man/DLBCLone_save_optimized.Rd @@ -11,21 +11,11 @@ DLBCLone_save_optimized( ) } \arguments{ +\item{optimized_params}{List containing the optimized parameters from DLBCLone_optimize_params} + \item{path}{Path to save the files} \item{name_prefix}{Prefix for the saved files, all files will be in path and start with name_prefix} - -\item{combined_mutation_df}{Data frame containing the mutation status of the samples used} - -\item{metadata}{Metadata with truth labels in lymphgen column} - -\item{truth_classes}{Vector of classes to used for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other")} - -\item{optimized_out}{output of DLBCLone_optimize_params(), if set to NULL values will not be saved} - -\item{predict_single}{output of predict_single_sample_DLBCLone(), if set to NULL values will not be saved} - -\item{neighborhood_plot}{output of make_neighborhood_plot(), if set to NULL values will not be saved} } \value{ saves the files to the specified path @@ -35,12 +25,7 @@ modal storage for DLBCLone outputs } \examples{ DLBCLone_save_optimized( - combined_mutation_df = status_df, - metadata = status_metadata, - truth_classes = c("MCD","EZB","BN2","ST2","N1","Other"), - optimized_out = lymphgen_DLBCLone, - predict_single=NULL, - neighborhood_plot=NULL, + optimzied_params = optimized_params, path="/save_optimized/trial_folder", name_prefix="test_A" ) diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd index dff771b..3317fd0 100644 --- a/man/assemble_genetic_features.Rd +++ b/man/assemble_genetic_features.Rd @@ -33,12 +33,18 @@ assemble_genetic_features( \item{hotspot_genes}{Vector of hotspot genes.} +\item{genome_build}{Genome build to use, default: "grch37".} + \item{sv_value}{Value to assign for SV presence (default: 3).} \item{synon_value}{Value to assign for synonymous mutations (default: 1).} \item{coding_value}{Value to assign for coding mutations (default: 2).} +\item{include_ashm}{Logical if TRUE, includes aSHM counts in the feature matrix.} + +\item{annotated_sv}{Annotated SV data frame.} + \item{verbose}{Defaults to FALSE} } \value{ diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 03ad886..4d260ff 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -44,7 +44,7 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{init}{Passed to UMAP2. The initialization method for the UMAP algorithm.} -\item{ret_model}{additional argument} +\item{ret_model}{If TRUE, the function will return the UMAP model object in addition to the UMAP coordinates.} \item{na_vals}{How to deal with NA values. Two options are "drop", which will remove all columns containing at least one NA or "to_zero", which sets @@ -53,6 +53,17 @@ all NA to zero and leaves the column intact.} \item{join_column}{The column name in the metadata data frame that contains the sample IDs (default sample_id).} \item{seed}{Passed to UMAP2. The random seed for reproducibility.} + +\item{target_column}{If provided, the function will run a supervised UMAP +using the specified target column from the metadata. If not provided, the function will run an unsupervised UMAP.} + +\item{target_metric}{The distance metric to use for the supervised UMAP. +Default: "euclidean". Passed to UMAP2.} + +\item{target_weight}{The weight to assign to the target column in the supervised UMAP. +Default: 0.5. Passed to UMAP2.} + +\item{calc_dispersion}{If TRUE, calculates the pairwise dispersion of the UMAP coordinates} } \description{ Run UMAP and attach result to metadata diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index ce4db34..d053ba6 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -30,7 +30,11 @@ Must include elements \code{prediction} (data frame with prediction results) and \item{label_column}{Does nothing, i.e. this is not currently working.} -\item{drop_other}{Logical. If \code{TRUE}, samples with the "Other" label will be excluded from the plot.} +\item{point_size}{Numeric. Size of the points in the plot. Default: 0.5.} + +\item{point_alpha}{Numeric. Transparency of the points in the plot. Default: 0.9.} + +\item{line_alpha}{Numeric. Transparency of the lines connecting the sample to its neighbors. Default: 0.9.} } \value{ A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index 9938a51..8728384 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -17,19 +17,21 @@ make_umap_scatterplot( ) } \arguments{ -\item{df}{} +\item{df}{Data frame containing the UMAP coordinates and annotations.} -\item{drop_composite}{} +\item{title}{Title for the plot. Default: NULL.} -\item{colour_by}{} +\item{drop_composite}{If TRUE: removes composite labels from the lymphgen column.} -\item{drop_other}{} +\item{colour_by}{Column name to color the points by. Default: "lymphgen".} -\item{high_confidence}{} +\item{drop_other}{If TRUE: removes "Other" and "NOS" labels from the lymphgen column.} -\item{custom_colours}{} +\item{high_confidence}{If TRUE: filters the data to include only samples with confidence > 0.7.} -\item{add_labels}{} +\item{custom_colours}{Custom color palette for the plot. If not provided, uses default GAMBL colors.} + +\item{add_labels}{If TRUE: adds labels to the points based on the median coordinates of each group.} \item{facet}{If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely} } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 9ef8a93..16b8f75 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -56,6 +56,8 @@ samples to estimate overall accuracy} \item{title3}{additional argument} \item{seed}{Random seed for reproducibility} + +\item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} } \value{ a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index 74d7971..29da3e3 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -51,6 +51,11 @@ distance = 0. This is usually only relevant when re-classifying labeled samples to estimate overall accuracy} \item{track_neighbors}{Set to TRUE to include details for the nearest neighbors of each sample} + +\item{separate_other}{Set to TRUE to treat the "Other" class separately +when calculating the confidence.} + +\item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} } \value{ data frame with labels and confidence values for rows in test_coords From 2e7c39b46c4c22b19d4a210c01d3c4d1ef8882a2 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 1 Aug 2025 11:46:55 -0700 Subject: [PATCH 57/79] adding plotting.R with spacing fixes and circle fix to neighborhood plot and param documentation --- NAMESPACE | 3 + R/plotting.R | 733 ++++++++++++++++++++++++++++++++ man/DLBCLone_summarize_model.Rd | 33 ++ man/make_alluvial.Rd | 98 +++++ man/make_neighborhood_plot.Rd | 25 +- man/make_umap_scatterplot.Rd | 16 +- man/report_accuracy.Rd | 52 +++ 7 files changed, 958 insertions(+), 2 deletions(-) create mode 100644 R/plotting.R create mode 100644 man/DLBCLone_summarize_model.Rd create mode 100644 man/make_alluvial.Rd create mode 100644 man/report_accuracy.Rd diff --git a/NAMESPACE b/NAMESPACE index 850f7bc..dcf16d8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,6 +3,7 @@ export(DLBCLone_load_optimized) export(DLBCLone_optimize_params) export(DLBCLone_save_optimized) +export(DLBCLone_summarize_model) export(DLBCLone_train_test_plot) export(assemble_genetic_features) export(classify_bl) @@ -10,12 +11,14 @@ export(classify_dlbcl) export(classify_fl) export(complete_missing_from_matrix) export(construct_reduced_winning_version) +export(make_alluvial) export(make_and_annotate_umap) export(make_neighborhood_plot) export(make_umap_scatterplot) export(massage_matrix_for_clustering) export(optimize_outgroup) export(predict_single_sample_DLBCLone) +export(report_accuracy) export(summarize_all_ssm_status) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) diff --git a/R/plotting.R b/R/plotting.R new file mode 100644 index 0000000..072b579 --- /dev/null +++ b/R/plotting.R @@ -0,0 +1,733 @@ +#' Summarize and Export DLBCLone Model Results +#' +#' Generates and saves a set of summary plots and tables for a DLBCLone model, including UMAP scatterplots, alluvial plots, and oncoplots. +#' Results are saved as PDF files in a directory named after the provided base name. +#' +#' @param base_name Character. The base name (and directory) for saving output files. +#' @param optimized_model List. The output from DLBCLone optimization, containing predictions, features, and metadata. +#' +#' @details +#' - Creates a directory for results if it does not exist. +#' - Saves UMAP scatterplots for all samples and for non-"Other" samples. +#' - Generates alluvial plots for different DLBCLone predictions +#' - Exports an oncoplot summarizing mutation and classification results. +#' - Uses `make_umap_scatterplot`, `make_alluvial`, and `prettyOncoplot` for visualization. +#' +#' @return No return value. Side effect: writes multiple PDF files to disk. +#' +#' @examples +#' DLBCLone_summarize_model("Full_geneset_unweighted", optimized_model) +#' +#' @export +DLBCLone_summarize_model = function( + base_name, + optimized_model +){ + base_dir = here::here() + full_dir = paste0(base_dir,"/",base_name) + if(!dir.exists(full_dir)){ + dir.create(full_dir) + } + #save the predictions + pred_out = paste0(full_dir,"/DLBCLone_predictions_bulk.tsv") + print(paste("Saving predictions to",pred_out)) + to_save = optimized_model$predictions %>% + select(sample_id,!!sym(optimized_model$truth_column),DLBCLone_i:DLBCLone_wo,confidence:other_score,V1,V2) + + write_tsv(to_save, + file = pred_out) + umap1 = paste0(full_dir,"/UMAP_all.pdf") + cairo_pdf(umap1,width=8,height=8) + p = make_umap_scatterplot( + optimized_model$df, + colour_by = optimized_model$truth_column, + title="all samples, projected" + ) + print(p) + dev.off() + + umap2 = paste0(full_dir,"/UMAP_no_Other.pdf") + cairo_pdf(umap2,width=8,height=8) + p = make_umap_scatterplot( + optimized_model$df, + colour_by = optimized_model$truth_column, + drop_other = T, + title="non-Other samples, projected" + ) + print(p) + dev.off() + cairo_pdf(paste0(full_dir,"/Alluvial_DLBCLone_io.pdf"),width=8,height=12) + p = make_alluvial( + optimized_model, + pred_column = "DLBCLone_io", + truth_column = optimized_model$truth_column + ) + print(p) + dev.off() + cairo_pdf(paste0(full_dir,"/Alluvial_DLBCLone_wo.pdf"),width=8,height=12) + p = make_alluvial( + optimized_model, + pred_column = "DLBCLone_wo", + truth_column = optimized_model$truth_column + ) + print(p) + dev.off() + cairo_pdf(paste0(full_dir,"/Oncoplot.pdf"),width=16,height=14) + mc = c("lymphgen","DLBCLone_wo","DLBCLone_io","DLBCLone_w","DLBCLone_i") + sc = c("DLBCLone_wo","DLBCLone_io","lymphgen","DLBCLone_w","DLBCLone_i","confidence") + if("DLBClass" %in% colnames(optimized_model$predictions)){ + mc = c(mc,"DLBClass") + sc = c(sc,"DLBClass") + } + mc = mc[mc %in% colnames(optimized_model$predictions)] + sc = sc[sc %in% colnames(optimized_model$predictions)] + prettyOncoplot( + all_maf_with_s, + these_samples_metadata =optimized_model$predictions, + genes = colnames(optimized_model$features), + minMutationPercent = 1, + metadataColumns = mc, + sortByColumns = sc, + numericMetadataColumns = "confidence", + simplify_annotation = T, + cluster_rows=T + ) + dev.off() +} + +#' @title Make Neighborhood Plot +#' @description +#' Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. +#' +#' @param single_sample_prediction_output A list containing prediction results and annotation data frames. +#' Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations). +#' @param training_predictions The equivalent data frame of prediction results for all training samples (e.g. optimized_model$df) +#' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. +#' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. +#' @param add_circle Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable. +#' @param label_column Does nothing, i.e. this is not currently working. +#' +#' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. +#' +#' @details +#' The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. +#' +#' @import dplyr +#' @import ggplot2 +#' @importFrom rlang sym +#' +#' @export +#' @examples +#' +#' # Assuming 'optimization_result' is the output of DLBCLone_optimize_params +#' # and 'output' is the result of DLBCLone_predict_single_sample +#' # on sample_id "SAMPLE123": +#' make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") +make_neighborhood_plot <- function( + single_sample_prediction_output, + training_predictions, + this_sample_id, + prediction_in_title = TRUE, + add_circle = TRUE, + label_column = "DLBCLone_io", + point_size = 0.5, + point_alpha = 0.9, + line_alpha = 0.9 +){ + + circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ + r = diameter / 2 + tt <- seq(0,2*pi,length.out = npoints) + xx <- center[1] + r * cos(tt) + yy <- center[2] + r * sin(tt) + return(data.frame(x = xx, y = yy)) + } + if(missing(training_predictions)){ + #training_predictions = single_sample_prediction_output$anno_df + training_predictions = + left_join( + select(single_sample_prediction_output$anno_df,sample_id,lymphgen), + select(single_sample_prediction_output$df,sample_id,V1,V2) + ) + }else if(missing(single_sample_prediction_output)){ + #Just plot the single sample in the context of the rest based on the optimization + single_sample_prediction_output = list() + single_sample_prediction_output[["prediction"]] = filter(training_predictions, sample_id==this_sample_id) + single_sample_prediction_output[["anno_df"]] = training_predictions + }else{ + single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction, sample_id==this_sample_id) + } + xmin = min(training_predictions$V1, na.rm = TRUE) + xmax = max(training_predictions$V1, na.rm = TRUE) + ymin = min(training_predictions$V2, na.rm = TRUE) + ymax = max(training_predictions$V2, na.rm = TRUE) + #extract the sample_id for all the nearest neighbors with non-Other labels + my_neighbours = filter(single_sample_prediction_output$prediction,sample_id == this_sample_id) %>% + pull(neighbor_id) %>% strsplit(.,",") %>% unlist() + + #set up links connecting each neighbor to the sample's point + links_df = filter(training_predictions,sample_id %in% my_neighbours) %>% mutate(group=lymphgen) + my_x = filter(single_sample_prediction_output$anno_df,sample_id==this_sample_id) %>% pull(V1) + my_y = filter(single_sample_prediction_output$anno_df,sample_id==this_sample_id) %>% pull(V2) + if(prediction_in_title){ + title = paste(this_sample_id,pull(single_sample_prediction_output$prediction,!!sym(label_column))) + if(single_sample_prediction_output$prediction[[label_column]] == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ + title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") + } + + }else{ + title = this_sample_id + } + links_df = mutate(links_df,my_x=my_x,my_y=my_y) + links_df = links_df %>% select(V1,V2,my_x,my_y,group) %>% mutate(length = sqrt((V1 - my_x)^2 + (V2 - my_y)^2)) # Euclidean distance + + pp=ggplot(mutate(training_predictions,group=lymphgen),aes(x=V1,y=V2,colour=group)) + + geom_point(alpha=point_alpha,size=point_size) + + geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=line_alpha) + + scale_colour_manual(values=get_gambl_colours()) + + ggtitle(title) + + xlim(c(xmin,xmax)) + + ylim(c(ymin,ymax)) + + theme_minimal() + if(add_circle){ + #add a circle around the sample + d = max(links_df$length)*2.1 # adding a 10% spacer + circle = circleFun(c(my_x,my_y),diameter=d,npoints=100) + pp = pp + geom_path(data=circle,aes(x=x,y=y),colour="black",alpha=1,inherit.aes=FALSE) + } + return(pp) +} + +#' Make UMAP scatterplot +#' +#' @param df Data frame containing the UMAP coordinates and annotations. +#' @param drop_composite If TRUE: removes composite labels from the lymphgen column. +#' @param colour_by Column name to color the points by. Default: "lymphgen". +#' @param drop_other If TRUE: removes "Other" and "NOS" labels from the lymphgen column. +#' @param high_confidence If TRUE: filters the data to include only samples with confidence > 0.7. +#' @param custom_colours Custom color palette for the plot. If not provided, uses default GAMBL colors. +#' @param add_labels If TRUE: adds labels to the points based on the median coordinates of each group. +#' @param title Title for the plot. Default: NULL. +#' +#' @returns +#' @export +#' +#' @examples +make_umap_scatterplot = function( + df, + drop_composite = TRUE, + colour_by="lymphgen", + drop_other = FALSE, + high_confidence = FALSE, + custom_colours, + add_labels = FALSE, + title = NULL +){ + xmin = min(df$V1,na.rm=TRUE) + xmax = max(df$V1,na.rm=TRUE) + ymin = min(df$V2,na.rm=TRUE) + ymax = max(df$V2,na.rm=TRUE) + if(!missing(custom_colours)){ + cols = custom_colours + }else{ + cols = get_gambl_colours() + } + if(drop_composite){ + df = filter(df,!is.na(lymphgen),!grepl("COMP",lymphgen)) + } + if(drop_other){ + df = filter(df,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") + } + if(high_confidence){ + df = filter(df,Confidence > 0.7) + } + if(add_labels){ + labels = group_by(df,!!sym(colour_by)) %>% + summarise(median_x = median(V1),median_y = median(V2)) + } + unique_lg = unique(df$lymphgen) + if(any(!unique_lg %in% names(cols))){ + missing = unique_lg[!unique_lg %in% names(cols)] + print(paste("missing colour for:",paste(missing,collapse=","))) + } + p = ggplot(df,aes(x=V1,y=V2,colour=!!sym(colour_by),label=cohort)) + + geom_point(alpha=0.8) + + scale_colour_manual(values=cols) + theme_Morons() + + guides(colour = guide_legend(nrow = 1)) + + xlim(xmin,xmax) + + ylim(ymin,ymax) + + if(!is.null(title)){ + p = p + ggtitle(title) + } + if(add_labels){ + p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + } + ggMarginal(p,groupColour = TRUE,groupFill=TRUE) + +} + +#' Report Classification Accuracy and Per-Class Metrics +#' +#' Computes overall accuracy, balanced accuracy, and sensitivity for predicted vs. true class labels. +#' Optionally excludes samples assigned to the "Other" class from accuracy calculations. +#' +#' @param predictions Data frame containing predicted and true class labels. +#' @param truth Name of the column with true class labels (default: "lymphgen"). +#' @param pred Name of the column with predicted class labels (default: "predicted_label"). +#' @param per_group Logical; if TRUE, computes per-group accuracy metrics. +#' @param metric Character; type of accuracy to report ("accuracy" supported). +#' @param verbose Whether to print verbose outputs to console +#' +#' @return A list with: +#' \item{no_other}{Accuracy excluding samples assigned to "Other"} +#' \item{per_class}{Balanced accuracy per class} +#' \item{per_class_sensitivity}{Sensitivity per class} +#' \item{overall}{Overall accuracy including all samples} +#' +#' @details +#' - Uses confusion matrices to compute accuracy metrics. +#' - Excludes "Other" class for no_other accuracy. +#' - Returns per-class metrics for further analysis. +#' +#' @examples +#' result <- report_accuracy(predictions_df) +#' result$overall +#' result$per_class +#' +#' @export +report_accuracy = function( + predictions, + truth="lymphgen", + pred="DLBCLone_io", + per_group = FALSE, + metric = "accuracy", + verbose = FALSE +){ + if("BN2" %in% predictions[[pred]]){ + truth = "lymphgen" + } + all_classes = unique(predictions[[truth]]) + no_other_pred = filter(predictions,lymphgen != "Other") + if(verbose){ + print(paste(nrow(no_other_pred),"non-Other samples were assigned a label")) + print(paste(filter(no_other_pred,!!sym(pred) !="Other") %>% nrow(),"non-Other samples were assigned to a non-Other class")) + } + + conf_matrix_no <- confusionMatrix( + factor(no_other_pred[[pred]],levels=all_classes), + factor(no_other_pred[[truth]],levels=all_classes) + ) + #print(conf_matrix) + overall = conf_matrix_no$overall[["Accuracy"]] + + if(metric == "accuracy"){ + #bal_acc_no <- conf_matrix$byClass[, "Balanced Accuracy"] + acc_no = overall + }else{ + stop("unsupported metric") + } + + conf_matrix <- confusionMatrix( + factor(predictions[[pred]],levels=unique(predictions[[truth]])), + factor(predictions[[truth]],levels=unique(predictions[[truth]])) + ) + + if(metric == "accuracy"){ + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + sensitivity = conf_matrix$byClass[, "Sensitivity"] + }else{ + stop("unsupported metric") + } + overall = conf_matrix$overall[["Accuracy"]] + + return(list( + no_other=acc_no, + per_class=bal_acc, + mean_balanced_accuracy = mean(bal_acc,na.rm=TRUE), + per_class_sensitivity = sensitivity, + overall=overall, + confusion_matrix_no_other=conf_matrix_no, + confusion_matrix=conf_matrix + )) +} + +#' Create an Alluvial Plot Comparing Original and Predicted Classifications +#' +#' This function generates a detailed alluvial plot to visualize the concordance and discordance between original (e.g., Lymphgen) and predicted (e.g., DLBCLone) class assignments for samples. +#' It supports annotation of concordance rates, per-group accuracy, unclassified rates, and flexible labeling and coloring options. +#' +#' @param optimized List containing prediction results and metadata, typically output from a DLBCLone optimization function. +#' @param count_excluded_as_other Logical; if TRUE, samples excluded due to missing features are counted as "Other" in the plot. +#' @param title Plot title (default: empty string). +#' @param group_order Character vector specifying the order of groups/classes for axes and coloring. +#' @param add_accuracy_to_title Logical; if TRUE, adds accuracy/concordance rate to the plot title. +#' @param accuracy_per_group Logical; if TRUE, computes and displays per-group accuracy. +#' @param accuracy_type Type of accuracy to report (default: "sensitivity"). +#' @param truth_name Name for the original class column (default: "Lymphgen"). +#' @param truth_column Name for the original class column in the predictions data frame (default: "lymphgen"). +#' @param pred_name Name for the predicted class column (default: "DLBCLone"). +#' @param pred_column Name of the column in predictions to use for the predicted class (default: "predicted_label_optimized"). +#' @param nudge Amount to nudge labels horizontally (default: 0.03). +#' @param box_nudge Amount to nudge label boxes (default: 0.15). +#' @param min_flip_n Minimum number of samples for a flow to be labeled (default: 10). +#' @param add_percent Logical; if TRUE, adds percent concordance to labels. +#' @param add_unclass_rate Logical; if TRUE, adds unclassified rate annotation to the plot. +#' @param denom Denominator for stratum/flow width scaling (default: 20). +#' @param label_size Size of label text (default: 3). +#' @param label_lock_y Logical; if TRUE, locks label movement to the x-axis only. +#' @param label_line_flip_colour Logical; controls color assignment for label lines. +#' @param label_box_flip_colour Logical; controls color assignment for label boxes. +#' @param concordant_label_relative_pos Position for concordant labels: 0 (left), 0.5 (middle), or 1 (right). +#' @param verbose Whether to print verbose outputs to console +#' +#' @return A ggplot2 object representing the alluvial plot. +#' +#' @details +#' - Visualizes flows between original and predicted classes, highlighting concordant and discordant assignments. +#' - Annotates concordance rate, per-group accuracy, and unclassified rate as specified. +#' - Supports flexible labeling, coloring, and axis ordering for publication-quality plots. +#' +#' @examples +#' # Example usage: +#' # make_alluvial(optimized_result) +#' +#' @export +make_alluvial <- function( + optimized, + count_excluded_as_other = FALSE, + title = "", + group_order = NULL, + add_accuracy_to_title = TRUE, + accuracy_per_group = TRUE, + accuracy_type = "sensitivity", + truth_name = "Lymphgen", + truth_column = "lymphgen", + pred_name = "DLBCLone", + pred_column = "predicted_label_optimized", + nudge = 0.03, + box_nudge = 0.15, + min_flip_n = 10, + add_percent = TRUE, + add_unclass_rate = TRUE, + denom = 20, + label_size=3, + label_lock_y=TRUE, + label_line_flip_colour=TRUE, + label_box_flip_colour=TRUE, + concordant_label_relative_pos = 0.5, #we only handle 0, 0.5 and 1 + verbose = FALSE +){ + predictions = optimized$predictions + if(is.null(group_order)){ + group_order = optimized$truth_classes + print("setting group order:") + } + + #print(group_order) + + if(is.null(truth_column) && !is.null(optimized$truth_column)){ + truth_column = optimized$truth_column + } + if (accuracy_per_group) { + accuracies <- report_accuracy( + predictions, + truth = truth_column, + pred = pred_column, + per_group = accuracy_per_group + ) + } + #print("Done accuracyies") + if(count_excluded_as_other){ + excluded_meta = optimized$sample_metadata_no_features %>% + mutate(!!pred_column := "Other") + predictions = bind_rows(excluded_meta,predictions) + } + if("total_samples_available" %in% names(optimized)){ + full_denominator = optimized$total_samples_available + }else{ + full_denominator = nrow(predictions) + } + + xx <- predictions %>% + #group_by(lymphgen, predicted_label_optimized) %>% + #summarize(num = n(), .groups = "drop") %>% + rename( + !!truth_name := !!sym(truth_column), + #!!new_name := predicted_label_optimized + !!pred_name := !!sym(pred_column) + ) + #print("Done renaming") + xx <- xx %>% + group_by(!!sym(truth_name), !!sym(pred_name)) %>% + summarize(num = n(), .groups = "drop") + + + grid <- expand_grid( + !!sym(truth_name) := group_order, + !!sym(pred_name) := group_order + ) + #print(colnames(grid)) + #print(colnames(xx)) + xx <- grid %>% + left_join(xx, by = c(truth_name, pred_name)) %>% + mutate(num = replace_na(num, 0)) + #print("Done joining") + xx[[truth_name]] <- factor(xx[[truth_name]], levels = group_order) + xx[[pred_name]] <- factor(xx[[pred_name]], levels = group_order) + + xx = xx %>% mutate(pair = paste(!!sym(truth_name),!!sym(pred_name),sep="-")) + xy = filter(xx,!!sym(truth_name)!="Other") %>% + mutate(match=ifelse(!!sym(truth_name)==!!sym(pred_name),TRUE,FALSE)) %>% + group_by(match) %>% + summarise(concordant=sum(num)) %>% + ungroup() %>% + mutate(total=sum(concordant)) %>% + mutate(percent=100*concordant/total) + + pc_conc = filter(xy,match==TRUE) %>% pull(percent) %>% round(.,1) + title = paste0(title," Concordance (non-Other): ",pc_conc,"%") + bacc = round(accuracies$mean_balanced_accuracy,4) + + # One side sort + + xx <- xx %>% + group_by(!!sym(truth_name)) %>% + arrange(!!sym(pred_name), .by_group = TRUE) %>% + ungroup() %>% + mutate(flow_id = row_number(), flow_label = ifelse(num > min_flip_n, as.character(num), "")) + + xx = xx %>% + group_by(!!sym(pred_name)) %>% + arrange(!!sym(truth_name),.by_group=TRUE) %>% + ungroup() %>% + mutate(flow_id2 = row_number()) + + lodes <- ggalluvial::to_lodes_form( + xx, + key = "axis", + axes = c(truth_name, pred_name), + id = "flow_id" + ) %>% + mutate( + axis = as.numeric(axis), + nudge_dir = 0.1 + ) %>% + left_join(xx %>% select(flow_id, !!sym(truth_name), !!sym(pred_name)), by = "flow_id") + + stratum_bases <- lodes %>% + mutate(stratum = factor(stratum, levels = group_order)) %>% + group_by(axis, stratum) %>% + summarize(total = sum(num), .groups = "drop") %>% + arrange(axis, desc(stratum)) %>% + group_by(axis) %>% + mutate(stratum_base = cumsum(total) - total) %>% + ungroup() + + left_lodes <- lodes %>% + left_join(stratum_bases, by = c("axis", "stratum")) %>% + separate(pair,into=c("left_group","right_group"),sep = "-") %>% + mutate(right_group = factor(right_group,levels=levels(stratum))) %>% + mutate(left_group = factor(left_group,levels=levels(stratum))) %>% + group_by(axis, stratum) %>% + arrange( + desc(stratum), + desc(right_group), + num, + .by_group = TRUE + ) %>% + mutate( + flow_bottom = cumsum(num) - num, + flow_y = stratum_base + flow_bottom + num / 2 + ) %>% + ungroup() + + right_lodes <- lodes %>% + left_join(stratum_bases, by = c("axis", "stratum")) %>% + separate(pair,into=c("left_group","right_group"),sep = "-") %>% + mutate(right_group = factor(right_group,levels=levels(stratum))) %>% + mutate(left_group = factor(left_group,levels=levels(stratum))) %>% + group_by(axis, stratum) %>% + arrange( + desc(right_group), + desc(left_group), + num, + .by_group = TRUE) %>% + mutate( + flow_bottom = cumsum(num) - num, + flow_y = stratum_base + flow_bottom + num / 2 + ) %>% + ungroup() + + #print("HERE") + lodes_mean_left = group_by(left_lodes,right_group,left_group) %>% + mutate(y=mean(flow_y)) %>% ungroup() %>% + arrange(right_group,left_group) %>% + mutate(nudge_dir = -nudge) + #print("HERE") + lodes_mean_right = group_by(right_lodes,left_group,right_group) %>% + mutate(y=mean(flow_y)) %>% ungroup() %>% + arrange(left_group,right_group) %>% + mutate(nudge_dir = nudge) + + if(concordant_label_relative_pos == 0){ + lodes_mean = lodes_mean_left %>% mutate(y=flow_y) %>% + filter(axis==1) + #position on far left + }else if(concordant_label_relative_pos == 1){ + lodes_mean = lodes_mean_right %>% + mutate(y=flow_y) %>% + filter(axis==2) + #position on far right + }else if(concordant_label_relative_pos == 0.5){ + lodes_mean = bind_rows(filter(right_lodes,axis==2),filter(left_lodes,axis==1)) %>% + group_by(left_group,right_group) %>% + mutate(y=mean(flow_y)) %>% ungroup() %>% + arrange(left_group,right_group) %>% + filter(axis==2) %>% + mutate(nudge_dir = 0) + #middle + }else{ + stop("unhandled value for concordant_label_relative_pos. Provide 0 (left), 0.5 (middle) or 1 (right)") + } + #print("HERE") + lodes_right = filter(lodes_mean_right,axis==2,left_group!=right_group) %>% + arrange(flow_id2) %>% + mutate(nudge_dir = -nudge) %>% + mutate(left_char=as.character(left_group),right_char = as.character(right_group)) + + + lodes_left = filter(lodes_mean_left,axis==1,left_group!=right_group) %>% + arrange(flow_id) %>% + mutate(left_char=as.character(left_group),right_char = as.character(right_group)) %>% + ungroup() + + lodes_left = lodes_left %>% + mutate(segment_colour = if (!label_line_flip_colour) left_char else right_char) %>% + mutate(label_fill = if(label_box_flip_colour) right_group else left_group) %>% + mutate(nudge_dir = nudge) + + lodes_right = lodes_right %>% + mutate(segment_colour = if (label_line_flip_colour) left_char else right_char) %>% + mutate(label_fill = if(!label_box_flip_colour) right_group else left_group) %>% + mutate(nudge_dir = -nudge) + + lodes_match = filter(lodes_mean,left_group==right_group) %>% + mutate(nudge_dir=0) + #print(lodes_match) + #print("HERE!") + + xx_denom <- predictions %>% + group_by(!!sym(truth_column)) %>% + summarize(denom = n(), .groups = "drop") %>% + rename( + stratum = !!sym(truth_column) + ) + #print("HERE!!") + + if(add_percent){ + lodes_match = left_join(lodes_match,xx_denom,by="stratum") %>% + mutate(percent = round(100*num/denom,1)) %>% + mutate(flow_label = paste0(flow_label," (",percent,"%",")")) + } + if(add_unclass_rate){ + right_other = filter(lodes_mean_right,right_group=="Other",axis==2) %>% + summarize(total=sum(num)) %>% + mutate(unclass= round(100 * total / full_denominator,1)) %>% + mutate(label=paste0(total,"\n (",unclass,"%)")) %>% + mutate(!!sym(truth_name) := "Other",!!sym(pred_name) := "Other") + #print(right_other) + unclass = pull(right_other,unclass) + title = paste0(title,", Classification rate: ",100-unclass,"%") + title = paste0(title," Bal Acc: ",bacc) + left_other = filter(lodes_mean_left,left_group=="Other",axis==2) %>% + summarize(total=sum(num)) %>% + mutate(unclass= round(100 * total / full_denominator,1)) %>% + mutate(label=paste0(total,"\n (",unclass,"%)")) %>% + mutate(!!sym(truth_name) := "Other",!!sym(pred_name) := "Other") + } + + pp = ggplot(xx, aes( + axis1 = !!sym(truth_name), + axis2 = !!sym(pred_name), + y = num + )) + + geom_alluvium(aes(fill = !!sym(truth_name)), width = 1 / denom) + + geom_stratum(width = 1 / denom, aes(fill = after_stat(stratum)), color = "black") + + geom_label( + data = filter(lodes_match,left_group==right_group,flow_label!=""), + stat = "identity", + inherit.aes = FALSE, + colour="white", + size=label_size, + aes( + x = concordant_label_relative_pos + 1, + y, + label = flow_label, + fill = right_group + ) + ) + + geom_label_repel( + data = filter(lodes_right,flow_label!=""), + min.segment.length = 0, + stat = "identity", + inherit.aes = FALSE, + direction = ifelse(label_lock_y,"x","both"), + colour="white", + size=label_size, + nudge_x = box_nudge, + aes( + x = axis + nudge_dir, + flow_y, + label = flow_label, + fill = label_fill, + segment.colour = segment_colour + ) + ) + + geom_label_repel( + data = filter(lodes_left,flow_label!=""), + min.segment.length = 0, + stat = "identity", + inherit.aes = FALSE, + direction = ifelse(label_lock_y,"x","both"), + colour="white", + size=label_size, + nudge_x = -box_nudge, + aes( + x = axis + nudge_dir, + flow_y, + label = flow_label, + fill = label_fill, + segment.colour = segment_colour + ) + ) + + scale_x_discrete(limits = c(truth_name, pred_name), expand = c(.1, .05)) + + scale_fill_manual(values = get_gambl_colours()) + + scale_colour_manual(values = get_gambl_colours(),aesthetics = c("color", "segment.color")) + + labs( + y = "Number of Samples", + x = NULL + ) + + theme_minimal() + + theme(legend.position = "none") + + ggtitle(title) + + if(add_unclass_rate){ + pp = pp + + geom_label_repel( + data=left_other, + direction = "x", + x=1, + y=20, + aes(fill=!!sym(truth_name),label=label) + ) + + geom_label_repel( + data=right_other, + direction = "x", + x=2, + y=20, + aes(fill=!!sym(pred_name),label=label) + ) + } + pp +} diff --git a/man/DLBCLone_summarize_model.Rd b/man/DLBCLone_summarize_model.Rd new file mode 100644 index 0000000..7e77f99 --- /dev/null +++ b/man/DLBCLone_summarize_model.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{DLBCLone_summarize_model} +\alias{DLBCLone_summarize_model} +\title{Summarize and Export DLBCLone Model Results} +\usage{ +DLBCLone_summarize_model(base_name, optimized_model) +} +\arguments{ +\item{base_name}{Character. The base name (and directory) for saving output files.} + +\item{optimized_model}{List. The output from DLBCLone optimization, containing predictions, features, and metadata.} +} +\value{ +No return value. Side effect: writes multiple PDF files to disk. +} +\description{ +Generates and saves a set of summary plots and tables for a DLBCLone model, including UMAP scatterplots, alluvial plots, and oncoplots. +Results are saved as PDF files in a directory named after the provided base name. +} +\details{ +\itemize{ +\item Creates a directory for results if it does not exist. +\item Saves UMAP scatterplots for all samples and for non-"Other" samples. +\item Generates alluvial plots for different DLBCLone predictions +\item Exports an oncoplot summarizing mutation and classification results. +\item Uses \code{make_umap_scatterplot}, \code{make_alluvial}, and \code{prettyOncoplot} for visualization. +} +} +\examples{ +DLBCLone_summarize_model("Full_geneset_unweighted", optimized_model) + +} diff --git a/man/make_alluvial.Rd b/man/make_alluvial.Rd new file mode 100644 index 0000000..b8e1be9 --- /dev/null +++ b/man/make_alluvial.Rd @@ -0,0 +1,98 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{make_alluvial} +\alias{make_alluvial} +\title{Create an Alluvial Plot Comparing Original and Predicted Classifications} +\usage{ +make_alluvial( + optimized, + count_excluded_as_other = FALSE, + title = "", + group_order = NULL, + add_accuracy_to_title = TRUE, + accuracy_per_group = TRUE, + accuracy_type = "sensitivity", + truth_name = "Lymphgen", + truth_column = "lymphgen", + pred_name = "DLBCLone", + pred_column = "predicted_label_optimized", + nudge = 0.03, + box_nudge = 0.15, + min_flip_n = 10, + add_percent = TRUE, + add_unclass_rate = TRUE, + denom = 20, + label_size = 3, + label_lock_y = TRUE, + label_line_flip_colour = TRUE, + label_box_flip_colour = TRUE, + concordant_label_relative_pos = 0.5, + verbose = FALSE +) +} +\arguments{ +\item{optimized}{List containing prediction results and metadata, typically output from a DLBCLone optimization function.} + +\item{count_excluded_as_other}{Logical; if TRUE, samples excluded due to missing features are counted as "Other" in the plot.} + +\item{title}{Plot title (default: empty string).} + +\item{group_order}{Character vector specifying the order of groups/classes for axes and coloring.} + +\item{add_accuracy_to_title}{Logical; if TRUE, adds accuracy/concordance rate to the plot title.} + +\item{accuracy_per_group}{Logical; if TRUE, computes and displays per-group accuracy.} + +\item{accuracy_type}{Type of accuracy to report (default: "sensitivity").} + +\item{truth_name}{Name for the original class column (default: "Lymphgen").} + +\item{truth_column}{Name for the original class column in the predictions data frame (default: "lymphgen").} + +\item{pred_name}{Name for the predicted class column (default: "DLBCLone").} + +\item{pred_column}{Name of the column in predictions to use for the predicted class (default: "predicted_label_optimized").} + +\item{nudge}{Amount to nudge labels horizontally (default: 0.03).} + +\item{box_nudge}{Amount to nudge label boxes (default: 0.15).} + +\item{min_flip_n}{Minimum number of samples for a flow to be labeled (default: 10).} + +\item{add_percent}{Logical; if TRUE, adds percent concordance to labels.} + +\item{add_unclass_rate}{Logical; if TRUE, adds unclassified rate annotation to the plot.} + +\item{denom}{Denominator for stratum/flow width scaling (default: 20).} + +\item{label_size}{Size of label text (default: 3).} + +\item{label_lock_y}{Logical; if TRUE, locks label movement to the x-axis only.} + +\item{label_line_flip_colour}{Logical; controls color assignment for label lines.} + +\item{label_box_flip_colour}{Logical; controls color assignment for label boxes.} + +\item{concordant_label_relative_pos}{Position for concordant labels: 0 (left), 0.5 (middle), or 1 (right).} + +\item{verbose}{Whether to print verbose outputs to console} +} +\value{ +A ggplot2 object representing the alluvial plot. +} +\description{ +This function generates a detailed alluvial plot to visualize the concordance and discordance between original (e.g., Lymphgen) and predicted (e.g., DLBCLone) class assignments for samples. +It supports annotation of concordance rates, per-group accuracy, unclassified rates, and flexible labeling and coloring options. +} +\details{ +\itemize{ +\item Visualizes flows between original and predicted classes, highlighting concordant and discordant assignments. +\item Annotates concordance rate, per-group accuracy, and unclassified rate as specified. +\item Supports flexible labeling, coloring, and axis ordering for publication-quality plots. +} +} +\examples{ +# Example usage: +# make_alluvial(optimized_result) + +} diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index d053ba6..70037fc 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -1,9 +1,21 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/umap.R +% Please edit documentation in R/plotting.R, R/umap.R \name{make_neighborhood_plot} \alias{make_neighborhood_plot} \title{Make Neighborhood Plot} \usage{ +make_neighborhood_plot( + single_sample_prediction_output, + training_predictions, + this_sample_id, + prediction_in_title = TRUE, + add_circle = TRUE, + label_column = "predicted_label_optimized", + point_size = 0.5, + point_alpha = 0.9, + line_alpha = 0.9 +) + make_neighborhood_plot( single_sample_prediction_output, training_predictions, @@ -37,16 +49,27 @@ Must include elements \code{prediction} (data frame with prediction results) and \item{line_alpha}{Numeric. Transparency of the lines connecting the sample to its neighbors. Default: 0.9.} } \value{ +A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. + A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. } \description{ +Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. + Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. } \details{ +The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. + The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. } \examples{ +# Assuming 'optimization_result' is the output of DLBCLone_optimize_params +# and 'output' is the result of DLBCLone_predict_single_sample +# on sample_id "SAMPLE123": +make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") + # Assuming 'optimization_result' is the output of DLBCLone_optimize_params # and 'output' is the result of DLBCLone_predict_single_sample # on sample_id "SAMPLE123": diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index 8728384..cd0b1a1 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -1,9 +1,21 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/umap.R +% Please edit documentation in R/plotting.R, R/umap.R \name{make_umap_scatterplot} \alias{make_umap_scatterplot} \title{Make UMAP scatterplot} \usage{ +make_umap_scatterplot( + df, + title = NULL, + drop_composite = TRUE, + colour_by = "lymphgen", + drop_other = FALSE, + high_confidence = FALSE, + custom_colours, + add_labels = FALSE, + facet = FALSE +) + make_umap_scatterplot( df, title = NULL, @@ -36,5 +48,7 @@ make_umap_scatterplot( \item{facet}{If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely} } \description{ +Make UMAP scatterplot + Make UMAP scatterplot } diff --git a/man/report_accuracy.Rd b/man/report_accuracy.Rd new file mode 100644 index 0000000..eae4fe2 --- /dev/null +++ b/man/report_accuracy.Rd @@ -0,0 +1,52 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{report_accuracy} +\alias{report_accuracy} +\title{Report Classification Accuracy and Per-Class Metrics} +\usage{ +report_accuracy( + predictions, + truth = "lymphgen", + pred = "DLBCLone_io", + per_group = FALSE, + metric = "accuracy", + verbose = FALSE +) +} +\arguments{ +\item{predictions}{Data frame containing predicted and true class labels.} + +\item{truth}{Name of the column with true class labels (default: "lymphgen").} + +\item{pred}{Name of the column with predicted class labels (default: "predicted_label").} + +\item{per_group}{Logical; if TRUE, computes per-group accuracy metrics.} + +\item{metric}{Character; type of accuracy to report ("accuracy" supported).} + +\item{verbose}{Whether to print verbose outputs to console} +} +\value{ +A list with: +\item{no_other}{Accuracy excluding samples assigned to "Other"} +\item{per_class}{Balanced accuracy per class} +\item{per_class_sensitivity}{Sensitivity per class} +\item{overall}{Overall accuracy including all samples} +} +\description{ +Computes overall accuracy, balanced accuracy, and sensitivity for predicted vs. true class labels. +Optionally excludes samples assigned to the "Other" class from accuracy calculations. +} +\details{ +\itemize{ +\item Uses confusion matrices to compute accuracy metrics. +\item Excludes "Other" class for no_other accuracy. +\item Returns per-class metrics for further analysis. +} +} +\examples{ +result <- report_accuracy(predictions_df) +result$overall +result$per_class + +} From 3b69841b68cfe0c08b5a9c051aa5468f8df7ba3f Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 1 Aug 2025 11:53:04 -0700 Subject: [PATCH 58/79] fixing spacing, adding documentation for all params, removed ignore self, fully incorporated optimized_model to predict_single_sample --- NAMESPACE | 5 + R/umap.R | 2411 +++++++++++++------------ man/DLBCLone_load_optimized.Rd | 4 +- man/DLBCLone_optimize_params.Rd | 6 +- man/DLBCLone_predict_mixture_model.Rd | 51 + man/DLBCLone_save_optimized.Rd | 4 +- man/DLBCLone_train_mixture_model.Rd | 50 + man/assemble_genetic_features.Rd | 12 +- man/make_and_annotate_umap.Rd | 33 +- man/make_neighborhood_plot.Rd | 33 +- man/make_umap_scatterplot.Rd | 23 +- man/optimize_outgroup.Rd | 5 +- man/optimize_purity.Rd | 54 + man/predict_single_sample_DLBCLone.Rd | 19 +- man/process_votes.Rd | 55 + man/summarize_all_ssm_status.Rd | 6 - man/weighted_knn_predict_with_conf.Rd | 5 - 17 files changed, 1544 insertions(+), 1232 deletions(-) create mode 100644 man/DLBCLone_predict_mixture_model.Rd create mode 100644 man/DLBCLone_train_mixture_model.Rd create mode 100644 man/optimize_purity.Rd create mode 100644 man/process_votes.Rd diff --git a/NAMESPACE b/NAMESPACE index dcf16d8..e482049 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,8 +2,10 @@ export(DLBCLone_load_optimized) export(DLBCLone_optimize_params) +export(DLBCLone_predict_mixture_model) export(DLBCLone_save_optimized) export(DLBCLone_summarize_model) +export(DLBCLone_train_mixture_model) export(DLBCLone_train_test_plot) export(assemble_genetic_features) export(classify_bl) @@ -17,7 +19,9 @@ export(make_neighborhood_plot) export(make_umap_scatterplot) export(massage_matrix_for_clustering) export(optimize_outgroup) +export(optimize_purity) export(predict_single_sample_DLBCLone) +export(process_votes) export(report_accuracy) export(summarize_all_ssm_status) export(tabulate_ssm_status) @@ -26,6 +30,7 @@ import(GAMBLR.data) import(GAMBLR.helpers) import(dplyr) import(ggplot2) +import(mclust) import(randomForest, except = c("combine")) import(readr) import(tibble) diff --git a/R/umap.R b/R/umap.R index d64b23d..916f69f 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1,4 +1,3 @@ - #' Summarize SSM (Somatic Single Nucleotide Mutation) Status Across Samples #' #' This function summarizes the mutation status for a set of genes @@ -11,11 +10,11 @@ #' \code{Hugo_Symbol}, \code{Variant_Classification}, and \code{Tumor_Sample_Barcode}. #' @param these_samples_metadata A data frame containing metadata for the samples, #' with at least a \code{sample_id} column. -#' Any sample that does not have a matching sample_id in these_samples_metadata will be dropped. +#' Any sample that does not have a matching sample_id in these_samples_metadata will be dropped. #' @param genes_of_interest A character vector of gene symbols to include #' in the summary. If missing, defaults to all Tier 1 B-cell lymphoma genes. #' @param synon_genes (Optional) A character vector of gene symbols for which -#' synonymous mutations should be included. +#' synonymous mutations should be included. #' @param silent_maf_df (Optional) A separate data frame containing silent mutation data if #' the user doesn't want to pull silent mutation status from \code{maf_df}. #' This argument is useful when you want to combine mutations from @@ -23,8 +22,6 @@ #' @param separate_by_class_genes (Optional) A character vector of #' gene symbols for which mutations should be separated by class #' (e.g., "Nonsense_Mutation", "Missense_Mutation"). -#' @param hotspot_genes Vector of hotspot genes. -#' @param annotated_sv SV from provided annotated SV data frame #' @param count_hits Logical; if \code{TRUE}, counts the number of mutations #' per gene per sample. If \code{FALSE} (default), only presence/absence is recorded. #' @@ -60,7 +57,6 @@ #' #' @import dplyr tidyr tibble #' @export -#' summarize_all_ssm_status <- function( maf_df, these_samples_metadata, @@ -68,36 +64,40 @@ summarize_all_ssm_status <- function( synon_genes, silent_maf_df, separate_by_class_genes = NULL, - hotspot_genes = NULL, - annotated_sv, count_hits = FALSE ){ if(missing(genes_of_interest)){ message("defaulting to all Tier 1 B-cell lymphoma genes") - genes_of_interest = filter(lymphoma_genes, - DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 ) %>% + genes_of_interest = filter( + lymphoma_genes, + DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 + ) %>% pull(Gene) %>% unique() } - - if(missing(silent_maf_df)){ - silent_maf_df = maf_df - } - + maf_df = filter( maf_df, Hugo_Symbol %in% genes_of_interest ) - + if(missing(silent_maf_df)){ + silent_maf_df = maf_df + }else{ + silent_maf_df = filter(silent_maf_df,Hugo_Symbol %in% genes_of_interest) + } if(!missing(these_samples_metadata)){ - maf_df = filter(maf_df, - Tumor_Sample_Barcode %in% these_samples_metadata$sample_id) + maf_df = filter( + maf_df, + Tumor_Sample_Barcode %in% these_samples_metadata$sample_id + ) + silent_maf_df = filter( + silent_maf_df, + Tumor_Sample_Barcode %in% these_samples_metadata$sample_id + ) } - if(!missing(synon_genes)){ if(any(!synon_genes %in% silent_maf_df$Hugo_Symbol)){ missing = synon_genes[!synon_genes %in% silent_maf_df$Hugo_Symbol] - not_missing = synon_genes[synon_genes %in% silent_maf_df$Hugo_Symbol] - + not_missing = synon_genes[synon_genes %in% silent_maf_df$Hugo_Symbol] message("Warning: Some synonymous genes have no mutations in silent_maf_df") message(paste(missing,collapse=", ")) } @@ -105,31 +105,10 @@ summarize_all_ssm_status <- function( maf_silent = filter(silent_maf_df, ! Variant_Classification %in% vc_nonSynonymous, Hugo_Symbol %in% synon_genes) maf_df = bind_rows(maf_silent,maf_nonsilent) } - #Simplify annotations - silent_types = c( - "Silent", - "Intron", - "5'UTR", - "3'UTR", - "5'Flank", - "3'Flank" - ) - nonsense_types = c( - "Nonsense_Mutation", - "Translation_Start_Site", - "Frame_Shift_Del", - "Frame_Shift_Ins", - "Nonstop_Mutation", - "Splice_Site" - ) - missense_types = c( - "In_Frame_Del", - "In_Frame_Ins", - "Missense_Mutation", - "Splice_Region" - ) - + silent_types = c("Silent","Intron","5'UTR","3'UTR","5'Flank","3'Flank") + nonsense_types = c("Nonsense_Mutation","Frame_Shift_Del","Frame_Shift_Ins","Splice_Site") + missense_types = c("In_Frame_Del","In_Frame_Ins","Missense_Mutation","Splice_Region") gene_mutations = mutate( maf_df, mutation_type=case_when( @@ -140,7 +119,6 @@ summarize_all_ssm_status <- function( ) ) %>% mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) - separated_coding_maf = filter( gene_mutations, Hugo_Symbol %in% genes_of_interest, @@ -148,104 +126,28 @@ summarize_all_ssm_status <- function( mutation_type != "Silent" ) - unseparated_coding_silent_maf = filter( - gene_mutations, - Hugo_Symbol %in% genes_of_interest, - !Hugo_Symbol %in% separate_by_class_genes - ) %>% - mutate(mutation = paste(Hugo_Symbol,sep=":")) - - separated_silent_maf = filter( + unseparated_coding_maf = filter( gene_mutations, - Hugo_Symbol %in% synon_genes, - mutation_type == "Silent" + Hugo_Symbol %in% genes_of_interest, + !Hugo_Symbol %in% separate_by_class_genes, + mutation_type != "Silent" ) %>% + mutate(mutation = paste(Hugo_Symbol,"Coding",sep=":")) + separated_silent_maf = filter(gene_mutations,Hugo_Symbol %in% synon_genes, mutation_type == "Silent") %>% mutate(mutation = paste(Hugo_Symbol,"Silent",sep=":")) - - gene_mutations = bind_rows( - separated_coding_maf, - unseparated_coding_silent_maf, - separated_silent_maf - ) + gene_mutations = bind_rows(separated_coding_maf, unseparated_coding_maf,separated_silent_maf) - mutation_distinct = select( - gene_mutations, - mutation, - Tumor_Sample_Barcode - ) %>% + mutation_distinct = select(gene_mutations,mutation,Tumor_Sample_Barcode) %>% mutate(mutated = 1) %>% - group_by( - Tumor_Sample_Barcode, - mutation, - mutated - ) %>% - count() %>% - ungroup() - + group_by(Tumor_Sample_Barcode,mutation,mutated) %>% + count() %>% ungroup() if(count_hits){ mutation_distinct = mutation_distinct %>% select(-mutated) %>% rename(mutated=n) %>% distinct() } else{ mutation_distinct = mutation_distinct %>% select(-n) %>% distinct() } - mutation_wide = pivot_wider(mutation_distinct, names_from = "mutation",values_from = "mutated",values_fill = 0) %>% - column_to_rownames("Tumor_Sample_Barcode") - - if(!is.null(hotspot_genes)){ - hotspot_features = get_coding_ssm_status( - these_samples_metadata = sample_metadata, - maf_data = maf_df, - include_hotspots = TRUE, - genes_of_interest = hotspot_genes - ) - - hotspot_features <- hotspot_features %>% - column_to_rownames("sample_id") %>% - select(contains("HOTSPOT")) - - mutation_wide <- mutation_wide %>% rownames_to_column(var = "sample_id") - hotspot_features <- hotspot_features %>% rownames_to_column(var = "sample_id") - - mutation_wide <- left_join(mutation_wide, hotspot_features, by = "sample_id") - - # Replace NA in hotspot columns with 0 (if necessary) - hotspot_cols <- colnames(hotspot_features)[-1] # exclude sample_id - mutation_wide[hotspot_cols][is.na(mutation_wide[hotspot_cols])] <- 0 - - mutation_wide <- mutation_wide %>% column_to_rownames("sample_id") - } - - #TODO: generalize this to use the column names provided in metadata_columns - bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) - bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) - myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) - - # include SV from provided annotated SV data frame - if(!missing(annotated_sv)){ - annotated_sv = filter(annotated_sv,tumour_sample_id %in% these_samples_metadata$sample_id) - bcl2_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL2") %>% pull(tumour_sample_id) - bcl6_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL6") %>% pull(tumour_sample_id) - myc_sv_id = filter(annotated_sv,!is.na(partner),gene=="MYC") %>% pull(tumour_sample_id) - n_b = length(bcl2_id) - print(paste(n_b,"BCL2 FISH")) - bcl2_id = unique(c(bcl2_id,bcl2_sv_id)) - n_b = length(bcl2_id) - print(paste(n_b,"BCL2 FISH or SV")) - bcl6_id = unique(c(bcl6_id,bcl6_sv_id)) - myc_id = unique(c(myc_id,myc_sv_id)) - } - - mutation_wide[,"BCL2_SV"] = 0 - mutation_wide[bcl2_id,"BCL2_SV"] = 1 - - mutation_wide[,"MYC_SV"] = 0 - mutation_wide[myc_id,"MYC_SV"] = 1 - - mutation_wide[,"BCL6_SV"] = 0 - mutation_wide[bcl6_id,"BCL6_SV"] = 1 - - mutation_wide[is.na(mutation_wide)] <- 0 - + column_to_rownames("Tumor_Sample_Barcode") return(mutation_wide) } @@ -260,12 +162,14 @@ summarize_all_ssm_status <- function( #' @param synon_genes Vector of gene symbols for synonymous mutations. #' @param maf_with_synon MAF data frame including synonymous mutations. #' @param hotspot_genes Vector of hotspot genes. -#' @param genome_build Genome build to use, default: "grch37". +#' @param genome_build Genome build version (default: "grch37"). #' @param sv_value Value to assign for SV presence (default: 3). #' @param synon_value Value to assign for synonymous mutations (default: 1). #' @param coding_value Value to assign for coding mutations (default: 2). -#' @param include_ashm Logical if TRUE, includes aSHM counts in the feature matrix. -#' @param annotated_sv Annotated SV data frame. +#' @param include_ashm Logical; if TRUE, includes aSHM counts in the output matrix (default: TRUE). +#' @param annotated_sv Data frame with annotated structural variants. +#' @param include_GAMBL_sv Logical; if TRUE, includes GAMBL structural variants in the output matrix (default: TRUE). +#' @param review_hotspots Logical; if TRUE, reviews hotspot genes (default: TRUE). #' @param verbose Defaults to FALSE #' #' @return Matrix of assembled features for each sample. @@ -283,25 +187,34 @@ assemble_genetic_features <- function( coding_value = 2, include_ashm = TRUE, annotated_sv, + include_GAMBL_sv= TRUE, + review_hotspots = TRUE, verbose = FALSE ){ + #if("genome_build" %in% ) + if(!"maf_data" %in% class(maf_with_synon)){ + warning(paste("maf_with_synon should be a maf_data object, but is not.","Proceeding anyway with genome_build = ", genome_build)) + }else{ + genome_build = attr(maf_with_synon,"genome_build") + } if(include_ashm){ #TODO: ensure this supports both genome builds correctly some_regions = GAMBLR.utils::create_bed_data( - GAMBLR.data::grch37_ashm_regions, - fix_names = "concat", - concat_cols = c("gene","region"), - sep="-") + GAMBLR.data::grch37_ashm_regions, + fix_names = "concat", + concat_cols = c("gene","region"), + sep="-" + ) #make the aSHM count matrix and combine if necessary if ("genome" %in% these_samples_metadata$seq_type){ - ashm_matrix_genome <- get_ashm_count_matrix( - regions_bed = some_regions, - this_seq_type = "genome", - these_samples_metadata = these_samples_metadata - ) - - colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) + ashm_matrix_genome <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "genome", + these_samples_metadata = these_samples_metadata + ) + colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) } + if ("capture" %in% these_samples_metadata$seq_type){ ashm_matrix_cap <- get_ashm_count_matrix( regions_bed = some_regions, @@ -315,20 +228,19 @@ assemble_genetic_features <- function( }else if("genome" %in% these_samples_metadata$seq_type){ ashm_matrix = ashm_matrix_genome }else if("capture" %in% these_samples_metadata$seq_type){ - ashm_matrix = ashm_matrix_cap - + ashm_matrix = ashm_matrix_cap }else{ stop("no eligible seq_type provided in these_samples_metadata") } } - + include_hotspots = ifelse(!missing(hotspot_genes), TRUE, FALSE) status_with_silent = get_coding_ssm_status( these_samples_metadata = these_samples_metadata, # drop all coding variants from this one maf_data = maf_with_synon, - include_hotspots = TRUE, + include_hotspots = include_hotspots, genes_of_interest = hotspot_genes, include_silent_genes = synon_genes[synon_genes %in% genes], gene_symbols = genes @@ -338,7 +250,7 @@ assemble_genetic_features <- function( status_without_silent = get_coding_ssm_status( these_samples_metadata = these_samples_metadata, maf_data = maf_with_synon, - include_hotspots = TRUE, + include_hotspots = include_hotspots, genes_of_interest = hotspot_genes, include_silent = FALSE, gene_symbols = genes @@ -351,7 +263,7 @@ assemble_genetic_features <- function( print(colnames(status_with_silent)[!colnames(status_with_silent) %in% colnames(status_without_silent)]) stop("some columns are missing from the status_without_silent matrix") } - # Instead of just relying on the MAF(s) supplied by + # Instead of just relying on the MAF(s) supplied by the user if (include_ashm){ ashm_matrix = select(ashm_matrix, any_of(colnames(status_with_silent))) %>% select(any_of(synon_genes)) ashm_matrix[ashm_matrix>1]= 1 @@ -362,20 +274,23 @@ assemble_genetic_features <- function( } #fill in gaps from aSHM (other non-coding variants in the genes) - missing = status_with_silent[rownames(ashm_matrix), - colnames(ashm_matrix)]==0 & - ashm_matrix[rownames(ashm_matrix), - colnames(ashm_matrix)] > 0 - - + missing = status_with_silent[rownames(ashm_matrix),colnames(ashm_matrix)]==0 & + ashm_matrix[rownames(ashm_matrix),colnames(ashm_matrix)] > 0 fill = missing fill[]=0 fill[missing] = synon_value - status_with_silent[rownames(fill), - colnames(fill)] = fill + status_with_silent[rownames(fill),colnames(fill)] = fill + } + #ensure all columns in status_with_silent are present in status_without_silent + if (any(! colnames(status_without_silent) %in% colnames(status_with_silent))){ + print(colnames(status_without_silent)[!colnames(status_without_silent) %in% colnames(status_with_silent)]) + stop("some columns are missing from the status_with_silent matrix") } - + + # ensure column order is identical in the two matrices + + status_with_silent = status_with_silent[,colnames(status_without_silent)] status_combined = status_with_silent + status_without_silent if(coding_value == 1){ @@ -385,22 +300,21 @@ assemble_genetic_features <- function( bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) - + # include SV from provided annotated SV data frame - if(!missing(annotated_sv)){ + if(!missing(annotated_sv) & include_GAMBL_sv){ annotated_sv = filter(annotated_sv,tumour_sample_id %in% these_samples_metadata$sample_id) bcl2_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL2") %>% pull(tumour_sample_id) bcl6_sv_id = filter(annotated_sv,!is.na(partner),gene=="BCL6") %>% pull(tumour_sample_id) myc_sv_id = filter(annotated_sv,!is.na(partner),gene=="MYC") %>% pull(tumour_sample_id) n_b = length(bcl2_id) - print(paste(n_b,"BCL2 FISH")) + bcl2_id = unique(c(bcl2_id,bcl2_sv_id)) n_b = length(bcl2_id) - print(paste(n_b,"BCL2 FISH or SV")) + bcl6_id = unique(c(bcl6_id,bcl6_sv_id)) myc_id = unique(c(myc_id,myc_sv_id)) } - status_combined[,"BCL2_SV"] = 0 status_combined[bcl2_id,"BCL2_SV"] = sv_value @@ -518,6 +432,7 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' based on the specified metric (balanced accuracy or accuracy). #' This function is not generally meant to be called directly but rather is #' a helper function used by DLBCLone_optimize_params. +#' #' @param predicted_labels Vector of predicted labels for the samples #' @param true_labels Vector of true labels for the samples #' @param other_score Vector of scores for the "Other" class for each sample () @@ -530,6 +445,7 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @param exclude_other_for_accuracy Set to TRUE to exclude the #' "Other" class from the 'lymphgen' column when calculating accuracy metrics #' (passed to DLBCLone_optimize_params). Default: FALSE +#' @param verbose Set to TRUE to print additional information during the optimization process. #' #' @returns a list of data frames with the predictions and the UMAP input #' @export @@ -538,18 +454,12 @@ optimize_outgroup <- function( predicted_labels, true_labels, other_score, - all_classes = c( - "MCD", - "EZB", - "BN2", - "N1", - "ST2", - "Other" - ), + all_classes = c("MCD","EZB","BN2","N1","ST2","Other"), maximize ="balanced_accuracy", - exclude_other_for_accuracy = FALSE + exclude_other_for_accuracy = FALSE, + verbose = FALSE ){ - + #print(paste("Exclude Other?",exclude_other_for_accuracy)) rel_thresholds = seq(1,10,0.1) sens_df = data.frame() acc_df = data.frame() @@ -585,7 +495,9 @@ optimize_outgroup <- function( sn$threshold = threshold sens_df = bind_rows(sens_df,sn) } + if(maximize %in% c("balanced_accuracy","accuracy")){ + #print(best) best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) }else{ best = slice_head(arrange(sens_df,desc(average_sensitivity)),n=1) @@ -709,7 +621,6 @@ DLBCLone_train_test_plot = function(test_df, pp + guides(colour = guide_legend(nrow = 1)) } - #' Run UMAP and attach result to metadata #' #' @param df Feature matrix with one row per sample and one column per mutation @@ -718,24 +629,30 @@ DLBCLone_train_test_plot = function(test_df, #' @param umap_out Optional UMAP output from a previous run. If provided, the function #' will use this model to project the data instead of re-running UMAP. This is useful #' for reproducibility and for using the same UMAP model on different datasets. +#' @param core_features A vector of column names in df that should be multiplied by +#' core_feature_multiplier. These features are considered "core" and will be weighted +#' more heavily in the UMAP embedding. +#' @param core_feature_multiplier A numeric value that will be used to multiply the +#' core features specified in core_features. Default is 1.5. +#' @param hidden_features A vector of column names in df that should be dropped from the +#' UMAP input. These features are considered "hidden" and will not be used in the UMAP embedding. #' @param n_neighbors Passed to UMAP2. The number of neighbors to consider when calculating the UMAP embedding. #' @param min_dist Passed to UMAP2. The minimum distance between points in the UMAP embedding. #' @param metric Passed to UMAP2. The distance metric to use for calculating distances between points. #' @param n_epochs Passed to UMAP2. The number of epochs to run the UMAP algorithm. #' @param init Passed to UMAP2. The initialization method for the UMAP algorithm. -#' @param ret_model If TRUE, the function will return the UMAP model object in addition to the UMAP coordinates. +#' @param ret_model additional argument #' @param na_vals How to deal with NA values. Two options are "drop", which #' will remove all columns containing at least one NA or "to_zero", which sets #' all NA to zero and leaves the column intact. #' @param join_column The column name in the metadata data frame that contains the sample IDs (default sample_id). #' @param seed Passed to UMAP2. The random seed for reproducibility. -#' @param target_column If provided, the function will run a supervised UMAP -#' using the specified target column from the metadata. If not provided, the function will run an unsupervised UMAP. -#' @param target_metric The distance metric to use for the supervised UMAP. -#' Default: "euclidean". Passed to UMAP2. -#' @param target_weight The weight to assign to the target column in the supervised UMAP. -#' Default: 0.5. Passed to UMAP2. -#' @param calc_dispersion If TRUE, calculates the pairwise dispersion of the UMAP coordinates +#' @param target_column The column in the metadata that contains the target variable for classification. +#' @param target_metric The distance metric to use for calculating distances to the target variable. +#' @param target_weight The weight to assign to the target variable in the UMAP embedding. Default is 0.5. +#' @param calc_dispersion If TRUE, calculates the dispersion of the UMAP embedding. +#' @param algorithm The UMAP algorithm to use. Default is "tumap", which uses the TUMAP implementation. +#' @param make_plot If TRUE, creates a plot of the UMAP embedding. #' #' @import uwot #' @export @@ -757,6 +674,9 @@ make_and_annotate_umap = function( df, metadata, umap_out, + core_features = NULL, + core_feature_multiplier = 1.5, + hidden_features = NULL, n_neighbors=55, min_dist=0, metric="cosine", @@ -770,13 +690,13 @@ make_and_annotate_umap = function( target_metric="euclidean", target_weight=0.5, calc_dispersion = FALSE, - algorithm = "tumap" -){ - # Function to compute mean (or median) pairwise distance within a group - pairwise_dispersion <- function(df_group) { - coords <- as.matrix(df_group[, c("V1", "V2")]) - dists <- dist(coords) # Euclidean pairwise distances - return(median(dists)) + algorithm = "tumap", + make_plot = FALSE +){ + if(!missing(umap_out)){ + model_provided = TRUE + }else{ + model_provided = FALSE } if("sample_id" %in% colnames(df)){ df = df %>% column_to_rownames(var = "sample_id") @@ -788,41 +708,65 @@ make_and_annotate_umap = function( if(any(sapply(df, is.factor))){ numeric_cols = names(df)[!sapply(df, is.factor)] df <- df[, colSums(is.na(df[,numeric_cols])) == 0] - rs = rowSums(df[,numeric_cols],na.rm=TRUE) - df = df[rs>0,] - } else{ + rs = rowSums(df[,numeric_cols],na.rm=TRUE) + dropped_rows = df[rs==0,] + df = df[rs>0,] + }else{ df <- df[, colSums(is.na(df)) == 0] rs = rowSums(df,na.rm=TRUE) + dropped_rows = df[rs==0,] df = df[rs>0,] } } - + if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } - if (!missing(metadata)) { - df_sample_ids <- rownames(df) - metadata_sample_ids <- metadata[[join_column]] - - # overlaps and mismatches - shared_ids <- intersect(df_sample_ids, metadata_sample_ids) - missing_from_metadata <- setdiff(df_sample_ids, metadata_sample_ids) - missing_from_df <- setdiff(metadata_sample_ids, df_sample_ids) - - # shared rows - df <- df[shared_ids, , drop = FALSE] - metadata <- metadata %>% filter((!!sym(join_column)) %in% shared_ids) - - # otherwise message - message(length(missing_from_metadata), " samples were missing from metadata.") - message(length(missing_from_df), " samples in metadata were dropped as missing from the feature matrix.") - message("using ", length(shared_ids), " samples provided in metadata and having features.") + if(!is.null(core_features)){ + if(any(weighted_status>1)){ + stop("values > 1 detected. Weighting of core features is only supported for binary features (0/1).") + } + if(!is.numeric(core_feature_multiplier)){ + stop("core_feature_multiplier must be a numeric value") + } + if(!all(core_features %in% colnames(df))){ + stop("core_features must be a vector of column names in df") + } + #multiply the core features by the multiplier + ncore = length(core_features) + message(paste0("multiplying ",ncore," core features by ",core_feature_multiplier)) + df[core_features] = df[core_features] * core_feature_multiplier + } + if(!is.null(hidden_features)){ + if(!all(hidden_features %in% colnames(df))){ + stop("hidden_features must be a vector of column names in df") + } + + message(paste0("dropping ",length(hidden_features)," hidden features")) + df = df %>% select(-any_of(hidden_features)) + } + no_feat_samples = NULL + if(!missing(metadata)){ + if(nrow(df)< original_n){ + nrem = original_n-nrow(df) + pct_rem = round(nrem/original_n*100,2) + message(paste0("removed ",nrem," (",pct_rem,"%) rows from the data that had no features")) + no_feat_samples = rownames(dropped_rows) + } + + keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] + df= df[keep_rows,] + no_feat_metadata = filter(metadata,!!sym(join_column) %in% no_feat_samples) + metadata= filter(metadata,!!sym(join_column) %in% rownames(df)) + + message(paste("kept",nrow(metadata),"rows of the data that have features and match the metadata provided")) } if(missing(umap_out)){ if(missing(target_column)){ if(algorithm == "umap"){ - embedding = umap2( + message("running umap2") + umap_out = umap2( df %>% as.matrix(), n_neighbors = n_neighbors, min_dist = min_dist, @@ -834,14 +778,15 @@ make_and_annotate_umap = function( approx_pow=TRUE, init=init, seed = seed, - n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility + n_threads = 1, batch = TRUE, n_sgd_threads = 1, - rng_type = "deterministic" - ) # possibly add rng_type = "deterministic" - + rng_type = "deterministic" # possibly add rng_type = "deterministic" + ) + #IMPORTANT: n_threads must not be changed because it will break reproducibility }else if(algorithm == "tumap"){ #X = df %>% as.matrix() + message("running tumap") X = df umap_args = list( X=X, @@ -854,9 +799,9 @@ make_and_annotate_umap = function( n_threads = 1, batch = TRUE, n_sgd_threads = 1, - rng_type = "deterministic" - ) - embedding = tumap( + rng_type = "deterministic") + + umap_out = tumap( X, n_neighbors = n_neighbors, metric = metric, @@ -869,18 +814,18 @@ make_and_annotate_umap = function( n_sgd_threads = 1, rng_type = "deterministic" ) + message("done!") }else{ stop("unsupported algorithm option") } - umap_out = embedding }else{ #supervised if(missing(metadata)){ stop("metadata must be provided for supervised UMAP") } metadata[[target_column]] = factor(metadata[[target_column]]) - print(table(metadata[[target_column]])) - embedding = umap2( + #print(table(metadata[[target_column]])) + umap_out = umap2( df %>% as.matrix(), n_neighbors = n_neighbors, min_dist = min_dist, @@ -889,54 +834,384 @@ make_and_annotate_umap = function( n_epochs=n_epochs, init=init, seed = seed, - n_threads = 1, #IMPORTANT: n_threads must not be changed because it will break reproducibility + n_threads = 1, y = metadata[[target_column]], target_metric = target_metric, target_weight = target_weight - ) # possibly add rng_type = "deterministic" + ) # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must not be changed because it will break reproducibility } }else{ - embedding = umap_transform( - X=df, - model=umap_out, - seed=seed, - batch = TRUE, - n_threads = 1, - n_sgd_threads = 1 + message("transforming each data point individually using the provided UMAP model. This will take some time.") + umap_df = data.frame() + for(sample in rownames(df)){ + this_row = df[sample,] + this_umap_df = umap_transform( + X=this_row, + model=umap_out$model, + seed=seed, + batch = TRUE, + n_threads = 1, + n_sgd_threads = 1 ) + this_umap_df = as.data.frame(this_umap_df) + umap_df = bind_rows(umap_df,this_umap_df) + } + message("done") } - if(!is.null(names(embedding))){ - umap_df = as.data.frame(embedding$embedding) %>% rownames_to_column(join_column) + if(model_provided){ + # model was generated here + #message("model given to function") + umap_df = as.data.frame(umap_df) %>% rownames_to_column(var=join_column) }else{ - umap_df = as.data.frame(embedding) %>% rownames_to_column(join_column) + #message("model not provided, created here") + umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) + #umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) + #umap_df = as.data.frame(umap_df) %>% rownames_to_column(var=join_column) + #print(head(umap_df)) } if(!missing(metadata)){ umap_df = left_join(umap_df,metadata,by=join_column) } results = list() - if(calc_dispersion){ - print("calculating pairwise dispersion") - dispersion_df <- umap_df %>% - group_by(lymphgen) %>% - summarise( - n = n(), - mean_pairwise_distance = pairwise_dispersion(cur_data()) - ) %>% - arrange(mean_pairwise_distance) - results[["dispersion"]] = dispersion_df - } - results[["df"]]=umap_df results[["features"]] = df + results[["dropped_rows"]] = no_feat_samples + if(!missing(metadata)){ + results[["total_samples_available"]] = nrow(metadata) + nrow(no_feat_metadata) + results[["sample_metadata_no_features"]] = no_feat_metadata + } if(ret_model){ results[["model"]]= umap_out } + if(make_plot){ + ms = make_umap_scatterplot( + umap_df, + title = "UMAP projection" + ) + print(ms) + results[["plot"]] = ms + } return(results) } +#' Process KNN Vote Strings and Scores for Classification +#' +#' This function processes the raw neighbor label strings and weighted vote scores from k-nearest neighbor (KNN) classification results. +#' It computes per-class neighbor counts, weighted scores, and identifies the top group by count and score for each sample. +#' The function also supports custom logic for handling the "Other" class, including vote multipliers and purity requirements. +#' +#' @param df Data frame containing kNN results, including columns with neighbor labels and weighted votes. +#' @param raw_col Name of the column containing the comma-separated neighbor labels (default: "label"). +#' @param group_labels Character vector of all possible class labels to consider (default: c("EZB", "MCD", "ST2", "BN2", "N1", "Other")). +#' @param vote_labels_col Name of the column containing the comma-separated neighbor labels for weighted votes (default: "vote_labels"). +#' @param k Number of neighbors used in kNN (required). +#' @param other_vote_multiplier Multiplier for the "Other" class when determining if a sample should be reclassified as "Other" (default: 2). +#' @param score_purity_requirement Minimum ratio of top group score to "Other" score to assign a sample to the top group (default: 1). +#' @param weighted_votes_col Name of the column containing the comma-separated weighted votes (default: "weighted_votes"). +#' +#' @return Data frame with additional columns for per-class neighbor counts, scores, top group assignments, and summary statistics for each sample. +#' +#' @details +#' - Computes the number of neighbors for each class and the sum of weighted votes per class. +#' - Identifies the top group by count and by weighted score, and applies custom logic for the "Other" class if present. +#' - Adds columns for counts, scores, top group, top group score, score ratios, and optimized group assignments. +#' - Designed for downstream use in DLBCLone and similar kNN-based classification workflows. +#' +#' @examples +#' # Example usage: +#' # result <- process_votes(knn_output_df, k = 7) +#' +#' @export +process_votes <- function( + df, + raw_col = "label", + group_labels = c("EZB", "MCD", "ST2", "BN2", "N1", "Other"), + vote_labels_col = "vote_labels", + k, + other_vote_multiplier = 2, + score_purity_requirement = 1, + weighted_votes_col = "weighted_votes" +){ + if(missing(k)){ + stop("k value is required") + } + score_thresh = 2 * k + + count_labels_in_string <- function(string, labels) { + tokens <- str_split(string, ",")[[1]] + map_int(labels, ~ sum(tokens == .x)) + } + + extract_weighted_scores <- function(label_str, vote_str, labels) { + lbls <- str_split(label_str, ",")[[1]] + votes <- as.numeric(str_split(vote_str, ",")[[1]]) + map_dbl(labels, ~ sum(votes[lbls == .x])) %>% + set_names(paste0(labels, "_score")) + } + + get_top_score_group <- function(label_str, vote_str, labels) { + lbls <- str_split(label_str, ",")[[1]] + votes <- as.numeric(str_split(vote_str, ",")[[1]]) + scores_by_label <- set_names(map_dbl(labels, ~ sum(votes[lbls == .x])), labels) + top <- names(scores_by_label)[which.max(scores_by_label)] + value <- scores_by_label[[top]] + list(top_score_group = top, top_group_score = value) + } + + df_out <- df %>% + mutate(.id = row_number()) %>% + rowwise() %>% + mutate( + # 1) counts + counts = list( + set_names( + count_labels_in_string(.data[[raw_col]], group_labels), + paste0(group_labels, "_NN_count") + ) + ), + top_group = { + cnts <- count_labels_in_string(.data[[raw_col]], group_labels) + group_labels[which.max(cnts)] + }, + + # 2) scores + scores = list( + if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { + extract_weighted_scores( + .data[[vote_labels_col]], + .data[[weighted_votes_col]], + group_labels + ) + } else { + set_names(rep(0, length(group_labels)), paste0(group_labels, "_score")) + } + ), + + # 3) top score group summary + score_summary = list( + if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { + get_top_score_group( + .data[[vote_labels_col]], + .data[[weighted_votes_col]], + group_labels + ) + } else { + list(top_score_group = NA_character_, top_group_score = NA_real_) + } + ) + ) %>% + ungroup() %>% + unnest_wider(counts) %>% + unnest_wider(scores) %>% + unnest_wider(score_summary) %>% + rowwise() %>% + mutate( + top_group_count = get(paste0(top_group, "_NN_count")) + ) %>% + ungroup() + + # Optional adjustments based on external columns (if present) + if ("neighbors_other" %in% colnames(df)) { + df_out <- df_out %>% + mutate(Other_count = neighbors_other) + } + + if ("other_weighted_votes" %in% colnames(df)) { + df_out <- df_out %>% + mutate(Other_score = other_weighted_votes) + } + + # Optional override of top group if Other dominates by a fold + if (all(c("top_group_count", "Other_count") %in% colnames(df_out))) { + df_out <- df_out %>% + mutate(by_vote = top_group_count) %>% + mutate(by_vote_opt = ifelse(top_group_count * other_vote_multiplier > Other_count, top_group, "Other")) + } + + df_out = mutate( + df_out, + by_score = top_score_group, + score_ratio = top_group_score / Other_score, + by_score_opt=ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh,top_score_group, + "Other" + ) + ) + + return(df_out) +} + +#' Optimize Purity Threshold for Classification Assignment +#' +#' This function searches for the optimal purity threshold to assign samples to their predicted class or to "Other" based on the score ratio in processed kNN vote results. +#' It iteratively tests a range of purity thresholds, updating the predicted class if the score ratio meets or exceeds the threshold, and computes the accuracy for each threshold. +#' The function returns the best accuracy achieved and the corresponding purity threshold. +#' +#' @param optimized_model_object An object containing the optimized model parameters and predictions. +#' @param vote_df Data frame containing the kNN vote results, including columns for sample IDs, predicted labels, and scores. +#' @param mode The mode of operation, which determines the output column names (e.g., "DLBCLone_w"). +#' @param optimize_by The metric to optimize by, either "balanced_accuracy" +#' @param truth_column Name of the column in `processed_votes` containing the true class labels. +#' @param all_classes Vector of all possible class labels to consider (default: c("MCD", "EZB", "BN2", "N1", "ST2", "Other")). +#' @param k The number of neighbors used in the kNN classification. +#' @param exclude_other_for_accuracy Logical indicating whether to exclude the "Other" class from accuracy calculations (default: FALSE). +#' +#' @return A list with two elements: `best_accuracy` (numeric, the highest accuracy achieved) and `best_purity_threshold` (numeric, the threshold at which this accuracy was achieved). +#' +#' @details +#' - For each threshold in the range 0.1 to 0.95 (step 0.05), the function updates the prediction column to assign the class from `by_score_opt` if the score ratio meets the threshold, otherwise assigns "Other". +#' - Accuracy is computed as the proportion of correct assignments (diagonal of the confusion matrix). +#' - The function is intended for use in optimizing classification purity in kNN-based workflows, especially when distinguishing between confident class assignments and ambiguous ("Other") cases. +#' +#' @examples +#' # Example usage: +#' # result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") +#' +#' @export +optimize_purity <- function( + optimized_model_object, + vote_df, + mode, + optimize_by = "balanced_accuracy", #or overall_accuracy + truth_column, + all_classes = c("MCD","EZB","BN2","N1","ST2","Other"), + k, + exclude_other_for_accuracy = FALSE +){ + if(mode == "DLBCLone_w"){ + in_column = "by_score" + out_column = mode + } + if(!missing(optimized_model_object)){ + if(!is.null(optimized_model_object$best_params)){ + if(!missing(k)){ + message("k is provided in the optimized_model_object, ignoring the k parameter") + } + k = optimized_model_object$best_params$k + }else{ + stop("optimized_model_object must contain best_params with k value") + } + vote_df = optimized_model_object$predictions + } + + #all_classes = unique(vote_df[[truth_column]]) + some_classes = all_classes[all_classes != "Other"] + score_thresh = 2 * k + + processed_votes = process_votes( + vote_df, + group_labels=all_classes, + k=k, + score_purity_requirement=0.5 + ) + # Function to optimize purity based on processed votes and truth labels + if(!truth_column %in% colnames(processed_votes)){ + stop("truth_column must be a column in processed_votes") + } + best_accuracy <- 0 + best_purity_threshold <- 0 + + processed_votes = mutate(processed_votes,!!sym(truth_column):= as.character(!!sym(truth_column))) + + no_other_df = processed_votes %>% + filter(!!sym(truth_column) != "Other") + for(purity_threshold in seq(3, 0, -0.05)){ + updated_votes <- processed_votes %>% + mutate(!!sym(out_column) := ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, "Other")) + updated_no_other_df <- no_other_df %>% + mutate(!!sym(out_column) := ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, "Other")) + + if(!exclude_other_for_accuracy){ + + xx = select(updated_votes, sample_id, !!sym(truth_column), !!sym(out_column)) %>% + mutate(match=ifelse(!!sym(truth_column) == !!sym(out_column),TRUE,FALSE)) %>% + group_by(match) %>% + #summarise(concordant) %>% + summarise(concordant=sum(match==TRUE), discordant = sum(match==FALSE)) %>% + ungroup() %>% + summarise(all_conc = sum(concordant), dis = sum(discordant), total=all_conc+dis, percent=100*sum(concordant)/total) + pc_conc = xx$percent %>% round(.,1) + num_conc = xx$all_conc + num_dis = xx$dis + + # calculate accuracy + updated_votes = mutate(updated_votes,!!sym(out_column) := factor(!!sym(out_column),levels=all_classes)) + updated_votes = mutate(updated_votes,!!sym(truth_column) := factor(!!sym(truth_column),levels=all_classes)) + confusion_matrix <- table(updated_votes[[truth_column]],updated_votes[[out_column]]) + conf_matrix <- confusionMatrix(updated_votes[[out_column]], updated_votes[[truth_column]]) + + }else{ + xx = select(updated_no_other_df, sample_id, !!sym(truth_column), !!sym(out_column)) %>% + filter(!!sym(truth_column) != "Other") %>% + mutate(match=ifelse(!!sym(truth_column) == !!sym(out_column),TRUE,FALSE)) %>% + group_by(match) %>% + #summarise(concordant) %>% + summarise(concordant=sum(match==TRUE), discordant = sum(match==FALSE)) %>% + ungroup() %>% + summarise(all_conc = sum(concordant), dis = sum(discordant), total=all_conc+dis, percent=100*sum(concordant)/total) + updated_no_other_df = mutate(updated_no_other_df, + !!sym(out_column) := factor(!!sym(out_column),levels=all_classes), + !!sym(truth_column) := factor(!!sym(truth_column),levels=all_classes)) + confusion_matrix <- table(updated_no_other_df[[truth_column]],updated_no_other_df[[out_column]]) + conf_matrix <- confusionMatrix(updated_no_other_df[[out_column]], updated_no_other_df[[truth_column]]) + } + accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class + oa = conf_matrix$overall[["Accuracy"]] + pc_conc = xx$percent %>% round(.,1) + bal_acc = mean(bal_acc,na.rm=T) + + if(optimize_by == "balanced_accuracy"){ + accuracy = bal_acc + }else if(optimize_by == "overall_accuracy"){ + accuracy = oa + } + num_conc = xx$all_conc + num_dis = xx$dis + + #if(accuracy > best_accuracy){ + if(accuracy > best_accuracy){ + #print(confusion_matrix) + #print(bal_acc) + #print(paste("Percent: ",pc_conc,"Overall:",oa, "Balanced:",mean(bal_acc,na.rm=T))) + #print(conf_matrix) + #message(paste0("New best accuracy: ", round(accuracy, 3))) + #message(paste0("Matches:" ,num_conc, " %Concordance: ",pc_conc, " Mismatches: ",num_dis)) + #message(paste0(" at purity threshold: ", round(purity_threshold, 2))) + best_accuracy <- accuracy + best_purity_threshold <- purity_threshold + }else{ + #print(paste("No improvement", accuracy, "vs", best_accuracy, "at threshold", purity_threshold)) + } + + #print(confusion_matrix_ignore_other) + } + best_out = processed_votes %>% + mutate(!!sym(out_column) := ifelse(score_ratio >= best_purity_threshold | top_group_score > score_thresh, by_score, "Other")) + + if(missing(optimized_model_object)){ + optimized_model_object = list() + optimized_model_object$best_params = list( + k=k, + purity_threshold=best_purity_threshold, + accuracy=best_accuracy, + num_classes=length(unique(best_out$predicted_label)), + num_features=ncol(best_out)-3, #minus V1,V2,sample_id + seed=12345) + } + optimized_model_object$predictions = best_out + optimized_model_object$best_accuracy = best_accuracy + optimized_model_object$best_purity_threshold = best_purity_threshold + optimized_model_object$maximize = optimize_by + optimized_model_object$score_thresh = score_thresh + optimized_model_object$exclude_other_for_accuracy = exclude_other_for_accuracy + return(optimized_model_object) +} + #' Optimize parameters for classifying samples using UMAP and k-nearest neighbor #' #' @param combined_mutation_status_df Data frame with one row per sample and @@ -948,6 +1223,8 @@ make_and_annotate_umap = function( #' instead of re-running UMAP. #' @param truth_classes Vector of classes to use for training and testing. #' Default: c("EZB","MCD","ST2","N1","BN2","Other") +#' @param truth_column The column in the metadata_df that contains the true class labels. +#' Default: "lymphgen". #' @param optimize_for_other Set to TRUE to optimize the threshold for #' classifying samples as "Other" based on the relative proportion of #' samples near the sample in UMAP space with the "Other" label. Rather than @@ -1003,200 +1280,173 @@ make_and_annotate_umap = function( #' optimize_for_other = T, #' truth_classes = c("MCD","EZB","ST2","BN2","Other")) #' - DLBCLone_optimize_params = function( combined_mutation_status_df, metadata_df, umap_out, - truth_classes = c( - "EZB", - "MCD", - "ST2", - "N1", - "BN2", - "Other" - ), - optimize_for_other = FALSE, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), + truth_column = "lymphgen", + optimize_for_other = FALSE, eval_group = NULL, min_k=3, - max_k=33, + max_k=23, verbose = FALSE, seed = 12345, maximize = "balanced_accuracy", exclude_other_for_accuracy = FALSE, weights_opt = c(TRUE) ){ - if(optimize_for_other){ - exclude_other_for_accuracy = FALSE - }else{ - exclude_other_for_accuracy = TRUE - } + #if(optimize_for_other){ + # exclude_other_for_accuracy = FALSE + #}else{ + exclude_other_for_accuracy = TRUE + #} na_opt = c("drop") - num_class = length(truth_classes) - + num_class = length(truth_classes) threshs = seq(0,0.9,0.1) + #threshs = 0.5 ks = seq(min_k,max_k,2) results <- data.frame() best_params <- data.frame() - use_w = TRUE best_acc = 0 + best_acc_w = 0 + best_pred_w = NULL + best_k_w = 0 best_fit = NULL this_accuracy = 0 best_pred = NULL other_pred = NULL - - if("sample_id" %in% colnames(combined_mutation_status_df)){ - combined_mutation_status_df = combined_mutation_status_df %>% column_to_rownames(var = "sample_id") - } + best_w_score_thresh = NULL + best_w_purity = NULL + #ignore_self = TRUE for(na_option in na_opt){ if(missing(umap_out)){ stop("umap_out must be provided by running make_and_annotate_umap first") + }else{ + #project onto existing model instead of re-running UMAP + outs = make_and_annotate_umap( + df=combined_mutation_status_df, + umap_out = umap_out, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option + ) } - - #project onto existing model instead of re-running UMAP - outs = make_and_annotate_umap( - df=combined_mutation_status_df, - umap_out = umap_out$model, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option - ) - - ignore_top = FALSE - if(is.null(eval_group)){ - ignore_top = TRUE - } + for(use_w in weights_opt){ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - test_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(test_coords) = test_ids - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + for(k in ks){ + best_w_acc = NULL + message(paste("K:",k)) + + #for (threshold in threshs){ + test_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) + train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) + train_ids = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(sample_id) + test_ids = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(sample_id) rownames(train_coords) = train_ids - }else{ - test_coords = filter(outs$df,dataset == eval_group) %>% select(V1,V2) - test_ids = filter(outs$df,dataset == eval_group) %>% pull(sample_id) rownames(test_coords) = test_ids - train_coords = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids - } - pred_all = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=ks, - conf_threshold = -1, # Will threshold later - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose, - track_neighbors = optimize_for_other - ) + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=k, + conf_threshold =threshold, + na_label="Other", + use_weights = use_w, + #ignore_self = ignore_self, + verbose = verbose + ) - if(!"Other" %in% truth_classes){ - if(is.null(eval_group)){ - test_coords = filter(outs$df,lymphgen %in% "Other") %>% select(V1,V2) - test_ids = filter(outs$df,lymphgen %in% "Other") %>% pull(sample_id) - rownames(test_coords) = test_ids - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) - rownames(train_coords) = train_ids - }else{ - test_coords = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% select(V1,V2) - test_ids = filter(outs$df,dataset == eval_group,lymphgen %in% "Other") %>% pull(sample_id) - rownames(test_coords) = test_ids - train_coords = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% select(V1,V2) - train_labels = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(lymphgen) - train_ids = filter(outs$df,dataset == eval_group,lymphgen %in% unique(c("Other",truth_classes))) %>% pull(sample_id) - rownames(train_coords) = train_ids - } - n_other = nrow(test_coords) - - if(!"Other" %in% truth_classes && n_other > 0){ - pred_other_all = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=ks, - conf_threshold = -1, # Will threshold later - na_label="Other", - use_weights = use_w, - ignore_top = ignore_top, - verbose = verbose + for(threshold in threshs){ + if(verbose){ + print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) + } + pred_thresh = mutate( + pred, + predicted_label = ifelse(confidence >= threshold, predicted_label, "Other") ) - } - } - - for(k in ks){ - message(paste("K:",k)) - for (threshold in threshs){ - - if(verbose){ - print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) - } - - k_index <- which(ks == k) - pred <- pred_all[[k_index]] %>% - mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) - - if(is.null(eval_group)){ - xx_d = bind_cols(filter(outs$df,lymphgen %in% truth_classes) ,pred) - train_d = filter(outs$df,lymphgen %in% truth_classes) - }else{ - xx_d = bind_cols(filter(outs$df,dataset == eval_group) ,pred) - train_d = filter(outs$df,dataset != eval_group,lymphgen %in% truth_classes) - } - # Always set true label factor levels to truth_classes - xx_d$lymphgen = factor(xx_d$lymphgen, levels = truth_classes) - - # Set predicted labels to allow "Other" - pred_levels = union(truth_classes, "Other") - xx_d$predicted_label = factor(xx_d$predicted_label, levels = pred_levels) - - if(!"Other" %in% truth_classes && n_other > 0){ - pred_other <- pred_other_all[[k_index]] %>% - mutate(predicted_label = ifelse(confidence < threshold, "Other", predicted_label)) - - if(!is.null(eval_group)){ - xx_o = bind_cols( - filter(outs$df, dataset == eval_group, lymphgen == "Other"), - pred_other - ) - } else { - xx_o = bind_cols( - filter(outs$df, lymphgen == "Other"), - pred_other - ) + if(is.null(best_w_acc)){ + #DLBCLone_wo + some_outs = filter(outs$df,!!sym(truth_column) %in% truth_classes) + pred_with_truth = bind_cols(some_outs ,pred) + if(verbose){ + print(dim(pred_with_truth)) + print(table(pred_with_truth[[truth_column]])) } - xx_o$lymphgen = factor("Other", levels = pred_levels) - xx_o$predicted_label = factor(xx_o$predicted_label, levels = pred_levels) - xx_d = bind_rows(xx_d, xx_o) + #return(pred_with_truth) + pred_w = + #suppressMessages( + optimize_purity( + vote_df = pred_with_truth, + mode = "DLBCLone_w", + truth_column = truth_column, + all_classes = truth_classes, + optimize_by = maximize, + k = k + ) + #) + if(verbose){ + print("running optimize_purity for the first time at k:", k) + } + + #print(names(pred_w)) + best_w_acc = pred_w$best_accuracy + if(best_w_acc > best_acc_w){ + message(paste("best accuracy DLBCLone_wo:",pred_w$best_accuracy, "purity threshold:", pred_w$best_purity_threshold)) + best_acc_w = best_w_acc + best_k_w = k + best_fit = pred_with_truth + best_w_purity = pred_w$best_purity_threshold + best_w_score_thresh = pred_w$score_thresh + } + best_pred_w = pred_w$predictions } + + train_d = filter(outs$df,!!sym(truth_column) %in% truth_classes) + xx_d = bind_cols(train_d ,pred_thresh) - true_factor = xx_d$lymphgen - pred_factor = xx_d$predicted_label + if("Other" %in% truth_classes){ + xx_d[[truth_column]] = factor(xx_d[[truth_column]]) + }else{ + test_coords = filter(outs$df,!!sym(truth_column) %in% "Other" | is.na(!!sym(truth_column))) %>% select(V1,V2) + train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) + train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) + n_other = nrow(test_coords) + if(!"Other" %in% truth_classes && n_other > 0){ + pred_other = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=k, + conf_threshold =threshold, + na_label="Other", + use_weights = use_w, + #ignore_self = ignore_self, + verbose = verbose + ) - if (exclude_other_for_accuracy) { - keep_idx = which(true_factor != "Other") - true_factor = true_factor[keep_idx] - pred_factor = pred_factor[keep_idx] + #if(!is.null(eval_group)){ + # xx_o = bind_cols(filter(outs$df,dataset == eval_group,!!sym(truth_column) =="Other") ,pred_other) + #}else{ + xx_o = bind_cols(filter(outs$df,!!sym(truth_column) == "Other" | is.na(!!sym(truth_column))) ,pred_other) + #} + } + xx_d[[truth_column]] = factor(xx_d[[truth_column]],levels = c(unique(xx_d[[truth_column]]),"Other")) } + true_factor = xx_d[[truth_column]] + pred_factor = factor(xx_d$predicted_label,levels = levels(xx_d[[truth_column]])) if(verbose){ print("true_factor") @@ -1206,24 +1456,21 @@ DLBCLone_optimize_params = function( } conf_matrix <- confusionMatrix(pred_factor, true_factor) - + #print(conf_matrix) bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class sn <- conf_matrix$byClass[, "Sensitivity"] # one per class + #bal_acc = report_accuracy(xx_d,pred = "predicted_label")$mean_balanced_accuracy if(verbose){ print(bal_acc) + print(conf_matrix$overall) + print(sn) } - + overall_accuracy <- conf_matrix$overall[["Accuracy"]] - if(exclude_other_for_accuracy){ - mean_balanced_accuracy = mean(bal_acc[!names(bal_acc) == "Class: Other"], na.rm = TRUE) - }else{ - mean_balanced_accuracy = mean(bal_acc) - } overall_sensitivity<- mean(sn[!names(sn) == "Class: Other"], na.rm = TRUE) - + if(optimize_for_other){ - optimized_accuracy_and_thresh = optimize_outgroup( pred_factor, true_factor, @@ -1232,62 +1479,71 @@ DLBCLone_optimize_params = function( maximize = maximize, exclude_other_for_accuracy = exclude_other_for_accuracy ) + out_opt_thresh = optimized_accuracy_and_thresh$threshold - optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 - out_opt_acc = optimized_accuracy_and_thresh$average_accuracy - + #print(optimized_accuracy_and_thresh$average_accuracy) + + #optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 + out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + }else{ out_opt_acc = 0 out_opt_thresh = 0 } - + if(exclude_other_for_accuracy){ + mean_balanced_accuracy = mean(bal_acc[!names(bal_acc) == "Class: Other"], na.rm = TRUE) + }else{ + mean_balanced_accuracy = mean(bal_acc) + } if(maximize == "sensitivity"){ this_accuracy = overall_sensitivity }else if(maximize == "balanced_accuracy"){ this_accuracy =mean_balanced_accuracy - }else{ + }else{ this_accuracy = overall_accuracy } - + #print(paste("accuracy:",this_accuracy, "out_opt_acc",out_opt_acc)) if(out_opt_acc > this_accuracy){ this_accuracy = out_opt_acc } - row <- data.frame(k = k, - threshold = threshold, - use_weights = use_w, - optimized_accuracy = this_accuracy, - overall_accuracy = overall_accuracy, - mean_balanced_accuracy = mean_balanced_accuracy, - sensitivity = overall_sensitivity, - N1_sn = unname(sn["Class: N1"]), - BN2_sn= unname(sn["Class: BN2"]), - ST2_sn = unname(sn["Class: ST2"]), - BN2_bacc = unname(bal_acc["Class: BN2"]), - MCD_bacc = unname(bal_acc["Class: MCD"]), - EZB_bacc = unname(bal_acc["Class: EZB"]), - A53_bacc = unname(bal_acc["Class: A53"]), - Other_bacc = unname(bal_acc["Class: Other"]), - C1_bacc = unname(bal_acc["Class: C1"]), - C2_bacc = unname(bal_acc["Class: C2"]), - C3_bacc = unname(bal_acc["Class: C3"]), - C4_bacc = unname(bal_acc["Class: C4"]), - C5_bacc = unname(bal_acc["Class: C5"]), - ST2_bacc = unname(bal_acc["Class: ST2"]), - threshold_outgroup = out_opt_thresh, - accuracy_out = out_opt_acc, - na_option= na_option) - + row <- data.frame( + k = k, + threshold = threshold, + use_weights = use_w, + optimized_accuracy = this_accuracy, + overall_accuracy = overall_accuracy, + mean_balanced_accuracy = mean_balanced_accuracy, + sensitivity = overall_sensitivity, + N1_sn = unname(sn["Class: N1"]), + BN2_sn= unname(sn["Class: BN2"]), + ST2_sn = unname(sn["Class: ST2"]), + BN2_bacc = unname(bal_acc["Class: BN2"]), + MCD_bacc = unname(bal_acc["Class: MCD"]), + EZB_bacc = unname(bal_acc["Class: EZB"]), + A53_bacc = unname(bal_acc["Class: A53"]), + Other_bacc = unname(bal_acc["Class: Other"]), + C1_bacc = unname(bal_acc["Class: C1"]), + C2_bacc = unname(bal_acc["Class: C2"]), + C3_bacc = unname(bal_acc["Class: C3"]), + C4_bacc = unname(bal_acc["Class: C4"]), + C5_bacc = unname(bal_acc["Class: C5"]), + ST2_bacc = unname(bal_acc["Class: ST2"]), + threshold_outgroup = out_opt_thresh, + accuracy_out = out_opt_acc, + na_option= na_option + ) + if(this_accuracy > best_acc){ best_acc = this_accuracy - - message(paste("best accuracy:",best_acc,"k:",k,"threshold:",threshold,"Other_thresh:",out_opt_thresh,"na:",na_option,"Balanced accuracy:",overall_accuracy, "sensitivity:",overall_sensitivity)) - + #(xx_d)) + xx_d = mutate(xx_d, predicted_label_optimized = ifelse(other_score > out_opt_thresh, "Other", predicted_label)) + no_other_acc = report_accuracy(xx_d,pred = "predicted_label_optimized")$mean_balanced_accuracy + message(paste("best accuracy DLBCLone_io:",best_acc, "without other: ", no_other_acc, "threshold_outgroup:", row$threshold_outgroup)) best_fit = outs best_pred = xx_d if(!"Other" %in% truth_classes && n_other > 0){ other_pred = pred_other } - best_params = row } results <- rbind(results, row) @@ -1296,50 +1552,96 @@ DLBCLone_optimize_params = function( } } best_params$num_classes = num_class - best_params$num_features = ncol(best_fit$features) + best_params$num_features = ncol(umap_out$features) best_params$seed = seed - test_coords = outs$df %>% select(V1,V2) - test_ids = filter(outs$df) %>% pull(sample_id) - rownames(test_coords) = test_ids - train_coords = filter(outs$df,lymphgen %in% truth_classes) %>% select(V1,V2) - train_labels = filter(outs$df,lymphgen %in% truth_classes) %>% pull(lymphgen) - train_ids = filter(outs$df,lymphgen %in% truth_classes) %>% pull(sample_id) + #train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) + #train_ids = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(sample_id) + #rownames(train_coords) = train_ids + #train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) + train_coords = outs$df %>% select(V1,V2) + train_ids = outs$df %>% pull(sample_id) rownames(train_coords) = train_ids - + train_labels = outs$df %>% pull(!!sym(truth_column)) + rownames(test_coords) = outs$df %>% pull(sample_id) pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=best_params$k, - conf_threshold =best_params$threshold, - na_label="Other", - use_weights = best_params$use_weights, - ignore_top = ignore_top, - verbose = verbose, - track_neighbors = optimize_for_other + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=best_params$k, + conf_threshold =best_params$threshold, + #na_label="Other", + use_weights = best_params$use_weights, + #ignore_self = ignore_self, + verbose = verbose, + track_neighbors = TRUE ) - pred = pred[[1]] + + pred_with_truth_full = bind_cols(outs$df %>% select(sample_id, !!sym(truth_column)), pred) + + if(verbose){ + print(paste("TOP score threshold:",best_w_score_thresh, "purity:", best_w_purity)) + } + best_pred_w = process_votes( + df=pred_with_truth_full, + group_labels=truth_classes, + k=k + ) %>% + mutate( + DLBCLone_w = ifelse( + score_ratio >= best_w_purity | top_group_score > best_w_score_thresh, + by_score, + "Other" + ) + ) if(optimize_for_other){ - pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, - "Other", - predicted_label)) + + pred = mutate( + pred, + predicted_label_optimized = ifelse( + other_score > best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) }else{ pred = mutate(pred,predicted_label_optimized = predicted_label) } + #new naming convention + pred = mutate(pred, DLBCLone_i = predicted_label, DLBCLone_io = predicted_label_optimized) + best_pred_w = rename(best_pred_w, DLBCLone_wo = DLBCLone_w) #optimized + best_pred_w = rename(best_pred_w, DLBCLone_w = by_score) #greedy xx_d = bind_cols(outs$df,pred) - to_ret = list(params=results, - best_params = best_params, - model=best_fit$model, - features=best_fit$features, - df=outs$df, - truth_classes = truth_classes, - predictions=xx_d) + xx_d = left_join(xx_d, select(best_pred_w, sample_id, DLBCLone_w, DLBCLone_wo), by="sample_id") + to_ret = list( + params=results, + best_params = best_params, + model=umap_out$model, + features=umap_out$features, + k_DLBCLone_i = best_params$k, + threshold_DLBCLone_i = best_params$threshold, + theshold_outgroup_DLBCLone_i = best_params$threshold_outgroup, + k_DLBCLone_w = best_k_w, + purity_DLBCLone_w = best_w_purity, + score_thresh_DLBCLone_w = best_w_score_thresh, + df=outs$df, + predictions=xx_d + ) if(!"Other" %in% truth_classes && n_other > 0){ to_ret[["predictions_other"]] = xx_o to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) } + #TODO: roll components of umap_out into to_ret + if(any(!names(outs) %in% names(to_ret))){ + missing_names = names(outs)[!names(outs) %in% names(to_ret)] + for(mn in missing_names){ + to_ret[[mn]] = outs[[mn]] + } + } + to_ret$truth_classes = truth_classes + to_ret$optimize_for_other = optimize_for_other + to_ret$truth_column = truth_column return(to_ret) } @@ -1361,9 +1663,6 @@ DLBCLone_optimize_params = function( #' @param verbose Whether to print verbose outputs to console #' @param use_weights Set to FALSE for all neigbors to have equal weight when #' calculating the confidence -#' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with -#' distance = 0. This is usually only relevant when re-classifying labeled -#' samples to estimate overall accuracy #' @param track_neighbors Set to TRUE to include details for the nearest neighbors of each sample #' @param separate_other Set to TRUE to treat the "Other" class separately #' when calculating the confidence. @@ -1376,17 +1675,17 @@ weighted_knn_predict_with_conf <- function( train_coords, train_labels, test_coords, - k, # a vector + k, epsilon = 0.1, conf_threshold = NULL, na_label = "Other", verbose = FALSE, use_weights = TRUE, - ignore_top = FALSE, track_neighbors = TRUE, - separate_other = TRUE, #big change here. Other is considered separately for optimization + separate_other = TRUE, max_neighbors = 500 -) { +){ #big change here. Other is considered separately for optimization + #ignore_self = TRUE if (nrow(train_coords)==0 || nrow(test_coords) == 0) { print("train_coords:") print(nrow(train_coords)) @@ -1394,318 +1693,194 @@ weighted_knn_predict_with_conf <- function( print(nrow(test_coords)) stop("train_coords and test_coords must be data frames with at least one row") } - - train_labels = as.character(train_labels) + # get the 100 nearest neighbors nn <- get.knnx(train_coords, test_coords, max_neighbors) + all_neighbors = data.frame() + preds <- character(nrow(test_coords)) + confs <- numeric(nrow(test_coords)) - results_list <- list() - - for (curr_k in k) { - if (verbose) cat("Processing k =", curr_k, "\n") - - preds <- character(nrow(test_coords)) - names(preds) <- rownames(test_coords) # *IMPORTANT* preserve sample_id - - confs <- numeric(nrow(test_coords)) - names(confs) <- rownames(test_coords) # *IMPORTANT* preserve sample_id - - all_neighbors <- data.frame() + train_labels = as.character(train_labels) + for (i in 1:nrow(test_coords)) { - for (i in 1:nrow(test_coords)){ - if(verbose){ - print(paste("index:",i)) - print(test_coords[i,]) - } - neighbors <- nn$nn.index[i, ] - distances <- nn$nn.dist[i, ] - if(ignore_top){ - #ignore a neighbor if it has identical V1 and V2 - distances <- nn$nn.dist[i, ] - if(distances[1] == 0){ - neighbors = neighbors[-1] - distances = distances[-1] - } - } + self = rownames(test_coords)[i] - distances = distances + epsilon + if(verbose){ + print(paste("index:",i)) + print(test_coords[i,]) + } + neighbors <- nn$nn.index[i, ] + distances <- nn$nn.dist[i, ] +# if(ignore_self){ + # nns = length(neighbors) + # neighbor_ids = rownames(train_coords)[neighbors] + # neighbors <- neighbors[neighbor_ids != self] + # distances <- distances[neighbor_ids != self] + # if(length(neighbors) == nns){ +# print(paste("self was not dropped",paste(neighbor_ids,collapse=","),"i:",i,"self:",self)) + # print(paste(neighbor_ids,collapse=",")) + # stop() + # } +# } + + distances = distances + epsilon + weights <- 1 / distances + if(use_weights){ weights <- 1 / distances - if(use_weights){ - weights <- 1 / distances - }else{ - weights <- rep(1,length(distances)) - } - - neighbor_labels <- train_labels[neighbors] - - if(verbose){ - print("neighbors:") - print(neighbors) - print("distances:") - print(distances) - print("weights:") - print(weights) - print("labels:") - print(neighbor_labels) - } - - # Remove NAs (just in case) - valid <- !is.na(neighbor_labels) - # num_other_neighbors = sum(neighbor_labels == "Other") - other_dists = distances[neighbor_labels == "Other"] - if(separate_other){ - valid = valid & neighbor_labels != "Other" - } - - neighbor_labels <- neighbor_labels[valid] - weights <- weights[valid] - distances <- distances[valid] - neighbors <- neighbors[valid] - #number of neighbours should be, at least, k - 1. If less than that, warn the user - if(length(neighbors) < curr_k-1){ - print(paste("Warning: number of neighbors is less than k-1.")) - print(paste("i:", i,"k:",curr_k)) - print(paste("num_neighbors:",length(neighbors))) - print(table(valid)) - } + }else{ + weights <- rep(1,length(distances)) + } - #now take the first k neighbors - if(length(neighbor_labels) > curr_k){ - neighbor_labels = neighbor_labels[1:curr_k] - weights = weights[1:curr_k] - distances = distances[1:curr_k] - neighbors = neighbors[1:curr_k] - } + neighbor_labels <- train_labels[neighbors] - others_closer = which(other_dists < max(distances)) + if(verbose){ + print("neighbors:") + print(neighbors) + print("distances:") + print(distances) + print("weights:") + print(weights) + print("labels:") + print(neighbor_labels) + } - others_distances = other_dists[others_closer] - if(use_weights){ - others_weights = 1 / others_distances - }else{ - others_weights = rep(1,length(others_closer)) - } - neighbors_other = length(others_closer) - other_weighted_votes = sum(others_weights) - mean_other_dist = mean(others_distances) - - if (length(neighbor_labels) == 0) { - preds[i] <- "Other" - confs[i] <- 1 - if(track_neighbors){ - rel_other = 10 - neighbor_info <- data.frame( - other_score = rel_other, - neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), - weighted_votes = "", - neighbors_other = neighbors_other, - other_weighted_votes = 0, - mean_other_dist = mean_other_dist, - total_w = 1, - pred_w = 2 - ) - all_neighbors = bind_rows(all_neighbors, neighbor_info) - next; - } - } + # Remove NAs (just in case) + valid <- !is.na(neighbor_labels) + #num_other_neighbors = sum(neighbor_labels == "Other") + other_dists = distances[neighbor_labels == "Other"] + if(separate_other){ + valid = valid & neighbor_labels != "Other" + } + neighbor_labels <- neighbor_labels[valid] + weights <- weights[valid] + distances <- distances[valid] + neighbors <- neighbors[valid] + #number of neighbours should be, at least, k - 1. If less than that, warn the user + if(length(neighbors) < k-1){ + print(paste("Warning: number of neighbors is less than k-1.")) + print(paste("i:", i,"k:",k)) + print(paste("num_neighbors:",length(neighbors))) + print(table(valid)) + } + #now take the first k neighbors + if(length(neighbor_labels) > k){ + neighbor_labels = neighbor_labels[1:k] + weights = weights[1:k] + distances = distances[1:k] + neighbors = neighbors[1:k] + } - weighted_votes <- tapply(weights, neighbor_labels, sum) + others_closer = which(other_dists < max(distances)) - if (length(weighted_votes) == 0) { - preds[i] <- na_label - confs[i] <- NA - total_weight <- 0 - pred_weight <- 0 - } else { - predicted_label <- names(which.max(weighted_votes)) - total_weight <- sum(weighted_votes) - pred_weight <- weighted_votes[predicted_label] - confidence <- pred_weight / total_weight - - # Confidence thresholding - if (!is.null(conf_threshold) && confidence < conf_threshold) { - preds[i] <- na_label - confs[i] <- confidence - } else { - preds[i] <- predicted_label - confs[i] <- confidence - } - } - - if(track_neighbors){ - # Create a data frame to store neighbors, distances, and weights - if(separate_other){ - rel_other = other_weighted_votes / pred_weight - }else{ - rel_other = 0 - } - #rel_other = ifelse(rel_other==0,0,log(rel_other)) + others_distances = other_dists[others_closer] + if(use_weights){ + others_weights = 1 / others_distances + }else{ + others_weights = rep(1,length(others_closer)) + } + neighbors_other = length(others_closer) + other_weighted_votes = sum(others_weights) + mean_other_dist = mean(others_distances) + #other_weighted_votes = neighbors_other + # print(paste("other weighted votes:",other_weighted_votes)) + #} + if (length(neighbor_labels) == 0) { + preds[i] <- "Other" + confs[i] <- 1 + if(track_neighbors){ + rel_other = 10 neighbor_info <- data.frame( other_score = rel_other, neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), neighbor = paste(neighbors,collapse=","), distance = paste(round(distances, 3),collapse=","), label = paste(neighbor_labels,collapse=","), - vote_labels = paste(names(weighted_votes),collapse=","), - weighted_votes = paste(weighted_votes,collapse=","), + weighted_votes = "", neighbors_other = neighbors_other, - other_weighted_votes = other_weighted_votes, - total_w = total_weight, - pred_w = pred_weight + other_weighted_votes = 0, + mean_other_dist = mean_other_dist, + total_w = 1, + pred_w = 2 ) all_neighbors = bind_rows(all_neighbors, neighbor_info) + next; } } - to_return = data.frame( - predicted_label = preds, - confidence = confs - ) + weighted_votes <- tapply(weights, neighbor_labels, sum) + + if (length(weighted_votes) == 0) { + preds[i] <- na_label + confs[i] <- NA + total_weight <- 0 + pred_weight <- 0 + }else{ + #print(weighted_votes) + predicted_label <- names(which.max(weighted_votes)) + total_weight <- sum(weighted_votes) + pred_weight <- weighted_votes[predicted_label] + confidence <- pred_weight / total_weight + # Confidence thresholding + #if (!is.null(conf_threshold) && confidence < conf_threshold) { + # preds[i] <- na_label + # confs[i] <- confidence + #} else { + preds[i] <- predicted_label + confs[i] <- confidence + #} + } if(track_neighbors){ - if(!nrow(to_return)==nrow(all_neighbors)){ - print("mismatch in row number for to_return and all_neighbors") - print(nrow(to_return)) - print(nrow(all_neighbors)) - print(head(to_return)) - print(head(all_neighbors)) - stop("") - } - to_return = bind_cols(to_return,all_neighbors) - if(nrow(to_return ) != nrow(test_coords)){ - print("mismatch in row number for to_return and test_coords") - print(nrow(to_return)) - print(nrow(test_coords)) - print(head(to_return)) - print(head(test_coords)) - stop("") + # Create a data frame to store neighbors, distances, and weights + if(separate_other){ + rel_other = other_weighted_votes / pred_weight + }else{ + rel_other = 0 } - } - results_list[[paste0("k_", curr_k)]] <- to_return - } - return(results_list) -} - -#' @title Make Neighborhood Plot -#' @description -#' Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. -#' -#' @param single_sample_prediction_output A list containing prediction results and annotation data frames. -#' Must include elements \code{prediction} (data frame with prediction results) and \code{anno_df} (data frame with UMAP coordinates and annotations). -#' @param training_predictions The equivalent data frame of prediction results for all training samples (e.g. optimized_model$df) -#' @param this_sample_id Character. The sample ID for which the neighborhood plot will be generated. -#' @param prediction_in_title Logical. If \code{TRUE}, includes the predicted label in the plot title. -#' @param add_circle Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable. -#' @param label_column Does nothing, i.e. this is not currently working. -#' @param point_size Numeric. Size of the points in the plot. Default: 0.5. -#' @param point_alpha Numeric. Transparency of the points in the plot. Default: 0.9. -#' @param line_alpha Numeric. Transparency of the lines connecting the sample to its neighbors. Default: 0.9. -#' -#' @return A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. -#' -#' @details -#' The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. -#' -#' @import dplyr -#' @import ggplot2 -#' @importFrom rlang sym -#' -#' @export -#' @examples -#' -#' # Assuming 'optimization_result' is the output of DLBCLone_optimize_params -#' # and 'output' is the result of DLBCLone_predict_single_sample -#' # on sample_id "SAMPLE123": -#' make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") -make_neighborhood_plot <- function( - single_sample_prediction_output, - training_predictions, - this_sample_id, - prediction_in_title = TRUE, - add_circle = TRUE, - label_column = "predicted_label_optimized", - point_size = 0.5, - point_alpha = 0.9, - line_alpha = 0.9 -){ - - circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ - r = diameter / 2 - tt <- seq(0,2*pi,length.out = npoints) - xx <- center[1] + r * cos(tt) - yy <- center[2] + r * sin(tt) - return(data.frame(x = xx, y = yy)) - } - if(missing(training_predictions)){ - training_predictions = single_sample_prediction_output$anno_df - }else if(missing(single_sample_prediction_output)){ - #Just plot the single sample in the context of the rest based on the optimization - single_sample_prediction_output = list() - single_sample_prediction_output[["prediction"]] = filter( - training_predictions, - rownames(training_predictions) == this_sample_id - ) - single_sample_prediction_output[["anno_df"]] = training_predictions - }else{ - single_sample_prediction_output$prediction = filter( - single_sample_prediction_output$prediction, - rownames(single_sample_prediction_output$prediction) == this_sample_id - ) - } - - #extract the sample_id for all the nearest neighbors with non-Other labels - my_neighbours = filter( - single_sample_prediction_output$prediction, - rownames(single_sample_prediction_output$prediction) == this_sample_id - ) %>% - pull(neighbor_id) %>% - strsplit(.,",") %>% - unlist() - - #set up links connecting each neighbor to the sample's point - links_df = filter(training_predictions,rownames(training_predictions) %in% my_neighbours) %>% mutate(group=lymphgen) - - sample_row <- single_sample_prediction_output$anno_df %>% - filter(rownames(single_sample_prediction_output$anno_df) == this_sample_id) %>% - slice_head(n = 1) # ensures 1 sample_id selected. Duplicates may occur when ignore_top = TRUE. - my_x <- sample_row$V1 - my_y <- sample_row$V2 - - if(prediction_in_title){ - title = paste(this_sample_id, - pull(single_sample_prediction_output$prediction, - !!sym(label_column))) - if(single_sample_prediction_output$prediction$predicted_label_optimized == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ - title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") - } - - }else{ - title = this_sample_id + #print(head(rownames(train_coords))) + #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( + other_score = rel_other, + neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + vote_labels = paste(names(weighted_votes),collapse=","), + weighted_votes = paste(weighted_votes,collapse=","), + neighbors_other = neighbors_other, + other_weighted_votes = other_weighted_votes, + total_w = total_weight, + pred_w = pred_weight + ) + all_neighbors = bind_rows(all_neighbors, neighbor_info) + } } - links_df = mutate(links_df,my_x=my_x,my_y=my_y) - links_df = links_df %>% select(V1, V2, my_x, my_y, group) %>% mutate(length = sqrt((V1 - my_x)^2 + (V2 - my_y)^2)) # Euclidean distance - - # if(drop_other){ - # training_predictions = filter(training_predictions,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") - #} + to_return = data.frame( + predicted_label = preds, + confidence = confs + ) - pp=ggplot( - mutate(training_predictions,group=lymphgen), - aes(x=V1,y=V2,colour=group)) + - geom_point(alpha=point_alpha,size=point_size) + - geom_segment(data=links_df,aes(x=V1,y=V2,xend=my_x,yend=my_y),alpha=line_alpha)+ - scale_colour_manual(values=get_gambl_colours()) + - ggtitle(title)+ - theme_minimal() - if(add_circle){ - #add a circle around the sample - d = max(links_df$length)*2.1 # adding a 10% spacer - circle = circleFun(c(my_x,my_y),diameter=d,npoints=100) - pp = pp + geom_path(data=circle,aes(x=x,y=y),colour="black",alpha=1,inherit.aes=FALSE) + if(track_neighbors){ + #check for any missing points + if(!nrow(to_return)==nrow(all_neighbors)){ + print("mismatch in row number for to_return and all_neighbors") + print(nrow(to_return)) + print(nrow(all_neighbors)) + print(head(to_return)) + print(head(all_neighbors)) + stop("") + } + to_return = bind_cols(to_return,all_neighbors) + if(nrow(to_return ) != nrow(test_coords)){ + print("mismatch in row number for to_return and test_coords") + print(nrow(to_return)) + print(nrow(test_coords)) + print(head(to_return)) + print(head(test_coords)) + stop("") + } + rownames(to_return) <- rownames(test_coords) } - return(pp) + return(to_return) } #' Predict class for a single sample without using umap_transform and plot result of classification @@ -1713,12 +1888,10 @@ make_neighborhood_plot <- function( #' @param test_df Data frame containing the mutation status of the test sample #' @param train_df Data frame containing the mutation status of the training samples #' @param train_metadata Metadata for training samples with truth labels in lymphgen column -#' @param optimize_params list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +#' @param projection UMAP projection of the training data. If not provided, the function will run UMAP on the combined data. +#' @param optimized_model list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a #' previous, Data frame with the best parameters. useful for reproducibility. -#' @param other_df Data frame containing the predictions for samples in the "Other" class -#' @param ignore_top Set to TRUE to avoid considering a nearest neighbor with -#' distance = 0. This is usually only relevant when re-classifying labeled -#' samples to estimate overall accuracy +#' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") #' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data #' @param make_plot Set to TRUE to plot the UMAP projection and predictions @@ -1730,7 +1903,7 @@ make_neighborhood_plot <- function( #' @param seed Random seed for reproducibility #' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' -#' @returns a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object +#' @returns a list of data frames with the predictions, the UMAP input, the model, and a ggplot object #' @export #' #' @examples @@ -1739,7 +1912,8 @@ make_neighborhood_plot <- function( #' test_df = test_df, #' train_df = train_df, #' train_metadata = train_metadata, -#' optimize_params = optimize_params, +#' umap_out = umap_out, +#' best_params = best_params #' predictions_df = predictions_df, #' annotate_accuracy = TRUE #' ) @@ -1748,10 +1922,10 @@ predict_single_sample_DLBCLone <- function( test_df, train_df, train_metadata, - optimize_params, + projection, + optimized_model = NULL, other_df, - ignore_top = TRUE, - truth_classes = c("EZB","MCD","ST2","BN2"), + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), drop_unlabeled_from_training=TRUE, make_plot = TRUE, annotate_accuracy = FALSE, @@ -1762,86 +1936,35 @@ predict_single_sample_DLBCLone <- function( seed = 12345, max_neighbors = 500 ){ - - if(nrow(test_df)>1){ - message("Warning: you have supplied more than one sample to test with. Will proceed with all") + if(is.null(optimized_model)){ + warning("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } - # cannot have duplicate rownames, sample_id needs to be a column in function, outputted as rownames - if(!"sample_id" %in% colnames(train_df)){ - train_df = train_df %>% rownames_to_column("sample_id") - } - - if(!"sample_id" %in% colnames(test_df)){ - test_df = test_df %>% rownames_to_column("sample_id") - } - - trained_features = colnames(optimize_params$features) - train_df = train_df %>% column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% rownames_to_column("sample_id") train_id <- train_df$sample_id test_df = test_df %>% column_to_rownames("sample_id") %>% - select(all_of(trained_features)) %>% rownames_to_column("sample_id") test_id <- test_df$sample_id - - if(ignore_top){ - if(test_id %in% train_df$sample_id){ - combined_df = train_df - combined_metadata <- train_metadata - }else{ - combined_df = bind_rows(train_df,test_df) - - # Make dummy metadata for test samples - test_metadata <- data.frame( - sample_id = test_df$sample_id, - cohort = "Test-Sample", - lymphgen = NA # or any placeholder? - ) - # Ensure train_metadata has matching columns - train_metadata <- train_metadata %>% - select(sample_id, cohort, lymphgen) - combined_metadata <- bind_rows(train_metadata, test_metadata) - } - } else { - # Drop overlaps to prevent rowname collisions - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - warning(paste("Removing", length(dupes), - "overlapping samples from train_df to avoid duplicated rownames.\n", - "Consider setting ignore_top = TRUE to avoid inaccurate high confidence, and to keep all training samples.")) - train_df <- train_df %>% filter(!sample_id %in% dupes) - train_metadata <- train_metadata %>% filter(!sample_id %in% dupes) - } - combined_df <- bind_rows(train_df, test_df) - - # Make dummy metadata for test samples - test_metadata <- data.frame( - sample_id = test_df$sample_id, - cohort = "Test-Sample", - lymphgen = NA # or any placeholder? + + combined_df <- bind_rows(train_df, test_df) + + if(missing(projection)){ + projection <- make_and_annotate_umap( + df = combined_df, + umap_out = optimized_model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = optimized_model$best_params$na_option ) - # Ensure train_metadata has matching columns - train_metadata <- train_metadata %>% - select(sample_id, cohort, lymphgen) - combined_metadata <- bind_rows(train_metadata, test_metadata) + }else{ + message("Using provided projection for prediction") } - - projection <- make_and_annotate_umap( - df = combined_df, - metadata = combined_metadata, - umap_out = optimize_params$model, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = optimize_params$best_params$na_option - ) - + train_coords = dplyr::filter( projection$df, sample_id %in% train_id @@ -1849,335 +1972,151 @@ predict_single_sample_DLBCLone <- function( select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - train_labels = dplyr::filter( + train_df_proj = dplyr::filter( projection$df, sample_id %in% train_id ) %>% - select(sample_id,V1,V2) %>% - left_join( - combined_metadata %>% select( - sample_id, - lymphgen - ), - by = "sample_id" - ) %>% + select(sample_id,V1,V2) %>% + left_join( #Join to the incoming metadata rather than trusting the metadata in the projection + train_metadata %>% + select( + sample_id, + lymphgen + ), + by = "sample_id" + ) + + train_labels = train_df_proj %>% pull(lymphgen) + #Obtain UMAP coordinates for the test sample(s) + print(paste("num rows:",nrow(test_df))) + test_projection <- make_and_annotate_umap( + df = test_df, + umap_out = optimized_model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = optimized_model$best_params$na_option + ) test_coords = dplyr::filter( - projection$df, + test_projection$df, sample_id %in% test_id ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - + + predict_training = FALSE + if(predict_training){ + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = optimized_model$best_params$k, + conf_threshold = optimized_model$best_params$threshold, + na_label = "Other", + use_weights = optimized_model$best_params$use_weights + ) + + train_pred = rownames_to_column(train_pred, var = "sample_id") + } + test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, - k = optimize_params$best_params$k, - conf_threshold = optimize_params$best_params$threshold, + k = optimized_model$best_params$k, + conf_threshold = optimized_model$best_params$threshold, na_label = "Other", - use_weights = optimize_params$best_params$use_w, - ignore_top = ignore_top, + use_weights = optimized_model$best_params$use_weights, max_neighbors = max_neighbors ) - test_pred <- as.data.frame(test_pred) - prefix <- paste0("k_", optimize_params$best_params$k, ".") - colnames(test_pred) <- sub(prefix, "", colnames(test_pred)) + test_pred = rownames_to_column(test_pred, var = "sample_id") + #test_pred = bind_cols(test_pred, test_coords) + if(!is.null(optimized_model)){ + test_pred = mutate( + test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse( + other_score > optimized_model$best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) - if(optimize_params$best_params$threshold_outgroup > 0){ # optimize_for_other = T in DLBCLone_optimize_params - test_pred = mutate(test_pred,predicted_label_optimized = ifelse( - other_score > optimize_params$best_params$threshold_outgroup, - "Other", - predicted_label - )) - }else{ # optimize_for_other = F - test_pred = mutate(test_pred,predicted_label_optimized = predicted_label) + }else{ + test_pred = mutate( + test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse( + other_score > optimized_model$best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) } + + anno_umap = select(test_projection$df, sample_id, V1, V2) - anno_umap = select(projection$df, sample_id, V1, V2) - - anno_out = left_join(test_pred,anno_umap,by="sample_id") + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) anno_out = anno_out %>% mutate( - label = as.character(paste( - sample_id, - predicted_label_optimized, - round(confidence,3) - )), V1 = as.numeric(V1), - V2 = as.numeric(V2) - ) - - predictions_test_df <- left_join(test_pred, projection$df, by = "sample_id") - - test_pred <- test_pred %>% - column_to_rownames("sample_id") - - missing_cols <- setdiff(names(predictions_test_df), names(projection$df)) - na_cols <- setNames(rep(list(NA), length(missing_cols)), missing_cols) - projection_filled <- projection$df %>% - filter(sample_id %in% train_id) %>% - mutate(!!!na_cols) - - predictions_df <- bind_rows( - predictions_test_df, - projection_filled %>% filter(!(sample_id %in% predictions_test_df$sample_id)) + V2 = as.numeric(V2), + label = as.character(label) ) - - predictions_df <- predictions_df %>% - column_to_rownames("sample_id") - - if(make_plot){ - title = paste0( - "N_class:", optimize_params$best_params$num_classes," N_feats:",optimize_params$best_params$num_features, - " k=",optimize_params$best_params$k," threshold=",optimize_params$best_params$threshold," bacc=",round(optimize_params$best_params$accuracy,3) - ) - - if("BN2" %in% truth_classes){ - # print(optimize_params$best_params) - acc_df = data.frame( - lymphgen = c( - # "N1", - "BN2", - "EZB", - "MCD", - "ST2", - "Other", - "A53" - ), - accuracy = c( - # optimize_params$best_params$N1_bacc, - optimize_params$best_params$BN2_bacc, - optimize_params$best_params$EZB_bacc, - optimize_params$best_params$MCD_bacc, - optimize_params$best_params$ST2_bacc, - optimize_params$best_params$Other_bacc, - optimize_params$best_params$A53_bacc - ) - ) - }else if("C1" %in% truth_classes){ - acc_df = data.frame( - lymphgen = c( - "C1", - "C2", - "C3", - "C4", - "C5" - ), - accuracy = c( - optimize_params$best_params$C1_bacc, - optimize_params$best_params$C2_bacc, - optimize_params$best_params$C3_bacc, - optimize_params$best_params$C4_bacc, - optimize_params$best_params$C5_bacc - ) - ) - }else{ - stop("no labels to add?") - } - - # Add the predicted labels for Other (unclassified) cases, if provided - if(!missing(other_df)){ - in_df = bind_rows( - mutate(predictions_df,dataset=title2), - mutate(other_df,dataset=title3) - ) - }else{ - in_df = bind_rows( - mutate(predictions_df,dataset=title2) - ) - } - - pp = ggplot(in_df) + - geom_point(aes(x=V1,y=V2,colour=lymphgen),alpha=0.8) + - scale_colour_manual(values=get_gambl_colours()) + - facet_wrap(~dataset,ncol=1) + - theme_Morons() + ggtitle(title) - - if(annotate_accuracy){ - #add labels and set nudge direction based on what quadrant each group sits in - centroids = filter(predictions_df,lymphgen %in% truth_classes) %>% - group_by(lymphgen) %>% - summarise(mean_V1=median(V1),mean_V2=median(V2)) %>% - mutate(nudge_x=sign(mean_V1),nudge_y = sign(mean_V2)) - - centroids = left_join(centroids,acc_df) %>% - mutate(label=paste(lymphgen,":",round(accuracy,3))) - - pp = pp + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = -1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y < 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = -1 * label_offset, - nudge_x = 1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x < 0), - aes(x=mean_V1,y=mean_V2,label=label), - size=5, - nudge_y = 1 * label_offset, - nudge_x = -1 * label_offset - ) + - geom_label_repel( - data=filter(centroids,nudge_y > 0, nudge_x > 0), - aes(x=mean_V1,y=mean_V2,label=label), - fill="white", - size=5, - nudge_y = 1 * label_offset, - nudge_x = 1 * label_offset - ) + - geom_label_repel( - data = anno_out, - aes(x=V1,y=V2,label=label), - nudge_y = 1 * label_offset, - nudge_x = 1 * label_offset, - colour="red" - ) - } - } - - return(list( - prediction = test_pred, - umap_input_features = trained_features, - model=optimize_params$model, - plot = pp, - anno_df = predictions_df, - projection = projection$df - )) -} - -#' Make UMAP scatterplot -#' -#' @param df Data frame containing the UMAP coordinates and annotations. -#' @param title Title for the plot. Default: NULL. -#' @param drop_composite If TRUE: removes composite labels from the lymphgen column. -#' @param colour_by Column name to color the points by. Default: "lymphgen". -#' @param drop_other If TRUE: removes "Other" and "NOS" labels from the lymphgen column. -#' @param high_confidence If TRUE: filters the data to include only samples with confidence > 0.7. -#' @param custom_colours Custom color palette for the plot. If not provided, uses default GAMBL colors. -#' @param add_labels If TRUE: adds labels to the points based on the median coordinates of each group. -#' @param facet If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely -#' -#' @returns -#' @export -#' -#' @examples -make_umap_scatterplot = function( - df, - title = NULL, - drop_composite = TRUE, - colour_by="lymphgen", - drop_other = FALSE, - high_confidence = FALSE, - custom_colours, - add_labels = FALSE, - facet = FALSE # if TRUE: truth, predicted, predicted_optimized -){ - - if(!missing(custom_colours)){ - cols = custom_colours + if(predict_training){ + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") }else{ - cols = get_gambl_colours() - } - if(drop_composite){ - df = filter(df,!is.na(lymphgen),!grepl("COMP",lymphgen)) - } - if(drop_other){ - df = filter(df,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") - } - if(high_confidence){ - df = filter(df,Confidence > 0.7) - } - if(add_labels){ - labels = group_by(df,!!sym(colour_by)) %>% - summarise(median_x = median(V1),median_y = median(V2)) + predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% + select(sample_id, V1, V2) } + #This had assumed that projection$df contains the test samples! + #predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") - if(facet){ - truth <- df - p_truth = ggplot(truth,aes( - x=V1, - y=V2, - colour=!!sym(colour_by), - label=cohort - )) + - geom_point(alpha=0.8) + - labs(title = "Truth") + - scale_colour_manual(values=cols) + - theme_Morons() + - guides(colour = guide_legend(nrow = 1)) - if(add_labels){ - p_truth = p_truth + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) - } - - predicted <- df %>% mutate(lymphgen=predicted_label) - p_predicted = ggplot(predicted,aes( - x=V1, - y=V2, - colour=!!sym(colour_by), - label=cohort - )) + - geom_point(alpha=0.8) + - labs(title = "Predicted") + - scale_colour_manual(values=cols) + - theme_Morons() + - guides(colour = guide_legend(nrow = 1)) - if(add_labels){ - p_predicted = p_predicted + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) - } + predictions_df = bind_rows( + predictions_train_df %>% select(sample_id, V1, V2), + predictions_test_df %>% select(sample_id, V1, V2) + ) - predicted_optimized <- df %>% mutate(lymphgen=predicted_label_optimized) - p_predicted_optimized = ggplot(predicted_optimized,aes( - x=V1, - y=V2, - colour=!!sym(colour_by), - label=cohort - )) + - geom_point(alpha=0.8) + - labs(title = "Predicted_Optimized") + - scale_colour_manual(values=cols) + - theme_Morons() + - guides(colour = guide_legend(nrow = 1)) - if(add_labels){ - p_predicted_optimized = p_predicted_optimized + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + if(!is.null(optimized_model)){ + best_w_purity = optimized_model$best_w_purity + best_w_score_thresh = optimized_model$best_w_score_thresh + + predictions_test_df = process_votes( + df=predictions_test_df, + group_labels=optimized_model$truth_classes, + k=optimized_model$k_DLBCLone_w + ) + + predictions_test_df = predictions_test_df %>% + mutate( + DLBCLone_w = by_score, + DLBCLone_wo = ifelse( + score_ratio >= optimized_model$purity_DLBCLone_w | top_group_score > optimized_model$score_thresh_DLBCLone_w, + by_score, + "Other" + ) + ) } + to_return = list( + prediction = predictions_test_df, + projection = projection$df, + umap_input = optimized_model$features, + model=optimized_model$model, + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), + anno_out = anno_out + ) - ppp = ggarrange(p_truth,p_predicted,p_predicted_optimized, ncol = 2, nrow = 2) - return(ppp) - - }else{ - p = ggplot(df,aes( - x=V1, - y=V2, - colour=!!sym(colour_by), - label=cohort - )) + - geom_point(alpha=0.8) + - labs(title = title) + - scale_colour_manual(values=cols) + - theme_Morons() + - guides(colour = guide_legend(nrow = 1)) - if(add_labels){ - p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) - } - ggMarginal(p,groupColour = TRUE,groupFill=TRUE) - } + return(to_return) } - -#' modal storage for DLBCLone outputs +#' model storage for DLBCLone outputs #' #' @param optimized_params List containing the optimized parameters from DLBCLone_optimize_params #' @param path Path to save the files @@ -2197,7 +2136,6 @@ make_umap_scatterplot = function( #' name_prefix="test_A" #' ) #' - DLBCLone_save_optimized = function( optimized_params=NULL, path="models/", @@ -2225,8 +2163,7 @@ DLBCLone_save_optimized = function( write.table(optimized_params$truth_classes,file=out_classes,quote=F,row.names=F) } - -#' load DLBCLone_save_optimized's DLBCLone_optimize_params outputs +#' load previously saved DLBCLone model and parameters #' #' @param path Path to open saved the files #' @param name_prefix Prefix of the saved files, all files will be in path and start with name_prefix @@ -2245,7 +2182,6 @@ DLBCLone_save_optimized = function( #' name_prefix="test_A" #' ) #' - DLBCLone_load_optimized <- function( # set sample_id to rownames path="models/", name_prefix="test" @@ -2285,3 +2221,194 @@ DLBCLone_load_optimized <- function( # set sample_id to rownames predictions = predictions )) } + +#' Train a Gaussian Mixture Model for DLBCLone Classification +#' +#' Fits a supervised Gaussian mixture model (GMM) to UMAP-projected data for DLBCLone subtypes, excluding samples labeled "Other". +#' Assigns class predictions and optionally reclassifies samples as "Other" based on probability and density thresholds. +#' +#' @param umap_out List. Output from \code{make_and_annotate_umap}, containing a data frame with UMAP coordinates and truth labels. +#' @param probability_threshold Numeric. Minimum posterior probability required to assign a class (default: 0.5). +#' @param density_max_threshold Numeric. Minimum maximum density required to assign a class (default: 0.05). +#' @param truth_column Name for the original class column in the predictions data frame (default: "lymphgen"). +#' @param cohort Optional character. Cohort label to annotate predictions. +#' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other"). +#' +#' @details +#' - Uses \code{MclustDA} to fit a supervised mixture model to the UMAP coordinates (V1, V2) and class labels. +#' - Predicts class membership and computes per-class densities for each sample. +#' - Samples with low maximum probability or density are reclassified as "Other". +#' - Returns both raw and thresholded class assignments, respectively under the columns DLBCLone_g and DLBCLone_go. +#' +#' @return A list with: +#' \item{gaussian_mixture_model}{Fitted \code{MclustDA} model object} +#' \item{predictions}{Data frame with sample IDs, UMAP coordinates, true labels, predicted classes, and thresholded assignments} +#' \item{probability_threshold}{Probability threshold used for "Other" assignment} +#' +#' @examples +#' result <- DLBCLone_train_mixture_model(umap_out) +#' head(result$predictions) +#' @import mclust +#' +#' @export +#' +DLBCLone_train_mixture_model = function( + umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + truth_column = "lymphgen", + cohort = NULL, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other") +){ + df = umap_out$df %>% select(sample_id,!!sym(truth_column),V1,V2) %>% filter(!!sym(truth_column) != "Other") + df = filter(df,!!sym(truth_column) %in% truth_classes) + #mont_test_proj = montreal_gambl_c_mu$df %>% select(sample_id,lymphgen,V1,V2) + + df[[truth_column]] <- as.factor(df[[truth_column]]) + + #model without Others + gmm_supervised <- MclustDA( + df[, c("V1", "V2")], + class = df[[truth_column]], + modelType = "MclustDA" # instead of "EDDA" + ) + df = umap_out$df %>% select(sample_id,!!sym(truth_column),V1,V2) #%>% filter(!!sym(truth_column) != "Other") + + pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) + + + densities_list <- lapply(names(gmm_supervised$models), function(class_name) { + model <- gmm_supervised$models[[class_name]] + + modelName <- model$modelName + params <- model$parameters + + # Compute log-densities for each component + logdens <- cdens( + data = df[, c("V1", "V2")], + modelName = modelName, + parameters = params, + logarithm = TRUE + ) + + # cdens can return a matrix (samples x components); sum components + class_density <- exp(logdens) # back to raw densities + if (is.matrix(class_density)) { + class_density <- rowSums(class_density) + } + + class_density + }) + + densities <- do.call(cbind, densities_list) + colnames(densities) <- names(gmm_supervised$models) + + max_density <- apply(densities, 1, max) + max_prob <- apply(pred$z, 1, max) + + df$DLBCLone_g = pred$classification + + df$DLBCLone_go <- ifelse( + (max_prob < probability_threshold) | (max_density < density_max_threshold), + "Other", + as.character(pred$classification) + ) + + df$cohort = cohort + to_return = list( + gaussian_mixture_model = gmm_supervised, + predictions = df, + truth_classes = truth_classes, + truth_column = truth_column, + probability_threshold = probability_threshold + ) + +return(to_return) +} + +#' Predict DLBCLone Class Membership Using a Trained Gaussian Mixture Model +#' +#' Applies a previously trained supervised Gaussian mixture model (GMM) to UMAP-projected data for DLBCLone subtypes. +#' Assigns class predictions and optionally reclassifies samples as "Other" based on probability and density thresholds. +#' +#' @param model Fitted \code{MclustDA} model object, as returned by \code{DLBCLone_train_mixture_model}. +#' @param umap_out List. Output from \code{make_and_annotate_umap}, containing a data frame with UMAP +#' coordinates for the samples to be classified with the model. This must be projected using the same UMAP model +#' that was generated using the training data. +#' @param probability_threshold Numeric. Minimum posterior probability required to assign a class (default: 0.5). +#' @param density_max_threshold Numeric. Minimum maximum density required to assign a class (default: 0.05). +#' @param cohort Optional character. Cohort label to annotate predictions. +#' +#' @details +#' - Uses the provided \code{MclustDA} model to predict class membership for each sample in the UMAP projection. +#' - Computes per-class densities and posterior probabilities for each sample. +#' - Samples with low maximum probability or density are reclassified as "Other". +#' - Returns both raw and thresholded class assignments, respectively under the columns DLBCLone_g and DLBCLone_go. +#' +#' @return A list with: +#' \item{gaussian_mixture_model}{Fitted \code{MclustDA} model object} +#' \item{predictions}{Data frame with sample IDs, UMAP coordinates, predicted classes, and thresholded assignments} +#' \item{probability_threshold}{Probability threshold used for "Other" assignment} +#' +#' @examples +#' # Predict on new UMAP data using a trained mixture model: +#' result <- DLBCLone_predict_mixture_model(model, umap_out) +#' head(result$predictions) +#' +#' @import mclust +#' @export +DLBCLone_predict_mixture_model = function( + model, + umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + cohort = NULL +){ + df = umap_out$df %>% select(sample_id,V1,V2) + gmm_supervised = model + pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) + + densities_list <- lapply(names(gmm_supervised$models), function(class_name) { + model <- gmm_supervised$models[[class_name]] + modelName <- model$modelName + params <- model$parameters + + # Compute log-densities for each component + logdens <- cdens( + data = df[, c("V1", "V2")], + modelName = modelName, + parameters = params, + logarithm = TRUE + ) + + # cdens can return a matrix (samples x components); sum components + class_density <- exp(logdens) # back to raw densities + if (is.matrix(class_density)) { + class_density <- rowSums(class_density) + } + + class_density + }) + + densities <- do.call(cbind, densities_list) + colnames(densities) <- names(gmm_supervised$models) + + max_density <- apply(densities, 1, max) + max_prob <- apply(pred$z, 1, max) + + df$DLBCLone_g = pred$classification + + df$DLBCLone_go <- ifelse( + (max_prob < probability_threshold) | (max_density < density_max_threshold), + "Other", + as.character(pred$classification) + ) + + df$cohort = cohort + to_return = list( + gaussian_mixture_model = gmm_supervised, + predictions = df, + probability_threshold = probability_threshold + ) + return(to_return) +} diff --git a/man/DLBCLone_load_optimized.Rd b/man/DLBCLone_load_optimized.Rd index f5a8d7e..7f79416 100644 --- a/man/DLBCLone_load_optimized.Rd +++ b/man/DLBCLone_load_optimized.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/umap.R \name{DLBCLone_load_optimized} \alias{DLBCLone_load_optimized} -\title{load DLBCLone_save_optimized's DLBCLone_optimize_params outputs} +\title{load previously saved DLBCLone model and parameters} \usage{ DLBCLone_load_optimized(path = "models/", name_prefix = "test") } @@ -15,7 +15,7 @@ DLBCLone_load_optimized(path = "models/", name_prefix = "test") saves the files to the specified path } \description{ -load DLBCLone_save_optimized's DLBCLone_optimize_params outputs +load previously saved DLBCLone model and parameters } \examples{ load_optimized <- DLBCLone_load_optimized( diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 674dcab..7b9ee48 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -9,10 +9,11 @@ DLBCLone_optimize_params( metadata_df, umap_out, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), + truth_column = "lymphgen", optimize_for_other = FALSE, eval_group = NULL, min_k = 3, - max_k = 33, + max_k = 23, verbose = FALSE, seed = 12345, maximize = "balanced_accuracy", @@ -34,6 +35,9 @@ instead of re-running UMAP.} \item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other")} +\item{truth_column}{The column in the metadata_df that contains the true class labels. +Default: "lymphgen".} + \item{optimize_for_other}{Set to TRUE to optimize the threshold for classifying samples as "Other" based on the relative proportion of samples near the sample in UMAP space with the "Other" label. Rather than diff --git a/man/DLBCLone_predict_mixture_model.Rd b/man/DLBCLone_predict_mixture_model.Rd new file mode 100644 index 0000000..3d94ea5 --- /dev/null +++ b/man/DLBCLone_predict_mixture_model.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_predict_mixture_model} +\alias{DLBCLone_predict_mixture_model} +\title{Predict DLBCLone Class Membership Using a Trained Gaussian Mixture Model} +\usage{ +DLBCLone_predict_mixture_model( + model, + umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + cohort = NULL +) +} +\arguments{ +\item{model}{Fitted \code{MclustDA} model object, as returned by \code{DLBCLone_train_mixture_model}.} + +\item{umap_out}{List. Output from \code{make_and_annotate_umap}, containing a data frame with UMAP +coordinates for the samples to be classified with the model. This must be projected using the same UMAP model +that was generated using the training data.} + +\item{probability_threshold}{Numeric. Minimum posterior probability required to assign a class (default: 0.5).} + +\item{density_max_threshold}{Numeric. Minimum maximum density required to assign a class (default: 0.05).} + +\item{cohort}{Optional character. Cohort label to annotate predictions.} +} +\value{ +A list with: +\item{gaussian_mixture_model}{Fitted \code{MclustDA} model object} +\item{predictions}{Data frame with sample IDs, UMAP coordinates, predicted classes, and thresholded assignments} +\item{probability_threshold}{Probability threshold used for "Other" assignment} +} +\description{ +Applies a previously trained supervised Gaussian mixture model (GMM) to UMAP-projected data for DLBCLone subtypes. +Assigns class predictions and optionally reclassifies samples as "Other" based on probability and density thresholds. +} +\details{ +\itemize{ +\item Uses the provided \code{MclustDA} model to predict class membership for each sample in the UMAP projection. +\item Computes per-class densities and posterior probabilities for each sample. +\item Samples with low maximum probability or density are reclassified as "Other". +\item Returns both raw and thresholded class assignments, respectively under the columns DLBCLone_g and DLBCLone_go. +} +} +\examples{ +# Predict on new UMAP data using a trained mixture model: +result <- DLBCLone_predict_mixture_model(model, umap_out) +head(result$predictions) + +} diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd index 57fcde6..d4a3c5e 100644 --- a/man/DLBCLone_save_optimized.Rd +++ b/man/DLBCLone_save_optimized.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/umap.R \name{DLBCLone_save_optimized} \alias{DLBCLone_save_optimized} -\title{modal storage for DLBCLone outputs} +\title{model storage for DLBCLone outputs} \usage{ DLBCLone_save_optimized( optimized_params = NULL, @@ -21,7 +21,7 @@ DLBCLone_save_optimized( saves the files to the specified path } \description{ -modal storage for DLBCLone outputs +model storage for DLBCLone outputs } \examples{ DLBCLone_save_optimized( diff --git a/man/DLBCLone_train_mixture_model.Rd b/man/DLBCLone_train_mixture_model.Rd new file mode 100644 index 0000000..77d611c --- /dev/null +++ b/man/DLBCLone_train_mixture_model.Rd @@ -0,0 +1,50 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_train_mixture_model} +\alias{DLBCLone_train_mixture_model} +\title{Train a Gaussian Mixture Model for DLBCLone Classification} +\usage{ +DLBCLone_train_mixture_model( + umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + truth_column = "lymphgen", + cohort = NULL, + truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other") +) +} +\arguments{ +\item{umap_out}{List. Output from \code{make_and_annotate_umap}, containing a data frame with UMAP coordinates and truth labels.} + +\item{probability_threshold}{Numeric. Minimum posterior probability required to assign a class (default: 0.5).} + +\item{density_max_threshold}{Numeric. Minimum maximum density required to assign a class (default: 0.05).} + +\item{truth_column}{Name for the original class column in the predictions data frame (default: "lymphgen").} + +\item{cohort}{Optional character. Cohort label to annotate predictions.} + +\item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2","Other").} +} +\value{ +A list with: +\item{gaussian_mixture_model}{Fitted \code{MclustDA} model object} +\item{predictions}{Data frame with sample IDs, UMAP coordinates, true labels, predicted classes, and thresholded assignments} +\item{probability_threshold}{Probability threshold used for "Other" assignment} +} +\description{ +Fits a supervised Gaussian mixture model (GMM) to UMAP-projected data for DLBCLone subtypes, excluding samples labeled "Other". +Assigns class predictions and optionally reclassifies samples as "Other" based on probability and density thresholds. +} +\details{ +\itemize{ +\item Uses \code{MclustDA} to fit a supervised mixture model to the UMAP coordinates (V1, V2) and class labels. +\item Predicts class membership and computes per-class densities for each sample. +\item Samples with low maximum probability or density are reclassified as "Other". +\item Returns both raw and thresholded class assignments, respectively under the columns DLBCLone_g and DLBCLone_go. +} +} +\examples{ +result <- DLBCLone_train_mixture_model(umap_out) +head(result$predictions) +} diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd index 3317fd0..3c6d5a9 100644 --- a/man/assemble_genetic_features.Rd +++ b/man/assemble_genetic_features.Rd @@ -17,6 +17,8 @@ assemble_genetic_features( coding_value = 2, include_ashm = TRUE, annotated_sv, + include_GAMBL_sv = TRUE, + review_hotspots = TRUE, verbose = FALSE ) } @@ -33,7 +35,7 @@ assemble_genetic_features( \item{hotspot_genes}{Vector of hotspot genes.} -\item{genome_build}{Genome build to use, default: "grch37".} +\item{genome_build}{Genome build version (default: "grch37").} \item{sv_value}{Value to assign for SV presence (default: 3).} @@ -41,9 +43,13 @@ assemble_genetic_features( \item{coding_value}{Value to assign for coding mutations (default: 2).} -\item{include_ashm}{Logical if TRUE, includes aSHM counts in the feature matrix.} +\item{include_ashm}{Logical; if TRUE, includes aSHM counts in the output matrix (default: TRUE).} -\item{annotated_sv}{Annotated SV data frame.} +\item{annotated_sv}{Data frame with annotated structural variants.} + +\item{include_GAMBL_sv}{Logical; if TRUE, includes GAMBL structural variants in the output matrix (default: TRUE).} + +\item{review_hotspots}{Logical; if TRUE, reviews hotspot genes (default: TRUE).} \item{verbose}{Defaults to FALSE} } diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 4d260ff..f3a9f03 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -8,6 +8,9 @@ make_and_annotate_umap( df, metadata, umap_out, + core_features = NULL, + core_feature_multiplier = 1.5, + hidden_features = NULL, n_neighbors = 55, min_dist = 0, metric = "cosine", @@ -21,7 +24,8 @@ make_and_annotate_umap( target_metric = "euclidean", target_weight = 0.5, calc_dispersion = FALSE, - algorithm = "tumap" + algorithm = "tumap", + make_plot = FALSE ) } \arguments{ @@ -34,6 +38,16 @@ matches the row names of df. This data frame will be joined to the UMAP output.} will use this model to project the data instead of re-running UMAP. This is useful for reproducibility and for using the same UMAP model on different datasets.} +\item{core_features}{A vector of column names in df that should be multiplied by +core_feature_multiplier. These features are considered "core" and will be weighted +more heavily in the UMAP embedding.} + +\item{core_feature_multiplier}{A numeric value that will be used to multiply the +core features specified in core_features. Default is 1.5.} + +\item{hidden_features}{A vector of column names in df that should be dropped from the +UMAP input. These features are considered "hidden" and will not be used in the UMAP embedding.} + \item{n_neighbors}{Passed to UMAP2. The number of neighbors to consider when calculating the UMAP embedding.} \item{min_dist}{Passed to UMAP2. The minimum distance between points in the UMAP embedding.} @@ -44,7 +58,7 @@ for reproducibility and for using the same UMAP model on different datasets.} \item{init}{Passed to UMAP2. The initialization method for the UMAP algorithm.} -\item{ret_model}{If TRUE, the function will return the UMAP model object in addition to the UMAP coordinates.} +\item{ret_model}{additional argument} \item{na_vals}{How to deal with NA values. Two options are "drop", which will remove all columns containing at least one NA or "to_zero", which sets @@ -54,16 +68,17 @@ all NA to zero and leaves the column intact.} \item{seed}{Passed to UMAP2. The random seed for reproducibility.} -\item{target_column}{If provided, the function will run a supervised UMAP -using the specified target column from the metadata. If not provided, the function will run an unsupervised UMAP.} +\item{target_column}{The column in the metadata that contains the target variable for classification.} + +\item{target_metric}{The distance metric to use for calculating distances to the target variable.} + +\item{target_weight}{The weight to assign to the target variable in the UMAP embedding. Default is 0.5.} -\item{target_metric}{The distance metric to use for the supervised UMAP. -Default: "euclidean". Passed to UMAP2.} +\item{calc_dispersion}{If TRUE, calculates the dispersion of the UMAP embedding.} -\item{target_weight}{The weight to assign to the target column in the supervised UMAP. -Default: 0.5. Passed to UMAP2.} +\item{algorithm}{The UMAP algorithm to use. Default is "tumap", which uses the TUMAP implementation.} -\item{calc_dispersion}{If TRUE, calculates the pairwise dispersion of the UMAP coordinates} +\item{make_plot}{If TRUE, creates a plot of the UMAP embedding.} } \description{ Run UMAP and attach result to metadata diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index 70037fc..2dea4be 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plotting.R, R/umap.R +% Please edit documentation in R/plotting.R \name{make_neighborhood_plot} \alias{make_neighborhood_plot} \title{Make Neighborhood Plot} @@ -10,19 +10,7 @@ make_neighborhood_plot( this_sample_id, prediction_in_title = TRUE, add_circle = TRUE, - label_column = "predicted_label_optimized", - point_size = 0.5, - point_alpha = 0.9, - line_alpha = 0.9 -) - -make_neighborhood_plot( - single_sample_prediction_output, - training_predictions, - this_sample_id, - prediction_in_title = TRUE, - add_circle = TRUE, - label_column = "predicted_label_optimized", + label_column = "DLBCLone_io", point_size = 0.5, point_alpha = 0.9, line_alpha = 0.9 @@ -41,35 +29,18 @@ Must include elements \code{prediction} (data frame with prediction results) and \item{add_circle}{Plot will include a circle surrounding the set of neighbors. Set to FALSE to disable.} \item{label_column}{Does nothing, i.e. this is not currently working.} - -\item{point_size}{Numeric. Size of the points in the plot. Default: 0.5.} - -\item{point_alpha}{Numeric. Transparency of the points in the plot. Default: 0.9.} - -\item{line_alpha}{Numeric. Transparency of the lines connecting the sample to its neighbors. Default: 0.9.} } \value{ -A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. - A \code{ggplot2} object representing the UMAP plot with the selected sample and its neighbors highlighted. } \description{ -Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. - Generates a UMAP plot highlighting the neighborhood of a given sample, showing its nearest neighbors and their group assignments. } \details{ -The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. - The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. } \examples{ -# Assuming 'optimization_result' is the output of DLBCLone_optimize_params -# and 'output' is the result of DLBCLone_predict_single_sample -# on sample_id "SAMPLE123": -make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") - # Assuming 'optimization_result' is the output of DLBCLone_optimize_params # and 'output' is the result of DLBCLone_predict_single_sample # on sample_id "SAMPLE123": diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index cd0b1a1..bb7d982 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -1,38 +1,23 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plotting.R, R/umap.R +% Please edit documentation in R/plotting.R \name{make_umap_scatterplot} \alias{make_umap_scatterplot} \title{Make UMAP scatterplot} \usage{ make_umap_scatterplot( df, - title = NULL, drop_composite = TRUE, colour_by = "lymphgen", drop_other = FALSE, high_confidence = FALSE, custom_colours, add_labels = FALSE, - facet = FALSE -) - -make_umap_scatterplot( - df, - title = NULL, - drop_composite = TRUE, - colour_by = "lymphgen", - drop_other = FALSE, - high_confidence = FALSE, - custom_colours, - add_labels = FALSE, - facet = FALSE + title = NULL ) } \arguments{ \item{df}{Data frame containing the UMAP coordinates and annotations.} -\item{title}{Title for the plot. Default: NULL.} - \item{drop_composite}{If TRUE: removes composite labels from the lymphgen column.} \item{colour_by}{Column name to color the points by. Default: "lymphgen".} @@ -45,10 +30,8 @@ make_umap_scatterplot( \item{add_labels}{If TRUE: adds labels to the points based on the median coordinates of each group.} -\item{facet}{If TRUE: truth, predicted, predicted_optimized, ggmarginal not used in order to fit nicely} +\item{title}{Title for the plot. Default: NULL.} } \description{ -Make UMAP scatterplot - Make UMAP scatterplot } diff --git a/man/optimize_outgroup.Rd b/man/optimize_outgroup.Rd index 3b2637a..c442aa9 100644 --- a/man/optimize_outgroup.Rd +++ b/man/optimize_outgroup.Rd @@ -10,7 +10,8 @@ optimize_outgroup( other_score, all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), maximize = "balanced_accuracy", - exclude_other_for_accuracy = FALSE + exclude_other_for_accuracy = FALSE, + verbose = FALSE ) } \arguments{ @@ -31,6 +32,8 @@ Default: "balanced_accuracy"} \item{exclude_other_for_accuracy}{Set to TRUE to exclude the "Other" class from the 'lymphgen' column when calculating accuracy metrics (passed to DLBCLone_optimize_params). Default: FALSE} + +\item{verbose}{Set to TRUE to print additional information during the optimization process.} } \value{ a list of data frames with the predictions and the UMAP input diff --git a/man/optimize_purity.Rd b/man/optimize_purity.Rd new file mode 100644 index 0000000..1dd331b --- /dev/null +++ b/man/optimize_purity.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{optimize_purity} +\alias{optimize_purity} +\title{Optimize Purity Threshold for Classification Assignment} +\usage{ +optimize_purity( + optimized_model_object, + vote_df, + mode, + optimize_by = "balanced_accuracy", + truth_column, + all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), + k, + exclude_other_for_accuracy = FALSE +) +} +\arguments{ +\item{optimized_model_object}{An object containing the optimized model parameters and predictions.} + +\item{vote_df}{Data frame containing the kNN vote results, including columns for sample IDs, predicted labels, and scores.} + +\item{mode}{The mode of operation, which determines the output column names (e.g., "DLBCLone_w").} + +\item{optimize_by}{The metric to optimize by, either "balanced_accuracy"} + +\item{truth_column}{Name of the column in \code{processed_votes} containing the true class labels.} + +\item{all_classes}{Vector of all possible class labels to consider (default: c("MCD", "EZB", "BN2", "N1", "ST2", "Other")).} + +\item{k}{The number of neighbors used in the kNN classification.} + +\item{exclude_other_for_accuracy}{Logical indicating whether to exclude the "Other" class from accuracy calculations (default: FALSE).} +} +\value{ +A list with two elements: \code{best_accuracy} (numeric, the highest accuracy achieved) and \code{best_purity_threshold} (numeric, the threshold at which this accuracy was achieved). +} +\description{ +This function searches for the optimal purity threshold to assign samples to their predicted class or to "Other" based on the score ratio in processed kNN vote results. +It iteratively tests a range of purity thresholds, updating the predicted class if the score ratio meets or exceeds the threshold, and computes the accuracy for each threshold. +The function returns the best accuracy achieved and the corresponding purity threshold. +} +\details{ +\itemize{ +\item For each threshold in the range 0.1 to 0.95 (step 0.05), the function updates the prediction column to assign the class from \code{by_score_opt} if the score ratio meets the threshold, otherwise assigns "Other". +\item Accuracy is computed as the proportion of correct assignments (diagonal of the confusion matrix). +\item The function is intended for use in optimizing classification purity in kNN-based workflows, especially when distinguishing between confident class assignments and ambiguous ("Other") cases. +} +} +\examples{ +# Example usage: +# result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") + +} diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 16b8f75..2e339fb 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -8,10 +8,10 @@ predict_single_sample_DLBCLone( test_df, train_df, train_metadata, - optimize_params, + projection, + optimized_model = NULL, other_df, - ignore_top = TRUE, - truth_classes = c("EZB", "MCD", "ST2", "BN2"), + truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), drop_unlabeled_from_training = TRUE, make_plot = TRUE, annotate_accuracy = FALSE, @@ -30,15 +30,13 @@ predict_single_sample_DLBCLone( \item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} -\item{optimize_params}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +\item{projection}{UMAP projection of the training data. If not provided, the function will run UMAP on the combined data.} + +\item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a previous, Data frame with the best parameters. useful for reproducibility.} \item{other_df}{Data frame containing the predictions for samples in the "Other" class} -\item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with -distance = 0. This is usually only relevant when re-classifying labeled -samples to estimate overall accuracy} - \item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} \item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} @@ -60,7 +58,7 @@ samples to estimate overall accuracy} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} } \value{ -a list of data frames with the predictions, the UMAP input, the UMAP projected output, the model, and a ggplot object +a list of data frames with the predictions, the UMAP input, the model, and a ggplot object } \description{ Predict class for a single sample without using umap_transform and plot result of classification @@ -71,7 +69,8 @@ predict_single_sample_DLBCLone( test_df = test_df, train_df = train_df, train_metadata = train_metadata, - optimize_params = optimize_params, + umap_out = umap_out, + best_params = best_params predictions_df = predictions_df, annotate_accuracy = TRUE ) diff --git a/man/process_votes.Rd b/man/process_votes.Rd new file mode 100644 index 0000000..c8e23df --- /dev/null +++ b/man/process_votes.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{process_votes} +\alias{process_votes} +\title{Process KNN Vote Strings and Scores for Classification} +\usage{ +process_votes( + df, + raw_col = "label", + group_labels = c("EZB", "MCD", "ST2", "BN2", "N1", "Other"), + vote_labels_col = "vote_labels", + k, + other_vote_multiplier = 2, + score_purity_requirement = 1, + weighted_votes_col = "weighted_votes" +) +} +\arguments{ +\item{df}{Data frame containing kNN results, including columns with neighbor labels and weighted votes.} + +\item{raw_col}{Name of the column containing the comma-separated neighbor labels (default: "label").} + +\item{group_labels}{Character vector of all possible class labels to consider (default: c("EZB", "MCD", "ST2", "BN2", "N1", "Other")).} + +\item{vote_labels_col}{Name of the column containing the comma-separated neighbor labels for weighted votes (default: "vote_labels").} + +\item{k}{Number of neighbors used in kNN (required).} + +\item{other_vote_multiplier}{Multiplier for the "Other" class when determining if a sample should be reclassified as "Other" (default: 2).} + +\item{score_purity_requirement}{Minimum ratio of top group score to "Other" score to assign a sample to the top group (default: 1).} + +\item{weighted_votes_col}{Name of the column containing the comma-separated weighted votes (default: "weighted_votes").} +} +\value{ +Data frame with additional columns for per-class neighbor counts, scores, top group assignments, and summary statistics for each sample. +} +\description{ +This function processes the raw neighbor label strings and weighted vote scores from k-nearest neighbor (KNN) classification results. +It computes per-class neighbor counts, weighted scores, and identifies the top group by count and score for each sample. +The function also supports custom logic for handling the "Other" class, including vote multipliers and purity requirements. +} +\details{ +\itemize{ +\item Computes the number of neighbors for each class and the sum of weighted votes per class. +\item Identifies the top group by count and by weighted score, and applies custom logic for the "Other" class if present. +\item Adds columns for counts, scores, top group, top group score, score ratios, and optimized group assignments. +\item Designed for downstream use in DLBCLone and similar kNN-based classification workflows. +} +} +\examples{ +# Example usage: +# result <- process_votes(knn_output_df, k = 7) + +} diff --git a/man/summarize_all_ssm_status.Rd b/man/summarize_all_ssm_status.Rd index 2203ca8..ec7b844 100644 --- a/man/summarize_all_ssm_status.Rd +++ b/man/summarize_all_ssm_status.Rd @@ -11,8 +11,6 @@ summarize_all_ssm_status( synon_genes, silent_maf_df, separate_by_class_genes = NULL, - hotspot_genes = NULL, - annotated_sv, count_hits = FALSE ) } @@ -40,10 +38,6 @@ the output of get_coding_ssm and get_ssm_by_region or get_ssm_by_gene} gene symbols for which mutations should be separated by class (e.g., "Nonsense_Mutation", "Missense_Mutation").} -\item{hotspot_genes}{Vector of hotspot genes.} - -\item{annotated_sv}{SV from provided annotated SV data frame} - \item{count_hits}{Logical; if \code{TRUE}, counts the number of mutations per gene per sample. If \code{FALSE} (default), only presence/absence is recorded.} } diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index 29da3e3..1ac48a0 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -14,7 +14,6 @@ weighted_knn_predict_with_conf( na_label = "Other", verbose = FALSE, use_weights = TRUE, - ignore_top = FALSE, track_neighbors = TRUE, separate_other = TRUE, max_neighbors = 500 @@ -46,10 +45,6 @@ Default: Other} \item{use_weights}{Set to FALSE for all neigbors to have equal weight when calculating the confidence} -\item{ignore_top}{Set to TRUE to avoid considering a nearest neighbor with -distance = 0. This is usually only relevant when re-classifying labeled -samples to estimate overall accuracy} - \item{track_neighbors}{Set to TRUE to include details for the nearest neighbors of each sample} \item{separate_other}{Set to TRUE to treat the "Other" class separately From 0615bfcc9acb8c5cab670077ccf5cb392c4daf9b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 1 Aug 2025 13:30:40 -0700 Subject: [PATCH 59/79] #-out-ed projection from predict_single_sample --- R/umap.R | 62 +++++++++++++-------------- man/predict_single_sample_DLBCLone.Rd | 5 +-- 2 files changed, 33 insertions(+), 34 deletions(-) diff --git a/R/umap.R b/R/umap.R index 916f69f..d954e68 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1922,7 +1922,7 @@ predict_single_sample_DLBCLone <- function( test_df, train_df, train_metadata, - projection, + #projection, optimized_model = NULL, other_df, truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), @@ -1952,28 +1952,28 @@ predict_single_sample_DLBCLone <- function( combined_df <- bind_rows(train_df, test_df) - if(missing(projection)){ - projection <- make_and_annotate_umap( - df = combined_df, - umap_out = optimized_model, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = optimized_model$best_params$na_option - ) - }else{ - message("Using provided projection for prediction") - } +# if(missing(projection)){ + # projection <- make_and_annotate_umap( + # df = combined_df, + # umap_out = optimized_model, + # ret_model = FALSE, +# seed = seed, + # join_column = "sample_id", + # na_vals = optimized_model$best_params$na_option + # ) +# }else{ + # message("Using provided projection for prediction") +# } train_coords = dplyr::filter( - projection$df, + optimized_model$df, sample_id %in% train_id ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") train_df_proj = dplyr::filter( - projection$df, + optimized_model$df, sample_id %in% train_id ) %>% select(sample_id,V1,V2) %>% @@ -1989,18 +1989,18 @@ predict_single_sample_DLBCLone <- function( train_labels = train_df_proj %>% pull(lymphgen) - #Obtain UMAP coordinates for the test sample(s) - print(paste("num rows:",nrow(test_df))) - test_projection <- make_and_annotate_umap( - df = test_df, - umap_out = optimized_model, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = optimized_model$best_params$na_option - ) +# #Obtain UMAP coordinates for the test sample(s) + # print(paste("num rows:",nrow(test_df))) +# test_projection <- make_and_annotate_umap( + # df = test_df, + # umap_out = optimized_model, + # ret_model = FALSE, +# seed = seed, + # join_column = "sample_id", + # na_vals = optimized_model$best_params$na_option +# ) test_coords = dplyr::filter( - test_projection$df, + optimized_model$df, sample_id %in% test_id ) %>% select(sample_id,V1,V2) %>% @@ -2057,7 +2057,7 @@ predict_single_sample_DLBCLone <- function( ) } - anno_umap = select(test_projection$df, sample_id, V1, V2) + anno_umap = select(optimized_model$df, sample_id, V1, V2) %>% filter(sample_id %in% test_id) anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% mutate(label = paste(sample_id,predicted_label,round(confidence,3))) @@ -2069,14 +2069,14 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) if(predict_training){ - predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + predictions_train_df = left_join(train_pred, optimized_model$df, by = "sample_id") }else{ - predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% + predictions_train_df = filter(optimized_model$df, sample_id %in% train_id) %>% select(sample_id, V1, V2) } #This had assumed that projection$df contains the test samples! #predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") - predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") + predictions_test_df = left_join(test_pred, optimized_model$df, by = "sample_id") predictions_df = bind_rows( predictions_train_df %>% select(sample_id, V1, V2), @@ -2105,7 +2105,7 @@ predict_single_sample_DLBCLone <- function( } to_return = list( prediction = predictions_test_df, - projection = projection$df, + projection = optimized_model$df, umap_input = optimized_model$features, model=optimized_model$model, df = predictions_df, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 2e339fb..f7d720b 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -8,7 +8,6 @@ predict_single_sample_DLBCLone( test_df, train_df, train_metadata, - projection, optimized_model = NULL, other_df, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), @@ -30,8 +29,6 @@ predict_single_sample_DLBCLone( \item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} -\item{projection}{UMAP projection of the training data. If not provided, the function will run UMAP on the combined data.} - \item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a previous, Data frame with the best parameters. useful for reproducibility.} @@ -56,6 +53,8 @@ previous, Data frame with the best parameters. useful for reproducibility.} \item{seed}{Random seed for reproducibility} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} + +\item{projection}{UMAP projection of the training data. If not provided, the function will run UMAP on the combined data.} } \value{ a list of data frames with the predictions, the UMAP input, the model, and a ggplot object From 277fbe21a505d78266fbd500d4f4b1b8e63e57dd Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Wed, 6 Aug 2025 22:38:32 -0700 Subject: [PATCH 60/79] further code clean up, and removing colname sample_id as input to predict_single_sample function --- R/umap.R | 53 ++++++++++++----------------------- man/make_and_annotate_umap.Rd | 1 - 2 files changed, 18 insertions(+), 36 deletions(-) diff --git a/R/umap.R b/R/umap.R index d954e68..c731ba8 100644 --- a/R/umap.R +++ b/R/umap.R @@ -57,6 +57,7 @@ #' #' @import dplyr tidyr tibble #' @export +#' summarize_all_ssm_status <- function( maf_df, these_samples_metadata, @@ -174,6 +175,7 @@ summarize_all_ssm_status <- function( #' #' @return Matrix of assembled features for each sample. #' @export +#' assemble_genetic_features <- function( these_samples_metadata, metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), @@ -669,7 +671,6 @@ DLBCLone_train_test_plot = function(test_df, #' ret_model=T, #' metric="cosine") #' -#' make_and_annotate_umap = function( df, metadata, @@ -926,6 +927,7 @@ make_and_annotate_umap = function( #' # result <- process_votes(knn_output_df, k = 7) #' #' @export +#' process_votes <- function( df, raw_col = "label", @@ -1071,6 +1073,7 @@ process_votes <- function( #' # result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") #' #' @export +#' optimize_purity <- function( optimized_model_object, vote_df, @@ -1502,7 +1505,7 @@ DLBCLone_optimize_params = function( }else{ this_accuracy = overall_accuracy } - #print(paste("accuracy:",this_accuracy, "out_opt_acc",out_opt_acc)) + if(out_opt_acc > this_accuracy){ this_accuracy = out_opt_acc } @@ -1940,17 +1943,15 @@ predict_single_sample_DLBCLone <- function( warning("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } - train_df = train_df %>% - column_to_rownames("sample_id") %>% - rownames_to_column("sample_id") - train_id <- train_df$sample_id + train_id <- train_df %>% + rownames_to_column("sample_id") %>% + pull(sample_id) - test_df = test_df %>% - column_to_rownames("sample_id") %>% - rownames_to_column("sample_id") - test_id <- test_df$sample_id + test_id <- test_df %>% + rownames_to_column("sample_id") %>% + pull(sample_id) - combined_df <- bind_rows(train_df, test_df) +# combined_df <- bind_rows(train_df, test_df) # if(missing(projection)){ # projection <- make_and_annotate_umap( @@ -2005,21 +2006,6 @@ predict_single_sample_DLBCLone <- function( ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") - - predict_training = FALSE - if(predict_training){ - train_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = train_coords, # <- predicitng training on self - k = optimized_model$best_params$k, - conf_threshold = optimized_model$best_params$threshold, - na_label = "Other", - use_weights = optimized_model$best_params$use_weights - ) - - train_pred = rownames_to_column(train_pred, var = "sample_id") - } test_pred = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -2068,14 +2054,10 @@ predict_single_sample_DLBCLone <- function( V2 = as.numeric(V2), label = as.character(label) ) - if(predict_training){ - predictions_train_df = left_join(train_pred, optimized_model$df, by = "sample_id") - }else{ - predictions_train_df = filter(optimized_model$df, sample_id %in% train_id) %>% - select(sample_id, V1, V2) - } - #This had assumed that projection$df contains the test samples! - #predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + + predictions_train_df = filter(optimized_model$df, sample_id %in% train_id) %>% + select(sample_id, V1, V2) + predictions_test_df = left_join(test_pred, optimized_model$df, by = "sample_id") predictions_df = bind_rows( @@ -2323,7 +2305,7 @@ DLBCLone_train_mixture_model = function( probability_threshold = probability_threshold ) -return(to_return) + return(to_return) } #' Predict DLBCLone Class Membership Using a Trained Gaussian Mixture Model @@ -2357,6 +2339,7 @@ return(to_return) #' #' @import mclust #' @export +#' DLBCLone_predict_mixture_model = function( model, umap_out, diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index f3a9f03..8de1b83 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -95,5 +95,4 @@ umap_outs = make_and_annotate_umap(df=gambl_train_lymphgen, ret_model=T, metric="cosine") - } From 349d118765ca8010b9ed3475daf0f1bc15277349 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 7 Aug 2025 10:53:45 -0700 Subject: [PATCH 61/79] return of test_projection to predict_single_sample --- R/umap.R | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/R/umap.R b/R/umap.R index c731ba8..ae24cbc 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1990,18 +1990,19 @@ predict_single_sample_DLBCLone <- function( train_labels = train_df_proj %>% pull(lymphgen) -# #Obtain UMAP coordinates for the test sample(s) - # print(paste("num rows:",nrow(test_df))) -# test_projection <- make_and_annotate_umap( - # df = test_df, - # umap_out = optimized_model, - # ret_model = FALSE, -# seed = seed, - # join_column = "sample_id", - # na_vals = optimized_model$best_params$na_option -# ) + #Obtain UMAP coordinates for the test sample(s) + print(paste("num rows:",nrow(test_df))) + test_projection <- make_and_annotate_umap( + df = test_df, + umap_out = optimized_model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = optimized_model$best_params$na_option + ) + test_coords = dplyr::filter( - optimized_model$df, + test_projection$df, sample_id %in% test_id ) %>% select(sample_id,V1,V2) %>% @@ -2043,7 +2044,7 @@ predict_single_sample_DLBCLone <- function( ) } - anno_umap = select(optimized_model$df, sample_id, V1, V2) %>% filter(sample_id %in% test_id) + anno_umap = select(test_projection$df, sample_id, V1, V2) anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% mutate(label = paste(sample_id,predicted_label,round(confidence,3))) @@ -2058,7 +2059,7 @@ predict_single_sample_DLBCLone <- function( predictions_train_df = filter(optimized_model$df, sample_id %in% train_id) %>% select(sample_id, V1, V2) - predictions_test_df = left_join(test_pred, optimized_model$df, by = "sample_id") + predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") predictions_df = bind_rows( predictions_train_df %>% select(sample_id, V1, V2), From c6200edcb6d8426022182bd0833f04418b535f11 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 7 Aug 2025 15:30:45 -0700 Subject: [PATCH 62/79] train_df removed, stop dependency added, all to predicit_single_sample --- R/umap.R | 19 +++++++++++-------- man/predict_single_sample_DLBCLone.Rd | 5 ++--- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/R/umap.R b/R/umap.R index ae24cbc..0fc72a2 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1923,7 +1923,7 @@ weighted_knn_predict_with_conf <- function( #' predict_single_sample_DLBCLone <- function( test_df, - train_df, + #train_df, train_metadata, #projection, optimized_model = NULL, @@ -1940,12 +1940,12 @@ predict_single_sample_DLBCLone <- function( max_neighbors = 500 ){ if(is.null(optimized_model)){ - warning("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") + stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } - train_id <- train_df %>% - rownames_to_column("sample_id") %>% - pull(sample_id) +# train_id <- train_df %>% + # rownames_to_column("sample_id") %>% + # pull(sample_id) test_id <- test_df %>% rownames_to_column("sample_id") %>% @@ -1968,14 +1968,14 @@ predict_single_sample_DLBCLone <- function( train_coords = dplyr::filter( optimized_model$df, - sample_id %in% train_id + !(sample_id %in% test_id) ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") train_df_proj = dplyr::filter( optimized_model$df, - sample_id %in% train_id + !(sample_id %in% test_id) ) %>% select(sample_id,V1,V2) %>% left_join( #Join to the incoming metadata rather than trusting the metadata in the projection @@ -2056,7 +2056,10 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) - predictions_train_df = filter(optimized_model$df, sample_id %in% train_id) %>% + predictions_train_df = dplyr::filter( + optimized_model$df, + !(sample_id %in% test_id) + ) %>% select(sample_id, V1, V2) predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index f7d720b..d0da829 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -6,7 +6,6 @@ \usage{ predict_single_sample_DLBCLone( test_df, - train_df, train_metadata, optimized_model = NULL, other_df, @@ -25,8 +24,6 @@ predict_single_sample_DLBCLone( \arguments{ \item{test_df}{Data frame containing the mutation status of the test sample} -\item{train_df}{Data frame containing the mutation status of the training samples} - \item{train_metadata}{Metadata for training samples with truth labels in lymphgen column} \item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a @@ -54,6 +51,8 @@ previous, Data frame with the best parameters. useful for reproducibility.} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} +\item{train_df}{Data frame containing the mutation status of the training samples} + \item{projection}{UMAP projection of the training data. If not provided, the function will run UMAP on the combined data.} } \value{ From a0a5a92842e138227b9ad1b2d165d3848c1efe45 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 7 Aug 2025 15:35:20 -0700 Subject: [PATCH 63/79] removing redundant comments --- R/umap.R | 26 ++------------------------ man/predict_single_sample_DLBCLone.Rd | 4 ---- 2 files changed, 2 insertions(+), 28 deletions(-) diff --git a/R/umap.R b/R/umap.R index 0fc72a2..0272c73 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1889,9 +1889,7 @@ weighted_knn_predict_with_conf <- function( #' Predict class for a single sample without using umap_transform and plot result of classification #' #' @param test_df Data frame containing the mutation status of the test sample -#' @param train_df Data frame containing the mutation status of the training samples #' @param train_metadata Metadata for training samples with truth labels in lymphgen column -#' @param projection UMAP projection of the training data. If not provided, the function will run UMAP on the combined data. #' @param optimized_model list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a #' previous, Data frame with the best parameters. useful for reproducibility. #' @param other_df Data frame containing the predictions for samples in the "Other" class @@ -1923,9 +1921,7 @@ weighted_knn_predict_with_conf <- function( #' predict_single_sample_DLBCLone <- function( test_df, - #train_df, train_metadata, - #projection, optimized_model = NULL, other_df, truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), @@ -1939,33 +1935,15 @@ predict_single_sample_DLBCLone <- function( seed = 12345, max_neighbors = 500 ){ + if(is.null(optimized_model)){ stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } -# train_id <- train_df %>% - # rownames_to_column("sample_id") %>% - # pull(sample_id) - test_id <- test_df %>% rownames_to_column("sample_id") %>% pull(sample_id) -# combined_df <- bind_rows(train_df, test_df) - -# if(missing(projection)){ - # projection <- make_and_annotate_umap( - # df = combined_df, - # umap_out = optimized_model, - # ret_model = FALSE, -# seed = seed, - # join_column = "sample_id", - # na_vals = optimized_model$best_params$na_option - # ) -# }else{ - # message("Using provided projection for prediction") -# } - train_coords = dplyr::filter( optimized_model$df, !(sample_id %in% test_id) @@ -2020,7 +1998,7 @@ predict_single_sample_DLBCLone <- function( ) test_pred = rownames_to_column(test_pred, var = "sample_id") - #test_pred = bind_cols(test_pred, test_coords) + if(!is.null(optimized_model)){ test_pred = mutate( test_pred, diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index d0da829..a6e2f16 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -50,10 +50,6 @@ previous, Data frame with the best parameters. useful for reproducibility.} \item{seed}{Random seed for reproducibility} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} - -\item{train_df}{Data frame containing the mutation status of the training samples} - -\item{projection}{UMAP projection of the training data. If not provided, the function will run UMAP on the combined data.} } \value{ a list of data frames with the predictions, the UMAP input, the model, and a ggplot object From 778d07cc0d5369cfa6bc9cdd12a87f2a48c13818 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 7 Aug 2025 15:43:30 -0700 Subject: [PATCH 64/79] documentation update, and updated example provided to predict_single_sample --- R/umap.R | 28 ++++------------------ man/predict_single_sample_DLBCLone.Rd | 34 +++------------------------ 2 files changed, 7 insertions(+), 55 deletions(-) diff --git a/R/umap.R b/R/umap.R index 0272c73..528343a 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1892,15 +1892,7 @@ weighted_knn_predict_with_conf <- function( #' @param train_metadata Metadata for training samples with truth labels in lymphgen column #' @param optimized_model list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a #' previous, Data frame with the best parameters. useful for reproducibility. -#' @param other_df Data frame containing the predictions for samples in the "Other" class #' @param truth_classes Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2") -#' @param drop_unlabeled_from_training Set to TRUE to drop unlabeled samples from the training data -#' @param make_plot Set to TRUE to plot the UMAP projection and predictions -#' @param annotate_accuracy Set to true to add labels with accuracy values -#' @param label_offset Length of the label offset for the accuracy labels -#' @param title1 additional argument -#' @param title2 additional argument -#' @param title3 additional argument #' @param seed Random seed for reproducibility #' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' @@ -1909,33 +1901,21 @@ weighted_knn_predict_with_conf <- function( #' #' @examples #' predict_single_sample_DLBCLone( -#' seed = 1234, #' test_df = test_df, -#' train_df = train_df, -#' train_metadata = train_metadata, -#' umap_out = umap_out, -#' best_params = best_params -#' predictions_df = predictions_df, -#' annotate_accuracy = TRUE +#' train_metadata = dlbcl_meta_clean, +#' optimized_model = weighted_lyseq_opt, +#' seed = 1234 #' ) #' predict_single_sample_DLBCLone <- function( test_df, train_metadata, optimized_model = NULL, - other_df, truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE, - annotate_accuracy = FALSE, - label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other", seed = 12345, max_neighbors = 500 ){ - + if(is.null(optimized_model)){ stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index a6e2f16..ff07c73 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -8,15 +8,7 @@ predict_single_sample_DLBCLone( test_df, train_metadata, optimized_model = NULL, - other_df, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), - drop_unlabeled_from_training = TRUE, - make_plot = TRUE, - annotate_accuracy = FALSE, - label_offset = 2, - title1 = "GAMBL", - title2 = "predicted_class_for_HighConf", - title3 = "predicted_class_for_Other", seed = 12345, max_neighbors = 500 ) @@ -29,24 +21,8 @@ predict_single_sample_DLBCLone( \item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a previous, Data frame with the best parameters. useful for reproducibility.} -\item{other_df}{Data frame containing the predictions for samples in the "Other" class} - \item{truth_classes}{Vector of classes to use for training and testing. Default: c("EZB","MCD","ST2","N1","BN2")} -\item{drop_unlabeled_from_training}{Set to TRUE to drop unlabeled samples from the training data} - -\item{make_plot}{Set to TRUE to plot the UMAP projection and predictions} - -\item{annotate_accuracy}{Set to true to add labels with accuracy values} - -\item{label_offset}{Length of the label offset for the accuracy labels} - -\item{title1}{additional argument} - -\item{title2}{additional argument} - -\item{title3}{additional argument} - \item{seed}{Random seed for reproducibility} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} @@ -59,14 +35,10 @@ Predict class for a single sample without using umap_transform and plot result o } \examples{ predict_single_sample_DLBCLone( - seed = 1234, test_df = test_df, - train_df = train_df, - train_metadata = train_metadata, - umap_out = umap_out, - best_params = best_params - predictions_df = predictions_df, - annotate_accuracy = TRUE + train_metadata = dlbcl_meta_clean, + optimized_model = weighted_lyseq_opt, + seed = 1234 ) } From 6e88c0ef746176b338fd2a782df8573bf1b44421 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 11 Aug 2025 11:38:34 -0700 Subject: [PATCH 65/79] current working state --- R/umap.R | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/R/umap.R b/R/umap.R index 528343a..056f0e5 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1915,7 +1915,6 @@ predict_single_sample_DLBCLone <- function( seed = 12345, max_neighbors = 500 ){ - if(is.null(optimized_model)){ stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } @@ -1978,7 +1977,6 @@ predict_single_sample_DLBCLone <- function( ) test_pred = rownames_to_column(test_pred, var = "sample_id") - if(!is.null(optimized_model)){ test_pred = mutate( test_pred, @@ -2014,7 +2012,7 @@ predict_single_sample_DLBCLone <- function( label = as.character(label) ) - predictions_train_df = dplyr::filter( + predictions_train_df = filter( optimized_model$df, !(sample_id %in% test_id) ) %>% @@ -2103,8 +2101,8 @@ DLBCLone_save_optimized = function( out_pred = paste0(prefix,"_optimized_pred.tsv") write_tsv(optimized_params$predictions,file=out_pred) - out_classes = paste0(prefix,"_classes.txt") - write.table(optimized_params$truth_classes,file=out_classes,quote=F,row.names=F) + out_classes = paste0(prefix,"_classes.rds") + saveRDS(optimized_params$truth_classes,file=out_classes) } #' load previously saved DLBCLone model and parameters @@ -2135,7 +2133,7 @@ DLBCLone_load_optimized <- function( # set sample_id to rownames load_mut = paste0(prefix,"_mutation_status_df.tsv") load_meta = paste0(prefix,"_metadata.tsv") - load_classes = paste0(prefix,"_classes.txt") + load_classes = paste0(prefix,"_classes.rds") load_param = paste0(prefix,"_optimized_best_params.rds") load_model = paste0(prefix,"_optimized_uwot.rds") load_pred = paste0(prefix,"_optimized_pred.tsv") @@ -2151,14 +2149,14 @@ DLBCLone_load_optimized <- function( # set sample_id to rownames mut_df <- read_tsv(load_mut) metadata <- read_tsv(load_meta) %>% mutate(lymphgen = as.factor(lymphgen)) - classes <- read.table(load_classes) + classes <- readRDS(load_classes) best_params <- readRDS(load_param) uwot_model <- load_uwot(load_model) predictions <- read_tsv(load_pred) return(list( - df = mut_df, - metadata = metadata, + features = mut_df, + df = metadata, truth_classes = classes, best_params = best_params, model = uwot_model, From 822293c9b0b8be794b931bd855686494a7ceef4a Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 11 Aug 2025 16:28:11 -0700 Subject: [PATCH 66/79] restricting V1 and V2 to 9 decimals, optimized_params$ features now an .rds for reproducibility, , , and also added to DLBCLone_save_optimized for reproducibility + running without error when running predict_single_sample --- R/umap.R | 44 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 35 insertions(+), 9 deletions(-) diff --git a/R/umap.R b/R/umap.R index 056f0e5..a9b98e2 100644 --- a/R/umap.R +++ b/R/umap.R @@ -876,6 +876,13 @@ make_and_annotate_umap = function( if(!missing(metadata)){ umap_df = left_join(umap_df,metadata,by=join_column) } + + umap_df = umap_df %>% + mutate( + V1 = round(V1, 9), + V2 = round(V2, 9) + ) + results = list() results[["df"]]=umap_df results[["features"]] = df @@ -1033,12 +1040,13 @@ process_votes <- function( mutate(by_vote = top_group_count) %>% mutate(by_vote_opt = ifelse(top_group_count * other_vote_multiplier > Other_count, top_group, "Other")) } - + df_out = mutate( df_out, by_score = top_score_group, score_ratio = top_group_score / Other_score, - by_score_opt=ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh,top_score_group, + by_score_opt=ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh, + top_score_group, "Other" ) ) @@ -2086,8 +2094,8 @@ DLBCLone_save_optimized = function( # all files will be in path and start with name_prefix prefix = paste0(path,"/",name_prefix) - out_mut = paste0(prefix,"_mutation_status_df.tsv") - write_tsv(optimized_params$features,file=out_mut) + out_mut = paste0(prefix,"_mutation_status_df.rds") + saveRDS(optimized_params$features,file=out_mut) out_meta = paste0(prefix,"_metadata.tsv") write_tsv(optimized_params$df,file=out_meta) @@ -2103,6 +2111,15 @@ DLBCLone_save_optimized = function( out_classes = paste0(prefix,"_classes.rds") saveRDS(optimized_params$truth_classes,file=out_classes) + + out_k_w = paste0(prefix,"_optimized_k_w.rds") + saveRDS(optimized_params$k_DLBCLone_w,file=out_k_w) + + purity_w = paste0(prefix,"_purity_w.rds") + saveRDS(optimized_params$purity_DLBCLone_w,file=purity_w) + + score_thresh_w = paste0(prefix,"_score_thresh_w.rds") + saveRDS(optimized_params$score_thresh_DLBCLone_w,file=score_thresh_w) } #' load previously saved DLBCLone model and parameters @@ -2124,21 +2141,24 @@ DLBCLone_save_optimized = function( #' name_prefix="test_A" #' ) #' -DLBCLone_load_optimized <- function( # set sample_id to rownames +DLBCLone_load_optimized <- function( path="models/", name_prefix="test" ){ #all files will be in path and start with name_prefix prefix = paste0(path,"/",name_prefix) - load_mut = paste0(prefix,"_mutation_status_df.tsv") + load_mut = paste0(prefix,"_mutation_status_df.rds") load_meta = paste0(prefix,"_metadata.tsv") load_classes = paste0(prefix,"_classes.rds") load_param = paste0(prefix,"_optimized_best_params.rds") load_model = paste0(prefix,"_optimized_uwot.rds") load_pred = paste0(prefix,"_optimized_pred.tsv") + load_k_w = paste0(prefix,"_optimized_k_w.rds") + load_purity_w = paste0(prefix,"_purity_w.rds") + load_score_thresh_w = paste0(prefix,"_score_thresh_w.rds") - required_files <- c(load_mut, load_meta, load_classes, load_param, load_model, load_pred) + required_files <- c(load_mut, load_meta, load_classes, load_param, load_model, load_pred, load_k_w, load_purity_w, load_score_thresh_w) # Check existence missing_files <- required_files[!file.exists(required_files)] @@ -2146,13 +2166,16 @@ DLBCLone_load_optimized <- function( # set sample_id to rownames stop("The following required files are missing:\n", paste(missing_files, collapse = "\n")) } - mut_df <- read_tsv(load_mut) + mut_df <- readRDS(load_mut) metadata <- read_tsv(load_meta) %>% mutate(lymphgen = as.factor(lymphgen)) classes <- readRDS(load_classes) best_params <- readRDS(load_param) uwot_model <- load_uwot(load_model) predictions <- read_tsv(load_pred) + k_DLBCLone_w <- readRDS(load_k_w) + best_w_purity <- readRDS(load_purity_w) + best_w_score_thresh <- readRDS(load_score_thresh_w) return(list( features = mut_df, @@ -2160,7 +2183,10 @@ DLBCLone_load_optimized <- function( # set sample_id to rownames truth_classes = classes, best_params = best_params, model = uwot_model, - predictions = predictions + predictions = predictions, + k_DLBCLone_w = k_DLBCLone_w, + purity_DLBCLone_w = best_w_purity, + score_thresh_DLBCLone_w = best_w_score_thresh )) } From ccb466514ba0552d7fc42f2d6609ec3d66714277 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 12 Aug 2025 11:13:59 -0700 Subject: [PATCH 67/79] dealing with samples when test_df has zero features --- R/umap.R | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/R/umap.R b/R/umap.R index a9b98e2..30ec988 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1927,6 +1927,10 @@ predict_single_sample_DLBCLone <- function( stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } + bad_samps <- rowSums(test_df) == 0 + bad_test_df <- test_df[bad_samps, ] + test_df <- test_df[!bad_samps, ] + test_id <- test_df %>% rownames_to_column("sample_id") %>% pull(sample_id) @@ -2053,6 +2057,17 @@ predict_single_sample_DLBCLone <- function( ) ) } + + predictions_test_df = predictions_test_df %>% + bind_rows( + bad_test_df %>% + rownames_to_column("sample_id") %>% + mutate( + predicted_label = "Other", + confidence = 1 + ) + ) + to_return = list( prediction = predictions_test_df, projection = optimized_model$df, From a1ddec3d1190697f2136452c84a801c66a559395 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 12 Aug 2025 13:40:12 -0700 Subject: [PATCH 68/79] removing redundant code from predict_single_sample, train data can now be used as test_df for training puposes reproducibly --- R/umap.R | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/R/umap.R b/R/umap.R index 30ec988..3fed3db 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1936,15 +1936,13 @@ predict_single_sample_DLBCLone <- function( pull(sample_id) train_coords = dplyr::filter( - optimized_model$df, - !(sample_id %in% test_id) + optimized_model$df ) %>% select(sample_id,V1,V2) %>% column_to_rownames("sample_id") train_df_proj = dplyr::filter( - optimized_model$df, - !(sample_id %in% test_id) + optimized_model$df ) %>% select(sample_id,V1,V2) %>% left_join( #Join to the incoming metadata rather than trusting the metadata in the projection @@ -2025,8 +2023,7 @@ predict_single_sample_DLBCLone <- function( ) predictions_train_df = filter( - optimized_model$df, - !(sample_id %in% test_id) + optimized_model$df ) %>% select(sample_id, V1, V2) From 2562e1d4501c3dc3b66977421926aa4824605997 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 12 Aug 2025 23:11:24 -0700 Subject: [PATCH 69/79] fixing how is handled in predict_single_sample_DLBCLone --- .RData | Bin 0 -> 2595 bytes .Rhistory | 5 +++++ R/umap.R | 5 +++-- 3 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 .RData create mode 100644 .Rhistory diff --git a/.RData b/.RData new file mode 100644 index 0000000000000000000000000000000000000000..fff6b6dfe11d98a5289473f1d5ce5d44d3d4b56b GIT binary patch literal 2595 zcmV+;3f%P{iwFP!000000|5*Q^Yv0hSThP(3IG5A0{{d80{{aB000001yxi=EjR!G z1Ofm60096500{s901PftVQyq^Z7y?VWn=&V01W^D0&)NVD5C%X0Tl-)$0^<7_EBS@ zi9+t*7Q$4HBfx-Qex>h~@wCN{A1JiLELrL(c@)V?ttv+yf*tOB{d$K|0i*B*I&+24RPM^(e&b~-q&BC~5NA}^uj%~K*Gc&6< z%`xwVfF%XAe)kJ*uO~Z>E*$w_o#ccqltdtcvQJij{gRi*d8O7z+kw~wP$|M@5vURh zZ7G6wb$5UtrOtIk0G{kZsvDbOZ+^xy;cw4N?0E}GGSbUczgvDia>h_vOXlYezkEgu zv-dRpb2MUb9&-2x<&w7EI34PI;pnl0f%`8>q)ZkrfrWU z9AQb{_!rdzo+PD|khOV3aTV5TeEQ|P(3i#mOjSKguuK!?k!}VqT#p(#tq#w{9D1P1v9)` z0up|#71h9XCnHDL9f0v~7XFt+>|h+h|4?GzZj~(PGrU8eGu@#Q!sqD2VbY2>jf0|V zMRc}B0Zp^~Hc>c83$XYJA)F=ZehN|X0fL^dW$Ai>3%c-_@A%IF_rdZQ45}w>_Kv80 zVg_Yf?!@#H_v{}~tdEOfy=;#wNNma$LZ>m3+2{)In1^_Ita)Fj{a4I2WcW>)z)fMh z)3WC|+8P#}AZ%d@U^1P2GT*lk$ZC1JROG{?6KasM7cSA}nG|;9%}}o+&MbkUbvQ;-VB;eEK`~SA*U{iqyjWcu z@4xEkYQhX(is2d;XdBLw4{qJ|x+c?#>F+9B>X+8+>xCiOq#UWE*(8@1*43fDYq|R| zhEj3X*hzjlgra`9#N>G{isxt`9{m^=C2lZTy!RgIny#|T(EG=-PnU{NeA;}IfKJmA zg}%>4zy60soQz+nAR6*?uMw`ns(RmZgICNzVubz07vd8W(KY_L9I@KMaGZU#?1$<_ z$gLj|!KlFD7A$*$$7Vrs$hl@f@a?ZXN7C*mGe?*}UJr03Ug)L3c&I|g@F4RZLO{p!B+)gRzewU54m2;E5dNLM(z2A&Y2uJE1DA+PvGRahO6tyg&voD zIS-;V{E>$~R_SmBS)_R?gP8^Gp2j337)HdCO-^(K#IsC3kU7O6QzKq((RNtz;PkHF za)X19Y^eP8x6OKSVkX|&xvOTRvOxS?2-o zvy1wk~>Qb5yxKx*Wp9YYj3htrK#HVq6$O|NIg!(UR7 z1CVJtmxA3~XrFW8lS*PY>2C!w%k6@WyH{WnQf&3pk4h;Df3jVc1(LSeH<^rHnW18D zx(YZ~?}IPtkB_Gcg=*joH!i-F0T|jj5Xmz zNYG9OP{{$|K}o6ftL)GE1dA^EOyIX0Y4_H*hQi9^VyD-EM`gKeBBQxGdSZF%fDXWh z(wU0Ups(XC;E!Mf4J+EU_~9fXr~!P;GUZ*zP%o_fBHx{9xO%wR4Vhup~r27PqoVH~Cavzlea!Lp{eaMM9{hxPjqsCciX0WMqP<;Ynu#j;eK`I=uq&r~ z@8?z*K%W+7$EA|~Q^BoUefP-6|Bq!O)u->VZHq10_m8z{fr|8a*;P+BPe#ew&FM$m zwZMckxNhBe-l}JSCA^k0u^c1}AL@v-D?)8hUR`9s2&}omtr+VA;#Unj&f)LJ5`?f( z|HD&kOto<_nei%rJ$IZeihgZO<5Hfy0c9ixYN2`X~no_k=e| zG`&?Mu}hhxp60>hb%*a}1}N0LMk{wEYuiB3HuhKpFND7tqi~k@F$a+zw0}p{g)Vl! zOk_qYRq${l*NGF-%`n5_m3ZN-UrH?tHl-23UgIMURJ7aRofBY2i4klT6ls#Zu>4M3 zvq>qAn@yN^N5?qioMwD-sap+ZEGnI~EC?-DW2B^aI?rFWyfn`ckgiKbvw*!42Gj`d zf?P#c$e`U5KZ8{d@U*L6K)vC=zqy=^PS56=O80`f% + rownames_to_column("sample_id") %>% + select(sample_id) test_df <- test_df[!bad_samps, ] test_id <- test_df %>% @@ -2058,7 +2060,6 @@ predict_single_sample_DLBCLone <- function( predictions_test_df = predictions_test_df %>% bind_rows( bad_test_df %>% - rownames_to_column("sample_id") %>% mutate( predicted_label = "Other", confidence = 1 From 0c933667cd45f843856f36b088e4ff3115eb7e6b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Thu, 14 Aug 2025 12:11:19 -0700 Subject: [PATCH 70/79] lukes current edition of nearest_neighbor_heatmap --- NAMESPACE | 5 + R/plotting.R | 243 ++++++++++++++++++++++++++++++++ R/umap.R | 3 +- man/basic_umap_scatterplot.Rd | 43 ++++++ man/nearest_neighbor_heatmap.Rd | 54 +++++++ 5 files changed, 347 insertions(+), 1 deletion(-) create mode 100644 man/basic_umap_scatterplot.Rd create mode 100644 man/nearest_neighbor_heatmap.Rd diff --git a/NAMESPACE b/NAMESPACE index e482049..3b265f0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,6 +8,7 @@ export(DLBCLone_summarize_model) export(DLBCLone_train_mixture_model) export(DLBCLone_train_test_plot) export(assemble_genetic_features) +export(basic_umap_scatterplot) export(classify_bl) export(classify_dlbcl) export(classify_fl) @@ -37,4 +38,8 @@ import(tibble) import(tidyr) import(tidyselect) import(uwot) +importFrom(ComplexHeatmap,Heatmap) +importFrom(circlize,colorRamp2) +importFrom(dplyr,filter) +importFrom(grid,gpar) importFrom(rlang,sym) diff --git a/R/plotting.R b/R/plotting.R index 072b579..16ff14b 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -1,3 +1,246 @@ +#' Heatmap visualization of mutations in nearest neighbors for a sample +#' +#' Generates a heatmap of feature values for the nearest neighbors of a specified sample, +#' based on a DLBCLone model object. This visualization helps to inspect the feature profiles +#' of samples most similar to the query sample. +#' +#' @param this_sample_id Character. The sample ID for which to plot the nearest neighbor heatmap. +#' @param single_sample_prediction_output A list containing the prediction results from predict_single_sample_DLBCLone, +#' including the feature matrix and predictions. +#' @param optimized_model list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +#' previous, Data frame with the best parameters. useful for reproducibility. +#' @param truth_column Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen"). +#' @param pred_column Character. The column name in the predictions data frame that contains the predicted labels. +#' @param clustering_distance Character. The distance metric to use for clustering rows. Default is "binary". +#' @param metadata_cols Optional character vector of additional metadata columns to include in the heatmap annotations. +#' +#' @return A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. +#' +#' @details +#' - For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. +#' - For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. +#' - The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, +#' and plots a heatmap of features with nonzero values. +#' +#' @importFrom dplyr filter +#' @importFrom ComplexHeatmap Heatmap +#' @importFrom grid gpar +#' @importFrom circlize colorRamp2 +#' +#' @examples +#' # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: +#' nearest_neighbor_heatmap(sample_id, model) +#' +nearest_neighbor_heatmap <- function( + this_sample_id, + single_sample_prediction_output, + optimized_model, + truth_column = "lymphgen", + pred_column, + clustering_distance = "binary", + metadata_cols = NULL +){ + + # Gather the base columns + cols_to_select <- c("sample_id", truth_column) + + # Add any non-null metadata columns + extra_cols <- c(pred_column,metadata_cols) + extra_cols <- extra_cols[!is.null(extra_cols)] + cols_to_select <- c(cols_to_select, extra_cols) + + single_sample_prediction_output$prediction <- single_sample_prediction_output$prediction %>% + filter(sample_id %in% this_sample_id) + + test_feats <- single_sample_prediction_output$test_feats %>% + filter(rownames(single_sample_prediction_output$test_feats) %in% this_sample_id) + + predicted_neighbor_ids <- single_sample_prediction_output$prediction %>% + pull(neighbor_id) %>% + strsplit(",") %>% + unlist() + + neighbor_feats <- optimized_model$features %>% + rownames_to_column("sample_id") %>% + filter(sample_id %in% predicted_neighbor_ids) %>% + column_to_rownames("sample_id") + + feats <- bind_rows(test_feats, neighbor_feats) + + top = max(feats) + mid = top/2 + col_fun = circlize::colorRamp2(c(0, mid, top), c("white", "#FFB3B3", "red")) + + row_df = optimized_model$predictions %>% + select(all_of(cols_to_select)) %>% + filter(sample_id %in% rownames(neighbor_feats)) + + if(!this_sample_id %in% row_df$sample_id){ + new_row <- single_sample_prediction_output$prediction %>% + filter(sample_id %in% this_sample_id) %>% + mutate( + !!sym(truth_column) := NA, + !!sym(pred_column) := single_sample_prediction_output$prediction$predicted_label + ) + + # ensure extra metadata columns exist in the new row + for(col in extra_cols){ + if(!col %in% names(new_row)){ + new_row[[col]] <- NA + } + } + + new_row <- new_row %>% + select(all_of(cols_to_select)) + row_df <- bind_rows(row_df, new_row) + + }else{ + # create an NA row with all needed columns + missing_row <- as.data.frame(setNames(rep(NA, length(cols_to_select)), cols_to_select)) + missing_row$sample_id <- this_sample_id + row_df <- bind_rows(row_df, missing_row) + } + + row_df = row_df %>% + column_to_rownames("sample_id") + + anno_colours = get_gambl_colours() + + anno_list <- list() + + anno_list[[truth_column]] <- anno_colours + + for (col in extra_cols) { + anno_list[[col]] <- anno_colours + } + + row_anno = rowAnnotation( + df = row_df[rownames(feats),,drop=FALSE], + col = anno_list, + annotation_name_gp = gpar(fontsize = 12), + show_legend = TRUE + ) + + title_text = paste( + "Sample", + this_sample_id, + "classified as", + single_sample_prediction_output$prediction$predicted_label + ) + + ht <- Heatmap( + feats[,colSums(feats)>0], + col = col_fun, + column_names_gp = gpar(fontsize=12), + right_annotation = row_anno, + clustering_distance_rows = clustering_distance, + show_heatmap_legend = FALSE, + column_title = title_text + ) + + draw( + ht, + heatmap_legend_side = "bottom", + annotation_legend_side = "bottom" + ) +} + +#' Basic UMAP Scatterplot +#' +#' Generates a simple UMAP scatterplot for visualizing sample clustering or separation. +#' +#' @param optimized Data frame containing at least V1, V2, sample_id, and grouping columns. +#' @param plot_samples Optional character vector of sample_ids to annotate. +#' @param colour_by Column name to color points by. Defaults to `truth_column`. +#' @param truth_column Name of the truth/ground-truth column (default: "lymphgen"). +#' @param pred_column Name of the predicted-class column (default: "DLBCLone_ko"). +#' @param other_label Label used for the outgroup/unclassified class (default: "Other"). +#' @param title Plot title. +#' @param use_plotly Logical; if FALSE and `plot_samples` provided, draw static labels. +#' @param custom_colours Optional named vector of colors for groups; falls back to `get_gambl_colours()`. +#' +#' @return A ggplot object. +#' @export +#' +basic_umap_scatterplot <- function( + optimized, + plot_samples = NULL, + colour_by = NULL, + truth_column = "lymphgen", + pred_column = "DLBCLone_ko", + other_label = "Other", + title = "UMAP based on selected features", + use_plotly = TRUE, + custom_colours = NULL +){ + stopifnot(all(c("V1","V2","sample_id") %in% colnames(optimized))) + stopifnot(is.data.frame(optimized)) + stopifnot(all(c("V1", "V2") %in% colnames(optimized))) + message("colour_by: ", colour_by) + colour_by <- colour_by %||% truth_column + + # dynamic label text (e.g., "lymphgen" and "DLBCLone_ko") + optimized_label <- optimized %>% + mutate( + label = paste0( + sample_id,"\n",truth_column, ":\n",.data[[truth_column]]," ",pred_column,":\n",.data[[pred_column]] + ) + ) + + # points to label + label_points <- dplyr::filter(optimized_label, sample_id %in% (plot_samples %||% character())) %>% + mutate(label_x = V1 + 0.75, label_y = V2 - 0.75) + + # base mapping: color by chosen column + aes_cols <- aes( + x = V1, y = V2, + sample_id = sample_id, + color = .data[[colour_by]] + ) + + # draw outgroup first (by truth column) to keep it visually underneath + p <- ggplot() + p <- p + + geom_point( + data = dplyr::filter(optimized, .data[[truth_column]] == other_label), + mapping = aes_cols + ) + + geom_point( + data = dplyr::filter(optimized, .data[[truth_column]] != other_label), + mapping = aes_cols + ) + + # palette + pal <- custom_colours %||% get_gambl_colours() + p <- p + + scale_color_manual(values = pal) + + guides(color = guide_legend(title = if (identical(colour_by, truth_column)) "Original Class" else "Predicted Class")) + + # optional static labels when not using plotly + if (!is.null(plot_samples) && length(plot_samples) > 0 && !isTRUE(use_plotly)) { + p <- p + + geom_segment( + data = label_points, + aes(x = V1, y = V2, xend = label_x, yend = label_y), + arrow = arrow(length = unit(0.02, "npc")), + color = "black" + ) + + geom_label( + data = label_points, + aes(x = label_x, y = label_y, label = label), + size = 3, + fill = "white", + label.size = 0.25 + ) + } + + p <- p + + labs(title = title) + + theme_minimal() + + return(p) +} + #' Summarize and Export DLBCLone Model Results #' #' Generates and saves a set of summary plots and tables for a DLBCLone model, including UMAP scatterplots, alluvial plots, and oncoplots. diff --git a/R/umap.R b/R/umap.R index 664763a..7358777 100644 --- a/R/umap.R +++ b/R/umap.R @@ -2073,7 +2073,8 @@ predict_single_sample_DLBCLone <- function( model=optimized_model$model, df = predictions_df, anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), - anno_out = anno_out + anno_out = anno_out, + test_feats = test_df ) return(to_return) diff --git a/man/basic_umap_scatterplot.Rd b/man/basic_umap_scatterplot.Rd new file mode 100644 index 0000000..a5d507f --- /dev/null +++ b/man/basic_umap_scatterplot.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{basic_umap_scatterplot} +\alias{basic_umap_scatterplot} +\title{Basic UMAP Scatterplot} +\usage{ +basic_umap_scatterplot( + optimized, + plot_samples = NULL, + colour_by = NULL, + truth_column = "lymphgen", + pred_column = "DLBCLone_ko", + other_label = "Other", + title = "UMAP based on selected features", + use_plotly = TRUE, + custom_colours = NULL +) +} +\arguments{ +\item{optimized}{Data frame containing at least V1, V2, sample_id, and grouping columns.} + +\item{plot_samples}{Optional character vector of sample_ids to annotate.} + +\item{colour_by}{Column name to color points by. Defaults to \code{truth_column}.} + +\item{truth_column}{Name of the truth/ground-truth column (default: "lymphgen").} + +\item{pred_column}{Name of the predicted-class column (default: "DLBCLone_ko").} + +\item{other_label}{Label used for the outgroup/unclassified class (default: "Other").} + +\item{title}{Plot title.} + +\item{use_plotly}{Logical; if FALSE and \code{plot_samples} provided, draw static labels.} + +\item{custom_colours}{Optional named vector of colors for groups; falls back to \code{get_gambl_colours()}.} +} +\value{ +A ggplot object. +} +\description{ +Generates a simple UMAP scatterplot for visualizing sample clustering or separation. +} diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd new file mode 100644 index 0000000..5a794a3 --- /dev/null +++ b/man/nearest_neighbor_heatmap.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{nearest_neighbor_heatmap} +\alias{nearest_neighbor_heatmap} +\title{Heatmap visualization of mutations in nearest neighbors for a sample} +\usage{ +nearest_neighbor_heatmap( + this_sample_id, + single_sample_prediction_output, + optimized_model, + truth_column = "lymphgen", + pred_column, + clustering_distance = "binary", + metadata_cols = NULL +) +} +\arguments{ +\item{this_sample_id}{Character. The sample ID for which to plot the nearest neighbor heatmap.} + +\item{single_sample_prediction_output}{A list containing the prediction results from predict_single_sample_DLBCLone, +including the feature matrix and predictions.} + +\item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a +previous, Data frame with the best parameters. useful for reproducibility.} + +\item{truth_column}{Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} + +\item{pred_column}{Character. The column name in the predictions data frame that contains the predicted labels.} + +\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} + +\item{metadata_cols}{Optional character vector of additional metadata columns to include in the heatmap annotations.} +} +\value{ +A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. +} +\description{ +Generates a heatmap of feature values for the nearest neighbors of a specified sample, +based on a DLBCLone model object. This visualization helps to inspect the feature profiles +of samples most similar to the query sample. +} +\details{ +\itemize{ +\item For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. +\item For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. +\item The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, +and plots a heatmap of features with nonzero values. +} +} +\examples{ +# Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: +nearest_neighbor_heatmap(sample_id, model) + +} From 9e2a3c6c3774ae516b2e93ba3d420723d9c8c563 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 15 Aug 2025 11:18:09 -0700 Subject: [PATCH 71/79] adding DLBCLone_KNN functions, also removing unecessary predict_single_sample list output objects and adding necessary objects --- NAMESPACE | 4 +- R/plotting.R | 213 +++++---- R/umap.R | 766 +++++++++++++++++++++++++++++++- man/DLBCLone_KNN.Rd | 91 ++++ man/DLBCLone_KNN_predict.Rd | 61 +++ man/basic_umap_scatterplot.Rd | 43 -- man/nearest_neighbor_heatmap.Rd | 41 +- 7 files changed, 1070 insertions(+), 149 deletions(-) create mode 100644 man/DLBCLone_KNN.Rd create mode 100644 man/DLBCLone_KNN_predict.Rd delete mode 100644 man/basic_umap_scatterplot.Rd diff --git a/NAMESPACE b/NAMESPACE index 3b265f0..be1f67f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,7 @@ # Generated by roxygen2: do not edit by hand +export(DLBCLone_KNN) +export(DLBCLone_KNN_predict) export(DLBCLone_load_optimized) export(DLBCLone_optimize_params) export(DLBCLone_predict_mixture_model) @@ -8,7 +10,6 @@ export(DLBCLone_summarize_model) export(DLBCLone_train_mixture_model) export(DLBCLone_train_test_plot) export(assemble_genetic_features) -export(basic_umap_scatterplot) export(classify_bl) export(classify_dlbcl) export(classify_fl) @@ -27,6 +28,7 @@ export(report_accuracy) export(summarize_all_ssm_status) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) +import(ComplexHeatmap) import(GAMBLR.data) import(GAMBLR.helpers) import(dplyr) diff --git a/R/plotting.R b/R/plotting.R index 16ff14b..9344091 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -149,96 +149,125 @@ nearest_neighbor_heatmap <- function( #' #' Generates a simple UMAP scatterplot for visualizing sample clustering or separation. #' -#' @param optimized Data frame containing at least V1, V2, sample_id, and grouping columns. -#' @param plot_samples Optional character vector of sample_ids to annotate. -#' @param colour_by Column name to color points by. Defaults to `truth_column`. -#' @param truth_column Name of the truth/ground-truth column (default: "lymphgen"). -#' @param pred_column Name of the predicted-class column (default: "DLBCLone_ko"). -#' @param other_label Label used for the outgroup/unclassified class (default: "Other"). -#' @param title Plot title. -#' @param use_plotly Logical; if FALSE and `plot_samples` provided, draw static labels. -#' @param custom_colours Optional named vector of colors for groups; falls back to `get_gambl_colours()`. -#' -#' @return A ggplot object. -#' @export -#' -basic_umap_scatterplot <- function( - optimized, - plot_samples = NULL, - colour_by = NULL, - truth_column = "lymphgen", - pred_column = "DLBCLone_ko", - other_label = "Other", - title = "UMAP based on selected features", - use_plotly = TRUE, - custom_colours = NULL -){ - stopifnot(all(c("V1","V2","sample_id") %in% colnames(optimized))) - stopifnot(is.data.frame(optimized)) - stopifnot(all(c("V1", "V2") %in% colnames(optimized))) - message("colour_by: ", colour_by) - colour_by <- colour_by %||% truth_column - - # dynamic label text (e.g., "lymphgen" and "DLBCLone_ko") - optimized_label <- optimized %>% - mutate( - label = paste0( - sample_id,"\n",truth_column, ":\n",.data[[truth_column]]," ",pred_column,":\n",.data[[pred_column]] - ) - ) - - # points to label - label_points <- dplyr::filter(optimized_label, sample_id %in% (plot_samples %||% character())) %>% - mutate(label_x = V1 + 0.75, label_y = V2 - 0.75) - - # base mapping: color by chosen column - aes_cols <- aes( - x = V1, y = V2, - sample_id = sample_id, - color = .data[[colour_by]] - ) - - # draw outgroup first (by truth column) to keep it visually underneath - p <- ggplot() - p <- p + - geom_point( - data = dplyr::filter(optimized, .data[[truth_column]] == other_label), - mapping = aes_cols - ) + - geom_point( - data = dplyr::filter(optimized, .data[[truth_column]] != other_label), - mapping = aes_cols - ) +#' @param this_sample_id Character. The sample ID for which to plot the nearest neighbor heatmap. +#' @param DLBCLone_model List. A DLBCLone model object, either from \code{DLBCLone_optimize_params} +#' or \code{DLBCLone_KNN} (with \code{predict_unlabeled = TRUE}). +#' +#' @return A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. +#' +#' @details +#' - For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. +#' - For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. +#' - The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, +#' and plots a heatmap of features with nonzero values. +#' +#' @importFrom dplyr filter +#' @import ComplexHeatmap +#' @importFrom grid gpar +#' @importFrom circlize colorRamp2 +#' +#' @examples +#' # Assuming 'predicted_out' is a the output of DLBCLone_KNN_predict +#' and "SomeSample_ID" is a valid sample ID from among the test (not training) samples +#' \dontrun{ +#' nearest_neighbor_heatmap("SomeSample_ID", predicted_out) +#'} +nearest_neighbor_heatmap <- function(this_sample_id, + DLBCLone_model, + truth_column = "lymphgen", + clustering_distance = "binary", + font_size = 14){ + pred_name = NULL + if(!missing(DLBCLone_model) && "type" %in% names(DLBCLone_model)){ + if(DLBCLone_model$type == "DLBCLone_optimize_params"){ + neighbor_df = DLBCLone_model$neighbors + lyseq_status = DLBCLone_model$lyseq_status + }else if(DLBCLone_model$type == "DLBCLone_KNN"){ + if(!"unlabeled_neighbors" %in% names(DLBCLone_model)){ + print(names(DLBCLone_model)) + stop("DLBCLone_model must be the output of DLBCLone_KNN with predict_unlabeled = TRUE") + }else if(is.null(DLBCLone_model$unlabeled_neighbors)){ + #no neighbors found for any of the incoming samples + message("No neighbors found for any sample. Returning NULL.") + return(NULL) + } + neighbor_df = DLBCLone_model$unlabeled_neighbors + neighbor_transpose = filter(neighbor_df,sample_id==this_sample_id) + if(nrow(neighbor_transpose) == 0){ + message("No neighbors found for sample ", this_sample_id, ". Returning NULL.") + return(NULL) + } + neighbor_transpose = neighbor_transpose %>% t() + #deal with fewer than K neighbours + neighbor_transpose = neighbor_transpose[!is.na(neighbor_transpose)] + pred_name = "DLBCLone_ko" + } - # palette - pal <- custom_colours %||% get_gambl_colours() - p <- p + - scale_color_manual(values = pal) + - guides(color = guide_legend(title = if (identical(colour_by, truth_column)) "Original Class" else "Predicted Class")) - - # optional static labels when not using plotly - if (!is.null(plot_samples) && length(plot_samples) > 0 && !isTRUE(use_plotly)) { - p <- p + - geom_segment( - data = label_points, - aes(x = V1, y = V2, xend = label_x, yend = label_y), - arrow = arrow(length = unit(0.02, "npc")), - color = "black" - ) + - geom_label( - data = label_points, - aes(x = label_x, y = label_y, label = label), - size = 3, - fill = "white", - label.size = 0.25 - ) + }else{ + stop("DLBCLone_model must be the output of DLBCLone_optimize_params or DLBCLone_KNN") + } + + xx=DLBCLone_model$features_df[neighbor_transpose,] + if(any(is.na(rownames(xx)))){ + print(neighbor_transpose) + stop("something went wrong. Some samples are missing from features_df") } + top = max(xx) + mid = top/2 + col_fun = circlize::colorRamp2(c(0, mid, top), c("white", "#FFB3B3", "red")) + if(!truth_column %in% colnames(DLBCLone_model$predictions)){ + print(head(DLBCLone_model$predictions)) + stop("missing",truth_column) + } + row_df = select(DLBCLone_model$predictions, sample_id, !!sym(truth_column), !!sym(pred_name)) %>% + filter(sample_id %in% rownames(xx)) + if(!this_sample_id %in% row_df$sample_id){ + if("unlabeled_predictions" %in% names(DLBCLone_model)){ + print("=====") + row_df = bind_rows(row_df, + select(DLBCLone_model$unlabeled_predictions, sample_id, !!sym(truth_column), !!sym(pred_name)) %>% + filter(sample_id %in% rownames(xx)) + ) + print(row_df) + sample_class = filter(DLBCLone_model$unlabeled_predictions, sample_id == this_sample_id) %>% + pull(!!sym(pred_name)) + print(sample_class) + + }else{ + sample_class = NULL + row_df = bind_rows(row_df, + tibble(sample_id = this_sample_id, + !!rlang::sym(truth_column) := NA_character_, + !!rlang::sym(pred_name) := NA_character_)) + } + } + row_df = row_df %>% + column_to_rownames("sample_id") + anno_colours = get_gambl_colours() + anno_list = list() - p <- p + - labs(title = title) + - theme_minimal() + anno_list[[truth_column]] = anno_colours + if (!is.null(pred_name)) { + anno_list[[pred_name]] = anno_colours + } + xx = xx[,colSums(xx)>0,drop=FALSE] + row_anno = rowAnnotation( + df = row_df[rownames(xx),,drop=FALSE], + col = anno_list, + #annotation_name_side = "left", + annotation_name_gp = gpar(fontsize = font_size), + show_legend = FALSE + ) - return(p) + title_text = paste("Sample", this_sample_id, "classified as", sample_class) + Heatmap(xx, + col = col_fun, + right_annotation = row_anno, + clustering_distance_rows = clustering_distance, + show_heatmap_legend = FALSE, + column_title = title_text, + column_title_gp = gpar(fontsize=font_size), + column_names_gp = gpar(fontsize=font_size)) } #' Summarize and Export DLBCLone Model Results @@ -395,17 +424,17 @@ make_neighborhood_plot <- function( }else if(missing(single_sample_prediction_output)){ #Just plot the single sample in the context of the rest based on the optimization single_sample_prediction_output = list() - single_sample_prediction_output[["prediction"]] = filter(training_predictions, sample_id==this_sample_id) + single_sample_prediction_output[["predictions"]] = filter(training_predictions, sample_id==this_sample_id) single_sample_prediction_output[["anno_df"]] = training_predictions }else{ - single_sample_prediction_output$prediction = filter(single_sample_prediction_output$prediction, sample_id==this_sample_id) + single_sample_prediction_output$predictions = filter(single_sample_prediction_output$predictions, sample_id==this_sample_id) } xmin = min(training_predictions$V1, na.rm = TRUE) xmax = max(training_predictions$V1, na.rm = TRUE) ymin = min(training_predictions$V2, na.rm = TRUE) ymax = max(training_predictions$V2, na.rm = TRUE) #extract the sample_id for all the nearest neighbors with non-Other labels - my_neighbours = filter(single_sample_prediction_output$prediction,sample_id == this_sample_id) %>% + my_neighbours = filter(single_sample_prediction_output$predictions,sample_id == this_sample_id) %>% pull(neighbor_id) %>% strsplit(.,",") %>% unlist() #set up links connecting each neighbor to the sample's point @@ -413,9 +442,9 @@ make_neighborhood_plot <- function( my_x = filter(single_sample_prediction_output$anno_df,sample_id==this_sample_id) %>% pull(V1) my_y = filter(single_sample_prediction_output$anno_df,sample_id==this_sample_id) %>% pull(V2) if(prediction_in_title){ - title = paste(this_sample_id,pull(single_sample_prediction_output$prediction,!!sym(label_column))) - if(single_sample_prediction_output$prediction[[label_column]] == "Other" && single_sample_prediction_output$prediction$predicted_label !="Other"){ - title = paste(title,"(",single_sample_prediction_output$prediction$predicted_label,")") + title = paste(this_sample_id,pull(single_sample_prediction_output$predictions,!!sym(label_column))) + if(single_sample_prediction_output$predictions[[label_column]] == "Other" && single_sample_prediction_output$predictions$predicted_label !="Other"){ + title = paste(title,"(",single_sample_prediction_output$predictions$predicted_label,")") } }else{ diff --git a/R/umap.R b/R/umap.R index 7358777..a6b68a3 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1223,6 +1223,744 @@ optimize_purity <- function( return(optimized_model_object) } +#' Predict DLBCLone Classes for New Samples Using a Trained KNN Model +#' +#' Applies a previously optimized DLBCLone KNN model to predict class labels for new (test) samples. +#' This function combines the training and test feature matrices, ensures feature compatibility, and uses the +#' parameters from a DLBCLone KNN optimization run to classify the test samples. Optionally, runs in iterative mode +#' for more stable results when predicting multiple samples. +#' +#' @param train_df Data frame or matrix of features for training samples (rows = samples, columns = features). +#' @param test_df Data frame or matrix of features for test samples to be classified. +#' @param metadata Data frame with metadata for all samples, including at least a \code{sample_id} column. +#' @param core_features Optional character vector of feature names to upweight in the KNN calculation. +#' @param core_feature_multiplier Numeric. Multiplier to apply to core features (default: 1.5). +#' @param hidden_features Optional character vector of feature names to exclude from the analysis. +#' @param DLBCLone_KNN_out List. Output from a previous call to \code{DLBCLone_KNN} containing optimized parameters. (Required) +#' @param mode Character. If \code{"iterative"}, runs KNN prediction for each test sample individually (recommended for stability). +#' +#' @return A list containing the KNN prediction results for the test samples, including predicted class labels and scores. +#' +#' @details +#' - Ensures that the feature columns in \code{train_df} and \code{test_df} are compatible. +#' - If \code{mode = "iterative"}, runs KNN prediction for each test sample one at a time. +#' - Uses the parameters (e.g., k, feature weights) from the provided \code{DLBCLone_KNN_out} object. +#' - Returns the same structure as \code{DLBCLone_KNN}, with predictions for the test samples. +#' +#' @examples +#' # Assuming you have run DLBCLone_KNN to get optimized parameters: +#' # model_out <- DLBCLone_KNN(train_features, train_metadata, ...) +#' # Predict on new samples: +#' predictions <- DLBCLone_KNN_predict( +#' train_df = train_features, +#' test_df = new_samples, +#' metadata = sample_metadata, +#' DLBCLone_KNN_out = model_out +#' +#' @export +#' +DLBCLone_KNN_predict <- function(train_df, + test_df, + metadata, + DLBCLone_KNN_out, + mode = "batch", + truth_column = "lymphgen", + other_class = "Other") { # <--- NEW ARG + if(missing(DLBCLone_KNN_out)){ + stop("DLBCLone_KNN_out must be provided, run DLBCLone_KNN first to get the optimal parameters") + } + nsamp = nrow(test_df) + message(paste0("Running DLBCLone KNN individually on ", nsamp, " samples")) + if(nsamp > 1){ + if(mode != "iterative"){ + warning("Running DLBCLone KNN on multiple samples at once is not recommended as the result may be unstable", + " and may not reflect the true classification of each sample. ", + "Running in iterative mode is recommended for more stable results.") + } + } + + if(any(!colnames(test_df) %in% colnames(train_df))){ + stop("test_df should not contain any features that are not in train_df. ", + "Please check the column names of test_df and train_df.") + } + combined_df = bind_rows(train_df, test_df) + if(any(!colnames(train_df) %in% colnames(test_df))){ + message("filling in missing features in test_df with zeros") + combined_df[is.na(combined_df)] = 0 + } + if(mode == "iterative"){ + predictions_list = list() + for(i in seq_len(nrow(test_df))){ + message("iteration:", i, "of", nrow(test_df)) + combined_df = bind_rows(train_df, test_df[i,]) + model_out = DLBCLone_KNN( + features_df = combined_df, + metadata = metadata, + DLBCLone_KNN_out = DLBCLone_KNN_out, + predict_unlabeled = TRUE, + other_class = other_class # <--- pass it in + ) + predictions_list[[i]] = model_out$unlabeled_predictions + } + all_predictions = do.call("bind_rows", predictions_list) + return(all_predictions) + } else { + model_out = DLBCLone_KNN( + features_df = combined_df, + metadata = metadata, + DLBCLone_KNN_out = DLBCLone_KNN_out, + truth_column = DLBCLone_KNN_out$truth_column, + truth_classes = DLBCLone_KNN_out$truth_classes, + predict_unlabeled = TRUE, + other_class = DLBCLone_KNN_out$other_class + ) + return(model_out) + } +} + + +#' Run DLBCLone KNN Classification +#' +#' Weighted KNN on a feature (mutation) matrix with optional upweighting of +#' user-specified "core" features, optional exclusion of "hidden" features, +#' and optional optimization of an explicit outgroup (e.g. "Other"). +#' +#' This version removes hard-coded LymphGen class names and instead derives the +#' in-group classes and the outgroup column name from the arguments +#' \code{truth_classes} and \code{other_class}. It keeps backward compatibility +#' for the default LymphGen-like usage. +#' +#' @param features_df Numeric matrix/data.frame (rows = samples, cols = features). +#' Row names must be sample IDs. +#' @param metadata Data frame with at least \code{sample_id} and the ground-truth +#' label column given in \code{truth_column}. +#' @param core_features Character vector of feature names to upweight (optional). +#' @param core_feature_multiplier Numeric multiplier for \code{core_features}. +#' @param hidden_features Character vector of feature names to drop (optional). +#' @param min_k,max_k Integer K range to explore when optimizing. +#' @param truth_column Name of metadata column with ground-truth class labels. +#' @param truth_classes Character vector of all classes to consider (including +#' \code{other_class} if you intend to optimize for it). +#' @param other_class Name of the explicit outgroup class (default: "Other"). +#' @param optimize_for_other Logical; if TRUE, computes a separate "other" +#' score (ratio) and searches a purity threshold; if FALSE, treats all +#' classes symmetrically. +#' @param predict_unlabeled If TRUE, re-runs KNN to classify samples that were +#' present in \code{features_df} but not in \code{metadata}. +#' @param plot_samples Optional vector of sample_ids to keep in example plots. +#' @param DLBCLone_KNN_out Optional prior result; if supplied, its learned +#' parameters are reused (skip optimization). +#' @param seed Random seed. +#' @param epsilon Small value added to distances before weighting. +#' @param weighted_votes If FALSE, neighbors are unweighted (equal votes). +#' @param skip_umap If TRUE, skip layout optimization plots at the end. +#' +#' @return A list with fields including: +#' \item{predictions}{Per-sample vote/score summary and predicted labels} +#' \item{DLBCLone_k_best_k}{Best K found} +#' \item{DLBCLone_k_purity_threshold}{Best purity threshold (if applicable)} +#' \item{DLBCLone_k_accuracy}{Best accuracy metric achieved} +#' \item{truth_classes, truth_column}{Echoed arguments} +#' \item{unlabeled_predictions}{Predictions for unlabeled samples (if requested)} +#' \item{df}{Annotated layout for plotting (when built in this run)} +#' \item{plot_truth, plot_predicted}{ggplots when built in this run} +#' +#' @export +DLBCLone_KNN <- function(features_df, + metadata, + core_features = NULL, + core_feature_multiplier = 1.5, + hidden_features = NULL, + min_k = 5, + max_k = 60, + truth_column = "lymphgen", + truth_classes = c("EZB", "BN2", "ST2", "MCD", "N1", "Other"), + other_class = "Other", + optimize_for_other = TRUE, + predict_unlabeled = FALSE, + plot_samples = NULL, + DLBCLone_KNN_out = NULL, + seed = 12345, + epsilon = 0.001, + weighted_votes = TRUE, + skip_umap = FALSE){ + + # In-group classes (exclude the explicit outgroup label if present) + class_levels <- setdiff(truth_classes, other_class) + + if(!missing(DLBCLone_KNN_out)){ + core_features <- DLBCLone_KNN_out$core_features + core_feature_multiplier <- DLBCLone_KNN_out$core_feature_multiplier + hidden_features <- DLBCLone_KNN_out$hidden_features + optimize_for_other <- DLBCLone_KNN_out$optimize_for_other + } + + # Upweight core features, drop hidden features + if(!is.null(core_features)){ + if(!is.numeric(core_feature_multiplier)){ + stop("core_feature_multiplier must be a numeric value") + } + if(!all(core_features %in% colnames(features_df))){ + stop("core_features must be a vector of column names in features_df") + } + ncore <- length(core_features) + message(paste0("multiplying ", ncore, " core features by ", core_feature_multiplier)) + features_df[, core_features] <- features_df[, core_features] * core_feature_multiplier + } + if(!is.null(hidden_features)){ + if(!all(hidden_features %in% colnames(features_df))){ + stop("hidden_features must be a vector of column names in features_df") + } + message(paste0("dropping ", length(hidden_features), " hidden features")) + features_df <- features_df %>% dplyr::select(-dplyr::any_of(hidden_features)) + } + + # Partition rows by presence in metadata and by empty feature rows + exclude_df <- features_df[!row.names(features_df) %in% metadata$sample_id, , drop = FALSE] + features_df <- features_df[ rownames(features_df) %in% metadata$sample_id, , drop = FALSE] + + df_empty <- features_df[rowSums(features_df) == 0, , drop = FALSE] + sample_metadata_no_features <- dplyr::filter(metadata, sample_id %in% rownames(df_empty)) + features_df <- features_df[rowSums(features_df) > 0, , drop = FALSE] + + exclude_empty <- exclude_df[rowSums(exclude_df) == 0, , drop = FALSE] + exclude_df <- exclude_df[rowSums(exclude_df) > 0, , drop = FALSE] + + if (is.null(DLBCLone_KNN_out)) { + # ------------------------ + # Optimize over K (and purity threshold if requested) + # ------------------------ + overall_best_acc_k <- 0 + overall_best_acc <- 0 + overall_best_thresh <- 0 + best_pred <- NULL + + metadata <- metadata %>% dplyr::filter(sample_id %in% rownames(features_df)) + metadata_simple <- metadata %>% dplyr::select(sample_id, !!rlang::sym(truth_column)) + + message("Finding all nearest neighbors up to k=", max_k + 1, " using cosine distance") + nn_u <- uwot::umap(features_df, + n_neighbors = max_k + 1, + ret_nn = TRUE, + metric = "cosine", + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") + + fkn_ids <- nn_u$nn$cosine$idx + fkn_dists <- nn_u$nn$cosine$dist + rownames(fkn_dists) <- rownames(features_df) + rownames(fkn_ids) <- rownames(features_df) + + fkn_dists <- as.data.frame(fkn_dists) %>% dplyr::select(-1) + if (weighted_votes) { + fkn_weighted <- round(1 / (fkn_dists + epsilon), 7) + } else { + fkn_weighted <- fkn_dists + fkn_weighted[] <- 1 + } + + fkn_ids <- as.data.frame(fkn_ids) %>% dplyr::select(-1) + truth_index <- metadata[[truth_column]] + names(truth_index) <- metadata$sample_id + + fkn_ids_named <- apply(fkn_ids, 2, function(x) rownames(fkn_ids)[x]) + rownames(fkn_ids_named) <- rownames(fkn_ids) + + fkn_ids_truth <- apply(fkn_ids_named, 2, function(x) truth_index[x]) + rownames(fkn_ids_truth) <- rownames(fkn_ids) + fkn_ids_truth <- as.data.frame(fkn_ids_truth) + + fkn_ids_long <- tibble::rownames_to_column(fkn_ids_truth, "sample_id") %>% + tidyr::pivot_longer(-sample_id, names_to = "column", values_to = "class") + fkn_weighted_long <- tibble::rownames_to_column(fkn_weighted, "sample_id") %>% + tidyr::pivot_longer(-sample_id, names_to = "column", values_to = "vote") + + fkn_votes <- dplyr::left_join(fkn_ids_long, fkn_weighted_long, + by = c("sample_id", "column")) + + for (k in seq(max_k, min_k, by = -5)) { + message(paste0("Running DLBCLone KNN with k=", k)) + + fkn_weighted <- fkn_votes %>% + dplyr::group_by(sample_id) %>% + dplyr::slice_head(n = k) %>% + dplyr::ungroup() %>% + dplyr::group_by(sample_id, class) %>% + dplyr::summarize(weighted_vote = sum(vote), .groups = "drop") + + if (optimize_for_other) { + # Treat outgroup separately; compute top group among in-groups only + fkn_weighted <- fkn_weighted %>% + tidyr::pivot_wider( + id_cols = "sample_id", + names_from = "class", + values_from = "weighted_vote", + values_fill = 0 + ) %>% + dplyr::select(sample_id, dplyr::all_of(union(class_levels, other_class))) %>% + dplyr::rowwise() %>% + dplyr::mutate( + idx = which.max(dplyr::c_across(dplyr::all_of(class_levels))), + top_class = class_levels[idx], + top_class_count = dplyr::c_across(dplyr::all_of(class_levels))[idx] + ) %>% + dplyr::select(-idx) %>% + dplyr::ungroup() %>% + dplyr::mutate( + top_class = ifelse(top_class_count == 0, other_class, top_class), + "{other_class}" := round(.data[[other_class]], 4), + top_class_count = round(top_class_count, 4) + ) %>% + dplyr::rename( + "{other_class}_score" := dplyr::all_of(other_class) + ) %>% + dplyr::mutate( + by_score = top_class, + top_group_score = top_class_count, + score_ratio = round(top_group_score / .data[[paste0(other_class, "_score")]], 4) + ) + + } else { + # All classes symmetric; still provide an {other_class}_score column + fkn_weighted <- fkn_weighted %>% + tidyr::pivot_wider( + id_cols = "sample_id", + names_from = "class", + values_from = "weighted_vote", + values_fill = 0 + ) %>% + dplyr::select(sample_id, dplyr::all_of(union(class_levels, other_class))) %>% + dplyr::rowwise() %>% + dplyr::mutate( + idx = which.max(dplyr::c_across(dplyr::all_of(class_levels))), + top_class = class_levels[idx], + top_class_count = dplyr::c_across(dplyr::all_of(class_levels))[idx] + ) %>% + dplyr::select(-idx) %>% + dplyr::ungroup() %>% + dplyr::mutate( + top_class = ifelse(top_class_count == 0, other_class, top_class), + "{other_class}_score" := dplyr::if_else( + !is.na(.data[[other_class]]), .data[[other_class]], 0 + ) + ) %>% + dplyr::mutate( + by_score = top_class, + top_group_score = top_class_count, + score_ratio = 10 + ) + } + + # Attach truth labels for reporting + fkn_weighted <- dplyr::left_join( + fkn_weighted, + dplyr::select(metadata, sample_id, !!rlang::sym(truth_column)), + by = "sample_id" + ) + + score_thresh <- 1.5 * k + + # Grid search purity threshold if outgroup optimization is on + if (optimize_for_other) { + best <- 0 + best_thresh <- 0 + for (purity_threshold in seq(3, 0, -0.05)) { + updated_votes <- fkn_weighted %>% + dplyr::mutate(min_score = score_thresh) %>% + dplyr::mutate( + by_score_opt = ifelse( + score_ratio >= purity_threshold | top_group_score > score_thresh, + by_score, other_class + ) + ) + + acc <- report_accuracy(updated_votes, pred = "by_score_opt", truth = truth_column) + if (acc$mean_balanced_accuracy > best) { + best <- acc$mean_balanced_accuracy + best_thresh <- purity_threshold + } + } + } else { + updated_votes <- dplyr::mutate(fkn_weighted, by_score_opt = by_score) + acc <- report_accuracy(updated_votes, pred = "by_score_opt", truth = truth_column) + best <- acc$mean_balanced_accuracy + best_thresh <- 0 + } + + classes_for_span <- setdiff(intersect(class_levels, names(fkn_weighted)), other_class) + if (best > overall_best_acc) { + overall_best_acc <- best + overall_best_thresh <- best_thresh + overall_best_acc_k <- k + + updated_votes <- fkn_weighted %>% + dplyr::mutate(min_score = score_thresh) %>% + dplyr::mutate(DLBCLone_k = by_score) %>% + dplyr::mutate(DLBCLone_ko = ifelse( + score_ratio >= best_thresh | top_group_score > score_thresh, + by_score, other_class + )) %>% + dplyr::rowwise() %>% + dplyr::mutate( + valid_classes = { + scores <- dplyr::c_across(dplyr::all_of(classes_for_span)) + keep_idx <- which(replace(scores > score_thresh, is.na(scores), FALSE)) + if (length(keep_idx) == 0) other_class else paste(classes_for_span[keep_idx], collapse = "/") + } + ) %>% + dplyr::ungroup() + + best_pred <- updated_votes + message(paste0( + "New best accuracy: ", round(best, 3), + " at k=", k, " with purity threshold: ", round(best_thresh, 2) + )) + } else { + message(paste( + "best accuracy did not improve at k=", k, + " with purity threshold:", round(best_thresh, 2), + " accuracy:", round(best, 3) + )) + } + } # end for K + + } else { + message("Using DLBCLone_KNN_out provided, skipping KNN run") + best_pred <- DLBCLone_KNN_out$predictions + optimized_layout <- DLBCLone_KNN_out$df + overall_best_acc_k <- DLBCLone_KNN_out$DLBCLone_k_best_k + overall_best_acc <- DLBCLone_KNN_out$DLBCLone_k_accuracy + overall_best_thresh <- DLBCLone_KNN_out$DLBCLone_k_purity_threshold + } + + # ------------------------ + # Predict for unlabeled samples (optional) + # ------------------------ + unlabeled_predictions <- data.frame() + samples_no_metadata <- c( + rownames(exclude_df)[!rownames(exclude_df) %in% metadata$sample_id], + rownames(exclude_empty)[!rownames(exclude_empty) %in% metadata$sample_id] + ) + + if (predict_unlabeled && length(samples_no_metadata) > 0) { + message("Re-running KNN to include unlabeled samples. Will use K value and thresholds from optimized model, if provided") + message("will use newly provided features rather than recycling!") + + if (!is.null(DLBCLone_KNN_out)) { + k <- DLBCLone_KNN_out$DLBCLone_k_best_k + overall_best_thresh <- DLBCLone_KNN_out$DLBCLone_k_purity_threshold + best_thresh <- overall_best_thresh + } + + # Build an augmented metadata where the unlabeled have NA in the truth column + placeholder_metadata <- data.frame(sample_id = samples_no_metadata, stringsAsFactors = FALSE) + placeholder_metadata[[truth_column]] <- NA + metadata_merge <- dplyr::bind_rows(metadata, placeholder_metadata) + + k_buffer <- min(length(samples_no_metadata), overall_best_acc_k) + generous_k <- overall_best_acc_k + k_buffer + message("Finding all nearest neighbors up to k=", generous_k + 1, " using cosine distance") + + if (nrow(exclude_df) > 0) { + features_df_merge <- dplyr::bind_rows(features_df, exclude_df) + + nn_u <- uwot::umap(features_df_merge, + n_neighbors = generous_k + 1, + ret_nn = TRUE, + metric = "cosine", + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") + fkn_ids <- nn_u$nn$cosine$idx + fkn_dists <- nn_u$nn$cosine$dist + rownames(fkn_dists) <- rownames(features_df_merge) + rownames(fkn_ids) <- rownames(features_df_merge) + + fkn_dists <- as.data.frame(fkn_dists) %>% dplyr::select(-1) + if (weighted_votes) { + fkn_weighted <- round(1 / (fkn_dists + epsilon), 7) + } else { + fkn_weighted <- fkn_dists + fkn_weighted[] <- 1 + } + + fkn_ids <- as.data.frame(fkn_ids) %>% dplyr::select(-1) + + truth_index <- metadata_merge[[truth_column]] + names(truth_index) <- metadata_merge$sample_id + + fkn_ids_named <- apply(fkn_ids, 2, function(x) rownames(fkn_ids)[x]) + rownames(fkn_ids_named) <- rownames(fkn_ids) + + fkn_ids_truth <- apply(fkn_ids_named, 2, function(x) truth_index[x]) + rownames(fkn_ids_truth) <- rownames(fkn_ids) + fkn_ids_truth <- as.data.frame(fkn_ids_truth) + + fk_neighbors_long <- as.data.frame(fkn_ids_named) %>% + tibble::rownames_to_column("sample_id") %>% + tidyr::pivot_longer(-sample_id, names_to = "column", values_to = "neighbor") + + fkn_ids_long <- tibble::rownames_to_column(fkn_ids_truth, "sample_id") %>% + tidyr::pivot_longer(-sample_id, names_to = "column", values_to = "class") + + fkn_weighted_long <- tibble::rownames_to_column(fkn_weighted, "sample_id") %>% + tidyr::pivot_longer(-sample_id, names_to = "column", values_to = "vote") + + fkn_votes <- dplyr::left_join(fkn_ids_long, fkn_weighted_long, by = c("sample_id", "column")) %>% + dplyr::left_join(fk_neighbors_long, by = c("sample_id", "column")) %>% + dplyr::filter(!is.na(class)) + + k <- overall_best_acc_k + + fkn_weighted <- fkn_votes %>% + dplyr::group_by(sample_id) %>% + dplyr::slice_head(n = k) %>% + dplyr::mutate(neighbor_id = paste(neighbor, collapse = ",")) %>% + dplyr::ungroup() + + fkn_weighted_neighbors <- fkn_weighted %>% + dplyr::select(sample_id, neighbor_id) %>% + dplyr::distinct() %>% + dplyr::filter(sample_id %in% rownames(exclude_df)) + + fkn_weighted <- fkn_weighted %>% + dplyr::group_by(sample_id, class) %>% + dplyr::summarize(weighted_vote = sum(vote), .groups = "drop") + + if (optimize_for_other) { + fkn_weighted <- fkn_weighted %>% + tidyr::pivot_wider( + id_cols = "sample_id", + names_from = "class", + values_from = "weighted_vote", + values_fill = 0 + ) %>% + dplyr::select(sample_id, dplyr::all_of(union(class_levels, other_class))) %>% + dplyr::rowwise() %>% + dplyr::mutate( + idx = which.max(dplyr::c_across(dplyr::all_of(class_levels))), + top_class = class_levels[idx], + top_class_count = dplyr::c_across(dplyr::all_of(class_levels))[idx] + ) %>% + dplyr::select(-idx) %>% + dplyr::ungroup() %>% + dplyr::mutate( + top_class = ifelse(top_class_count == 0, other_class, top_class), + "{other_class}" := round(.data[[other_class]], 4), + top_class_count = round(top_class_count, 4) + ) %>% + dplyr::rename( + "{other_class}_score" := dplyr::all_of(other_class) + ) %>% + dplyr::mutate( + by_score = top_class, + top_group_score = top_class_count, + score_ratio = round(top_group_score / .data[[paste0(other_class, "_score")]], 4) + ) + } else { + fkn_weighted <- fkn_weighted %>% + tidyr::pivot_wider( + id_cols = "sample_id", + names_from = "class", + values_from = "weighted_vote", + values_fill = 0 + ) %>% + dplyr::select(sample_id, dplyr::all_of(union(class_levels, other_class))) %>% + dplyr::rowwise() %>% + dplyr::mutate( + idx = which.max(dplyr::c_across(dplyr::all_of(class_levels))), + top_class = class_levels[idx], + top_class_count = dplyr::c_across(dplyr::all_of(class_levels))[idx] + ) %>% + dplyr::select(-idx) %>% + dplyr::ungroup() %>% + dplyr::mutate( + top_class = ifelse(top_class_count == 0, other_class, top_class), + "{other_class}_score" := dplyr::if_else( + !is.na(.data[[other_class]]), .data[[other_class]], 0 + ) + ) %>% + dplyr::mutate( + by_score = top_class, + top_group_score = top_class_count, + score_ratio = 10 + ) + } + + fkn_weighted <- fkn_weighted %>% + dplyr::filter(sample_id %in% rownames(exclude_df)) + + fkn_weighted <- dplyr::left_join( + fkn_weighted, + dplyr::select(metadata_merge, sample_id, !!rlang::sym(truth_column)), + by = "sample_id" + ) + + score_thresh <- 1.5 * k + + if (optimize_for_other) { + unlabeled_predictions <- fkn_weighted %>% + dplyr::mutate(DLBCLone_k = by_score) %>% + dplyr::mutate(DLBCLone_ko = ifelse( + score_ratio >= overall_best_thresh | top_group_score > score_thresh, + by_score, other_class + )) %>% + dplyr::left_join(., fkn_weighted_neighbors, by = "sample_id") + } else { + classes_for_span <- intersect(class_levels, names(fkn_weighted)) + other_score_col <- paste0(other_class, "_score") + + unlabeled_predictions <- fkn_weighted %>% + dplyr::mutate(DLBCLone_k = by_score) %>% + dplyr::mutate(DLBCLone_ko = by_score) %>% + dplyr::rowwise() %>% + dplyr::mutate( + valid_classes = { + scores <- c_across(all_of(classes_for_span)) + keep_idx <- which(replace(scores > .data[[other_score_col]], is.na(scores), FALSE)) + if (length(keep_idx) == 0) other_class else paste(classes_for_span[keep_idx], collapse = "/") + } + ) %>% + dplyr::ungroup() %>% + dplyr::left_join(., fkn_weighted_neighbors, by = "sample_id") + } + } else { + other_score_col <- paste0(other_class, "_score") + unlabeled_predictions <- data.frame(sample_id = rownames(exclude_empty), stringsAsFactors = FALSE) + unlabeled_predictions[[truth_column]] <- NA + unlabeled_predictions <- unlabeled_predictions %>% + dplyr::mutate( + DLBCLone_k = other_class, + DLBCLone_ko = other_class, + by_score = NA, + top_group_score = NA, + score_ratio = NA + ) + # Ensure truth_classes columns exist as NA + for (cls in truth_classes) { + if (!cls %in% colnames(unlabeled_predictions)) { + unlabeled_predictions[[cls]] <- NA + } + } + unlabeled_predictions[[other_score_col]] <- NA + # no neighbor_id information in this branch + } + } + + # ------------------------ + # Assemble return object + # ------------------------ + if (is.null(DLBCLone_KNN_out)) { + + if (nrow(sample_metadata_no_features) > 0) { + sample_metadata_no_features <- sample_metadata_no_features %>% + dplyr::select(sample_id, !!rlang::sym(truth_column)) %>% + dplyr::mutate( + DLBCLone_k = NA, + DLBCLone_ko = other_class, + by_score = NA, + top_group_score = NA, + score_ratio = NA + ) + best_pred <- dplyr::bind_rows(best_pred, sample_metadata_no_features) + } + + format_for_output <- function(x) { + x_rounded <- round(x, 4) + ifelse( + is.na(x_rounded), + NA_character_, + ifelse( + x_rounded == 0, + "0", + sub("\\.?0+$", "", format(x_rounded, scientific = FALSE, trim = TRUE, nsmall = 0)) + ) + ) + } + + to_return <- list( + predictions = best_pred %>% + dplyr::mutate(dplyr::across(where(is.numeric), ~ format_for_output(.))), + DLBCLone_k_best_k = overall_best_acc_k, + DLBCLone_k_purity_threshold = overall_best_thresh, + DLBCLone_k_accuracy = overall_best_acc, + truth_classes = truth_classes, + truth_column = truth_column, + sample_metadata_no_features = sample_metadata_no_features, + core_feature_multiplier = core_feature_multiplier, + core_features = core_features, + hidden_features = hidden_features, + seed = seed, + optimize_for_other = optimize_for_other, + unlabeled_predictions = NULL + ) + } else { + to_return <- DLBCLone_KNN_out + } + + # Fill in neighbors / predictions for unlabeled (if requested) + to_return$unlabeled_neighbors <- NULL + if (predict_unlabeled && length(samples_no_metadata) > 0) { + if (exists("fkn_weighted_neighbors")) { + to_return$unlabeled_neighbors <- fkn_weighted_neighbors %>% + tidyr::separate(neighbor_id, into = paste0("N", c(1:k)), sep = ",") + } + to_return$unlabeled_predictions <- unlabeled_predictions + + if (is.null(DLBCLone_KNN_out) & !skip_umap) { + message("Optimizing graph layout for visualization, predict_unlabeled = TRUE") + df_show <- dplyr::bind_rows(features_df, exclude_df) + + optimized <- make_and_annotate_umap(df_show, metadata = metadata)$df + optimized <- optimized %>% + dplyr::left_join( + dplyr::bind_rows( + dplyr::select(best_pred, -!!rlang::sym(truth_column)), + dplyr::select(unlabeled_predictions, -!!rlang::sym(truth_column)) + ), + by = "sample_id" + ) %>% + dplyr::mutate(!!rlang::sym(truth_column) := ifelse(is.na(.data[[truth_column]]), DLBCLone_ko, .data[[truth_column]])) + } else { + optimized <- if (!is.null(DLBCLone_KNN_out)) DLBCLone_KNN_out$features_df else NULL + } + } else { + if (is.null(DLBCLone_KNN_out)) { + message("Optimizing graph layout for visualization") + optimized <- make_and_annotate_umap(features_df, metadata = metadata)$df + optimized <- optimized %>% + dplyr::left_join(dplyr::select(best_pred, -!!rlang::sym(truth_column)), by = "sample_id") %>% + dplyr::mutate(!!rlang::sym(truth_column) := ifelse(is.na(.data[[truth_column]]), DLBCLone_ko, .data[[truth_column]])) + } else { + message("Using DLBCLone_KNN_out provided, skipping graph layout optimization") + optimized <- DLBCLone_KNN_out$features_df + } + } + + if(!is.null(optimized) & is.null(DLBCLone_KNN_out)){ + to_return$plot_truth <- basic_umap_scatterplot(optimized, plot_samples, colour_by = truth_column) + to_return$plot_predicted <- basic_umap_scatterplot(optimized, plot_samples, colour_by = "DLBCLone_ko") + to_return$df <- optimized + } + + # Ensure features_df is included in the return for downstream plotting + if (predict_unlabeled && length(samples_no_metadata) > 0) { + to_return$features_df <- if (exists("features_df_merge")) features_df_merge else features_df + } else { + to_return$features_df <- features_df + } + + to_return$type <- "DLBCLone_KNN" + to_return$pred_column <- "DLBCLone_ko" + to_return$other_class = other_class + return(to_return) +} + #' Optimize parameters for classifying samples using UMAP and k-nearest neighbor #' #' @param combined_mutation_status_df Data frame with one row per sample and @@ -1927,6 +2665,13 @@ predict_single_sample_DLBCLone <- function( stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") } + if(any(!colnames(test_df) %in% colnames(optimized_model$features))){ + stop( + "test_df should not contain any features that are not in training data. ", + "Please check the column names of test_df and optimized_model$features." + ) + } + bad_samps <- rowSums(test_df) == 0 bad_test_df <- test_df[bad_samps, ] %>% rownames_to_column("sample_id") %>% @@ -2066,15 +2811,26 @@ predict_single_sample_DLBCLone <- function( ) ) + combined_df <- bind_rows( + optimized_model$features %>% rownames_to_column("sample_id"), + test_df %>% rownames_to_column("sample_id") + ) %>% + distinct(sample_id, .keep_all = TRUE) %>% # keep first occurrence + column_to_rownames("sample_id") + + if(any(!colnames(optimized_model$features) %in% colnames(test_df))){ + message("filling in missing features in test_df with zeros") + combined_df[is.na(combined_df)] = 0 + } + to_return = list( - prediction = predictions_test_df, - projection = optimized_model$df, - umap_input = optimized_model$features, - model=optimized_model$model, + predictions = predictions_test_df, df = predictions_df, anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), anno_out = anno_out, - test_feats = test_df + features_df = combined_df, + type = "predict_single_sample_DLBCLone", + optimized_predictions = optimized_model$predictions ) return(to_return) diff --git a/man/DLBCLone_KNN.Rd b/man/DLBCLone_KNN.Rd new file mode 100644 index 0000000..531a8d6 --- /dev/null +++ b/man/DLBCLone_KNN.Rd @@ -0,0 +1,91 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_KNN} +\alias{DLBCLone_KNN} +\title{Run DLBCLone KNN Classification} +\usage{ +DLBCLone_KNN( + features_df, + metadata, + core_features = NULL, + core_feature_multiplier = 1.5, + hidden_features = NULL, + min_k = 5, + max_k = 60, + truth_column = "lymphgen", + truth_classes = c("EZB", "BN2", "ST2", "MCD", "N1", "Other"), + other_class = "Other", + optimize_for_other = TRUE, + predict_unlabeled = FALSE, + plot_samples = NULL, + DLBCLone_KNN_out = NULL, + seed = 12345, + epsilon = 0.001, + weighted_votes = TRUE, + skip_umap = FALSE +) +} +\arguments{ +\item{features_df}{Numeric matrix/data.frame (rows = samples, cols = features). +Row names must be sample IDs.} + +\item{metadata}{Data frame with at least \code{sample_id} and the ground-truth +label column given in \code{truth_column}.} + +\item{core_features}{Character vector of feature names to upweight (optional).} + +\item{core_feature_multiplier}{Numeric multiplier for \code{core_features}.} + +\item{hidden_features}{Character vector of feature names to drop (optional).} + +\item{min_k, max_k}{Integer K range to explore when optimizing.} + +\item{truth_column}{Name of metadata column with ground-truth class labels.} + +\item{truth_classes}{Character vector of all classes to consider (including +\code{other_class} if you intend to optimize for it).} + +\item{other_class}{Name of the explicit outgroup class (default: "Other").} + +\item{optimize_for_other}{Logical; if TRUE, computes a separate "other" +score (ratio) and searches a purity threshold; if FALSE, treats all +classes symmetrically.} + +\item{predict_unlabeled}{If TRUE, re-runs KNN to classify samples that were +present in \code{features_df} but not in \code{metadata}.} + +\item{plot_samples}{Optional vector of sample_ids to keep in example plots.} + +\item{DLBCLone_KNN_out}{Optional prior result; if supplied, its learned +parameters are reused (skip optimization).} + +\item{seed}{Random seed.} + +\item{epsilon}{Small value added to distances before weighting.} + +\item{weighted_votes}{If FALSE, neighbors are unweighted (equal votes).} + +\item{skip_umap}{If TRUE, skip layout optimization plots at the end.} +} +\value{ +A list with fields including: +\item{predictions}{Per-sample vote/score summary and predicted labels} +\item{DLBCLone_k_best_k}{Best K found} +\item{DLBCLone_k_purity_threshold}{Best purity threshold (if applicable)} +\item{DLBCLone_k_accuracy}{Best accuracy metric achieved} +\item{truth_classes, truth_column}{Echoed arguments} +\item{unlabeled_predictions}{Predictions for unlabeled samples (if requested)} +\item{df}{Annotated layout for plotting (when built in this run)} +\item{plot_truth, plot_predicted}{ggplots when built in this run} +} +\description{ +Weighted KNN on a feature (mutation) matrix with optional upweighting of +user-specified "core" features, optional exclusion of "hidden" features, +and optional optimization of an explicit outgroup (e.g. "Other"). +} +\details{ +This version removes hard-coded LymphGen class names and instead derives the +in-group classes and the outgroup column name from the arguments +\code{truth_classes} and \code{other_class}. It keeps backward compatibility +for the default LymphGen-like usage. +} diff --git a/man/DLBCLone_KNN_predict.Rd b/man/DLBCLone_KNN_predict.Rd new file mode 100644 index 0000000..ccfc263 --- /dev/null +++ b/man/DLBCLone_KNN_predict.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/umap.R +\name{DLBCLone_KNN_predict} +\alias{DLBCLone_KNN_predict} +\title{Predict DLBCLone Classes for New Samples Using a Trained KNN Model} +\usage{ +DLBCLone_KNN_predict( + train_df, + test_df, + metadata, + DLBCLone_KNN_out, + mode = "batch", + truth_column = "lymphgen", + other_class = "Other" +) +} +\arguments{ +\item{train_df}{Data frame or matrix of features for training samples (rows = samples, columns = features).} + +\item{test_df}{Data frame or matrix of features for test samples to be classified.} + +\item{metadata}{Data frame with metadata for all samples, including at least a \code{sample_id} column.} + +\item{DLBCLone_KNN_out}{List. Output from a previous call to \code{DLBCLone_KNN} containing optimized parameters. (Required)} + +\item{mode}{Character. If \code{"iterative"}, runs KNN prediction for each test sample individually (recommended for stability).} + +\item{core_features}{Optional character vector of feature names to upweight in the KNN calculation.} + +\item{core_feature_multiplier}{Numeric. Multiplier to apply to core features (default: 1.5).} + +\item{hidden_features}{Optional character vector of feature names to exclude from the analysis.} +} +\value{ +A list containing the KNN prediction results for the test samples, including predicted class labels and scores. +} +\description{ +Applies a previously optimized DLBCLone KNN model to predict class labels for new (test) samples. +This function combines the training and test feature matrices, ensures feature compatibility, and uses the +parameters from a DLBCLone KNN optimization run to classify the test samples. Optionally, runs in iterative mode +for more stable results when predicting multiple samples. +} +\details{ +\itemize{ +\item Ensures that the feature columns in \code{train_df} and \code{test_df} are compatible. +\item If \code{mode = "iterative"}, runs KNN prediction for each test sample one at a time. +\item Uses the parameters (e.g., k, feature weights) from the provided \code{DLBCLone_KNN_out} object. +\item Returns the same structure as \code{DLBCLone_KNN}, with predictions for the test samples. +} +} +\examples{ +# Assuming you have run DLBCLone_KNN to get optimized parameters: +# model_out <- DLBCLone_KNN(train_features, train_metadata, ...) +# Predict on new samples: +predictions <- DLBCLone_KNN_predict( + train_df = train_features, + test_df = new_samples, + metadata = sample_metadata, + DLBCLone_KNN_out = model_out + +} diff --git a/man/basic_umap_scatterplot.Rd b/man/basic_umap_scatterplot.Rd deleted file mode 100644 index a5d507f..0000000 --- a/man/basic_umap_scatterplot.Rd +++ /dev/null @@ -1,43 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plotting.R -\name{basic_umap_scatterplot} -\alias{basic_umap_scatterplot} -\title{Basic UMAP Scatterplot} -\usage{ -basic_umap_scatterplot( - optimized, - plot_samples = NULL, - colour_by = NULL, - truth_column = "lymphgen", - pred_column = "DLBCLone_ko", - other_label = "Other", - title = "UMAP based on selected features", - use_plotly = TRUE, - custom_colours = NULL -) -} -\arguments{ -\item{optimized}{Data frame containing at least V1, V2, sample_id, and grouping columns.} - -\item{plot_samples}{Optional character vector of sample_ids to annotate.} - -\item{colour_by}{Column name to color points by. Defaults to \code{truth_column}.} - -\item{truth_column}{Name of the truth/ground-truth column (default: "lymphgen").} - -\item{pred_column}{Name of the predicted-class column (default: "DLBCLone_ko").} - -\item{other_label}{Label used for the outgroup/unclassified class (default: "Other").} - -\item{title}{Plot title.} - -\item{use_plotly}{Logical; if FALSE and \code{plot_samples} provided, draw static labels.} - -\item{custom_colours}{Optional named vector of colors for groups; falls back to \code{get_gambl_colours()}.} -} -\value{ -A ggplot object. -} -\description{ -Generates a simple UMAP scatterplot for visualizing sample clustering or separation. -} diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd index 5a794a3..b6e4e1c 100644 --- a/man/nearest_neighbor_heatmap.Rd +++ b/man/nearest_neighbor_heatmap.Rd @@ -6,40 +6,60 @@ \usage{ nearest_neighbor_heatmap( this_sample_id, - single_sample_prediction_output, - optimized_model, + DLBCLone_model, truth_column = "lymphgen", - pred_column, clustering_distance = "binary", - metadata_cols = NULL + font_size = 14 +) + +nearest_neighbor_heatmap( + this_sample_id, + DLBCLone_model, + truth_column = "lymphgen", + clustering_distance = "binary", + font_size = 14 ) } \arguments{ \item{this_sample_id}{Character. The sample ID for which to plot the nearest neighbor heatmap.} +\item{DLBCLone_model}{List. A DLBCLone model object, either from \code{DLBCLone_optimize_params} +or \code{DLBCLone_KNN} (with \code{predict_unlabeled = TRUE}).} + +\item{truth_column}{Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} + +\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} + \item{single_sample_prediction_output}{A list containing the prediction results from predict_single_sample_DLBCLone, including the feature matrix and predictions.} \item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a previous, Data frame with the best parameters. useful for reproducibility.} -\item{truth_column}{Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} - \item{pred_column}{Character. The column name in the predictions data frame that contains the predicted labels.} -\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} - \item{metadata_cols}{Optional character vector of additional metadata columns to include in the heatmap annotations.} } \value{ +A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. + A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. } \description{ Generates a heatmap of feature values for the nearest neighbors of a specified sample, based on a DLBCLone model object. This visualization helps to inspect the feature profiles of samples most similar to the query sample. + +Generates a simple UMAP scatterplot for visualizing sample clustering or separation. } \details{ +\itemize{ +\item For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. +\item For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. +\item The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, +and plots a heatmap of features with nonzero values. +} + \itemize{ \item For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. \item For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. @@ -51,4 +71,9 @@ and plots a heatmap of features with nonzero values. # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: nearest_neighbor_heatmap(sample_id, model) +# Assuming 'predicted_out' is a the output of DLBCLone_KNN_predict +and "SomeSample_ID" is a valid sample ID from among the test (not training) samples +\dontrun{ +nearest_neighbor_heatmap("SomeSample_ID", predicted_out) +} } From 5e7e05ab54c5eed6e19c237d2b8eff1b4a4c069c Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Fri, 15 Aug 2025 12:16:57 -0700 Subject: [PATCH 72/79] nearest_neighbor_heatmap update --- NAMESPACE | 2 +- R/plotting.R | 375 +++++++++++++++++++------------- R/umap.R | 1 + man/basic_umap_scatterplot.Rd | 53 +++++ man/nearest_neighbor_heatmap.Rd | 40 +--- 5 files changed, 281 insertions(+), 190 deletions(-) create mode 100644 man/basic_umap_scatterplot.Rd diff --git a/NAMESPACE b/NAMESPACE index be1f67f..633c8ac 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,6 +10,7 @@ export(DLBCLone_summarize_model) export(DLBCLone_train_mixture_model) export(DLBCLone_train_test_plot) export(assemble_genetic_features) +export(basic_umap_scatterplot) export(classify_bl) export(classify_dlbcl) export(classify_fl) @@ -28,7 +29,6 @@ export(report_accuracy) export(summarize_all_ssm_status) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) -import(ComplexHeatmap) import(GAMBLR.data) import(GAMBLR.helpers) import(dplyr) diff --git a/R/plotting.R b/R/plotting.R index 9344091..9f16977 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -5,14 +5,11 @@ #' of samples most similar to the query sample. #' #' @param this_sample_id Character. The sample ID for which to plot the nearest neighbor heatmap. -#' @param single_sample_prediction_output A list containing the prediction results from predict_single_sample_DLBCLone, -#' including the feature matrix and predictions. -#' @param optimized_model list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a -#' previous, Data frame with the best parameters. useful for reproducibility. +#' @param DLBCLone_model A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, \code{DLBCLone_KNN}, or \code{predict_single_sample_DLBCLone}. #' @param truth_column Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen"). -#' @param pred_column Character. The column name in the predictions data frame that contains the predicted labels. -#' @param clustering_distance Character. The distance metric to use for clustering rows. Default is "binary". #' @param metadata_cols Optional character vector of additional metadata columns to include in the heatmap annotations. +#' @param clustering_distance Character. The distance metric to use for clustering rows. Default is "binary". +#' @param font_size Numeric. Font size for heatmap text (default: 14). #' #' @return A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. #' @@ -33,14 +30,70 @@ #' nearest_neighbor_heatmap <- function( this_sample_id, - single_sample_prediction_output, - optimized_model, + DLBCLone_model, truth_column = "lymphgen", - pred_column, + metadata_cols = NULL, clustering_distance = "binary", - metadata_cols = NULL + font_size = 14 ){ + if(!missing(DLBCLone_model) && "type" %in% names(DLBCLone_model)){ + if(DLBCLone_model$type == "DLBCLone_optimize_params"){ + neighbor_df = DLBCLone_model$neighbors + lyseq_status = DLBCLone_model$lyseq_status + }else if(DLBCLone_model$type == "DLBCLone_KNN"){ + + if(!"unlabeled_neighbors" %in% names(DLBCLone_model)){ + print(names(DLBCLone_model)) + stop("DLBCLone_model must be the output of DLBCLone_KNN with predict_unlabeled = TRUE") + }else if(is.null(DLBCLone_model$unlabeled_neighbors)){ + #no neighbors found for any of the incoming samples + message("No neighbors found for any sample. Returning NULL.") + return(NULL) + } + neighbor_df = DLBCLone_model$unlabeled_neighbors + neighbor_transpose = filter(neighbor_df,sample_id==this_sample_id) + if(nrow(neighbor_transpose) == 0){ + message("No neighbors found for sample ", this_sample_id, ". Returning NULL.") + return(NULL) + } + neighbor_transpose = neighbor_transpose %>% t() + #deal with fewer than K neighbours + neighbor_transpose = neighbor_transpose[!is.na(neighbor_transpose)] + pred_column = "DLBCLone_ko" + + }else if(DLBCLone_model$type == "predict_single_sample_DLBCLone"){ + DLBCLone_model$predictions <- DLBCLone_model$predictions %>% + filter(sample_id %in% this_sample_id) + + neighbor_ids = DLBCLone_model$predictions %>% + pull(neighbor_id) %>% + strsplit(",") %>% + unlist() + + neighbor_df <- DLBCLone_model$features_df %>% + rownames_to_column("sample_id") %>% + filter(sample_id %in% neighbor_ids) %>% + column_to_rownames("sample_id") + + neighbor_transpose = DLBCLone_model$features_df %>% + filter(rownames(DLBCLone_model$features_df) == this_sample_id) + + if(nrow(neighbor_transpose) == 0){ + message("No features found for sample ", this_sample_id, ". Returning NULL.") + return(NULL) + } + + neighbor_transpose = neighbor_transpose %>% t() + #deal with fewer than K neighbours + neighbor_transpose = neighbor_transpose[!is.na(neighbor_transpose)] + + pred_column = "predicted_label" + } + }else{ + stop("DLBCLone_model must be the output of DLBCLone_optimize_params, DLBCLone_KNN or predict_single_sample_DLBCLone") + } + # Gather the base columns cols_to_select <- c("sample_id", truth_column) @@ -49,40 +102,31 @@ nearest_neighbor_heatmap <- function( extra_cols <- extra_cols[!is.null(extra_cols)] cols_to_select <- c(cols_to_select, extra_cols) - single_sample_prediction_output$prediction <- single_sample_prediction_output$prediction %>% - filter(sample_id %in% this_sample_id) - - test_feats <- single_sample_prediction_output$test_feats %>% - filter(rownames(single_sample_prediction_output$test_feats) %in% this_sample_id) - - predicted_neighbor_ids <- single_sample_prediction_output$prediction %>% - pull(neighbor_id) %>% - strsplit(",") %>% - unlist() - - neighbor_feats <- optimized_model$features %>% - rownames_to_column("sample_id") %>% - filter(sample_id %in% predicted_neighbor_ids) %>% - column_to_rownames("sample_id") - - feats <- bind_rows(test_feats, neighbor_feats) + xx = DLBCLone_model$features_df[neighbor_transpose,] + + if(any(is.na(rownames(xx)))){ + print(neighbor_transpose) + stop("something went wrong. Some samples are missing from features_df") + } - top = max(feats) + top = max(xx) mid = top/2 col_fun = circlize::colorRamp2(c(0, mid, top), c("white", "#FFB3B3", "red")) - row_df = optimized_model$predictions %>% + if(DLBCLone_model$type == "predict_single_sample_DLBCLone"){ + + row_df = DLBCLone_model$optimized_predictions %>% select(all_of(cols_to_select)) %>% - filter(sample_id %in% rownames(neighbor_feats)) + filter(sample_id %in% rownames(neighbor_df)) if(!this_sample_id %in% row_df$sample_id){ - new_row <- single_sample_prediction_output$prediction %>% + new_row <- DLBCLone_model$predictions %>% filter(sample_id %in% this_sample_id) %>% mutate( !!sym(truth_column) := NA, - !!sym(pred_column) := single_sample_prediction_output$prediction$predicted_label + !!sym(pred_column) := DLBCLone_model$predictions$predicted_label ) - + # ensure extra metadata columns exist in the new row for(col in extra_cols){ if(!col %in% names(new_row)){ @@ -101,35 +145,78 @@ nearest_neighbor_heatmap <- function( row_df <- bind_rows(row_df, missing_row) } - row_df = row_df %>% - column_to_rownames("sample_id") + }else{ + + #row_df = DLBCLone_model$predictions %>% + row_df = left_join(DLBCLone_model$predictions, DLBCLone_model$metadata) %>% + select(all_of(cols_to_select)) %>% + filter(sample_id %in% rownames(xx)) + if(!this_sample_id %in% row_df$sample_id){ + if("unlabeled_predictions" %in% names(DLBCLone_model)){ + print("=====") + row_df = bind_rows( + row_df, + select( + DLBCLone_model$unlabeled_predictions, + sample_id, + !!sym(truth_column), + !!sym(pred_column) + ) %>% + filter(sample_id %in% rownames(xx)) + ) + print(row_df) + sample_class = filter(DLBCLone_model$unlabeled_predictions, sample_id == this_sample_id) %>% + pull(!!sym(pred_column)) + print(sample_class) + + }else{ + sample_class = NULL + row_df = bind_rows( + row_df, + tibble( + sample_id = this_sample_id, + !!rlang::sym(truth_column) := NA_character_, + !!rlang::sym(pred_column) := NA_character_) + ) + } + } + } + + feats_df <- DLBCLone_model$features_df %>% + rownames_to_column("sample_id") %>% + filter(sample_id %in% row_df$sample_id) %>% + column_to_rownames("sample_id") + + row_df = row_df %>% + column_to_rownames("sample_id") + anno_colours = get_gambl_colours() - anno_list <- list() + anno_list = list() anno_list[[truth_column]] <- anno_colours - for (col in extra_cols) { + for(col in extra_cols){ anno_list[[col]] <- anno_colours } row_anno = rowAnnotation( - df = row_df[rownames(feats),,drop=FALSE], + df = row_df[rownames(feats_df),,drop=FALSE], col = anno_list, annotation_name_gp = gpar(fontsize = 12), show_legend = TRUE ) - title_text = paste( + title_text = paste( "Sample", this_sample_id, "classified as", - single_sample_prediction_output$prediction$predicted_label + pred_column ) ht <- Heatmap( - feats[,colSums(feats)>0], + feats_df[,colSums(feats_df)>0], col = col_fun, column_names_gp = gpar(fontsize=12), right_annotation = row_anno, @@ -149,127 +236,107 @@ nearest_neighbor_heatmap <- function( #' #' Generates a simple UMAP scatterplot for visualizing sample clustering or separation. #' -#' @param this_sample_id Character. The sample ID for which to plot the nearest neighbor heatmap. -#' @param DLBCLone_model List. A DLBCLone model object, either from \code{DLBCLone_optimize_params} -#' or \code{DLBCLone_KNN} (with \code{predict_unlabeled = TRUE}). -#' -#' @return A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. -#' -#' @details -#' - For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. -#' - For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. -#' - The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, -#' and plots a heatmap of features with nonzero values. -#' -#' @importFrom dplyr filter -#' @import ComplexHeatmap -#' @importFrom grid gpar -#' @importFrom circlize colorRamp2 +#' @param optimized Data frame containing at least V1, V2, sample_id, and grouping columns. +#' @param plot_samples Optional character vector of sample_ids to label in the plot +#' @param colour_by Column name to color points by. Defaults to `truth_column`. +#' @param truth_column Name of the truth/ground-truth column (default: "lymphgen"). +#' @param pred_column Name of the predicted-class column (default: "DLBCLone_ko"). +#' @param other_label Label used for the outgroup/unclassified class (default: "Other"). +#' @param title Plot title. +#' @param use_plotly Logical; if FALSE and `plot_samples` provided, draw static labels. +#' @param custom_colours Optional named vector of colors for groups; falls back to `get_gambl_colours()`. #' -#' @examples -#' # Assuming 'predicted_out' is a the output of DLBCLone_KNN_predict -#' and "SomeSample_ID" is a valid sample ID from among the test (not training) samples +#' @return A ggplot object. +#' @export +#' +#' @examples +#' #' \dontrun{ -#' nearest_neighbor_heatmap("SomeSample_ID", predicted_out) -#'} -nearest_neighbor_heatmap <- function(this_sample_id, - DLBCLone_model, - truth_column = "lymphgen", - clustering_distance = "binary", - font_size = 14){ - pred_name = NULL - if(!missing(DLBCLone_model) && "type" %in% names(DLBCLone_model)){ - if(DLBCLone_model$type == "DLBCLone_optimize_params"){ - neighbor_df = DLBCLone_model$neighbors - lyseq_status = DLBCLone_model$lyseq_status - }else if(DLBCLone_model$type == "DLBCLone_KNN"){ - if(!"unlabeled_neighbors" %in% names(DLBCLone_model)){ - print(names(DLBCLone_model)) - stop("DLBCLone_model must be the output of DLBCLone_KNN with predict_unlabeled = TRUE") - }else if(is.null(DLBCLone_model$unlabeled_neighbors)){ - #no neighbors found for any of the incoming samples - message("No neighbors found for any sample. Returning NULL.") - return(NULL) - } - neighbor_df = DLBCLone_model$unlabeled_neighbors - neighbor_transpose = filter(neighbor_df,sample_id==this_sample_id) - if(nrow(neighbor_transpose) == 0){ - message("No neighbors found for sample ", this_sample_id, ". Returning NULL.") - return(NULL) - } - neighbor_transpose = neighbor_transpose %>% t() - #deal with fewer than K neighbours - neighbor_transpose = neighbor_transpose[!is.na(neighbor_transpose)] - pred_name = "DLBCLone_ko" - } +#' my_umap = make_and_annotate_umap(my_data, my_metadata) +#' +#' basic_umap_scatterplot(my_umap$df, #the data frame containing V1 and V2 from UMAP +#' plot_samples = "some_sample_ID", +#' colour_by = "DLBCLone_ko") +#' } +basic_umap_scatterplot <- function(optimized, + plot_samples = NULL, + colour_by = NULL, + truth_column = "lymphgen", + pred_column = "DLBCLone_ko", + other_label = "Other", + title = "UMAP based on selected features", + use_plotly = TRUE, + custom_colours = NULL) { + stopifnot(all(c("V1","V2","sample_id") %in% colnames(optimized))) + stopifnot(is.data.frame(optimized)) + stopifnot(all(c("V1", "V2") %in% colnames(optimized))) + message("colour_by: ", colour_by) + colour_by <- colour_by %||% truth_column - }else{ - stop("DLBCLone_model must be the output of DLBCLone_optimize_params or DLBCLone_KNN") - } - - xx=DLBCLone_model$features_df[neighbor_transpose,] - if(any(is.na(rownames(xx)))){ - print(neighbor_transpose) - stop("something went wrong. Some samples are missing from features_df") - } - top = max(xx) - mid = top/2 - col_fun = circlize::colorRamp2(c(0, mid, top), c("white", "#FFB3B3", "red")) - if(!truth_column %in% colnames(DLBCLone_model$predictions)){ - print(head(DLBCLone_model$predictions)) - stop("missing",truth_column) - } - row_df = select(DLBCLone_model$predictions, sample_id, !!sym(truth_column), !!sym(pred_name)) %>% - filter(sample_id %in% rownames(xx)) - if(!this_sample_id %in% row_df$sample_id){ - if("unlabeled_predictions" %in% names(DLBCLone_model)){ - print("=====") - row_df = bind_rows(row_df, - select(DLBCLone_model$unlabeled_predictions, sample_id, !!sym(truth_column), !!sym(pred_name)) %>% - filter(sample_id %in% rownames(xx)) - ) - print(row_df) - sample_class = filter(DLBCLone_model$unlabeled_predictions, sample_id == this_sample_id) %>% - pull(!!sym(pred_name)) - print(sample_class) - - }else{ - sample_class = NULL - row_df = bind_rows(row_df, - tibble(sample_id = this_sample_id, - !!rlang::sym(truth_column) := NA_character_, - !!rlang::sym(pred_name) := NA_character_)) - } - } - row_df = row_df %>% - column_to_rownames("sample_id") - anno_colours = get_gambl_colours() - anno_list = list() + # dynamic label text (e.g., "lymphgen" and "DLBCLone_ko") + optimized_label <- optimized %>% + mutate( + label = paste0( + sample_id, + "\n", truth_column, ":\n", .data[[truth_column]], + " ", pred_column, ":\n", .data[[pred_column]] + ) + ) - anno_list[[truth_column]] = anno_colours - if (!is.null(pred_name)) { - anno_list[[pred_name]] = anno_colours - } - xx = xx[,colSums(xx)>0,drop=FALSE] - row_anno = rowAnnotation( - df = row_df[rownames(xx),,drop=FALSE], - col = anno_list, - #annotation_name_side = "left", - annotation_name_gp = gpar(fontsize = font_size), - show_legend = FALSE + # points to label + label_points <- dplyr::filter(optimized_label, sample_id %in% (plot_samples %||% character())) %>% + mutate(label_x = V1 + 0.75, label_y = V2 - 0.75) + + # base mapping: color by chosen column + aes_cols <- aes( + x = V1, y = V2, + sample_id = sample_id, + color = .data[[colour_by]] ) - title_text = paste("Sample", this_sample_id, "classified as", sample_class) - Heatmap(xx, - col = col_fun, - right_annotation = row_anno, - clustering_distance_rows = clustering_distance, - show_heatmap_legend = FALSE, - column_title = title_text, - column_title_gp = gpar(fontsize=font_size), - column_names_gp = gpar(fontsize=font_size)) + # draw outgroup first (by truth column) to keep it visually underneath + p <- ggplot() + p <- p + + geom_point( + data = dplyr::filter(optimized, .data[[truth_column]] == other_label), + mapping = aes_cols + ) + + geom_point( + data = dplyr::filter(optimized, .data[[truth_column]] != other_label), + mapping = aes_cols + ) + + # palette + pal <- custom_colours %||% get_gambl_colours() + p <- p + scale_color_manual(values = pal) + + guides(color = guide_legend(title = if (identical(colour_by, truth_column)) "Original Class" else "Predicted Class")) + + # optional static labels when not using plotly + if (!is.null(plot_samples) && length(plot_samples) > 0 && !isTRUE(use_plotly)) { + p <- p + + geom_segment( + data = label_points, + aes(x = V1, y = V2, xend = label_x, yend = label_y), + arrow = arrow(length = unit(0.02, "npc")), + color = "black" + ) + + geom_label( + data = label_points, + aes(x = label_x, y = label_y, label = label), + size = 3, + fill = "white", + label.size = 0.25 + ) + } + + p <- p + + labs(title = title) + + theme_minimal() + + return(p) } + #' Summarize and Export DLBCLone Model Results #' #' Generates and saves a set of summary plots and tables for a DLBCLone model, including UMAP scatterplots, alluvial plots, and oncoplots. diff --git a/R/umap.R b/R/umap.R index a6b68a3..103da09 100644 --- a/R/umap.R +++ b/R/umap.R @@ -1895,6 +1895,7 @@ DLBCLone_KNN <- function(features_df, core_feature_multiplier = core_feature_multiplier, core_features = core_features, hidden_features = hidden_features, + metadata = metadata, seed = seed, optimize_for_other = optimize_for_other, unlabeled_predictions = NULL diff --git a/man/basic_umap_scatterplot.Rd b/man/basic_umap_scatterplot.Rd new file mode 100644 index 0000000..86a2d3e --- /dev/null +++ b/man/basic_umap_scatterplot.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{basic_umap_scatterplot} +\alias{basic_umap_scatterplot} +\title{Basic UMAP Scatterplot} +\usage{ +basic_umap_scatterplot( + optimized, + plot_samples = NULL, + colour_by = NULL, + truth_column = "lymphgen", + pred_column = "DLBCLone_ko", + other_label = "Other", + title = "UMAP based on selected features", + use_plotly = TRUE, + custom_colours = NULL +) +} +\arguments{ +\item{optimized}{Data frame containing at least V1, V2, sample_id, and grouping columns.} + +\item{plot_samples}{Optional character vector of sample_ids to label in the plot} + +\item{colour_by}{Column name to color points by. Defaults to \code{truth_column}.} + +\item{truth_column}{Name of the truth/ground-truth column (default: "lymphgen").} + +\item{pred_column}{Name of the predicted-class column (default: "DLBCLone_ko").} + +\item{other_label}{Label used for the outgroup/unclassified class (default: "Other").} + +\item{title}{Plot title.} + +\item{use_plotly}{Logical; if FALSE and \code{plot_samples} provided, draw static labels.} + +\item{custom_colours}{Optional named vector of colors for groups; falls back to \code{get_gambl_colours()}.} +} +\value{ +A ggplot object. +} +\description{ +Generates a simple UMAP scatterplot for visualizing sample clustering or separation. +} +\examples{ + +\dontrun{ +my_umap = make_and_annotate_umap(my_data, my_metadata) + +basic_umap_scatterplot(my_umap$df, #the data frame containing V1 and V2 from UMAP + plot_samples = "some_sample_ID", + colour_by = "DLBCLone_ko") +} +} diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd index b6e4e1c..16333dc 100644 --- a/man/nearest_neighbor_heatmap.Rd +++ b/man/nearest_neighbor_heatmap.Rd @@ -8,14 +8,7 @@ nearest_neighbor_heatmap( this_sample_id, DLBCLone_model, truth_column = "lymphgen", - clustering_distance = "binary", - font_size = 14 -) - -nearest_neighbor_heatmap( - this_sample_id, - DLBCLone_model, - truth_column = "lymphgen", + metadata_cols = NULL, clustering_distance = "binary", font_size = 14 ) @@ -23,43 +16,25 @@ nearest_neighbor_heatmap( \arguments{ \item{this_sample_id}{Character. The sample ID for which to plot the nearest neighbor heatmap.} -\item{DLBCLone_model}{List. A DLBCLone model object, either from \code{DLBCLone_optimize_params} -or \code{DLBCLone_KNN} (with \code{predict_unlabeled = TRUE}).} +\item{DLBCLone_model}{A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, \code{DLBCLone_KNN}, or \code{predict_single_sample_DLBCLone}.} \item{truth_column}{Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} -\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} - -\item{single_sample_prediction_output}{A list containing the prediction results from predict_single_sample_DLBCLone, -including the feature matrix and predictions.} - -\item{optimized_model}{list of parameters from DLBCLone_optimize_params, neccessary UMAP output from a -previous, Data frame with the best parameters. useful for reproducibility.} +\item{metadata_cols}{Optional character vector of additional metadata columns to include in the heatmap annotations.} -\item{pred_column}{Character. The column name in the predictions data frame that contains the predicted labels.} +\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} -\item{metadata_cols}{Optional character vector of additional metadata columns to include in the heatmap annotations.} +\item{font_size}{Numeric. Font size for heatmap text (default: 14).} } \value{ -A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. - A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. } \description{ Generates a heatmap of feature values for the nearest neighbors of a specified sample, based on a DLBCLone model object. This visualization helps to inspect the feature profiles of samples most similar to the query sample. - -Generates a simple UMAP scatterplot for visualizing sample clustering or separation. } \details{ -\itemize{ -\item For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. -\item For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. -\item The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, -and plots a heatmap of features with nonzero values. -} - \itemize{ \item For models of type \code{DLBCLone_optimize_params}, uses the \code{neighbors} and \code{lyseq_status} fields. \item For models of type \code{DLBCLone_KNN}, uses the \code{unlabeled_neighbors} field. @@ -71,9 +46,4 @@ and plots a heatmap of features with nonzero values. # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: nearest_neighbor_heatmap(sample_id, model) -# Assuming 'predicted_out' is a the output of DLBCLone_KNN_predict -and "SomeSample_ID" is a valid sample ID from among the test (not training) samples -\dontrun{ -nearest_neighbor_heatmap("SomeSample_ID", predicted_out) -} } From 0b7d6c814ca466d32766177b2c38456a1bbd3f4b Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 18 Aug 2025 09:45:55 -0700 Subject: [PATCH 73/79] stacked_bar_plot --- R/plotting.R | 158 +++++++++++++++++++++++++++++++++++++++- man/stacked_bar_plot.Rd | 43 +++++++++++ 2 files changed, 198 insertions(+), 3 deletions(-) create mode 100644 man/stacked_bar_plot.Rd diff --git a/R/plotting.R b/R/plotting.R index 9f16977..e2ee53f 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -204,11 +204,11 @@ nearest_neighbor_heatmap <- function( row_anno = rowAnnotation( df = row_df[rownames(feats_df),,drop=FALSE], col = anno_list, - annotation_name_gp = gpar(fontsize = 12), + annotation_name_gp = gpar(fontsize = font_size), show_legend = TRUE ) - title_text = paste( + title_text = paste( "Sample", this_sample_id, "classified as", @@ -218,7 +218,7 @@ nearest_neighbor_heatmap <- function( ht <- Heatmap( feats_df[,colSums(feats_df)>0], col = col_fun, - column_names_gp = gpar(fontsize=12), + column_names_gp = gpar(fontsize = font_size), right_annotation = row_anno, clustering_distance_rows = clustering_distance, show_heatmap_legend = FALSE, @@ -1070,3 +1070,155 @@ make_alluvial <- function( } pp } + +#' Create a stacked bar plot of top features per subtype +#' +#' This function generates a stacked bar plot showing the top features (genes) for each subtype based on their prevalence in the dataset. +#' +#' @param optimized_model output from DLBCLone_optimize_params function +#' @param truth_column Name of the column containing the true class labels (default: "lymphgen"). +#' @param truth_classes Vector of class labels to consider (default: c("BN2","EZB","MCD","ST2")). +#' @param method Method to determine top features: "common" for most common features, "chi_square" for subtype vs rest significance (default : "common"). +#' @param num_feats Number of top features to display per subtype (default: 10). +#' @param title Title for the plot (default: NULL). +#' +#' @return A ggplot2 object representing the stacked bar plot. +#' +#' @examples +#' stacked_bar_plot( +#' all_features_optimized, +#' method = "chi_square", +#' num_feats = 10, +#' title = "LymphGen" +#' ) +#' + +stacked_bar_plot <- function( + optimized_model, + truth_column = "lymphgen", + truth_classes = c("BN2","EZB","MCD","ST2"), + method = "common", + num_feats = 10, + title = NULL +){ + + bad_cols <- colSums(optimized_model$features) <= 0.02 * nrow(optimized_model$features) + optimized_model$features <- optimized_model$features[, !bad_cols] + + annotated_feats <- optimized_model$features %>% + rownames_to_column("sample_id") %>% + left_join( + optimized_model$df %>% select(sample_id, !!sym(truth_column)), + by = "sample_id" + ) %>% + column_to_rownames("sample_id") + + annotated_feats[[truth_column]] <- as.factor(annotated_feats[[truth_column]]) + + gene_cols <- setdiff(colnames(annotated_feats), c("sample_id", truth_column)) + subtypes <- truth_classes + + top_genes_per_subtype <- list() + + if(method == "common"){ + + for(subtype in subtypes){ + subtype_samples <- annotated_feats[annotated_feats[[truth_column]] == subtype, ] + + gene_counts <- colSums(subtype_samples[, gene_cols, drop = FALSE]) + gene_ranking <- order(gene_counts, decreasing = TRUE) + + top_genes_per_subtype[[subtype]] <- gene_cols[gene_ranking[1:num_feats]] + } + + }else if(method == "chi_square"){ + + for(subtype in subtypes){ + # Create binary class: subtype vs rest + y_binary <- factor(ifelse(annotated_feats[[truth_column]] == subtype, subtype, paste0("not_", subtype))) + + chi_stats <- numeric(length(gene_cols)) + + for(i in seq_along(gene_cols)){ + gene <- gene_cols[i] + tbl <- table(annotated_feats[[gene]], y_binary) + test <- suppressWarnings(chisq.test(tbl)) + chi_stats[i] <- test$statistic + } + + gene_ranking <- order(chi_stats, decreasing = TRUE) + + # Top genes for this subtype + top_genes_per_subtype[[subtype]] <- gene_cols[gene_ranking[1:num_feats]] + } + + }else{ + stop( + paste( + "Must select a valid method:", + "'common' - for most common features", + "'chi_square' - for subtype vs rest significance", + sep = "\n" + ) + ) + } + + plot_df <- bind_rows(lapply(names(top_genes_per_subtype), function(subtype){ + genes <- top_genes_per_subtype[[subtype]] + subtype_samples <- annotated_feats[annotated_feats[[truth_column]] == subtype, ] + n_subtype <- nrow(subtype_samples) # total samples in this subtype + + counts <- colSums(subtype_samples[, genes, drop = FALSE] > 0) # ensure 0/1 + plot_df <- tibble( + subtype = subtype, + gene = names(counts), + count = as.numeric(counts), + prop_gene = as.numeric(counts) / n_subtype + ) + })) + + plot_df <- plot_df %>% + group_by(subtype) %>% + mutate( + prop = count / sum(count), + rank = rank(-count, ties.method = "first") # rank genes within subtype + ) %>% + ungroup() + + # cumulative position for placing labels + plot_df <- plot_df %>% + group_by(subtype) %>% + arrange(rank) %>% + mutate( + cum_prop = cumsum(prop), + pos = cum_prop - prop / 2 + ) %>% + ungroup() + + n_colors <- max(plot_df$rank) + palette <- colorRampPalette(c("darkred","pink","white"))(n_colors) + + ggplot(plot_df, aes(x = subtype, y = prop, fill = factor(rank))) + + geom_bar( + stat = "identity", + color = "black", + position = position_stack(reverse = TRUE) + ) + + geom_text( + aes(label = paste0(gene, " ", scales::percent(prop_gene, accuracy = 1))), + position = position_stack(vjust = 0.5, reverse = TRUE), + size = 3 + ) + + scale_fill_manual(values = palette) + + labs( + title = paste(title, ", Top", num_feats, "genes per subtype (method:", method, ")"), + x = "Subtype", + y = "Proportion of Samples with Mutation" + ) + + theme_minimal() + + theme( + axis.text.x = element_text(angle = 45, hjust = 1), + legend.position = "none" + ) + +} diff --git a/man/stacked_bar_plot.Rd b/man/stacked_bar_plot.Rd new file mode 100644 index 0000000..e3b5779 --- /dev/null +++ b/man/stacked_bar_plot.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{stacked_bar_plot} +\alias{stacked_bar_plot} +\title{Create a stacked bar plot of top features per subtype} +\usage{ +stacked_bar_plot( + optimized_model, + truth_column = "lymphgen", + truth_classes = c("BN2", "EZB", "MCD", "ST2"), + method = "common", + num_feats = 10, + title = NULL +) +} +\arguments{ +\item{optimized_model}{output from DLBCLone_optimize_params function} + +\item{truth_column}{Name of the column containing the true class labels (default: "lymphgen").} + +\item{truth_classes}{Vector of class labels to consider (default: c("BN2","EZB","MCD","ST2")).} + +\item{method}{Method to determine top features: "common" for most common features, "chi_square" for subtype vs rest significance (default : "common").} + +\item{num_feats}{Number of top features to display per subtype (default: 10).} + +\item{title}{Title for the plot (default: NULL).} +} +\value{ +A ggplot2 object representing the stacked bar plot. +} +\description{ +This function generates a stacked bar plot showing the top features (genes) for each subtype based on their prevalence in the dataset. +} +\examples{ +stacked_bar_plot( + all_features_optimized, + method = "chi_square", + num_feats = 10, + title = "LymphGen" +) + +} From 0d7906ccdc764609deef5617669a91ac9f977ec3 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Mon, 18 Aug 2025 10:26:35 -0700 Subject: [PATCH 74/79] stacked barplot updates --- R/plotting.R | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/R/plotting.R b/R/plotting.R index e2ee53f..12de589 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -1195,21 +1195,29 @@ stacked_bar_plot <- function( ) %>% ungroup() - n_colors <- max(plot_df$rank) - palette <- colorRampPalette(c("darkred","pink","white"))(n_colors) + colours <- get_gambl_colours() + n_colours <- max(plot_df$rank) - ggplot(plot_df, aes(x = subtype, y = prop, fill = factor(rank))) + - geom_bar( - stat = "identity", - color = "black", - position = position_stack(reverse = TRUE) - ) + + fill_map <- plot_df %>% + group_by(subtype) %>% + mutate( + base_col = ifelse(!is.na(colours[subtype]), colours[subtype], "grey"), + ramp = list(colorRampPalette(c(first(base_col), "white"))(max(rank))), + fill_color = ramp[[1]][rank] + ) %>% + ungroup() + + # merge colors back into plot_df + plot_df$fill_color <- fill_map$fill_color + + ggplot(plot_df, aes(x = subtype, y = prop, fill = fill_color)) + + geom_bar(stat = "identity", color = "black", position = position_stack(reverse = TRUE)) + geom_text( aes(label = paste0(gene, " ", scales::percent(prop_gene, accuracy = 1))), position = position_stack(vjust = 0.5, reverse = TRUE), size = 3 ) + - scale_fill_manual(values = palette) + + scale_fill_identity() + labs( title = paste(title, ", Top", num_feats, "genes per subtype (method:", method, ")"), x = "Subtype", @@ -1217,7 +1225,7 @@ stacked_bar_plot <- function( ) + theme_minimal() + theme( - axis.text.x = element_text(angle = 45, hjust = 1), + axis.text.x = element_text(angle = 0, hjust = 1), legend.position = "none" ) From aa61e7d893b3012637ddb7930efa110f74e2b1af Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 19 Aug 2025 14:24:23 -0700 Subject: [PATCH 75/79] updating params for make_umap_scatterplot and updating stacked barplot to accept DLBCLone_KNN and DLBCLone_optimize_params list outputs --- R/plotting.R | 47 +++++++++++++++++++-------------- man/make_umap_scatterplot.Rd | 6 ++--- man/nearest_neighbor_heatmap.Rd | 6 ++--- man/stacked_bar_plot.Rd | 4 +-- 4 files changed, 35 insertions(+), 28 deletions(-) diff --git a/R/plotting.R b/R/plotting.R index 12de589..6bbba49 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -6,10 +6,10 @@ #' #' @param this_sample_id Character. The sample ID for which to plot the nearest neighbor heatmap. #' @param DLBCLone_model A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, \code{DLBCLone_KNN}, or \code{predict_single_sample_DLBCLone}. -#' @param truth_column Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen"). +#' @param truth_column The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen"). #' @param metadata_cols Optional character vector of additional metadata columns to include in the heatmap annotations. -#' @param clustering_distance Character. The distance metric to use for clustering rows. Default is "binary". -#' @param font_size Numeric. Font size for heatmap text (default: 14). +#' @param clustering_distance The distance metric to use for clustering rows. Default is "binary". +#' @param font_size Font size for heatmap text (default: 14). #' #' @return A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. #' @@ -540,8 +540,8 @@ make_neighborhood_plot <- function( #' Make UMAP scatterplot #' #' @param df Data frame containing the UMAP coordinates and annotations. +#' @param truth_column Name of the column containing the labels to be plotted (default: "lymphgen"). #' @param drop_composite If TRUE: removes composite labels from the lymphgen column. -#' @param colour_by Column name to color the points by. Default: "lymphgen". #' @param drop_other If TRUE: removes "Other" and "NOS" labels from the lymphgen column. #' @param high_confidence If TRUE: filters the data to include only samples with confidence > 0.7. #' @param custom_colours Custom color palette for the plot. If not provided, uses default GAMBL colors. @@ -552,10 +552,11 @@ make_neighborhood_plot <- function( #' @export #' #' @examples +#' make_umap_scatterplot = function( df, + truth_column = "lymphgen", drop_composite = TRUE, - colour_by="lymphgen", drop_other = FALSE, high_confidence = FALSE, custom_colours, @@ -572,24 +573,24 @@ make_umap_scatterplot = function( cols = get_gambl_colours() } if(drop_composite){ - df = filter(df,!is.na(lymphgen),!grepl("COMP",lymphgen)) + df = filter(df,!is.na(!!sym(truth_column)),!grepl("COMP",!!sym(truth_column))) } if(drop_other){ - df = filter(df,!is.na(lymphgen),lymphgen!="Other",lymphgen!="NOS") + df = filter(df,!is.na(!!sym(truth_column)),!!sym(truth_column)!="Other",!!sym(truth_column)!="NOS") } if(high_confidence){ df = filter(df,Confidence > 0.7) } if(add_labels){ - labels = group_by(df,!!sym(colour_by)) %>% + labels = group_by(df,!!sym(truth_column)) %>% summarise(median_x = median(V1),median_y = median(V2)) } - unique_lg = unique(df$lymphgen) + unique_lg = unique(df[[truth_column]]) if(any(!unique_lg %in% names(cols))){ missing = unique_lg[!unique_lg %in% names(cols)] print(paste("missing colour for:",paste(missing,collapse=","))) } - p = ggplot(df,aes(x=V1,y=V2,colour=!!sym(colour_by),label=cohort)) + + p = ggplot(df,aes(x=V1,y=V2,colour=!!sym(truth_column),label=cohort)) + geom_point(alpha=0.8) + scale_colour_manual(values=cols) + theme_Morons() + guides(colour = guide_legend(nrow = 1)) + @@ -600,10 +601,9 @@ make_umap_scatterplot = function( p = p + ggtitle(title) } if(add_labels){ - p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(colour_by))) + p = p + geom_label_repel(data=labels,aes(x=median_x,y=median_y,label=!!sym(truth_column))) } ggMarginal(p,groupColour = TRUE,groupFill=TRUE) - } #' Report Classification Accuracy and Per-Class Metrics @@ -1075,7 +1075,7 @@ make_alluvial <- function( #' #' This function generates a stacked bar plot showing the top features (genes) for each subtype based on their prevalence in the dataset. #' -#' @param optimized_model output from DLBCLone_optimize_params function +#' @param DLBCLone_model A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, or \code{DLBCLone_KNN} #' @param truth_column Name of the column containing the true class labels (default: "lymphgen"). #' @param truth_classes Vector of class labels to consider (default: c("BN2","EZB","MCD","ST2")). #' @param method Method to determine top features: "common" for most common features, "chi_square" for subtype vs rest significance (default : "common"). @@ -1094,21 +1094,28 @@ make_alluvial <- function( #' stacked_bar_plot <- function( - optimized_model, + DLBCLone_model, truth_column = "lymphgen", truth_classes = c("BN2","EZB","MCD","ST2"), method = "common", num_feats = 10, title = NULL ){ + if(!missing(DLBCLone_model) && "type" %in% names(DLBCLone_model) && DLBCLone_model$type == "predict_single_sample_DLBCLone"){ + stop("DLBCLone_model must be the output of DLBCLone_optimize_params, or DLBCLone_KNN") + } - bad_cols <- colSums(optimized_model$features) <= 0.02 * nrow(optimized_model$features) - optimized_model$features <- optimized_model$features[, !bad_cols] + if ("type" %in% names(DLBCLone_model) && DLBCLone_model$type == "DLBCLone_KNN") { + DLBCLone_model$features <- DLBCLone_model$features_df + } + + bad_cols <- colSums(DLBCLone_model$features) <= 0.02 * nrow(DLBCLone_model$features) + DLBCLone_model$features <- DLBCLone_model$features[, !bad_cols] - annotated_feats <- optimized_model$features %>% + annotated_feats <- DLBCLone_model$features %>% rownames_to_column("sample_id") %>% left_join( - optimized_model$df %>% select(sample_id, !!sym(truth_column)), + DLBCLone_model$df %>% select(sample_id, !!sym(truth_column)), by = "sample_id" ) %>% column_to_rownames("sample_id") @@ -1128,6 +1135,7 @@ stacked_bar_plot <- function( gene_counts <- colSums(subtype_samples[, gene_cols, drop = FALSE]) gene_ranking <- order(gene_counts, decreasing = TRUE) + # Top genes for this subtype top_genes_per_subtype[[subtype]] <- gene_cols[gene_ranking[1:num_feats]] } @@ -1219,7 +1227,7 @@ stacked_bar_plot <- function( ) + scale_fill_identity() + labs( - title = paste(title, ", Top", num_feats, "genes per subtype (method:", method, ")"), + title = paste(title, " Top", num_feats, "genes per subtype (method:", method, ")"), x = "Subtype", y = "Proportion of Samples with Mutation" ) + @@ -1228,5 +1236,4 @@ stacked_bar_plot <- function( axis.text.x = element_text(angle = 0, hjust = 1), legend.position = "none" ) - } diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index bb7d982..038ea6c 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -6,8 +6,8 @@ \usage{ make_umap_scatterplot( df, + truth_column = "lymphgen", drop_composite = TRUE, - colour_by = "lymphgen", drop_other = FALSE, high_confidence = FALSE, custom_colours, @@ -18,9 +18,9 @@ make_umap_scatterplot( \arguments{ \item{df}{Data frame containing the UMAP coordinates and annotations.} -\item{drop_composite}{If TRUE: removes composite labels from the lymphgen column.} +\item{truth_column}{Name of the column containing the labels to be plotted (default: "lymphgen").} -\item{colour_by}{Column name to color the points by. Default: "lymphgen".} +\item{drop_composite}{If TRUE: removes composite labels from the lymphgen column.} \item{drop_other}{If TRUE: removes "Other" and "NOS" labels from the lymphgen column.} diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd index 16333dc..28e4610 100644 --- a/man/nearest_neighbor_heatmap.Rd +++ b/man/nearest_neighbor_heatmap.Rd @@ -18,13 +18,13 @@ nearest_neighbor_heatmap( \item{DLBCLone_model}{A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, \code{DLBCLone_KNN}, or \code{predict_single_sample_DLBCLone}.} -\item{truth_column}{Character. The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} +\item{truth_column}{The column name in the predictions data frame that contains the ground truth labels (default: "lymphgen").} \item{metadata_cols}{Optional character vector of additional metadata columns to include in the heatmap annotations.} -\item{clustering_distance}{Character. The distance metric to use for clustering rows. Default is "binary".} +\item{clustering_distance}{The distance metric to use for clustering rows. Default is "binary".} -\item{font_size}{Numeric. Font size for heatmap text (default: 14).} +\item{font_size}{Font size for heatmap text (default: 14).} } \value{ A ComplexHeatmap object showing the feature matrix for the nearest neighbors of the sample. diff --git a/man/stacked_bar_plot.Rd b/man/stacked_bar_plot.Rd index e3b5779..96c1dc4 100644 --- a/man/stacked_bar_plot.Rd +++ b/man/stacked_bar_plot.Rd @@ -5,7 +5,7 @@ \title{Create a stacked bar plot of top features per subtype} \usage{ stacked_bar_plot( - optimized_model, + DLBCLone_model, truth_column = "lymphgen", truth_classes = c("BN2", "EZB", "MCD", "ST2"), method = "common", @@ -14,7 +14,7 @@ stacked_bar_plot( ) } \arguments{ -\item{optimized_model}{output from DLBCLone_optimize_params function} +\item{DLBCLone_model}{A DLBCLone model object, which can be the output of \code{DLBCLone_optimize_params}, or \code{DLBCLone_KNN}} \item{truth_column}{Name of the column containing the true class labels (default: "lymphgen").} From b22b25c69c69977f785462e25230c7939a756170 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Tue, 2 Sep 2025 17:11:57 -0700 Subject: [PATCH 76/79] adding appropriate @examples to each function in umap.r and plotting.r --- R/plotting.R | 64 ++++++++++++++--- R/umap.R | 100 +++++++++++++++++--------- man/DLBCLone_KNN.Rd | 10 +++ man/DLBCLone_KNN_predict.Rd | 10 +-- man/DLBCLone_optimize_params.Rd | 32 +++++---- man/DLBCLone_summarize_model.Rd | 2 + man/assemble_genetic_features.Rd | 27 +++++++ man/basic_umap_scatterplot.Rd | 12 ++-- man/make_alluvial.Rd | 5 +- man/make_and_annotate_umap.Rd | 13 ++-- man/make_neighborhood_plot.Rd | 17 ++++- man/make_umap_scatterplot.Rd | 14 ++++ man/nearest_neighbor_heatmap.Rd | 8 ++- man/predict_single_sample_DLBCLone.Rd | 9 ++- man/report_accuracy.Rd | 2 + man/stacked_bar_plot.Rd | 4 +- 16 files changed, 245 insertions(+), 84 deletions(-) diff --git a/R/plotting.R b/R/plotting.R index 6bbba49..ac89cc4 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -26,7 +26,13 @@ #' #' @examples #' # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: -#' nearest_neighbor_heatmap(sample_id, model) +#' \dontrun{ +#' nearest_neighbor_heatmap( +#' this_sample_id = "CCS_0680_lst", +#' DLBCLone_model = predict_single, +#' font_size = 10 +#' ) +#' } #' nearest_neighbor_heatmap <- function( this_sample_id, @@ -252,11 +258,15 @@ nearest_neighbor_heatmap <- function( #' @examples #' #' \dontrun{ -#' my_umap = make_and_annotate_umap(my_data, my_metadata) +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) #' -#' basic_umap_scatterplot(my_umap$df, #the data frame containing V1 and V2 from UMAP -#' plot_samples = "some_sample_ID", -#' colour_by = "DLBCLone_ko") +#' basic_umap_scatterplot( +#' make_umap$df, #the data frame containing V1 and V2 from UMAP +#' plot_samples = "some_sample_ID", +#' colour_by = "DLBCLone_ko") #' } basic_umap_scatterplot <- function(optimized, plot_samples = NULL, @@ -355,8 +365,10 @@ basic_umap_scatterplot <- function(optimized, #' @return No return value. Side effect: writes multiple PDF files to disk. #' #' @examples +#' \dontrun{ #' DLBCLone_summarize_model("Full_geneset_unweighted", optimized_model) -#' +#'} +#' #' @export DLBCLone_summarize_model = function( base_name, @@ -461,7 +473,22 @@ DLBCLone_summarize_model = function( #' # Assuming 'optimization_result' is the output of DLBCLone_optimize_params #' # and 'output' is the result of DLBCLone_predict_single_sample #' # on sample_id "SAMPLE123": -#' make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") +#' \dontrun{ +#' predict_single <- predict_single_sample_DLBCLone( +#' test_df = optimize_params$features[1,], +#' train_metadata = dlbcl_meta, +#' optimized_model = optimize_params +#' ) +#' +#' make_neighborhood_plot( +#' single_sample_prediction_output = predict_single, +#' training_predictions = optimize_params$df, +#' this_sample_id = "SAMPLE123", +#' prediction_in_title = TRUE, +#' add_circle = TRUE +#' ) +#' } +#' make_neighborhood_plot <- function( single_sample_prediction_output, training_predictions, @@ -552,6 +579,17 @@ make_neighborhood_plot <- function( #' @export #' #' @examples +#' \dontrun{ +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) +#' +#' make_umap_scatterplot( +#' make_umap$df, +#' drop_other = F +#' ) +#' } #' make_umap_scatterplot = function( df, @@ -630,9 +668,11 @@ make_umap_scatterplot = function( #' - Returns per-class metrics for further analysis. #' #' @examples +#' \dontrun{ #' result <- report_accuracy(predictions_df) #' result$overall #' result$per_class +#' } #' #' @export report_accuracy = function( @@ -728,8 +768,9 @@ report_accuracy = function( #' - Supports flexible labeling, coloring, and axis ordering for publication-quality plots. #' #' @examples -#' # Example usage: -#' # make_alluvial(optimized_result) +#' \dontrun{ +#' make_alluvial(optimize_params) +#' } #' #' @export make_alluvial <- function( @@ -1085,14 +1126,15 @@ make_alluvial <- function( #' @return A ggplot2 object representing the stacked bar plot. #' #' @examples +#' \dontrun{ #' stacked_bar_plot( -#' all_features_optimized, +#' optimize_params, #' method = "chi_square", #' num_feats = 10, #' title = "LymphGen" #' ) +#' } #' - stacked_bar_plot <- function( DLBCLone_model, truth_column = "lymphgen", diff --git a/R/umap.R b/R/umap.R index 103da09..06ba67a 100644 --- a/R/umap.R +++ b/R/umap.R @@ -174,6 +174,33 @@ summarize_all_ssm_status <- function( #' @param verbose Defaults to FALSE #' #' @return Matrix of assembled features for each sample. +#' +#' @examples +#' \dontrun{ +#' ordered_genes = filter(lymphoma_genes,DLBCL_Tier==1 | FL_Tier==1|BL_Tier==1) %>% pull(Gene) +#' synon_genes = unique(grch37_ashm_regions$gene) +#' synon_genes = synon_genes[synon_genes %in% ordered_genes] +#' +#' all_sv_anno = get_combined_sv(these_samples_metadata = dlbcl_meta) +#' +#' all_sv_anno = annotate_sv(all_sv_anno) +#' +#' all_full_status = assemble_genetic_features( +#' these_samples_metadata = dlbcl_meta, +#' metadata_columns = c("BCL2_SV","BCL6_SV"), +#' synon_genes = synon_genes, +#' synon_value = 1, +#' coding_value = 2, +#' genes = ordered_genes, +#' hotspot_genes = c("MYD88"), +#' maf_with_synon = all_maf_with_s, +#' include_ashm = F, +#' sv_value = 2, +#' verbose=F, +#' annotated_sv = all_sv_anno +#' ) +#' } +#' #' @export #' assemble_genetic_features <- function( @@ -661,15 +688,10 @@ DLBCLone_train_test_plot = function(test_df, #' #' @examples #' -#' umap_outs = make_and_annotate_umap(df=gambl_train_lymphgen, -#' min_dist = 0, -#' n_neighbors = 55, -#' init="spectral", -#' n_epochs = 1500, -#' #seed=best_params$seed, -#' metadata=gambl_train_meta_dlbclass, -#' ret_model=T, -#' metric="cosine") +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#') #' make_and_annotate_umap = function( df, @@ -1252,10 +1274,12 @@ optimize_purity <- function( #' # model_out <- DLBCLone_KNN(train_features, train_metadata, ...) #' # Predict on new samples: #' predictions <- DLBCLone_KNN_predict( -#' train_df = train_features, -#' test_df = new_samples, -#' metadata = sample_metadata, -#' DLBCLone_KNN_out = model_out +#' train_df = all_full_status, +#' test_df = lyseq_validation_data, +#' metadata = dlbcl_meta, +#' DLBCLone_KNN_out = dlbcl_knn, +#' mode = "batch" +#' ) #' #' @export #' @@ -1364,6 +1388,15 @@ DLBCLone_KNN_predict <- function(train_df, #' \item{unlabeled_predictions}{Predictions for unlabeled samples (if requested)} #' \item{df}{Annotated layout for plotting (when built in this run)} #' \item{plot_truth, plot_predicted}{ggplots when built in this run} +#' +#' @examples +#' dlbcl_knn <- DLBCLone_KNN( +#' features_df = all_full_status, +#' metadata = dlbcl_meta, +#' min_k = 5, +#' max_k = 21, +#' optimize_for_other = TRUE +#' ) #' #' @export DLBCLone_KNN <- function(features_df, @@ -2011,24 +2044,28 @@ DLBCLone_KNN <- function(features_df, #' #' #' # Aim to maximize classification of samples into non-Other class -#' lymphgen_lyseq_no_other = -#' GAMBLR.predict::DLBCLone_optimize_params( -#' dlbcl_status_combined_lyseq, -#' dlbcl_meta_lyseq_train, -#' min_k = 5,max_k=23, -#' optimize_for_other = F, -#' truth_classes = c("MCD","EZB","ST2","BN2")) +#' optimize_params_F <- DLBCLone_optimize_params( +#' make_umap$features, +#' dlbcl_meta, +#' umap_out = make_umap, +#' truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), +#' optimize_for_other = F, +#' min_k=5, +#' max_k=23 +#' ) #' #' # Aim to maximize balanced accuracy while allowing samples to be #' # unclassified (assigned to "Other") #' -#' DLBCLone_lymphgen_lyseq_prime_opt = -#' GAMBLR.predict::DLBCLone_optimize_params( -#' dlbcl_status_combined_lyseq_prime, -#' dlbcl_meta_lyseq_train, -#' min_k = 5,max_k=23, -#' optimize_for_other = T, -#' truth_classes = c("MCD","EZB","ST2","BN2","Other")) +#' optimize_params_T <- DLBCLone_optimize_params( +#' make_umap$features, +#' dlbcl_meta, +#' umap_out = make_umap, +#' truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), +#' optimize_for_other = T, +#' min_k=5, +#' max_k=23 +#' ) #' DLBCLone_optimize_params = function( combined_mutation_status_df, @@ -2647,11 +2684,10 @@ weighted_knn_predict_with_conf <- function( #' @export #' #' @examples -#' predict_single_sample_DLBCLone( -#' test_df = test_df, -#' train_metadata = dlbcl_meta_clean, -#' optimized_model = weighted_lyseq_opt, -#' seed = 1234 +#' predict_single <- predict_single_sample_DLBCLone( +#' test_df = optimize_params$features[1,], +#' train_metadata = dlbcl_meta, +#' optimized_model = optimize_params_T #' ) #' predict_single_sample_DLBCLone <- function( diff --git a/man/DLBCLone_KNN.Rd b/man/DLBCLone_KNN.Rd index 531a8d6..c6eca6b 100644 --- a/man/DLBCLone_KNN.Rd +++ b/man/DLBCLone_KNN.Rd @@ -89,3 +89,13 @@ in-group classes and the outgroup column name from the arguments \code{truth_classes} and \code{other_class}. It keeps backward compatibility for the default LymphGen-like usage. } +\examples{ +dlbcl_knn <- DLBCLone_KNN( + features_df = all_full_status, + metadata = dlbcl_meta, + min_k = 5, + max_k = 21, + optimize_for_other = TRUE +) + +} diff --git a/man/DLBCLone_KNN_predict.Rd b/man/DLBCLone_KNN_predict.Rd index ccfc263..e49e5eb 100644 --- a/man/DLBCLone_KNN_predict.Rd +++ b/man/DLBCLone_KNN_predict.Rd @@ -53,9 +53,11 @@ for more stable results when predicting multiple samples. # model_out <- DLBCLone_KNN(train_features, train_metadata, ...) # Predict on new samples: predictions <- DLBCLone_KNN_predict( - train_df = train_features, - test_df = new_samples, - metadata = sample_metadata, - DLBCLone_KNN_out = model_out + train_df = all_full_status, + test_df = lyseq_validation_data, + metadata = dlbcl_meta, + DLBCLone_KNN_out = dlbcl_knn, + mode = "batch" +) } diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 7b9ee48..42e2cdc 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -84,23 +84,27 @@ Optimize parameters for classifying samples using UMAP and k-nearest neighbor # Aim to maximize classification of samples into non-Other class -lymphgen_lyseq_no_other = -GAMBLR.predict::DLBCLone_optimize_params( - dlbcl_status_combined_lyseq, - dlbcl_meta_lyseq_train, - min_k = 5,max_k=23, - optimize_for_other = F, - truth_classes = c("MCD","EZB","ST2","BN2")) +optimize_params_F <- DLBCLone_optimize_params( + make_umap$features, + dlbcl_meta, + umap_out = make_umap, + truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), + optimize_for_other = F, + min_k=5, + max_k=23 +) # Aim to maximize balanced accuracy while allowing samples to be # unclassified (assigned to "Other") -DLBCLone_lymphgen_lyseq_prime_opt = -GAMBLR.predict::DLBCLone_optimize_params( - dlbcl_status_combined_lyseq_prime, - dlbcl_meta_lyseq_train, - min_k = 5,max_k=23, - optimize_for_other = T, - truth_classes = c("MCD","EZB","ST2","BN2","Other")) +optimize_params_T <- DLBCLone_optimize_params( + make_umap$features, + dlbcl_meta, + umap_out = make_umap, + truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), + optimize_for_other = T, + min_k=5, + max_k=23 +) } diff --git a/man/DLBCLone_summarize_model.Rd b/man/DLBCLone_summarize_model.Rd index 7e77f99..d9566d0 100644 --- a/man/DLBCLone_summarize_model.Rd +++ b/man/DLBCLone_summarize_model.Rd @@ -28,6 +28,8 @@ Results are saved as PDF files in a directory named after the provided base name } } \examples{ +\dontrun{ DLBCLone_summarize_model("Full_geneset_unweighted", optimized_model) +} } diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd index 3c6d5a9..3accacc 100644 --- a/man/assemble_genetic_features.Rd +++ b/man/assemble_genetic_features.Rd @@ -60,3 +60,30 @@ Matrix of assembled features for each sample. This function assembles a matrix of genetic features for each sample, including mutation status, aSHM counts, and structural variant status for BCL2, BCL6, and MYC. It supports both genome and capture sequencing types. } +\examples{ +\dontrun{ +ordered_genes = filter(lymphoma_genes,DLBCL_Tier==1 | FL_Tier==1|BL_Tier==1) \%>\% pull(Gene) +synon_genes = unique(grch37_ashm_regions$gene) +synon_genes = synon_genes[synon_genes \%in\% ordered_genes] + +all_sv_anno = get_combined_sv(these_samples_metadata = dlbcl_meta) + +all_sv_anno = annotate_sv(all_sv_anno) + +all_full_status = assemble_genetic_features( + these_samples_metadata = dlbcl_meta, + metadata_columns = c("BCL2_SV","BCL6_SV"), + synon_genes = synon_genes, + synon_value = 1, + coding_value = 2, + genes = ordered_genes, + hotspot_genes = c("MYD88"), + maf_with_synon = all_maf_with_s, + include_ashm = F, + sv_value = 2, + verbose=F, + annotated_sv = all_sv_anno +) +} + +} diff --git a/man/basic_umap_scatterplot.Rd b/man/basic_umap_scatterplot.Rd index 86a2d3e..c236157 100644 --- a/man/basic_umap_scatterplot.Rd +++ b/man/basic_umap_scatterplot.Rd @@ -44,10 +44,14 @@ Generates a simple UMAP scatterplot for visualizing sample clustering or separat \examples{ \dontrun{ -my_umap = make_and_annotate_umap(my_data, my_metadata) +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) -basic_umap_scatterplot(my_umap$df, #the data frame containing V1 and V2 from UMAP - plot_samples = "some_sample_ID", - colour_by = "DLBCLone_ko") +basic_umap_scatterplot( + make_umap$df, #the data frame containing V1 and V2 from UMAP + plot_samples = "some_sample_ID", + colour_by = "DLBCLone_ko") } } diff --git a/man/make_alluvial.Rd b/man/make_alluvial.Rd index b8e1be9..7e35e76 100644 --- a/man/make_alluvial.Rd +++ b/man/make_alluvial.Rd @@ -92,7 +92,8 @@ It supports annotation of concordance rates, per-group accuracy, unclassified ra } } \examples{ -# Example usage: -# make_alluvial(optimized_result) +\dontrun{ +make_alluvial(optimize_params) +} } diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 8de1b83..70aecd7 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -85,14 +85,9 @@ Run UMAP and attach result to metadata } \examples{ -umap_outs = make_and_annotate_umap(df=gambl_train_lymphgen, - min_dist = 0, - n_neighbors = 55, - init="spectral", - n_epochs = 1500, - #seed=best_params$seed, - metadata=gambl_train_meta_dlbclass, - ret_model=T, - metric="cosine") +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) } diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index 2dea4be..a955d5e 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -44,5 +44,20 @@ The function extracts the nearest neighbors of the specified sample, draws segme # Assuming 'optimization_result' is the output of DLBCLone_optimize_params # and 'output' is the result of DLBCLone_predict_single_sample # on sample_id "SAMPLE123": -make_neighborhood_plot(output, optimization_result$df, "SAMPLE123") +\dontrun{ +predict_single <- predict_single_sample_DLBCLone( + test_df = optimize_params$features[1,], + train_metadata = dlbcl_meta, + optimized_model = optimize_params +) + +make_neighborhood_plot( + single_sample_prediction_output = predict_single, + training_predictions = optimize_params$df, + this_sample_id = "SAMPLE123", + prediction_in_title = TRUE, + add_circle = TRUE +) +} + } diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index 038ea6c..90e4e51 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -35,3 +35,17 @@ make_umap_scatterplot( \description{ Make UMAP scatterplot } +\examples{ +\dontrun{ +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) + +make_umap_scatterplot( + make_umap$df, + drop_other = F +) +} + +} diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd index 28e4610..b4d3087 100644 --- a/man/nearest_neighbor_heatmap.Rd +++ b/man/nearest_neighbor_heatmap.Rd @@ -44,6 +44,12 @@ and plots a heatmap of features with nonzero values. } \examples{ # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: -nearest_neighbor_heatmap(sample_id, model) +\dontrun{ +nearest_neighbor_heatmap( + this_sample_id = "CCS_0680_lst", + DLBCLone_model = predict_single, + font_size = 10 +) +} } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index ff07c73..ab62760 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -34,11 +34,10 @@ a list of data frames with the predictions, the UMAP input, the model, and a ggp Predict class for a single sample without using umap_transform and plot result of classification } \examples{ -predict_single_sample_DLBCLone( - test_df = test_df, - train_metadata = dlbcl_meta_clean, - optimized_model = weighted_lyseq_opt, - seed = 1234 +predict_single <- predict_single_sample_DLBCLone( + test_df = optimize_params$features[1,], + train_metadata = dlbcl_meta, + optimized_model = optimize_params_T ) } diff --git a/man/report_accuracy.Rd b/man/report_accuracy.Rd index eae4fe2..934591e 100644 --- a/man/report_accuracy.Rd +++ b/man/report_accuracy.Rd @@ -45,8 +45,10 @@ Optionally excludes samples assigned to the "Other" class from accuracy calculat } } \examples{ +\dontrun{ result <- report_accuracy(predictions_df) result$overall result$per_class +} } diff --git a/man/stacked_bar_plot.Rd b/man/stacked_bar_plot.Rd index 96c1dc4..eaa5615 100644 --- a/man/stacked_bar_plot.Rd +++ b/man/stacked_bar_plot.Rd @@ -33,11 +33,13 @@ A ggplot2 object representing the stacked bar plot. This function generates a stacked bar plot showing the top features (genes) for each subtype based on their prevalence in the dataset. } \examples{ +\dontrun{ stacked_bar_plot( - all_features_optimized, + optimize_params, method = "chi_square", num_feats = 10, title = "LymphGen" ) +} } From a31fed403baf0ad51ff4fe6d285d6f86515d21ad Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Sat, 6 Sep 2025 09:58:25 -0700 Subject: [PATCH 77/79] more documentation updates --- NAMESPACE | 19 +- R/plotting.R | 49 +- R/umap.R | 2206 ++++++++++++++----------- man/DLBCLone_KNN.Rd | 8 + man/DLBCLone_KNN_predict.Rd | 9 + man/DLBCLone_load_optimized.Rd | 3 + man/DLBCLone_optimize_params.Rd | 11 +- man/DLBCLone_predict_mixture_model.Rd | 4 + man/DLBCLone_save_optimized.Rd | 3 + man/DLBCLone_train_mixture_model.Rd | 4 + man/DLBCLone_train_test_plot.Rd | 3 + man/assemble_genetic_features.Rd | 5 +- man/basic_umap_scatterplot.Rd | 3 + man/make_alluvial.Rd | 2 + man/make_and_annotate_umap.Rd | 14 +- man/make_neighborhood_plot.Rd | 2 + man/make_umap_scatterplot.Rd | 5 + man/nearest_neighbor_heatmap.Rd | 4 +- man/optimize_outgroup.Rd | 4 +- man/optimize_purity.Rd | 10 +- man/predict_single_sample_DLBCLone.Rd | 23 +- man/process_votes.Rd | 10 +- man/report_accuracy.Rd | 2 + man/stacked_bar_plot.Rd | 2 + man/summarize_all_ssm_status.Rd | 3 + man/weighted_knn_predict_with_conf.Rd | 25 +- 26 files changed, 1418 insertions(+), 1015 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 633c8ac..9cdcf5b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -24,24 +24,33 @@ export(massage_matrix_for_clustering) export(optimize_outgroup) export(optimize_purity) export(predict_single_sample_DLBCLone) +export(prepare_single_sample_DLBCLone) export(process_votes) export(report_accuracy) +export(stacked_bar_plot) export(summarize_all_ssm_status) export(tabulate_ssm_status) export(weighted_knn_predict_with_conf) +import(ComplexHeatmap) +import(FNN) import(GAMBLR.data) import(GAMBLR.helpers) +import(caret) +import(circlize) import(dplyr) +import(ggExtra) +import(ggalluvial) import(ggplot2) +import(ggrepel) +import(ggside) +import(grid) import(mclust) +import(purrr) import(randomForest, except = c("combine")) import(readr) +import(rlang) +import(stringr) import(tibble) import(tidyr) import(tidyselect) import(uwot) -importFrom(ComplexHeatmap,Heatmap) -importFrom(circlize,colorRamp2) -importFrom(dplyr,filter) -importFrom(grid,gpar) -importFrom(rlang,sym) diff --git a/R/plotting.R b/R/plotting.R index ac89cc4..4fd8980 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -19,14 +19,13 @@ #' - The function extracts the feature matrix rows corresponding to the nearest neighbors of the specified sample, #' and plots a heatmap of features with nonzero values. #' -#' @importFrom dplyr filter -#' @importFrom ComplexHeatmap Heatmap -#' @importFrom grid gpar -#' @importFrom circlize colorRamp2 +#' @import dplyr tidyr ComplexHeatmap grid circlize tibble #' #' @examples #' # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: -#' \dontrun{ +#' \dontrun{ +#' library(GAMBLR.predict) +#' #' nearest_neighbor_heatmap( #' this_sample_id = "CCS_0680_lst", #' DLBCLone_model = predict_single, @@ -253,11 +252,15 @@ nearest_neighbor_heatmap <- function( #' @param custom_colours Optional named vector of colors for groups; falls back to `get_gambl_colours()`. #' #' @return A ggplot object. +#' +#' @import dplyr ggplot2 ggExtra #' @export #' #' @examples #' #' \dontrun{ +#' library(GAMBLR.predict) +#' #' make_umap <- make_and_annotate_umap( #' df=all_full_status, #' metadata=dlbcl_meta @@ -268,6 +271,7 @@ nearest_neighbor_heatmap <- function( #' plot_samples = "some_sample_ID", #' colour_by = "DLBCLone_ko") #' } +#' basic_umap_scatterplot <- function(optimized, plot_samples = NULL, colour_by = NULL, @@ -364,12 +368,14 @@ basic_umap_scatterplot <- function(optimized, #' #' @return No return value. Side effect: writes multiple PDF files to disk. #' +#' @import dplyr ggplot2 ComplexHeatmap rlang +#' @export +#' #' @examples #' \dontrun{ #' DLBCLone_summarize_model("Full_geneset_unweighted", optimized_model) #'} #' -#' @export DLBCLone_summarize_model = function( base_name, optimized_model @@ -463,17 +469,17 @@ DLBCLone_summarize_model = function( #' @details #' The function extracts the nearest neighbors of the specified sample, draws segments connecting the sample to its neighbors, and colors points by group (e.g., lymphgen subtype). The plot title can optionally include the predicted label. #' -#' @import dplyr -#' @import ggplot2 -#' @importFrom rlang sym -#' +#' @import dplyr ggplot2 rlang ggExtra #' @export +#' #' @examples #' #' # Assuming 'optimization_result' is the output of DLBCLone_optimize_params #' # and 'output' is the result of DLBCLone_predict_single_sample #' # on sample_id "SAMPLE123": #' \dontrun{ +#' library(GAMBLR.predict) +#' #' predict_single <- predict_single_sample_DLBCLone( #' test_df = optimize_params$features[1,], #' train_metadata = dlbcl_meta, @@ -575,11 +581,15 @@ make_neighborhood_plot <- function( #' @param add_labels If TRUE: adds labels to the points based on the median coordinates of each group. #' @param title Title for the plot. Default: NULL. #' -#' @returns +#' @returns A ggplot object representing the UMAP scatterplot with marginal histograms. +#' +#' @import dplyr ggplot2 ggExtra ggside rlang #' @export #' #' @examples #' \dontrun{ +#' library(GAMBLR.predict) +#' #' make_umap <- make_and_annotate_umap( #' df=all_full_status, #' metadata=dlbcl_meta @@ -666,15 +676,19 @@ make_umap_scatterplot = function( #' - Uses confusion matrices to compute accuracy metrics. #' - Excludes "Other" class for no_other accuracy. #' - Returns per-class metrics for further analysis. +#' +#' @import dplyr caret rlang +#' @export #' #' @examples #' \dontrun{ +#' library(GAMBLR.predict) +#' #' result <- report_accuracy(predictions_df) #' result$overall #' result$per_class #' } #' -#' @export report_accuracy = function( predictions, truth="lymphgen", @@ -767,12 +781,16 @@ report_accuracy = function( #' - Annotates concordance rate, per-group accuracy, and unclassified rate as specified. #' - Supports flexible labeling, coloring, and axis ordering for publication-quality plots. #' +#' @import dplyr ggplot2 ggalluvial rlang tidyr +#' @export +#' #' @examples #' \dontrun{ +#' library(GAMBLR.predict) +#' #' make_alluvial(optimize_params) #' } #' -#' @export make_alluvial <- function( optimized, count_excluded_as_other = FALSE, @@ -1124,9 +1142,14 @@ make_alluvial <- function( #' @param title Title for the plot (default: NULL). #' #' @return A ggplot2 object representing the stacked bar plot. +#' +#' @import dplyr ggplot2 tidyr rlang +#' @export #' #' @examples #' \dontrun{ +#' library(GAMBLR.predict) +#' #' stacked_bar_plot( #' optimize_params, #' method = "chi_square", diff --git a/R/umap.R b/R/umap.R index 06ba67a..543c2ce 100644 --- a/R/umap.R +++ b/R/umap.R @@ -36,11 +36,17 @@ #' - Synonymous mutations can be counted as another separate feature for genes specified by \code{synon_genes} genes. #' - The function simplifies mutation annotations and pivots the data to a wide format suitable for downstream analysis. #' +#' @import dplyr tidyr tibble +#' @export +#' #' @examples #' # A basic example, using only the output of get_all_coding_ssm #' # Since the only non-coding class this function handles is Silent, #' # we will be missing most non-coding types such as Intron, UTR, Flank +#' #' \dontrun{ +#' library(GAMBLR.predict) +#' #' sample_metadata = get_sample_metadata() %>% filter(seq_type!= "mrna") #' #' maf_data = get_all_coding_ssm(sample_metadata) @@ -54,51 +60,39 @@ #' count_hits = FALSE #' ) #' } -#' -#' @import dplyr tidyr tibble -#' @export #' -summarize_all_ssm_status <- function( - maf_df, - these_samples_metadata, - genes_of_interest, - synon_genes, - silent_maf_df, - separate_by_class_genes = NULL, - count_hits = FALSE -){ +summarize_all_ssm_status <- function(maf_df, + these_samples_metadata, + genes_of_interest, + synon_genes, + silent_maf_df, + separate_by_class_genes = NULL, + count_hits = FALSE){ if(missing(genes_of_interest)){ message("defaulting to all Tier 1 B-cell lymphoma genes") - genes_of_interest = filter( - lymphoma_genes, - DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 - ) %>% + genes_of_interest = filter(GAMBLR.data::lymphoma_genes, + DLBCL_Tier==1 | FL_Tier == 1 | BL_Tier == 1 ) %>% pull(Gene) %>% unique() } - maf_df = filter( - maf_df, - Hugo_Symbol %in% genes_of_interest - ) + maf_df = filter(maf_df, + Hugo_Symbol %in% genes_of_interest) if(missing(silent_maf_df)){ silent_maf_df = maf_df }else{ silent_maf_df = filter(silent_maf_df,Hugo_Symbol %in% genes_of_interest) } if(!missing(these_samples_metadata)){ - maf_df = filter( - maf_df, - Tumor_Sample_Barcode %in% these_samples_metadata$sample_id - ) - silent_maf_df = filter( - silent_maf_df, - Tumor_Sample_Barcode %in% these_samples_metadata$sample_id - ) + maf_df = filter(maf_df, + Tumor_Sample_Barcode %in% these_samples_metadata$sample_id) + silent_maf_df = filter(silent_maf_df, + Tumor_Sample_Barcode %in% these_samples_metadata$sample_id) } if(!missing(synon_genes)){ if(any(!synon_genes %in% silent_maf_df$Hugo_Symbol)){ missing = synon_genes[!synon_genes %in% silent_maf_df$Hugo_Symbol] - not_missing = synon_genes[synon_genes %in% silent_maf_df$Hugo_Symbol] + not_missing = synon_genes[synon_genes %in% silent_maf_df$Hugo_Symbol] + message("Warning: Some synonymous genes have no mutations in silent_maf_df") message(paste(missing,collapse=", ")) } @@ -109,30 +103,25 @@ summarize_all_ssm_status <- function( #Simplify annotations silent_types = c("Silent","Intron","5'UTR","3'UTR","5'Flank","3'Flank") nonsense_types = c("Nonsense_Mutation","Frame_Shift_Del","Frame_Shift_Ins","Splice_Site") - missense_types = c("In_Frame_Del","In_Frame_Ins","Missense_Mutation","Splice_Region") - gene_mutations = mutate( - maf_df, - mutation_type=case_when( - Variant_Classification %in% nonsense_types ~ "Nonsense_Mutation", - Variant_Classification %in% missense_types ~ "Missense_Mutation", - Variant_Classification %in% silent_types ~ "Silent", - TRUE ~ Variant_Classification - ) - ) %>% - mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) - separated_coding_maf = filter( - gene_mutations, - Hugo_Symbol %in% genes_of_interest, - Hugo_Symbol %in% separate_by_class_genes, - mutation_type != "Silent" - ) + missense_types = c("In_Frame_Del", + "In_Frame_Ins", + "Missense_Mutation", + "Splice_Region") + gene_mutations = mutate(maf_df, + mutation_type=case_when( + Variant_Classification %in% nonsense_types ~ "Nonsense_Mutation", + Variant_Classification %in% missense_types ~ "Missense_Mutation", + Variant_Classification %in% silent_types ~ "Silent", + TRUE ~ Variant_Classification) + ) %>% + mutate(mutation=paste(Hugo_Symbol,mutation_type,sep=":")) + separated_coding_maf = filter(gene_mutations, + Hugo_Symbol %in% genes_of_interest, + Hugo_Symbol %in% separate_by_class_genes, + mutation_type != "Silent") - unseparated_coding_maf = filter( - gene_mutations, - Hugo_Symbol %in% genes_of_interest, - !Hugo_Symbol %in% separate_by_class_genes, - mutation_type != "Silent" - ) %>% + unseparated_coding_maf = filter(gene_mutations, + Hugo_Symbol %in% genes_of_interest, !Hugo_Symbol %in% separate_by_class_genes, mutation_type != "Silent") %>% mutate(mutation = paste(Hugo_Symbol,"Coding",sep=":")) separated_silent_maf = filter(gene_mutations,Hugo_Symbol %in% synon_genes, mutation_type == "Silent") %>% mutate(mutation = paste(Hugo_Symbol,"Silent",sep=":")) @@ -149,7 +138,7 @@ summarize_all_ssm_status <- function( } mutation_wide = pivot_wider(mutation_distinct, names_from = "mutation",values_from = "mutated",values_fill = 0) %>% column_to_rownames("Tumor_Sample_Barcode") - return(mutation_wide) + return(mutation_wide) } #' Assemble genetic features for UMAP input @@ -175,8 +164,14 @@ summarize_all_ssm_status <- function( #' #' @return Matrix of assembled features for each sample. #' +#' @import dplyr tibble tidyr +#' @export +#' #' @examples +#' #' \dontrun{ +#' library(GAMBLR.predict) +#' #' ordered_genes = filter(lymphoma_genes,DLBCL_Tier==1 | FL_Tier==1|BL_Tier==1) %>% pull(Gene) #' synon_genes = unique(grch37_ashm_regions$gene) #' synon_genes = synon_genes[synon_genes %in% ordered_genes] @@ -200,40 +195,38 @@ summarize_all_ssm_status <- function( #' annotated_sv = all_sv_anno #' ) #' } -#' -#' @export -#' -assemble_genetic_features <- function( - these_samples_metadata, - metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), - genes, - synon_genes, - maf_with_synon, - hotspot_genes, - genome_build = "grch37", - sv_value = 3, - synon_value = 1, - coding_value = 2, - include_ashm = TRUE, - annotated_sv, - include_GAMBL_sv= TRUE, - review_hotspots = TRUE, - verbose = FALSE -){ - #if("genome_build" %in% ) +#' +assemble_genetic_features <- function(these_samples_metadata, + metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), + genes, + synon_genes, + maf_with_synon, + hotspot_genes, + genome_build = "grch37", + sv_value = 3, + synon_value = 1, + coding_value = 2, + include_ashm = TRUE, + annotated_sv, + include_GAMBL_sv= TRUE, + review_hotspots = TRUE, + verbose = FALSE){ + if(!"maf_data" %in% class(maf_with_synon)){ - warning(paste("maf_with_synon should be a maf_data object, but is not.","Proceeding anyway with genome_build = ", genome_build)) + warning(paste("maf_with_synon should be a maf_data object, but is not.", + "Proceeding anyway with genome_build = ", genome_build)) }else{ genome_build = attr(maf_with_synon,"genome_build") } if(include_ashm){ - #TODO: ensure this supports both genome builds correctly + stopifnot( genome_build == "grch37", "other genome builds are not yet supported for aSHM") + #TODO: consider shifting this function to GAMBLR.results + #TODO: ensure this supports both genome builds correctly. This is currently hard-coded some_regions = GAMBLR.utils::create_bed_data( - GAMBLR.data::grch37_ashm_regions, - fix_names = "concat", - concat_cols = c("gene","region"), - sep="-" - ) + GAMBLR.data::grch37_ashm_regions, + fix_names = "concat", + concat_cols = c("gene","region"), + sep="-") #make the aSHM count matrix and combine if necessary if ("genome" %in% these_samples_metadata$seq_type){ ashm_matrix_genome <- get_ashm_count_matrix( @@ -241,27 +234,29 @@ assemble_genetic_features <- function( this_seq_type = "genome", these_samples_metadata = these_samples_metadata ) + colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) } - if ("capture" %in% these_samples_metadata$seq_type){ - ashm_matrix_cap <- get_ashm_count_matrix( - regions_bed = some_regions, - this_seq_type = "capture", - these_samples_metadata = these_samples_metadata - ) - colnames(ashm_matrix_cap) = gsub("-.+","",colnames(ashm_matrix_cap)) - } - if ("genome" %in% these_samples_metadata$seq_type && "capture" %in% these_samples_metadata$seq_type){ - ashm_matrix = bind_rows(ashm_matrix_genome, ashm_matrix_cap) - }else if("genome" %in% these_samples_metadata$seq_type){ - ashm_matrix = ashm_matrix_genome - }else if("capture" %in% these_samples_metadata$seq_type){ - ashm_matrix = ashm_matrix_cap - }else{ - stop("no eligible seq_type provided in these_samples_metadata") + if ("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix_cap <- get_ashm_count_matrix( + regions_bed = some_regions, + this_seq_type = "capture", + these_samples_metadata = these_samples_metadata + ) + colnames(ashm_matrix_cap) = gsub("-.+","",colnames(ashm_matrix_cap)) + } + if ("genome" %in% these_samples_metadata$seq_type && "capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = bind_rows(ashm_matrix_genome, ashm_matrix_cap) + }else if("genome" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_genome + }else if("capture" %in% these_samples_metadata$seq_type){ + ashm_matrix = ashm_matrix_cap + + }else{ + stop("no eligible seq_type provided in these_samples_metadata") + } } - } include_hotspots = ifelse(!missing(hotspot_genes), TRUE, FALSE) @@ -303,12 +298,18 @@ assemble_genetic_features <- function( } #fill in gaps from aSHM (other non-coding variants in the genes) - missing = status_with_silent[rownames(ashm_matrix),colnames(ashm_matrix)]==0 & - ashm_matrix[rownames(ashm_matrix),colnames(ashm_matrix)] > 0 + missing = status_with_silent[rownames(ashm_matrix), + colnames(ashm_matrix)]==0 & + ashm_matrix[rownames(ashm_matrix), + colnames(ashm_matrix)] > 0 + + fill = missing fill[]=0 fill[missing] = synon_value - status_with_silent[rownames(fill),colnames(fill)] = fill + status_with_silent[rownames(fill), + colnames(fill)] = fill + } #ensure all columns in status_with_silent are present in status_without_silent @@ -344,100 +345,7 @@ assemble_genetic_features <- function( bcl6_id = unique(c(bcl6_id,bcl6_sv_id)) myc_id = unique(c(myc_id,myc_sv_id)) } - - status_combined[,"BCL2_SV"] = 0 - status_combined[bcl2_id,"BCL2_SV"] = sv_value - - status_combined[,"MYC_SV"] = 0 - status_combined[myc_id,"MYC_SV"] = sv_value - - status_combined[,"BCL6_SV"] = 0 - status_combined[bcl6_id,"BCL6_SV"] = sv_value - return(status_combined) - -} - -old_assemble_genetic_features <- function(these_samples_metadata, - metadata_columns = c("bcl2_ba","bcl6_ba","myc_ba"), - genes, - synon_genes, - maf_with_synon, - hotspot_genes, - sv_value = 3, - synon_value = 1, - coding_value = 2){ - #TODO: ensure this supports both genome builds correctly - some_regions = GAMBLR.utils::create_bed_data( - GAMBLR.data::grch37_ashm_regions, - fix_names = "concat", - concat_cols = c("gene","region"), - sep="-") - #make the aSHM count matrix and combine if necessary - if ("genome" %in% these_samples_metadata$seq_type){ - ashm_matrix_genome <- get_ashm_count_matrix( - regions_bed = some_regions, - this_seq_type = "genome", - these_samples_metadata = these_samples_metadata - ) - - colnames(ashm_matrix_genome) = gsub("-.+","",colnames(ashm_matrix_genome)) - } - if ("capture" %in% these_samples_metadata$seq_type){ - ashm_matrix_cap <- get_ashm_count_matrix( - regions_bed = some_regions, - this_seq_type = "capture", - these_samples_metadata = these_samples_metadata - ) - colnames(ashm_matrix_cap) = gsub("-.+","",colnames(ashm_matrix_cap)) - } - if ("genome" %in% these_samples_metadata$seq_type && "capture" %in% these_samples_metadata$seq_type){ - ashm_matrix = bind_rows(ashm_matrix_genome, ashm_matrix_cap) - }else if("genome" %in% these_samples_metadata$seq_type){ - ashm_matrix = ashm_matrix_genome - }else if("capture" %in% these_samples_metadata$seq_type){ - ashm_matrix = ashm_matrix_cap - }else{ - stop("no eligible seq_type provided in these_samples_metadata") - } - - status_with_silent = get_coding_ssm_status( - these_samples_metadata = these_samples_metadata, - maf_data = maf_with_synon, - include_hotspots = TRUE, - genes_of_interest = "MYD88", - include_silent_genes = synon_genes, - gene_symbols = genes - ) - status_with_silent = status_with_silent %>% column_to_rownames("sample_id") - - status_without_silent = get_coding_ssm_status( - these_samples_metadata = these_samples_metadata, - maf_data = maf_with_synon, - include_hotspots = TRUE, - genes_of_interest = "MYD88", - include_silent = FALSE, - gene_symbols = genes - ) - status_without_silent = status_without_silent %>% column_to_rownames("sample_id") - - status_combined = status_with_silent + status_without_silent - if(coding_value == 1){ - status_combined[status_combined > 1] = 1 - } - - #fill in gaps from aSHM (other non-coding variants in the genes) - - missing = status_with_silent[rownames(ashm_matrix), - colnames(ashm_matrix)]==0 & - ashm_matrix[rownames(ashm_matrix), - colnames(ashm_matrix)] == synon_value - - status_with_silent[rownames(missing), - colnames(missing)] = synon_value - bcl2_id = these_samples_metadata[which(these_samples_metadata$bcl2_ba=="POS"),] %>% pull(sample_id) - bcl6_id = these_samples_metadata[which(these_samples_metadata$bcl6_ba=="POS"),] %>% pull(sample_id) - myc_id = these_samples_metadata[which(these_samples_metadata$myc_ba=="POS"),] %>% pull(sample_id) status_combined[,"BCL2_SV"] = 0 status_combined[bcl2_id,"BCL2_SV"] = sv_value @@ -477,59 +385,71 @@ old_assemble_genetic_features <- function(these_samples_metadata, #' @param verbose Set to TRUE to print additional information during the optimization process. #' #' @returns a list of data frames with the predictions and the UMAP input +#' +#' @import dplyr tidyr caret #' @export #' -optimize_outgroup <- function( - predicted_labels, - true_labels, - other_score, - all_classes = c("MCD","EZB","BN2","N1","ST2","Other"), - maximize ="balanced_accuracy", - exclude_other_for_accuracy = FALSE, - verbose = FALSE -){ - #print(paste("Exclude Other?",exclude_other_for_accuracy)) +optimize_outgroup <- function(predicted_labels, + true_labels, + other_score, + all_classes = c("MCD", + "EZB", + "BN2", + "N1", + "ST2", + "Other"), + maximize ="balanced_accuracy", + exclude_other_for_accuracy = FALSE, + cap_classification_rate = 1, + verbose = FALSE, + other_class = "Other"){ rel_thresholds = seq(1,10,0.1) sens_df = data.frame() acc_df = data.frame() - predictions = data.frame( - predicted_label=as.character(predicted_labels), - true_label=as.character(true_labels) - ) + predictions = data.frame(predicted_label=as.character(predicted_labels), + true_label=as.character(true_labels)) for(threshold in rel_thresholds){ - predictions_new = mutate( - predictions, - predicted_label = ifelse( - other_score < threshold, - predicted_label, - "Other" - ) - ) - - pred = factor(predictions_new[["predicted_label"]],levels=all_classes) - truth = factor(predictions_new[["true_label"]],levels=all_classes) - conf_matrix <- confusionMatrix(pred, truth) - - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] - if(maximize == "balanced_accuracy"){ - bal_acc$average_accuracy = mean(bal_acc) - }else{ - bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] - } - bal_acc$threshold = threshold - acc_df = bind_rows(acc_df,bal_acc) - sn <- conf_matrix$byClass[, "Sensitivity"] - sn$average_sensitivity = mean(sn) - sn$threshold = threshold - sens_df = bind_rows(sens_df,sn) + predictions_new = mutate(predictions, + predicted_label = ifelse(other_score < threshold, + predicted_label, + other_class)) + all_acc = report_accuracy(predictions_new, + truth="true_label", + pred="predicted_label", + drop_other=FALSE) + pred = factor(predictions_new[["predicted_label"]],levels=all_classes) + truth = factor(predictions_new[["true_label"]],levels=all_classes) + conf_matrix <- confusionMatrix(pred, truth) + + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] + if(maximize == "balanced_accuracy"){ + bal_acc$average_accuracy = mean(bal_acc) + }else{ + bal_acc$average_accuracy = conf_matrix$overall[["Accuracy"]] + } + bal_acc$threshold = threshold + bal_acc$harmonic_mean = all_acc$harmonic_mean + bal_acc$classification_rate = all_acc$classification_rate + acc_df = bind_rows(acc_df,bal_acc) + sn <- conf_matrix$byClass[, "Sensitivity"] + sn$average_sensitivity = mean(sn) + sn$threshold = threshold + sn$harmonic_mean = all_acc$harmonic_mean + sn$classification_rate = all_acc$classification_rate + sens_df = bind_rows(sens_df,sn) } + if(maximize %in% c("balanced_accuracy","accuracy")){ - #print(best) + acc_df = filter(acc_df, classification_rate <= cap_classification_rate) best = slice_head(arrange(acc_df,desc(average_accuracy)),n=1) + }else if(maximize == "harmonic_mean"){ + acc_df = filter(sens_df, classification_rate <= cap_classification_rate) + best = slice_head(arrange(sens_df,desc(harmonic_mean)),n=1) }else{ best = slice_head(arrange(sens_df,desc(average_sensitivity)),n=1) + } return(best) @@ -550,10 +470,15 @@ optimize_outgroup <- function( #' @param title3 additional argument #' #' @returns a ggplot object +#' +#' @import ggplot2 dplyr ggrepel #' @export #' #' @examples #' #add the dataset name to the metadata if it's not already there (required for the plot work) +#' +#' library(GAMBLR.predict) +#' #' lymphgen_A53_DLBCLone$df$dataset = "GAMBL" #' #' DLBCLone_train_test_plot( @@ -584,7 +509,6 @@ DLBCLone_train_test_plot = function(test_df, } if(annotate_accuracy){ if("BN2" %in% classes){ - #print(details) acc_df = data.frame(lymphgen = classes, accuracy = c( details$BN2_bacc, @@ -683,39 +607,47 @@ DLBCLone_train_test_plot = function(test_df, #' @param algorithm The UMAP algorithm to use. Default is "tumap", which uses the TUMAP implementation. #' @param make_plot If TRUE, creates a plot of the UMAP embedding. #' -#' @import uwot +#' @import uwot dplyr tidyr tibble readr #' @export #' #' @examples +#' +#' library(GAMBLR.predict) #' -#' make_umap <- make_and_annotate_umap( -#' df=all_full_status, -#' metadata=dlbcl_meta -#') -#' -make_and_annotate_umap = function( - df, - metadata, - umap_out, - core_features = NULL, - core_feature_multiplier = 1.5, - hidden_features = NULL, - n_neighbors=55, - min_dist=0, - metric="cosine", - n_epochs=1500, - init="spca", - ret_model=TRUE, - na_vals = "drop", - join_column="sample_id", - seed=12345, - target_column, - target_metric="euclidean", - target_weight=0.5, - calc_dispersion = FALSE, - algorithm = "tumap", - make_plot = FALSE -){ +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' +#' my_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) +#' +make_and_annotate_umap = function(df, + metadata, + umap_out, + truth_column = "lymphgen", + core_features = NULL, + core_feature_multiplier = 1.5, + hidden_features = NULL, + n_neighbors=55, + min_dist=0, + metric="cosine", + n_epochs=1500, + init="spca", + ret_model=TRUE, + na_vals = "drop", + join_column="sample_id", + seed=12345, + target_column, + target_metric="euclidean", + target_weight=0.5, + calc_dispersion = FALSE, + algorithm = "tumap", + make_plot = FALSE + ){ + if(!missing(umap_out)){ model_provided = TRUE }else{ @@ -731,24 +663,24 @@ make_and_annotate_umap = function( if(any(sapply(df, is.factor))){ numeric_cols = names(df)[!sapply(df, is.factor)] df <- df[, colSums(is.na(df[,numeric_cols])) == 0] - rs = rowSums(df[,numeric_cols],na.rm=TRUE) - dropped_rows = df[rs==0,] - df = df[rs>0,] - }else{ + rs = rowSums(df[,numeric_cols],na.rm=TRUE) + dropped_rows = df[rs==0,] + df = df[rs>0,] + } else{ df <- df[, colSums(is.na(df)) == 0] rs = rowSums(df,na.rm=TRUE) dropped_rows = df[rs==0,] df = df[rs>0,] } + } + if(missing(df)){ stop("provide a data frame or matrix with one row for each sample and a numeric column for each mutation feature") } if(!is.null(core_features)){ - if(any(weighted_status>1)){ - stop("values > 1 detected. Weighting of core features is only supported for binary features (0/1).") - } + #print(paste(core_features,collapse=",")) if(!is.numeric(core_feature_multiplier)){ stop("core_feature_multiplier must be a numeric value") } @@ -775,6 +707,7 @@ make_and_annotate_umap = function( pct_rem = round(nrem/original_n*100,2) message(paste0("removed ",nrem," (",pct_rem,"%) rows from the data that had no features")) no_feat_samples = rownames(dropped_rows) + } keep_rows = rownames(df)[rownames(df) %in% metadata[[join_column]]] @@ -789,54 +722,50 @@ make_and_annotate_umap = function( if(missing(target_column)){ if(algorithm == "umap"){ message("running umap2") - umap_out = umap2( - df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - a=1.8956, - b = 0.806, - approx_pow=TRUE, - init=init, - seed = seed, - n_threads = 1, - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic" # possibly add rng_type = "deterministic" - ) - #IMPORTANT: n_threads must not be changed because it will break reproducibility + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + a=1.8956, + b = 0.806, + approx_pow=TRUE, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") # possibly add rng_type = "deterministic" + #IMPORTANT: n_threads must never be changed because it will break reproducibility + }else if(algorithm == "tumap"){ - #X = df %>% as.matrix() + message("running tumap") X = df - umap_args = list( - X=X, - n_neighbors = n_neighbors, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic") + umap_args = list(X=X, + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") - umap_out = tumap( - X, - n_neighbors = n_neighbors, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, - batch = TRUE, - n_sgd_threads = 1, - rng_type = "deterministic" - ) + umap_out = tumap(X, + n_neighbors = n_neighbors, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + batch = TRUE, + n_sgd_threads = 1, + rng_type = "deterministic") message("done!") }else{ stop("unsupported algorithm option") @@ -847,65 +776,62 @@ make_and_annotate_umap = function( stop("metadata must be provided for supervised UMAP") } metadata[[target_column]] = factor(metadata[[target_column]]) - #print(table(metadata[[target_column]])) - umap_out = umap2( - df %>% as.matrix(), - n_neighbors = n_neighbors, - min_dist = min_dist, - metric = metric, - ret_model = ret_model, - n_epochs=n_epochs, - init=init, - seed = seed, - n_threads = 1, - y = metadata[[target_column]], - target_metric = target_metric, - target_weight = target_weight - ) # possibly add rng_type = "deterministic" - #IMPORTANT: n_threads must not be changed because it will break reproducibility + + umap_out = umap2(df %>% as.matrix(), + n_neighbors = n_neighbors, + min_dist = min_dist, + metric = metric, + ret_model = ret_model, + n_epochs=n_epochs, + init=init, + seed = seed, + n_threads = 1, + y = metadata[[target_column]], + target_metric = target_metric, + target_weight = target_weight, + rng_type = "deterministic" + ) + #IMPORTANT: n_threads must never be changed because it will break reproducibility } + }else{ message("transforming each data point individually using the provided UMAP model. This will take some time.") umap_df = data.frame() for(sample in rownames(df)){ this_row = df[sample,] - this_umap_df = umap_transform( - X=this_row, - model=umap_out$model, - seed=seed, - batch = TRUE, - n_threads = 1, - n_sgd_threads = 1 - ) + this_umap_df = umap_transform(X=this_row, + model=umap_out$model, + seed=seed, + batch = TRUE, + n_threads = 1, + n_sgd_threads = 1) this_umap_df = as.data.frame(this_umap_df) + umap_df = bind_rows(umap_df,this_umap_df) } message("done") + + } + if(model_provided){ # model was generated here #message("model given to function") + umap_df = as.data.frame(umap_df) %>% rownames_to_column(var=join_column) + }else{ - #message("model not provided, created here") umap_df = as.data.frame(umap_out$embedding) %>% rownames_to_column(join_column) - #umap_df = as.data.frame(umap_out) %>% rownames_to_column(join_column) - #umap_df = as.data.frame(umap_df) %>% rownames_to_column(var=join_column) - #print(head(umap_df)) } if(!missing(metadata)){ umap_df = left_join(umap_df,metadata,by=join_column) } + results = list() - umap_df = umap_df %>% - mutate( - V1 = round(V1, 9), - V2 = round(V2, 9) - ) - results = list() + results[["df"]]=umap_df results[["features"]] = df results[["dropped_rows"]] = no_feat_samples @@ -920,6 +846,7 @@ make_and_annotate_umap = function( if(make_plot){ ms = make_umap_scatterplot( umap_df, + colour_by = truth_column, title = "UMAP projection" ) print(ms) @@ -951,22 +878,25 @@ make_and_annotate_umap = function( #' - Adds columns for counts, scores, top group, top group score, score ratios, and optimized group assignments. #' - Designed for downstream use in DLBCLone and similar kNN-based classification workflows. #' +#' @import dplyr tidyr stringr purrr +#' @export +#' #' @examples -#' # Example usage: -#' # result <- process_votes(knn_output_df, k = 7) +#' +#' library(GAMBLR.predict) +#' +#' result <- process_votes(knn_output_df, k = 7) #' -#' @export #' -process_votes <- function( - df, - raw_col = "label", - group_labels = c("EZB", "MCD", "ST2", "BN2", "N1", "Other"), - vote_labels_col = "vote_labels", - k, - other_vote_multiplier = 2, - score_purity_requirement = 1, - weighted_votes_col = "weighted_votes" -){ +process_votes <- function(df, + raw_col = "label", + group_labels = c("EZB", "MCD", "ST2", "BN2", "N1", "Other"), + vote_labels_col = "vote_labels", + k, + other_vote_multiplier = 2, + score_purity_requirement = 1, + weighted_votes_col = "weighted_votes", + other_class = "Other") { # <--- NEW ARG if(missing(k)){ stop("k value is required") } @@ -997,7 +927,6 @@ process_votes <- function( mutate(.id = row_number()) %>% rowwise() %>% mutate( - # 1) counts counts = list( set_names( count_labels_in_string(.data[[raw_col]], group_labels), @@ -1008,8 +937,6 @@ process_votes <- function( cnts <- count_labels_in_string(.data[[raw_col]], group_labels) group_labels[which.max(cnts)] }, - - # 2) scores scores = list( if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { extract_weighted_scores( @@ -1021,8 +948,6 @@ process_votes <- function( set_names(rep(0, length(group_labels)), paste0(group_labels, "_score")) } ), - - # 3) top score group summary score_summary = list( if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { get_top_score_group( @@ -1046,33 +971,33 @@ process_votes <- function( ungroup() # Optional adjustments based on external columns (if present) - if ("neighbors_other" %in% colnames(df)) { - df_out <- df_out %>% - mutate(Other_count = neighbors_other) - } - - if ("other_weighted_votes" %in% colnames(df)) { - df_out <- df_out %>% - mutate(Other_score = other_weighted_votes) - } - - # Optional override of top group if Other dominates by a fold - if (all(c("top_group_count", "Other_count") %in% colnames(df_out))) { - df_out <- df_out %>% - mutate(by_vote = top_group_count) %>% - mutate(by_vote_opt = ifelse(top_group_count * other_vote_multiplier > Other_count, top_group, "Other")) + if (!is.null(other_class) && other_class %in% group_labels) { # <--- CONDITIONAL + if ("neighbors_other" %in% colnames(df)) { + df_out <- df_out %>% + mutate(!!sym(paste0(other_class, "_count")) := neighbors_other) + } + if ("other_weighted_votes" %in% colnames(df)) { + df_out <- df_out %>% + mutate(!!sym(paste0(other_class, "_score")) := other_weighted_votes) + } + if (all(c("top_group_count", paste0(other_class, "_count")) %in% colnames(df_out))) { + df_out <- df_out %>% + mutate(by_vote = top_group_count) %>% + mutate(by_vote_opt = ifelse(top_group_count * other_vote_multiplier > !!sym(paste0(other_class, "_count")), top_group, other_class)) + } + df_out <- mutate(df_out, + by_score = top_score_group, + score_ratio = top_group_score / !!sym(paste0(other_class, "_score")), + by_score_opt = ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh, top_score_group, other_class)) + } else { + # Fallback: if no "other" class exists, keep by_score/by_vote as top_group + df_out <- mutate(df_out, + by_score = top_score_group, + score_ratio = NA_real_, + by_score_opt = top_score_group, + by_vote_opt = top_group) } - df_out = mutate( - df_out, - by_score = top_score_group, - score_ratio = top_group_score / Other_score, - by_score_opt=ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh, - top_score_group, - "Other" - ) - ) - return(df_out) } @@ -1098,26 +1023,28 @@ process_votes <- function( #' - Accuracy is computed as the proportion of correct assignments (diagonal of the confusion matrix). #' - The function is intended for use in optimizing classification purity in kNN-based workflows, especially when distinguishing between confident class assignments and ambiguous ("Other") cases. #' -#' @examples -#' # Example usage: -#' # result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") -#' +#' @import dplyr tidyr caret #' @export #' -optimize_purity <- function( - optimized_model_object, - vote_df, - mode, - optimize_by = "balanced_accuracy", #or overall_accuracy - truth_column, - all_classes = c("MCD","EZB","BN2","N1","ST2","Other"), - k, - exclude_other_for_accuracy = FALSE -){ - if(mode == "DLBCLone_w"){ - in_column = "by_score" - out_column = mode - } +#' @examples +#' +#' library(GAMBLR.predict) +#' +#' result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") +#' +optimize_purity <- function(optimized_model_object, + vote_df, + mode, + optimize_by = "balanced_accuracy", #allowed: harmonic_mean, overall_accuracy + truth_column, + all_classes = c("MCD","EZB","BN2","N1","ST2","Other"), + k, + cap_classification_rate = 1, + exclude_other_for_accuracy = FALSE, + other_class = "Other") { # <--- NEW ARG + + out_column = "DLBCLone_wo" + if(!missing(optimized_model_object)){ if(!is.null(optimized_model_object$best_params)){ if(!missing(k)){ @@ -1130,118 +1057,128 @@ optimize_purity <- function( vote_df = optimized_model_object$predictions } - #all_classes = unique(vote_df[[truth_column]]) - some_classes = all_classes[all_classes != "Other"] + some_classes <- if (!is.null(other_class) && other_class %in% all_classes) { + all_classes[all_classes != other_class] + } else { + all_classes + } score_thresh = 2 * k - processed_votes = process_votes( - vote_df, - group_labels=all_classes, - k=k, - score_purity_requirement=0.5 - ) - # Function to optimize purity based on processed votes and truth labels + processed_votes <- process_votes(vote_df, + group_labels = all_classes, + k = k, + score_purity_requirement = 0.5, + other_class = other_class) # <--- pass it through + if(!truth_column %in% colnames(processed_votes)){ stop("truth_column must be a column in processed_votes") } best_accuracy <- 0 best_purity_threshold <- 0 - processed_votes = mutate(processed_votes,!!sym(truth_column):= as.character(!!sym(truth_column))) + processed_votes <- mutate(processed_votes, !!sym(truth_column) := as.character(!!sym(truth_column))) - no_other_df = processed_votes %>% - filter(!!sym(truth_column) != "Other") + if (!is.null(other_class) && other_class %in% all_classes) { + no_other_df <- processed_votes %>% + filter(!!sym(truth_column) != other_class) + } else { + no_other_df <- processed_votes + } + for(purity_threshold in seq(3, 0, -0.05)){ updated_votes <- processed_votes %>% - mutate(!!sym(out_column) := ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, "Other")) + mutate(!!sym(out_column) := if (!is.null(other_class) && other_class %in% all_classes) { + ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, other_class) + } else { + by_score + }) + updated_no_other_df <- no_other_df %>% - mutate(!!sym(out_column) := ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, "Other")) + mutate(!!sym(out_column) := if (!is.null(other_class) && other_class %in% all_classes) { + ifelse(score_ratio >= purity_threshold | top_group_score > score_thresh, by_score, other_class) + } else { + by_score + }) - if(!exclude_other_for_accuracy){ - - xx = select(updated_votes, sample_id, !!sym(truth_column), !!sym(out_column)) %>% - mutate(match=ifelse(!!sym(truth_column) == !!sym(out_column),TRUE,FALSE)) %>% + if(!exclude_other_for_accuracy || is.null(other_class) || !(other_class %in% all_classes)){ + xx <- select(updated_votes, sample_id, !!sym(truth_column), !!sym(out_column)) %>% + mutate(match = !!sym(truth_column) == !!sym(out_column)) %>% group_by(match) %>% - #summarise(concordant) %>% - summarise(concordant=sum(match==TRUE), discordant = sum(match==FALSE)) %>% - ungroup() %>% - summarise(all_conc = sum(concordant), dis = sum(discordant), total=all_conc+dis, percent=100*sum(concordant)/total) - pc_conc = xx$percent %>% round(.,1) - num_conc = xx$all_conc - num_dis = xx$dis + summarise(concordant = sum(match == TRUE), discordant = sum(match == FALSE), .groups = "drop") %>% + summarise(all_conc = sum(concordant), dis = sum(discordant), total = all_conc + dis, percent = 100 * sum(concordant) / total) - # calculate accuracy - updated_votes = mutate(updated_votes,!!sym(out_column) := factor(!!sym(out_column),levels=all_classes)) - updated_votes = mutate(updated_votes,!!sym(truth_column) := factor(!!sym(truth_column),levels=all_classes)) - confusion_matrix <- table(updated_votes[[truth_column]],updated_votes[[out_column]]) + updated_votes <- mutate(updated_votes, !!sym(out_column) := factor(!!sym(out_column), levels = all_classes)) + updated_votes <- mutate(updated_votes, !!sym(truth_column) := factor(!!sym(truth_column), levels = all_classes)) + confusion_matrix <- table(updated_votes[[truth_column]], updated_votes[[out_column]]) conf_matrix <- confusionMatrix(updated_votes[[out_column]], updated_votes[[truth_column]]) - - }else{ - xx = select(updated_no_other_df, sample_id, !!sym(truth_column), !!sym(out_column)) %>% - filter(!!sym(truth_column) != "Other") %>% - mutate(match=ifelse(!!sym(truth_column) == !!sym(out_column),TRUE,FALSE)) %>% + } else { + xx <- select(updated_no_other_df, sample_id, !!sym(truth_column), !!sym(out_column)) %>% + filter(!!sym(truth_column) != other_class) %>% + mutate(match = !!sym(truth_column) == !!sym(out_column)) %>% group_by(match) %>% - #summarise(concordant) %>% - summarise(concordant=sum(match==TRUE), discordant = sum(match==FALSE)) %>% - ungroup() %>% - summarise(all_conc = sum(concordant), dis = sum(discordant), total=all_conc+dis, percent=100*sum(concordant)/total) - updated_no_other_df = mutate(updated_no_other_df, - !!sym(out_column) := factor(!!sym(out_column),levels=all_classes), - !!sym(truth_column) := factor(!!sym(truth_column),levels=all_classes)) - confusion_matrix <- table(updated_no_other_df[[truth_column]],updated_no_other_df[[out_column]]) + summarise(concordant = sum(match == TRUE), discordant = sum(match == FALSE), .groups = "drop") %>% + summarise(all_conc = sum(concordant), dis = sum(discordant), total = all_conc + dis, percent = 100 * sum(concordant) / total) + + updated_no_other_df <- mutate(updated_no_other_df, + !!sym(out_column) := factor(!!sym(out_column), levels = all_classes), + !!sym(truth_column) := factor(!!sym(truth_column), levels = all_classes)) + confusion_matrix <- table(updated_no_other_df[[truth_column]], updated_no_other_df[[out_column]]) conf_matrix <- confusionMatrix(updated_no_other_df[[out_column]], updated_no_other_df[[truth_column]]) } - accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix) - bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class - oa = conf_matrix$overall[["Accuracy"]] - pc_conc = xx$percent %>% round(.,1) - bal_acc = mean(bal_acc,na.rm=T) - - if(optimize_by == "balanced_accuracy"){ - accuracy = bal_acc - }else if(optimize_by == "overall_accuracy"){ - accuracy = oa - } - num_conc = xx$all_conc - num_dis = xx$dis + + acc_details <- report_accuracy(updated_votes, + truth = truth_column, + pred = out_column) - #if(accuracy > best_accuracy){ - if(accuracy > best_accuracy){ - #print(confusion_matrix) - #print(bal_acc) - #print(paste("Percent: ",pc_conc,"Overall:",oa, "Balanced:",mean(bal_acc,na.rm=T))) - #print(conf_matrix) - #message(paste0("New best accuracy: ", round(accuracy, 3))) - #message(paste0("Matches:" ,num_conc, " %Concordance: ",pc_conc, " Mismatches: ",num_dis)) - #message(paste0(" at purity threshold: ", round(purity_threshold, 2))) - best_accuracy <- accuracy - best_purity_threshold <- purity_threshold + bal_acc <- mean(conf_matrix$byClass[, "Balanced Accuracy"], na.rm = TRUE) + classification_rate = acc_details$classification_rate + if(classification_rate <= cap_classification_rate){ + if(optimize_by == "harmonic_mean"){ + accuracy = acc_details$harmonic_mean + }else if( optimize_by == "overall_accuracy"){ + accuracy = conf_matrix$overall[["Accuracy"]] + + }else{ + accuracy = bal_acc + } + + + if(accuracy > best_accuracy){ + best_accuracy <- accuracy + best_purity_threshold <- purity_threshold + } }else{ - #print(paste("No improvement", accuracy, "vs", best_accuracy, "at threshold", purity_threshold)) + message(paste0("skipping purity threshold ",purity_threshold," because classification rate ",round(classification_rate,3), + " exceeds cap of ",cap_classification_rate)) } - - #print(confusion_matrix_ignore_other) } - best_out = processed_votes %>% - mutate(!!sym(out_column) := ifelse(score_ratio >= best_purity_threshold | top_group_score > score_thresh, by_score, "Other")) + + best_out <- processed_votes %>% + mutate(!!sym(out_column) := if (!is.null(other_class) && other_class %in% all_classes) { + ifelse(score_ratio >= best_purity_threshold | top_group_score > score_thresh, by_score, other_class) + } else { + by_score + }) if(missing(optimized_model_object)){ - optimized_model_object = list() - optimized_model_object$best_params = list( - k=k, - purity_threshold=best_purity_threshold, - accuracy=best_accuracy, - num_classes=length(unique(best_out$predicted_label)), - num_features=ncol(best_out)-3, #minus V1,V2,sample_id - seed=12345) - } - optimized_model_object$predictions = best_out - optimized_model_object$best_accuracy = best_accuracy - optimized_model_object$best_purity_threshold = best_purity_threshold - optimized_model_object$maximize = optimize_by - optimized_model_object$score_thresh = score_thresh - optimized_model_object$exclude_other_for_accuracy = exclude_other_for_accuracy + optimized_model_object <- list() + optimized_model_object$best_params <- list( + k = k, + purity_threshold = best_purity_threshold, + accuracy = best_accuracy, + num_classes = length(unique(best_out$predicted_label)), + num_features = ncol(best_out) - 3, + seed = 12345 + ) + } + optimized_model_object$predictions <- best_out + optimized_model_object$best_accuracy <- best_accuracy + optimized_model_object$best_purity_threshold <- best_purity_threshold + optimized_model_object$maximize <- optimize_by + optimized_model_object$score_thresh <- score_thresh + optimized_model_object$exclude_other_for_accuracy <- exclude_other_for_accuracy + return(optimized_model_object) } @@ -1269,10 +1206,21 @@ optimize_purity <- function( #' - Uses the parameters (e.g., k, feature weights) from the provided \code{DLBCLone_KNN_out} object. #' - Returns the same structure as \code{DLBCLone_KNN}, with predictions for the test samples. #' +#' @import dplyr tidyr tibble readr +#' @export +#' #' @examples #' # Assuming you have run DLBCLone_KNN to get optimized parameters: #' # model_out <- DLBCLone_KNN(train_features, train_metadata, ...) #' # Predict on new samples: +#' +#' library(GAMBLR.predict) +#' +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' #' predictions <- DLBCLone_KNN_predict( #' train_df = all_full_status, #' test_df = lyseq_validation_data, @@ -1281,7 +1229,6 @@ optimize_purity <- function( #' mode = "batch" #' ) #' -#' @export #' DLBCLone_KNN_predict <- function(train_df, test_df, @@ -1342,7 +1289,6 @@ DLBCLone_KNN_predict <- function(train_df, } } - #' Run DLBCLone KNN Classification #' #' Weighted KNN on a feature (mutation) matrix with optional upweighting of @@ -1389,7 +1335,18 @@ DLBCLone_KNN_predict <- function(train_df, #' \item{df}{Annotated layout for plotting (when built in this run)} #' \item{plot_truth, plot_predicted}{ggplots when built in this run} #' +#' @import dplyr tidyr uwot ggplot2 purrr caret +#' @export +#' #' @examples +#' +#' library(GAMBLR.predict) +#' +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' #' dlbcl_knn <- DLBCLone_KNN( #' features_df = all_full_status, #' metadata = dlbcl_meta, @@ -1398,7 +1355,6 @@ DLBCLone_KNN_predict <- function(train_df, #' optimize_for_other = TRUE #' ) #' -#' @export DLBCLone_KNN <- function(features_df, metadata, core_features = NULL, @@ -1416,7 +1372,7 @@ DLBCLone_KNN <- function(features_df, seed = 12345, epsilon = 0.001, weighted_votes = TRUE, - skip_umap = FALSE){ + skip_umap = FALSE) { # In-group classes (exclude the explicit outgroup label if present) class_levels <- setdiff(truth_classes, other_class) @@ -1928,7 +1884,6 @@ DLBCLone_KNN <- function(features_df, core_feature_multiplier = core_feature_multiplier, core_features = core_features, hidden_features = hidden_features, - metadata = metadata, seed = seed, optimize_for_other = optimize_for_other, unlabeled_predictions = NULL @@ -2038,14 +1993,21 @@ DLBCLone_KNN <- function(features_df, #' UMAP output as a data frame. The list also includes the predictions for the #' "Other" class if it was included in the training and testing. #' +#' @import dplyr tidyr tibble #' @export #' #' @examples #' +#' library(GAMBLR.predict) +#' +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) #' #' # Aim to maximize classification of samples into non-Other class #' optimize_params_F <- DLBCLone_optimize_params( -#' make_umap$features, +#' all_full_status, #' dlbcl_meta, #' umap_out = make_umap, #' truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), @@ -2058,7 +2020,7 @@ DLBCLone_KNN <- function(features_df, #' # unclassified (assigned to "Other") #' #' optimize_params_T <- DLBCLone_optimize_params( -#' make_umap$features, +#' all_full_status, #' dlbcl_meta, #' umap_out = make_umap, #' truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), @@ -2067,34 +2029,68 @@ DLBCLone_KNN <- function(features_df, #' max_k=23 #' ) #' -DLBCLone_optimize_params = function( - combined_mutation_status_df, - metadata_df, - umap_out, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - truth_column = "lymphgen", - optimize_for_other = FALSE, - eval_group = NULL, - min_k=3, - max_k=23, - verbose = FALSE, - seed = 12345, - maximize = "balanced_accuracy", - exclude_other_for_accuracy = FALSE, - weights_opt = c(TRUE) -){ - #if(optimize_for_other){ - # exclude_other_for_accuracy = FALSE - #}else{ +DLBCLone_optimize_params = function(combined_mutation_status_df, + metadata_df, + umap_out, + truth_classes = c("EZB", + "MCD", + "ST2", + "N1", + "BN2", + "Other"), + truth_column = "lymphgen", + optimize_for_other = FALSE, + + eval_group = NULL, + min_k=3, + max_k=23, + verbose = FALSE, + seed = 12345, + maximize = "balanced_accuracy", #or "harmonic_mean" or "accuracy" + cap_classification_rate = 0.9, + exclude_other_for_accuracy = FALSE, + weights_opt = c(TRUE) + ) { + macro_f1 <- function(truth, pred, drop_other = TRUE, other_label = "Other", na_rm = TRUE) { + stopifnot(length(truth) == length(pred)) + truth <- factor(truth) + pred <- factor(pred, levels = levels(truth)) # align levels + + classes <- levels(truth) + if (drop_other && other_label %in% classes) { + classes <- setdiff(classes, other_label) + } + + f1_per_class <- sapply(classes, function(cls) { + tp <- sum(truth == cls & pred == cls) + fp <- sum(truth != cls & pred == cls) + fn <- sum(truth == cls & pred != cls) + + prec <- if ((tp + fp) == 0) NA_real_ else tp / (tp + fp) + rec <- if ((tp + fn) == 0) NA_real_ else tp / (tp + fn) + + if (is.na(prec) || is.na(rec) || (prec + rec) == 0) { + if (na_rm) return(NA_real_) else return(0) # choose policy + } + 2 * prec * rec / (prec + rec) + }) + + mean(f1_per_class, na.rm = na_rm) + + } + exclude_other_for_accuracy = TRUE - #} + na_opt = c("drop") - num_class = length(truth_classes) + num_class = length(truth_classes) + threshs = seq(0,0.9,0.1) - #threshs = 0.5 + + ks = seq(min_k,max_k,2) results <- data.frame() best_params <- data.frame() + use_w = TRUE best_acc = 0 best_acc_w = 0 @@ -2105,35 +2101,45 @@ DLBCLone_optimize_params = function( best_pred = NULL other_pred = NULL best_w_score_thresh = NULL - best_w_purity = NULL - #ignore_self = TRUE + best_w_purity = NULL + w_best_pred = NULL + ignore_self = TRUE for(na_option in na_opt){ if(missing(umap_out)){ - stop("umap_out must be provided by running make_and_annotate_umap first") + outs = make_and_annotate_umap(df=combined_mutation_status_df, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) }else{ #project onto existing model instead of re-running UMAP - outs = make_and_annotate_umap( - df=combined_mutation_status_df, - umap_out = umap_out, - min_dist = 0, - n_neighbors = 55, - n_epochs = 1500, - seed=seed, - metadata=metadata_df, - ret_model=T, - metric="cosine", - join_column="sample_id", - na_vals = na_option - ) + if(!missing(combined_mutation_status_df)){ + message("ignoring mutation status data frame and using features from umap_out instead") + } + outs = make_and_annotate_umap(df=umap_out$features, + umap_out = umap_out, + min_dist = 0, + n_neighbors = 55, + n_epochs = 1500, + seed=seed, + metadata=metadata_df, + ret_model=T, + metric="cosine", + join_column="sample_id", + na_vals = na_option) } + for(use_w in weights_opt){ for(k in ks){ best_w_acc = NULL message(paste("K:",k)) - - #for (threshold in threshs){ test_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) @@ -2142,26 +2148,25 @@ DLBCLone_optimize_params = function( rownames(train_coords) = train_ids rownames(test_coords) = test_ids + + pred = weighted_knn_predict_with_conf( train_coords = train_coords, train_labels = train_labels, test_coords = test_coords, k=k, conf_threshold =threshold, - na_label="Other", + other_class="Other", use_weights = use_w, - #ignore_self = ignore_self, - verbose = verbose - ) + ignore_self = ignore_self, + verbose = verbose) for(threshold in threshs){ if(verbose){ print(paste("k:",k,"threshold:",threshold,"use_weights:",use_w,"na_option:",na_option)) } - pred_thresh = mutate( - pred, - predicted_label = ifelse(confidence >= threshold, predicted_label, "Other") - ) + pred_thresh = mutate(pred, + predicted_label = ifelse(confidence >= threshold, predicted_label, "Other")) if(is.null(best_w_acc)){ #DLBCLone_wo @@ -2172,35 +2177,63 @@ DLBCLone_optimize_params = function( print(table(pred_with_truth[[truth_column]])) } - #return(pred_with_truth) pred_w = - #suppressMessages( optimize_purity( vote_df = pred_with_truth, mode = "DLBCLone_w", truth_column = truth_column, all_classes = truth_classes, optimize_by = maximize, + cap_classification_rate = cap_classification_rate, k = k ) - #) + if(verbose){ print("running optimize_purity for the first time at k:", k) } - - #print(names(pred_w)) + reported_accuracy = report_accuracy(pred_w$predictions,pred = "DLBCLone_wo") best_w_acc = pred_w$best_accuracy if(best_w_acc > best_acc_w){ - message(paste("best accuracy DLBCLone_wo:",pred_w$best_accuracy, "purity threshold:", pred_w$best_purity_threshold)) + best_acc_w = best_w_acc best_k_w = k best_fit = pred_with_truth + best_pred_w = pred_w$predictions best_w_purity = pred_w$best_purity_threshold best_w_score_thresh = pred_w$score_thresh + #print(head(pred_w$predictions)) + + conc = reported_accuracy$accuracy_no_other + f1 = macro_f1(pred_w$predictions[[truth_column]], + pred_w$predictions$DLBCLone_wo, + drop_other = T) + new_f1 = reported_accuracy$macro_f1 + harmonic_mean = reported_accuracy$harmonic_mean + mba = reported_accuracy$mean_balanced_accuracy + cr = reported_accuracy$classification_rate + + message("new best DLBCLone_wo:") + message(paste( + "Concordance:", + round(conc,3), + "F1:", + round(f1,5), + "MBA:", + round(best_w_acc,3), + "Classification rate:", + round(cr,5), + "Harmonic mean:", + round(harmonic_mean,5), + "purity:", + pred_w$best_purity_threshold, + "Score:", + best_w_score_thresh + )) } best_pred_w = pred_w$predictions + } - + train_d = filter(outs$df,!!sym(truth_column) %in% truth_classes) xx_d = bind_cols(train_d ,pred_thresh) @@ -2211,6 +2244,7 @@ DLBCLone_optimize_params = function( train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) n_other = nrow(test_coords) + if(!"Other" %in% truth_classes && n_other > 0){ pred_other = weighted_knn_predict_with_conf( train_coords = train_coords, @@ -2218,19 +2252,16 @@ DLBCLone_optimize_params = function( test_coords = test_coords, k=k, conf_threshold =threshold, - na_label="Other", + other_class="Other", use_weights = use_w, - #ignore_self = ignore_self, - verbose = verbose - ) + ignore_self = ignore_self, + verbose = verbose) - #if(!is.null(eval_group)){ - # xx_o = bind_cols(filter(outs$df,dataset == eval_group,!!sym(truth_column) =="Other") ,pred_other) - #}else{ xx_o = bind_cols(filter(outs$df,!!sym(truth_column) == "Other" | is.na(!!sym(truth_column))) ,pred_other) - #} + } xx_d[[truth_column]] = factor(xx_d[[truth_column]],levels = c(unique(xx_d[[truth_column]]),"Other")) + } true_factor = xx_d[[truth_column]] pred_factor = factor(xx_d$predicted_label,levels = levels(xx_d[[truth_column]])) @@ -2243,10 +2274,10 @@ DLBCLone_optimize_params = function( } conf_matrix <- confusionMatrix(pred_factor, true_factor) - #print(conf_matrix) + bal_acc <- conf_matrix$byClass[, "Balanced Accuracy"] # one per class sn <- conf_matrix$byClass[, "Sensitivity"] # one per class - #bal_acc = report_accuracy(xx_d,pred = "predicted_label")$mean_balanced_accuracy + if(verbose){ print(bal_acc) print(conf_matrix$overall) @@ -2258,21 +2289,23 @@ DLBCLone_optimize_params = function( overall_sensitivity<- mean(sn[!names(sn) == "Class: Other"], na.rm = TRUE) if(optimize_for_other){ - optimized_accuracy_and_thresh = optimize_outgroup( - pred_factor, - true_factor, - xx_d$other_score, - all_classes = truth_classes, - maximize = maximize, - exclude_other_for_accuracy = exclude_other_for_accuracy - ) + + optimized_accuracy_and_thresh = optimize_outgroup(pred_factor, + true_factor, + xx_d$other_score, + all_classes = truth_classes, + maximize = maximize, + cap_classification_rate = cap_classification_rate, + exclude_other_for_accuracy = exclude_other_for_accuracy) out_opt_thresh = optimized_accuracy_and_thresh$threshold - #print(optimized_accuracy_and_thresh$average_accuracy) - - #optimized_accuracy_and_thresh$average_accuracy[is.na(optimized_accuracy_and_thresh$average_accuracy)] = 0 - out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + if(maximize=="harmonic_mean"){ + out_opt_acc = optimized_accuracy_and_thresh$harmonic_mean + }else{ + out_opt_acc = optimized_accuracy_and_thresh$average_accuracy + } + }else{ out_opt_acc = 0 out_opt_thresh = 0 @@ -2285,52 +2318,62 @@ DLBCLone_optimize_params = function( if(maximize == "sensitivity"){ this_accuracy = overall_sensitivity }else if(maximize == "balanced_accuracy"){ + this_accuracy =mean_balanced_accuracy - }else{ + }else{ + this_accuracy = overall_accuracy } - if(out_opt_acc > this_accuracy){ + this_accuracy = out_opt_acc } - row <- data.frame( - k = k, - threshold = threshold, - use_weights = use_w, - optimized_accuracy = this_accuracy, - overall_accuracy = overall_accuracy, - mean_balanced_accuracy = mean_balanced_accuracy, - sensitivity = overall_sensitivity, - N1_sn = unname(sn["Class: N1"]), - BN2_sn= unname(sn["Class: BN2"]), - ST2_sn = unname(sn["Class: ST2"]), - BN2_bacc = unname(bal_acc["Class: BN2"]), - MCD_bacc = unname(bal_acc["Class: MCD"]), - EZB_bacc = unname(bal_acc["Class: EZB"]), - A53_bacc = unname(bal_acc["Class: A53"]), - Other_bacc = unname(bal_acc["Class: Other"]), - C1_bacc = unname(bal_acc["Class: C1"]), - C2_bacc = unname(bal_acc["Class: C2"]), - C3_bacc = unname(bal_acc["Class: C3"]), - C4_bacc = unname(bal_acc["Class: C4"]), - C5_bacc = unname(bal_acc["Class: C5"]), - ST2_bacc = unname(bal_acc["Class: ST2"]), - threshold_outgroup = out_opt_thresh, - accuracy_out = out_opt_acc, - na_option= na_option - ) - + row <- data.frame(k = k, + threshold = threshold, + use_weights = use_w, + optimized_accuracy = this_accuracy, + overall_accuracy = overall_accuracy, + mean_balanced_accuracy = mean_balanced_accuracy, + sensitivity = overall_sensitivity, + N1_sn = unname(sn["Class: N1"]), + BN2_sn= unname(sn["Class: BN2"]), + ST2_sn = unname(sn["Class: ST2"]), + BN2_bacc = unname(bal_acc["Class: BN2"]), + MCD_bacc = unname(bal_acc["Class: MCD"]), + EZB_bacc = unname(bal_acc["Class: EZB"]), + A53_bacc = unname(bal_acc["Class: A53"]), + Other_bacc = unname(bal_acc["Class: Other"]), + C1_bacc = unname(bal_acc["Class: C1"]), + C2_bacc = unname(bal_acc["Class: C2"]), + C3_bacc = unname(bal_acc["Class: C3"]), + C4_bacc = unname(bal_acc["Class: C4"]), + C5_bacc = unname(bal_acc["Class: C5"]), + ST2_bacc = unname(bal_acc["Class: ST2"]), + threshold_outgroup = out_opt_thresh, + accuracy_out = out_opt_acc, + na_option= na_option) + if(this_accuracy > best_acc){ best_acc = this_accuracy - #(xx_d)) + xx_d = mutate(xx_d, predicted_label_optimized = ifelse(other_score > out_opt_thresh, "Other", predicted_label)) no_other_acc = report_accuracy(xx_d,pred = "predicted_label_optimized")$mean_balanced_accuracy - message(paste("best accuracy DLBCLone_io:",best_acc, "without other: ", no_other_acc, "threshold_outgroup:", row$threshold_outgroup)) + message("new best accuracy DLBCLone_io:") + message(paste( + best_acc, + "without other: ", + no_other_acc, + "sensitivity:", + overall_sensitivity, + "threshold_outgroup:", + row$threshold_outgroup)) + best_fit = outs best_pred = xx_d if(!"Other" %in% truth_classes && n_other > 0){ other_pred = pred_other } + best_params = row } results <- rbind(results, row) @@ -2341,80 +2384,85 @@ DLBCLone_optimize_params = function( best_params$num_classes = num_class best_params$num_features = ncol(umap_out$features) best_params$seed = seed + test_coords = outs$df %>% select(V1,V2) - #train_coords = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% select(V1,V2) - #train_ids = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(sample_id) - #rownames(train_coords) = train_ids - #train_labels = filter(outs$df,!!sym(truth_column) %in% truth_classes) %>% pull(!!sym(truth_column)) + train_coords = outs$df %>% select(V1,V2) train_ids = outs$df %>% pull(sample_id) rownames(train_coords) = train_ids train_labels = outs$df %>% pull(!!sym(truth_column)) rownames(test_coords) = outs$df %>% pull(sample_id) - pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k=best_params$k, - conf_threshold =best_params$threshold, - #na_label="Other", - use_weights = best_params$use_weights, - #ignore_self = ignore_self, - verbose = verbose, - track_neighbors = TRUE - ) - pred_with_truth_full = bind_cols(outs$df %>% select(sample_id, !!sym(truth_column)), pred) - if(verbose){ print(paste("TOP score threshold:",best_w_score_thresh, "purity:", best_w_purity)) } - best_pred_w = process_votes( - df=pred_with_truth_full, - group_labels=truth_classes, - k=k - ) %>% - mutate( - DLBCLone_w = ifelse( - score_ratio >= best_w_purity | top_group_score > best_w_score_thresh, - by_score, - "Other" - ) - ) + + pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k=best_k_w,# re-run with best k for weighted voting + #conf_threshold =best_params$threshold, + use_weights = best_params$use_weights, + ignore_self = ignore_self, + verbose = verbose, + track_neighbors = TRUE) + + pred_with_truth_full = bind_cols(outs$df %>% select(sample_id, !!sym(truth_column)), pred) + + best_pred_w = process_votes(df=pred_with_truth_full, + group_labels=truth_classes, + k=best_k_w) + + best_pred_w = best_pred_w %>% + mutate(DLBCLone_w = ifelse(score_ratio >= best_w_purity | + top_group_score > best_w_score_thresh, + by_score, + "Other")) if(optimize_for_other){ - pred = mutate( - pred, - predicted_label_optimized = ifelse( - other_score > best_params$threshold_outgroup, - "Other", - predicted_label - ) - ) + pred = mutate(pred,predicted_label_optimized = ifelse(other_score > best_params$threshold_outgroup, + "Other", + predicted_label)) }else{ pred = mutate(pred,predicted_label_optimized = predicted_label) } + #new naming convention pred = mutate(pred, DLBCLone_i = predicted_label, DLBCLone_io = predicted_label_optimized) best_pred_w = rename(best_pred_w, DLBCLone_wo = DLBCLone_w) #optimized best_pred_w = rename(best_pred_w, DLBCLone_w = by_score) #greedy + #check accuracy again + print(dim(pred)) xx_d = bind_cols(outs$df,pred) - xx_d = left_join(xx_d, select(best_pred_w, sample_id, DLBCLone_w, DLBCLone_wo), by="sample_id") - to_ret = list( - params=results, - best_params = best_params, - model=umap_out$model, - features=umap_out$features, - k_DLBCLone_i = best_params$k, - threshold_DLBCLone_i = best_params$threshold, - theshold_outgroup_DLBCLone_i = best_params$threshold_outgroup, - k_DLBCLone_w = best_k_w, - purity_DLBCLone_w = best_w_purity, - score_thresh_DLBCLone_w = best_w_score_thresh, - df=outs$df, - predictions=xx_d - ) + print(dim(xx_d)) + xx_d = left_join(xx_d, select(best_pred_w, sample_id, score_ratio, top_group_score, DLBCLone_w, DLBCLone_wo), by="sample_id") + acc_check_w = report_accuracy(xx_d,pred = "DLBCLone_w") + acc_check_wo = report_accuracy(xx_d,pred = "DLBCLone_wo") + message(paste("DLBCLone_w accuracy:", + round(acc_check_w$mean_balanced_accuracy,3), + "DLBCLone_wo accuracy:", + round(acc_check_wo$mean_balanced_accuracy,3), + "DLBCLone_w Concordance:", round(acc_check_w$no_other,3), + "DLBCLone_wo Concordance:", round(acc_check_wo$no_other,3))) + to_ret = list(params=results, + best_params = best_params, + model=umap_out$model, + features=umap_out$features, + k_DLBCLone_i = best_params$k, + threshold_DLBCLone_i = best_params$threshold, + theshold_outgroup_DLBCLone_i = best_params$threshold_outgroup, + k_DLBCLone_w = best_k_w, + purity_DLBCLone_w = best_w_purity, + score_thresh_DLBCLone_w = best_w_score_thresh, + df=outs$df, + predictions=xx_d, + best_pred_w = best_pred_w, + truth_classes = truth_classes, + optimize_for_other = optimize_for_other, + truth_column = truth_column, + type = "DLBCLone_optimize_params") if(!"Other" %in% truth_classes && n_other > 0){ to_ret[["predictions_other"]] = xx_o to_ret[["predictions_combined"]] = bind_rows(xx_o,best_pred) @@ -2426,9 +2474,6 @@ DLBCLone_optimize_params = function( to_ret[[mn]] = outs[[mn]] } } - to_ret$truth_classes = truth_classes - to_ret$optimize_for_other = optimize_for_other - to_ret$truth_column = truth_column return(to_ret) } @@ -2455,24 +2500,39 @@ DLBCLone_optimize_params = function( #' when calculating the confidence. #' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' -#' @returns data frame with labels and confidence values for rows in test_coords +#' @return Data frame with rows = test samples and columns: +#' \item{predicted_label}{the predicted class} +#' \item{confidence}{predicted class weight / total weight} +#' If \code{track_neighbors = TRUE}, additional columns: +#' \item{other_score}{relative weight of outgroup vs predicted class} +#' \item{neighbor_id}{comma-separated neighbor sample IDs} +#' \item{neighbor}{comma-separated neighbor indices (in train order)} +#' \item{distance}{comma-separated neighbor distances} +#' \item{label}{comma-separated neighbor labels (in-group only if separate_other=TRUE)} +#' \item{vote_labels}{comma-separated unique labels contributing to weights} +#' \item{weighted_votes}{comma-separated weights per \code{vote_labels}} +#' \item{neighbors_other}{count of outgroup neighbors closer than the farthest in-group neighbor} +#' \item{other_weighted_votes}{sum of outgroup weights closer than the farthest in-group neighbor} +#' \item{total_w}{sum of weights for in-group neighbors used} +#' \item{pred_w}{weight supporting the predicted class} +#' +#' @import FNN dplyr tibble tidyr #' @export #' -weighted_knn_predict_with_conf <- function( - train_coords, - train_labels, - test_coords, - k, - epsilon = 0.1, - conf_threshold = NULL, - na_label = "Other", - verbose = FALSE, - use_weights = TRUE, - track_neighbors = TRUE, - separate_other = TRUE, - max_neighbors = 500 -){ #big change here. Other is considered separately for optimization - #ignore_self = TRUE +weighted_knn_predict_with_conf <- function(train_coords, + train_labels, + test_coords, + k, + epsilon = 0.1, + conf_threshold = NULL, + other_class = "Other", + verbose = FALSE, + use_weights = TRUE, + ignore_self = TRUE, + track_neighbors = TRUE, + separate_other = TRUE, + max_neighbors = 500) { + if (nrow(train_coords)==0 || nrow(test_coords) == 0) { print("train_coords:") print(nrow(train_coords)) @@ -2497,17 +2557,18 @@ weighted_knn_predict_with_conf <- function( } neighbors <- nn$nn.index[i, ] distances <- nn$nn.dist[i, ] -# if(ignore_self){ - # nns = length(neighbors) - # neighbor_ids = rownames(train_coords)[neighbors] - # neighbors <- neighbors[neighbor_ids != self] - # distances <- distances[neighbor_ids != self] - # if(length(neighbors) == nns){ -# print(paste("self was not dropped",paste(neighbor_ids,collapse=","),"i:",i,"self:",self)) - # print(paste(neighbor_ids,collapse=",")) - # stop() - # } -# } + if(ignore_self){ + nns = length(neighbors) + neighbor_ids = rownames(train_coords)[neighbors] + + neighbors <- neighbors[neighbor_ids != self] + distances <- distances[neighbor_ids != self] + neighbor_ids = neighbor_ids[neighbor_ids != self] + + neighbor_labels <- train_labels[neighbors] + + } + distances = distances + epsilon weights <- 1 / distances @@ -2517,6 +2578,7 @@ weighted_knn_predict_with_conf <- function( weights <- rep(1,length(distances)) } + neighbor_labels <- train_labels[neighbors] if(verbose){ @@ -2529,14 +2591,19 @@ weighted_knn_predict_with_conf <- function( print("labels:") print(neighbor_labels) } - # Remove NAs (just in case) + other_mask <- (neighbor_labels == other_class) + other_dists <- distances[other_mask] + other_ids <- neighbor_ids[other_mask] + other_labels <- neighbor_labels[other_mask] valid <- !is.na(neighbor_labels) - #num_other_neighbors = sum(neighbor_labels == "Other") - other_dists = distances[neighbor_labels == "Other"] - if(separate_other){ - valid = valid & neighbor_labels != "Other" + if(!all(other_labels == other_class)){ + stop("logic error: not all other_labels are other_class") } + #num_other_neighbors = sum(neighbor_labels == "Other") + #other_dists = distances[neighbor_labels == "Other"] + if (separate_other) valid <- valid & !other_mask + neighbor_labels <- neighbor_labels[valid] weights <- weights[valid] distances <- distances[valid] @@ -2559,6 +2626,7 @@ weighted_knn_predict_with_conf <- function( others_closer = which(other_dists < max(distances)) others_distances = other_dists[others_closer] + other_ids = other_ids[others_closer] if(use_weights){ others_weights = 1 / others_distances }else{ @@ -2567,18 +2635,18 @@ weighted_knn_predict_with_conf <- function( neighbors_other = length(others_closer) other_weighted_votes = sum(others_weights) mean_other_dist = mean(others_distances) - #other_weighted_votes = neighbors_other - # print(paste("other weighted votes:",other_weighted_votes)) - #} + if (length(neighbor_labels) == 0) { preds[i] <- "Other" confs[i] <- 1 - if(track_neighbors){ + if(track_neighbors){ + rel_other = 10 neighbor_info <- data.frame( other_score = rel_other, neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), neighbor = paste(neighbors,collapse=","), + other_neighbor = paste(other_ids,collapse=","), distance = paste(round(distances, 3),collapse=","), label = paste(neighbor_labels,collapse=","), weighted_votes = "", @@ -2586,7 +2654,8 @@ weighted_knn_predict_with_conf <- function( other_weighted_votes = 0, mean_other_dist = mean_other_dist, total_w = 1, - pred_w = 2 + pred_w = 2 + ) all_neighbors = bind_rows(all_neighbors, neighbor_info) next; @@ -2596,16 +2665,17 @@ weighted_knn_predict_with_conf <- function( weighted_votes <- tapply(weights, neighbor_labels, sum) if (length(weighted_votes) == 0) { - preds[i] <- na_label + preds[i] <- other_class confs[i] <- NA total_weight <- 0 pred_weight <- 0 - }else{ + } else { #print(weighted_votes) predicted_label <- names(which.max(weighted_votes)) total_weight <- sum(weighted_votes) pred_weight <- weighted_votes[predicted_label] confidence <- pred_weight / total_weight + # Confidence thresholding #if (!is.null(conf_threshold) && confidence < conf_threshold) { # preds[i] <- na_label @@ -2623,30 +2693,31 @@ weighted_knn_predict_with_conf <- function( }else{ rel_other = 0 } - #print(head(rownames(train_coords))) - #rel_other = ifelse(rel_other==0,0,log(rel_other)) + neighbor_info <- data.frame( other_score = rel_other, neighbor_id = paste(rownames(train_coords)[neighbors],collapse=","), - neighbor = paste(neighbors,collapse=","), - distance = paste(round(distances, 3),collapse=","), - label = paste(neighbor_labels,collapse=","), + neighbor = paste(neighbors,collapse=","), + distance = paste(round(distances, 3),collapse=","), + label = paste(neighbor_labels,collapse=","), + other_neighbor = paste(other_ids,collapse=","), vote_labels = paste(names(weighted_votes),collapse=","), weighted_votes = paste(weighted_votes,collapse=","), neighbors_other = neighbors_other, other_weighted_votes = other_weighted_votes, total_w = total_weight, - pred_w = pred_weight + pred_w = pred_weight + ) all_neighbors = bind_rows(all_neighbors, neighbor_info) - } + } + } to_return = data.frame( predicted_label = preds, - confidence = confs - ) - + confidence = confs) if(track_neighbors){ + #check for any missing points if(!nrow(to_return)==nrow(all_neighbors)){ print("mismatch in row number for to_return and all_neighbors") @@ -2670,6 +2741,27 @@ weighted_knn_predict_with_conf <- function( return(to_return) } +#' @export +prepare_single_sample_DLBCLone <- function(optimized_model,seed=12345){ + if(!optimized_model$type == "DLBCLone_optimize_params"){ + stop("Input must be the output of predict_single_sample_DLBCLone") + } + if(!"projection" %in% names(optimized_model)){ + projection <- make_and_annotate_umap( + df = optimized_model$features, + umap_out = optimized_model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = optimized_model$best_params$na_option + ) + }else{ + stop("Model already contains a projection. Nothing to do!") + } + optimized_model$projection = projection + return(optimized_model) +} + #' Predict class for a single sample without using umap_transform and plot result of classification #' #' @param test_df Data frame containing the mutation status of the test sample @@ -2681,196 +2773,235 @@ weighted_knn_predict_with_conf <- function( #' @param max_neighbors Maximum number of neighbors to consider for each sample. Default 500. #' #' @returns a list of data frames with the predictions, the UMAP input, the model, and a ggplot object +#' +#' @import dplyr tibble ggplot2 readr #' @export #' #' @examples +#' +#' library(GAMBLR.predict) +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' #' predict_single <- predict_single_sample_DLBCLone( #' test_df = optimize_params$features[1,], #' train_metadata = dlbcl_meta, -#' optimized_model = optimize_params_T +#' optimized_model = optimize_params #' ) #' predict_single_sample_DLBCLone <- function( - test_df, - train_metadata, - optimized_model = NULL, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - seed = 12345, - max_neighbors = 500 + test_df, + train_df, + train_metadata, + projection, + umap_out, + best_params, + optimized_model = NULL, + other_df, + ignore_self = FALSE, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), + drop_unlabeled_from_training=TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + label_offset = 2, + title1="GAMBL", + title2="predicted_class_for_HighConf", + title3 ="predicted_class_for_Other", + seed = 12345, + max_neighbors = 500, + other_class = "Other" ){ - if(is.null(optimized_model)){ - stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") - } + set.seed(seed) + if(is.null(optimized_model)){ + warning("optimized_model will become a required argument in the future. Please update your code accordingly") + } + if(ignore_self){ + # Allow overlapping samples: rename test duplicates temporarily + dupes <- intersect(train_df$sample_id, test_df$sample_id) + if(length(dupes) > 0){ + test_df <- test_df %>% + mutate(sample_id = ifelse( + sample_id %in% dupes, + paste0(sample_id, "_test"), + sample_id + )) + } + } else { + # Drop overlaps to prevent rowname collisions + dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(any(!colnames(test_df) %in% colnames(optimized_model$features))){ - stop( - "test_df should not contain any features that are not in training data. ", - "Please check the column names of test_df and optimized_model$features." - ) - } + } + + + train_df = train_df %>% + column_to_rownames("sample_id") %>% + + rownames_to_column("sample_id") + train_id <- train_df$sample_id - bad_samps <- rowSums(test_df) == 0 - bad_test_df <- test_df[bad_samps, ] %>% - rownames_to_column("sample_id") %>% - select(sample_id) - test_df <- test_df[!bad_samps, ] + test_df = test_df %>% + column_to_rownames("sample_id") %>% - test_id <- test_df %>% - rownames_to_column("sample_id") %>% - pull(sample_id) + rownames_to_column("sample_id") + test_id <- test_df$sample_id - train_coords = dplyr::filter( - optimized_model$df - ) %>% + combined_df <- bind_rows(train_df, test_df) + + if(missing(projection)){ + projection <- make_and_annotate_umap( + df = combined_df, + umap_out = umap_out, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + }else{ + message("Using provided projection for prediction") + } + + + train_coords = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + print("TRAIN:") + print(dim(train_coords)) + train_df_proj = dplyr::filter( + projection$df, + sample_id %in% train_id + ) %>% select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") + left_join( #Join to the incoming metadata rather than trusting the metadata in the projection + train_metadata %>% select( + sample_id, + lymphgen + ), + by = "sample_id" + ) - train_df_proj = dplyr::filter( - optimized_model$df - ) %>% - select(sample_id,V1,V2) %>% - left_join( #Join to the incoming metadata rather than trusting the metadata in the projection - train_metadata %>% - select( - sample_id, - lymphgen - ), - by = "sample_id" - ) + train_labels = train_df_proj %>% + pull(lymphgen) - train_labels = train_df_proj %>% - pull(lymphgen) - - #Obtain UMAP coordinates for the test sample(s) - print(paste("num rows:",nrow(test_df))) - test_projection <- make_and_annotate_umap( - df = test_df, - umap_out = optimized_model, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = optimized_model$best_params$na_option - ) + #Obtain UMAP coordinates for the test sample(s) + print(paste("num rows:",nrow(test_df))) + test_projection <- make_and_annotate_umap( + df = test_df, + umap_out = umap_out, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = best_params$na_option + ) + test_coords = dplyr::filter( + test_projection$df, + sample_id %in% test_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") + + predict_training = FALSE + if(predict_training){ + train_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = train_coords, # <- predicitng training on self + k = best_params$k, + conf_threshold = best_params$threshold, + other_class = other_class, + use_weights = best_params$use_weights, + ignore_self = ignore_self + ) - test_coords = dplyr::filter( - test_projection$df, - sample_id %in% test_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") + train_pred = rownames_to_column(train_pred, var = "sample_id") + } + - test_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k = optimized_model$best_params$k, - conf_threshold = optimized_model$best_params$threshold, - na_label = "Other", - use_weights = optimized_model$best_params$use_weights, - max_neighbors = max_neighbors - ) + test_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = best_params$k, + conf_threshold = best_params$threshold, + other_class = other_class, + use_weights = best_params$use_weights, + ignore_self = ignore_self, + max_neighbors = max_neighbors + ) - test_pred = rownames_to_column(test_pred, var = "sample_id") - if(!is.null(optimized_model)){ - test_pred = mutate( - test_pred, - DLBCLone_i = predicted_label, - DLBCLone_io = ifelse( - other_score > optimized_model$best_params$threshold_outgroup, - "Other", - predicted_label - ) - ) + test_pred = rownames_to_column(test_pred, var = "sample_id") + #test_pred = bind_cols(test_pred, test_coords) + if(!is.null(optimized_model)){ + test_pred = mutate(test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse(other_score > best_params$threshold_outgroup, + other_class, + predicted_label)) - }else{ - test_pred = mutate( - test_pred, - DLBCLone_i = predicted_label, - DLBCLone_io = ifelse( - other_score > optimized_model$best_params$threshold_outgroup, - "Other", - predicted_label - ) - ) - } + }else{ + test_pred = mutate(test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse(other_score > best_params$threshold_outgroup, + other_class, + predicted_label)) + } - anno_umap = select(test_projection$df, sample_id, V1, V2) + anno_umap = select(test_projection$df, sample_id, V1, V2) - anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - anno_out = anno_out %>% + anno_out = anno_out %>% mutate( - V1 = as.numeric(V1), - V2 = as.numeric(V2), - label = as.character(label) - ) - - predictions_train_df = filter( - optimized_model$df - ) %>% - select(sample_id, V1, V2) - - predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") + V1 = as.numeric(V1), + V2 = as.numeric(V2), + label = as.character(label) + ) + if(predict_training){ + predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") + }else{ + predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% + select(sample_id, V1, V2) + } + #This had assumed that projection$df contains the test samples! + #predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") + predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") - predictions_df = bind_rows( - predictions_train_df %>% select(sample_id, V1, V2), - predictions_test_df %>% select(sample_id, V1, V2) - ) + predictions_df = bind_rows(predictions_train_df %>% select(sample_id, V1, V2), + predictions_test_df %>% select(sample_id, V1, V2)) - if(!is.null(optimized_model)){ - best_w_purity = optimized_model$best_w_purity - best_w_score_thresh = optimized_model$best_w_score_thresh + if(!is.null(optimized_model)){ + best_w_purity = optimized_model$best_w_purity + best_w_score_thresh = optimized_model$best_w_score_thresh - predictions_test_df = process_votes( - df=predictions_test_df, - group_labels=optimized_model$truth_classes, - k=optimized_model$k_DLBCLone_w - ) + predictions_test_df = process_votes( + df=predictions_test_df, + group_labels=optimized_model$truth_classes, + k=optimized_model$k_DLBCLone_w) - predictions_test_df = predictions_test_df %>% - mutate( - DLBCLone_w = by_score, - DLBCLone_wo = ifelse( - score_ratio >= optimized_model$purity_DLBCLone_w | top_group_score > optimized_model$score_thresh_DLBCLone_w, - by_score, - "Other" - ) - ) - } - - predictions_test_df = predictions_test_df %>% - bind_rows( - bad_test_df %>% - mutate( - predicted_label = "Other", - confidence = 1 - ) - ) - - combined_df <- bind_rows( - optimized_model$features %>% rownames_to_column("sample_id"), - test_df %>% rownames_to_column("sample_id") - ) %>% - distinct(sample_id, .keep_all = TRUE) %>% # keep first occurrence - column_to_rownames("sample_id") + predictions_test_df = predictions_test_df %>% + mutate(DLBCLone_w = by_score, + DLBCLone_wo = ifelse(score_ratio >= optimized_model$purity_DLBCLone_w | + top_group_score > optimized_model$score_thresh_DLBCLone_w, + by_score, + other_class)) - if(any(!colnames(optimized_model$features) %in% colnames(test_df))){ - message("filling in missing features in test_df with zeros") - combined_df[is.na(combined_df)] = 0 } - to_return = list( - predictions = predictions_test_df, - df = predictions_df, - anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), - anno_out = anno_out, - features_df = combined_df, - type = "predict_single_sample_DLBCLone", - optimized_predictions = optimized_model$predictions - ) + prediction = predictions_test_df, + projection = projection$df, + umap_input = umap_out$features, + model=umap_out$model, + features_df = combined_df %>% column_to_rownames("sample_id"), + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), + anno_out = anno_out, + type = "predict_single_sample_DLBCLone" + ) - return(to_return) + return(to_return) } #' model storage for DLBCLone outputs @@ -2881,12 +3012,14 @@ predict_single_sample_DLBCLone <- function( #' #' @returns saves the files to the specified path #' -#' @import uwot -#' @import readr +#' @import uwot readr #' #' @export #' #' @examples +#' +#' library(GAMBLR.predict) +#' #' DLBCLone_save_optimized( #' optimzied_params = optimized_params, #' path="/save_optimized/trial_folder", @@ -2936,13 +3069,13 @@ DLBCLone_save_optimized = function( #' #' @returns saves the files to the specified path #' -#' @import uwot -#' @import readr -#' @import dplyr -#' +#' @import uwot readr dplyr #' @export #' #' @examples +#' +#' library(GAMBLR.predict) +#' #' load_optimized <- DLBCLone_load_optimized( #' path="/save_optimized/trial_folder", #' name_prefix="test_A" @@ -3020,21 +3153,24 @@ DLBCLone_load_optimized <- function( #' \item{predictions}{Data frame with sample IDs, UMAP coordinates, true labels, predicted classes, and thresholded assignments} #' \item{probability_threshold}{Probability threshold used for "Other" assignment} #' +#' @import mclust dplyr tibble +#' @export +#' #' @examples +#' +#' library(GAMBLR.predict) +#' #' result <- DLBCLone_train_mixture_model(umap_out) #' head(result$predictions) -#' @import mclust -#' -#' @export #' -DLBCLone_train_mixture_model = function( - umap_out, - probability_threshold = 0.5, - density_max_threshold = 0.05, - truth_column = "lymphgen", - cohort = NULL, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other") -){ +DLBCLone_train_mixture_model = function(umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + truth_column = "lymphgen", + cohort = NULL, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other") + ){ + df = umap_out$df %>% select(sample_id,!!sym(truth_column),V1,V2) %>% filter(!!sym(truth_column) != "Other") df = filter(df,!!sym(truth_column) %in% truth_classes) #mont_test_proj = montreal_gambl_c_mu$df %>% select(sample_id,lymphgen,V1,V2) @@ -3047,58 +3183,56 @@ DLBCLone_train_mixture_model = function( class = df[[truth_column]], modelType = "MclustDA" # instead of "EDDA" ) - df = umap_out$df %>% select(sample_id,!!sym(truth_column),V1,V2) #%>% filter(!!sym(truth_column) != "Other") - - pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) +df = umap_out$df %>% select(sample_id,!!sym(truth_column),V1,V2) #%>% filter(!!sym(truth_column) != "Other") +pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) - densities_list <- lapply(names(gmm_supervised$models), function(class_name) { - model <- gmm_supervised$models[[class_name]] +densities_list <- lapply(names(gmm_supervised$models), function(class_name) { + model <- gmm_supervised$models[[class_name]] - modelName <- model$modelName - params <- model$parameters + modelName <- model$modelName + params <- model$parameters - # Compute log-densities for each component - logdens <- cdens( - data = df[, c("V1", "V2")], - modelName = modelName, - parameters = params, - logarithm = TRUE - ) + # Compute log-densities for each component + logdens <- cdens( + data = df[, c("V1", "V2")], + modelName = modelName, + parameters = params, + logarithm = TRUE + ) - # cdens can return a matrix (samples x components); sum components - class_density <- exp(logdens) # back to raw densities - if (is.matrix(class_density)) { - class_density <- rowSums(class_density) - } + # cdens can return a matrix (samples x components); sum components + class_density <- exp(logdens) # back to raw densities + if (is.matrix(class_density)) { + class_density <- rowSums(class_density) + } - class_density - }) + class_density +}) - densities <- do.call(cbind, densities_list) - colnames(densities) <- names(gmm_supervised$models) +densities <- do.call(cbind, densities_list) +colnames(densities) <- names(gmm_supervised$models) - max_density <- apply(densities, 1, max) - max_prob <- apply(pred$z, 1, max) +max_density <- apply(densities, 1, max) +max_prob <- apply(pred$z, 1, max) - df$DLBCLone_g = pred$classification +df$DLBCLone_g = pred$classification - df$DLBCLone_go <- ifelse( - (max_prob < probability_threshold) | (max_density < density_max_threshold), - "Other", - as.character(pred$classification) - ) +df$DLBCLone_go <- ifelse( + (max_prob < probability_threshold) | (max_density < density_max_threshold), + "Other", + as.character(pred$classification) +) - df$cohort = cohort - to_return = list( - gaussian_mixture_model = gmm_supervised, - predictions = df, - truth_classes = truth_classes, - truth_column = truth_column, - probability_threshold = probability_threshold - ) +df$cohort = cohort +to_return = list(gaussian_mixture_model = gmm_supervised, + predictions = df, + truth_classes = truth_classes, + truth_column = truth_column, + probability_threshold = probability_threshold + ) - return(to_return) +return(to_return) } #' Predict DLBCLone Class Membership Using a Trained Gaussian Mixture Model @@ -3124,67 +3258,187 @@ DLBCLone_train_mixture_model = function( #' \item{gaussian_mixture_model}{Fitted \code{MclustDA} model object} #' \item{predictions}{Data frame with sample IDs, UMAP coordinates, predicted classes, and thresholded assignments} #' \item{probability_threshold}{Probability threshold used for "Other" assignment} -#' +#' +#' @import mclust dplyr tibble +#' @export +#' #' @examples #' # Predict on new UMAP data using a trained mixture model: +#' +#' library(GAMBLR.predict) +#' #' result <- DLBCLone_predict_mixture_model(model, umap_out) #' head(result$predictions) #' -#' @import mclust -#' @export #' -DLBCLone_predict_mixture_model = function( - model, - umap_out, - probability_threshold = 0.5, - density_max_threshold = 0.05, - cohort = NULL -){ +DLBCLone_predict_mixture_model = function(model, + umap_out, + probability_threshold = 0.5, + density_max_threshold = 0.05, + cohort = NULL + ){ df = umap_out$df %>% select(sample_id,V1,V2) gmm_supervised = model - pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) +pred <- predict(gmm_supervised, newdata = df[, c("V1", "V2")]) - densities_list <- lapply(names(gmm_supervised$models), function(class_name) { - model <- gmm_supervised$models[[class_name]] - modelName <- model$modelName - params <- model$parameters +densities_list <- lapply(names(gmm_supervised$models), function(class_name) { + model <- gmm_supervised$models[[class_name]] + modelName <- model$modelName + params <- model$parameters - # Compute log-densities for each component - logdens <- cdens( - data = df[, c("V1", "V2")], - modelName = modelName, - parameters = params, - logarithm = TRUE - ) + # Compute log-densities for each component + logdens <- cdens( + data = df[, c("V1", "V2")], + modelName = modelName, + parameters = params, + logarithm = TRUE + ) - # cdens can return a matrix (samples x components); sum components - class_density <- exp(logdens) # back to raw densities - if (is.matrix(class_density)) { - class_density <- rowSums(class_density) - } + # cdens can return a matrix (samples x components); sum components + class_density <- exp(logdens) # back to raw densities + if (is.matrix(class_density)) { + class_density <- rowSums(class_density) + } - class_density - }) + class_density +}) - densities <- do.call(cbind, densities_list) - colnames(densities) <- names(gmm_supervised$models) +densities <- do.call(cbind, densities_list) +colnames(densities) <- names(gmm_supervised$models) - max_density <- apply(densities, 1, max) - max_prob <- apply(pred$z, 1, max) +max_density <- apply(densities, 1, max) +max_prob <- apply(pred$z, 1, max) - df$DLBCLone_g = pred$classification - df$DLBCLone_go <- ifelse( - (max_prob < probability_threshold) | (max_density < density_max_threshold), - "Other", - as.character(pred$classification) - ) +df$DLBCLone_g = pred$classification - df$cohort = cohort - to_return = list( - gaussian_mixture_model = gmm_supervised, - predictions = df, - probability_threshold = probability_threshold - ) - return(to_return) + +df$DLBCLone_go <- ifelse( + (max_prob < probability_threshold) | (max_density < density_max_threshold), + "Other", + as.character(pred$classification) +) + + +df$cohort = cohort +to_return = list(gaussian_mixture_model = gmm_supervised, + predictions = df, + probability_threshold = probability_threshold + ) +return(to_return) +} + +# Some comment here +# + +old_process_votes <- function(df, + raw_col = "label", + group_labels = c("EZB", "MCD", "ST2", "BN2", "N1", "Other"), + vote_labels_col = "vote_labels", + k, + other_vote_multiplier = 2, + score_purity_requirement = 1, + weighted_votes_col = "weighted_votes") { + if(missing(k)){ + stop("k value is required") + } + score_thresh = 2 * k + + count_labels_in_string <- function(string, labels) { + tokens <- str_split(string, ",")[[1]] + map_int(labels, ~ sum(tokens == .x)) + } + + extract_weighted_scores <- function(label_str, vote_str, labels) { + lbls <- str_split(label_str, ",")[[1]] + votes <- as.numeric(str_split(vote_str, ",")[[1]]) + map_dbl(labels, ~ sum(votes[lbls == .x])) %>% + set_names(paste0(labels, "_score")) + } + + get_top_score_group <- function(label_str, vote_str, labels) { + lbls <- str_split(label_str, ",")[[1]] + votes <- as.numeric(str_split(vote_str, ",")[[1]]) + scores_by_label <- set_names(map_dbl(labels, ~ sum(votes[lbls == .x])), labels) + top <- names(scores_by_label)[which.max(scores_by_label)] + value <- scores_by_label[[top]] + list(top_score_group = top, top_group_score = value) + } + + df_out <- df %>% + mutate(.id = row_number()) %>% + rowwise() %>% + mutate( + # 1) counts + counts = list( + set_names( + count_labels_in_string(.data[[raw_col]], group_labels), + paste0(group_labels, "_NN_count") + ) + ), + top_group = { + cnts <- count_labels_in_string(.data[[raw_col]], group_labels) + group_labels[which.max(cnts)] + }, + + # 2) scores + scores = list( + if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { + extract_weighted_scores( + .data[[vote_labels_col]], + .data[[weighted_votes_col]], + group_labels + ) + } else { + set_names(rep(0, length(group_labels)), paste0(group_labels, "_score")) + } + ), + + # 3) top score group summary + score_summary = list( + if (!is.null(vote_labels_col) && !is.null(weighted_votes_col)) { + get_top_score_group( + .data[[vote_labels_col]], + .data[[weighted_votes_col]], + group_labels + ) + } else { + list(top_score_group = NA_character_, top_group_score = NA_real_) + } + ) + ) %>% + ungroup() %>% + unnest_wider(counts) %>% + unnest_wider(scores) %>% + unnest_wider(score_summary) %>% + rowwise() %>% +mutate( + top_group_count = get(paste0(top_group, "_NN_count")) +) %>% +ungroup() + + # Optional adjustments based on external columns (if present) + if ("neighbors_other" %in% colnames(df)) { + df_out <- df_out %>% + mutate(Other_count = neighbors_other) + } + + if ("other_weighted_votes" %in% colnames(df)) { + df_out <- df_out %>% + mutate(Other_score = other_weighted_votes) + } + + # Optional override of top group if Other dominates by a fold + if (all(c("top_group_count", "Other_count") %in% colnames(df_out))) { + df_out <- df_out %>% + mutate(by_vote = top_group_count) %>% + mutate(by_vote_opt = ifelse(top_group_count * other_vote_multiplier > Other_count, top_group, "Other")) + } + + df_out = mutate(df_out, + by_score = top_score_group, + score_ratio = top_group_score / Other_score, + by_score_opt=ifelse(score_ratio > score_purity_requirement | top_group_score > score_thresh,top_score_group,"Other")) + + return(df_out) } diff --git a/man/DLBCLone_KNN.Rd b/man/DLBCLone_KNN.Rd index c6eca6b..2beee40 100644 --- a/man/DLBCLone_KNN.Rd +++ b/man/DLBCLone_KNN.Rd @@ -90,6 +90,14 @@ in-group classes and the outgroup column name from the arguments for the default LymphGen-like usage. } \examples{ + +library(GAMBLR.predict) + +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + dlbcl_knn <- DLBCLone_KNN( features_df = all_full_status, metadata = dlbcl_meta, diff --git a/man/DLBCLone_KNN_predict.Rd b/man/DLBCLone_KNN_predict.Rd index e49e5eb..9e03b6a 100644 --- a/man/DLBCLone_KNN_predict.Rd +++ b/man/DLBCLone_KNN_predict.Rd @@ -52,6 +52,14 @@ for more stable results when predicting multiple samples. # Assuming you have run DLBCLone_KNN to get optimized parameters: # model_out <- DLBCLone_KNN(train_features, train_metadata, ...) # Predict on new samples: + +library(GAMBLR.predict) + +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + predictions <- DLBCLone_KNN_predict( train_df = all_full_status, test_df = lyseq_validation_data, @@ -60,4 +68,5 @@ predictions <- DLBCLone_KNN_predict( mode = "batch" ) + } diff --git a/man/DLBCLone_load_optimized.Rd b/man/DLBCLone_load_optimized.Rd index 7f79416..6fc4f65 100644 --- a/man/DLBCLone_load_optimized.Rd +++ b/man/DLBCLone_load_optimized.Rd @@ -18,6 +18,9 @@ saves the files to the specified path load previously saved DLBCLone model and parameters } \examples{ + +library(GAMBLR.predict) + load_optimized <- DLBCLone_load_optimized( path="/save_optimized/trial_folder", name_prefix="test_A" diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 42e2cdc..20301b3 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -17,6 +17,7 @@ DLBCLone_optimize_params( verbose = FALSE, seed = 12345, maximize = "balanced_accuracy", + cap_classification_rate = 0.9, exclude_other_for_accuracy = FALSE, weights_opt = c(TRUE) ) @@ -82,10 +83,16 @@ Optimize parameters for classifying samples using UMAP and k-nearest neighbor } \examples{ +library(GAMBLR.predict) + +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) # Aim to maximize classification of samples into non-Other class optimize_params_F <- DLBCLone_optimize_params( - make_umap$features, + all_full_status, dlbcl_meta, umap_out = make_umap, truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), @@ -98,7 +105,7 @@ optimize_params_F <- DLBCLone_optimize_params( # unclassified (assigned to "Other") optimize_params_T <- DLBCLone_optimize_params( - make_umap$features, + all_full_status, dlbcl_meta, umap_out = make_umap, truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), diff --git a/man/DLBCLone_predict_mixture_model.Rd b/man/DLBCLone_predict_mixture_model.Rd index 3d94ea5..77c8e0c 100644 --- a/man/DLBCLone_predict_mixture_model.Rd +++ b/man/DLBCLone_predict_mixture_model.Rd @@ -45,7 +45,11 @@ Assigns class predictions and optionally reclassifies samples as "Other" based o } \examples{ # Predict on new UMAP data using a trained mixture model: + +library(GAMBLR.predict) + result <- DLBCLone_predict_mixture_model(model, umap_out) head(result$predictions) + } diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd index d4a3c5e..90fef41 100644 --- a/man/DLBCLone_save_optimized.Rd +++ b/man/DLBCLone_save_optimized.Rd @@ -24,6 +24,9 @@ saves the files to the specified path model storage for DLBCLone outputs } \examples{ + +library(GAMBLR.predict) + DLBCLone_save_optimized( optimzied_params = optimized_params, path="/save_optimized/trial_folder", diff --git a/man/DLBCLone_train_mixture_model.Rd b/man/DLBCLone_train_mixture_model.Rd index 77d611c..68c8ebc 100644 --- a/man/DLBCLone_train_mixture_model.Rd +++ b/man/DLBCLone_train_mixture_model.Rd @@ -45,6 +45,10 @@ Assigns class predictions and optionally reclassifies samples as "Other" based o } } \examples{ + +library(GAMBLR.predict) + result <- DLBCLone_train_mixture_model(umap_out) head(result$predictions) + } diff --git a/man/DLBCLone_train_test_plot.Rd b/man/DLBCLone_train_test_plot.Rd index e29273e..b0d2e48 100644 --- a/man/DLBCLone_train_test_plot.Rd +++ b/man/DLBCLone_train_test_plot.Rd @@ -50,6 +50,9 @@ Plot the result of a DLBCLone classification } \examples{ #add the dataset name to the metadata if it's not already there (required for the plot work) + +library(GAMBLR.predict) + lymphgen_A53_DLBCLone$df$dataset = "GAMBL" DLBCLone_train_test_plot( diff --git a/man/assemble_genetic_features.Rd b/man/assemble_genetic_features.Rd index 3accacc..ab1596b 100644 --- a/man/assemble_genetic_features.Rd +++ b/man/assemble_genetic_features.Rd @@ -61,7 +61,10 @@ This function assembles a matrix of genetic features for each sample, including aSHM counts, and structural variant status for BCL2, BCL6, and MYC. It supports both genome and capture sequencing types. } \examples{ + \dontrun{ +library(GAMBLR.predict) + ordered_genes = filter(lymphoma_genes,DLBCL_Tier==1 | FL_Tier==1|BL_Tier==1) \%>\% pull(Gene) synon_genes = unique(grch37_ashm_regions$gene) synon_genes = synon_genes[synon_genes \%in\% ordered_genes] @@ -85,5 +88,5 @@ all_full_status = assemble_genetic_features( annotated_sv = all_sv_anno ) } - + } diff --git a/man/basic_umap_scatterplot.Rd b/man/basic_umap_scatterplot.Rd index c236157..7082a23 100644 --- a/man/basic_umap_scatterplot.Rd +++ b/man/basic_umap_scatterplot.Rd @@ -44,6 +44,8 @@ Generates a simple UMAP scatterplot for visualizing sample clustering or separat \examples{ \dontrun{ +library(GAMBLR.predict) + make_umap <- make_and_annotate_umap( df=all_full_status, metadata=dlbcl_meta @@ -54,4 +56,5 @@ basic_umap_scatterplot( plot_samples = "some_sample_ID", colour_by = "DLBCLone_ko") } + } diff --git a/man/make_alluvial.Rd b/man/make_alluvial.Rd index 7e35e76..cac4e43 100644 --- a/man/make_alluvial.Rd +++ b/man/make_alluvial.Rd @@ -93,6 +93,8 @@ It supports annotation of concordance rates, per-group accuracy, unclassified ra } \examples{ \dontrun{ +library(GAMBLR.predict) + make_alluvial(optimize_params) } diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 70aecd7..0fdb29c 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -8,6 +8,7 @@ make_and_annotate_umap( df, metadata, umap_out, + truth_column = "lymphgen", core_features = NULL, core_feature_multiplier = 1.5, hidden_features = NULL, @@ -85,9 +86,16 @@ Run UMAP and attach result to metadata } \examples{ -make_umap <- make_and_annotate_umap( - df=all_full_status, - metadata=dlbcl_meta +library(GAMBLR.predict) + +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + +my_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta ) } diff --git a/man/make_neighborhood_plot.Rd b/man/make_neighborhood_plot.Rd index a955d5e..37cabb7 100644 --- a/man/make_neighborhood_plot.Rd +++ b/man/make_neighborhood_plot.Rd @@ -45,6 +45,8 @@ The function extracts the nearest neighbors of the specified sample, draws segme # and 'output' is the result of DLBCLone_predict_single_sample # on sample_id "SAMPLE123": \dontrun{ +library(GAMBLR.predict) + predict_single <- predict_single_sample_DLBCLone( test_df = optimize_params$features[1,], train_metadata = dlbcl_meta, diff --git a/man/make_umap_scatterplot.Rd b/man/make_umap_scatterplot.Rd index 90e4e51..7c29ffb 100644 --- a/man/make_umap_scatterplot.Rd +++ b/man/make_umap_scatterplot.Rd @@ -32,11 +32,16 @@ make_umap_scatterplot( \item{title}{Title for the plot. Default: NULL.} } +\value{ +A ggplot object representing the UMAP scatterplot with marginal histograms. +} \description{ Make UMAP scatterplot } \examples{ \dontrun{ +library(GAMBLR.predict) + make_umap <- make_and_annotate_umap( df=all_full_status, metadata=dlbcl_meta diff --git a/man/nearest_neighbor_heatmap.Rd b/man/nearest_neighbor_heatmap.Rd index b4d3087..17e4858 100644 --- a/man/nearest_neighbor_heatmap.Rd +++ b/man/nearest_neighbor_heatmap.Rd @@ -44,7 +44,9 @@ and plots a heatmap of features with nonzero values. } \examples{ # Assuming 'model' is a DLBCLone model and 'sample_id' is a valid sample: -\dontrun{ +\dontrun{ +library(GAMBLR.predict) + nearest_neighbor_heatmap( this_sample_id = "CCS_0680_lst", DLBCLone_model = predict_single, diff --git a/man/optimize_outgroup.Rd b/man/optimize_outgroup.Rd index c442aa9..d7dd9bc 100644 --- a/man/optimize_outgroup.Rd +++ b/man/optimize_outgroup.Rd @@ -11,7 +11,9 @@ optimize_outgroup( all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), maximize = "balanced_accuracy", exclude_other_for_accuracy = FALSE, - verbose = FALSE + cap_classification_rate = 1, + verbose = FALSE, + other_class = "Other" ) } \arguments{ diff --git a/man/optimize_purity.Rd b/man/optimize_purity.Rd index 1dd331b..b90af3e 100644 --- a/man/optimize_purity.Rd +++ b/man/optimize_purity.Rd @@ -12,7 +12,9 @@ optimize_purity( truth_column, all_classes = c("MCD", "EZB", "BN2", "N1", "ST2", "Other"), k, - exclude_other_for_accuracy = FALSE + cap_classification_rate = 1, + exclude_other_for_accuracy = FALSE, + other_class = "Other" ) } \arguments{ @@ -48,7 +50,9 @@ The function returns the best accuracy achieved and the corresponding purity thr } } \examples{ -# Example usage: -# result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") + +library(GAMBLR.predict) + +result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index ab62760..fd47d83 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -6,11 +6,25 @@ \usage{ predict_single_sample_DLBCLone( test_df, + train_df, train_metadata, + projection, + umap_out, + best_params, optimized_model = NULL, + other_df, + ignore_self = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), + drop_unlabeled_from_training = TRUE, + make_plot = TRUE, + annotate_accuracy = FALSE, + label_offset = 2, + title1 = "GAMBL", + title2 = "predicted_class_for_HighConf", + title3 = "predicted_class_for_Other", seed = 12345, - max_neighbors = 500 + max_neighbors = 500, + other_class = "Other" ) } \arguments{ @@ -34,10 +48,15 @@ a list of data frames with the predictions, the UMAP input, the model, and a ggp Predict class for a single sample without using umap_transform and plot result of classification } \examples{ + +library(GAMBLR.predict) + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + predict_single <- predict_single_sample_DLBCLone( test_df = optimize_params$features[1,], train_metadata = dlbcl_meta, - optimized_model = optimize_params_T + optimized_model = optimize_params ) } diff --git a/man/process_votes.Rd b/man/process_votes.Rd index c8e23df..da0cdb4 100644 --- a/man/process_votes.Rd +++ b/man/process_votes.Rd @@ -12,7 +12,8 @@ process_votes( k, other_vote_multiplier = 2, score_purity_requirement = 1, - weighted_votes_col = "weighted_votes" + weighted_votes_col = "weighted_votes", + other_class = "Other" ) } \arguments{ @@ -49,7 +50,10 @@ The function also supports custom logic for handling the "Other" class, includin } } \examples{ -# Example usage: -# result <- process_votes(knn_output_df, k = 7) + +library(GAMBLR.predict) + +result <- process_votes(knn_output_df, k = 7) + } diff --git a/man/report_accuracy.Rd b/man/report_accuracy.Rd index 934591e..f9bdac1 100644 --- a/man/report_accuracy.Rd +++ b/man/report_accuracy.Rd @@ -46,6 +46,8 @@ Optionally excludes samples assigned to the "Other" class from accuracy calculat } \examples{ \dontrun{ +library(GAMBLR.predict) + result <- report_accuracy(predictions_df) result$overall result$per_class diff --git a/man/stacked_bar_plot.Rd b/man/stacked_bar_plot.Rd index eaa5615..3c86438 100644 --- a/man/stacked_bar_plot.Rd +++ b/man/stacked_bar_plot.Rd @@ -34,6 +34,8 @@ This function generates a stacked bar plot showing the top features (genes) for } \examples{ \dontrun{ +library(GAMBLR.predict) + stacked_bar_plot( optimize_params, method = "chi_square", diff --git a/man/summarize_all_ssm_status.Rd b/man/summarize_all_ssm_status.Rd index ec7b844..98f48a0 100644 --- a/man/summarize_all_ssm_status.Rd +++ b/man/summarize_all_ssm_status.Rd @@ -65,7 +65,10 @@ class for genes specified in \code{separate_by_class_genes} # A basic example, using only the output of get_all_coding_ssm # Since the only non-coding class this function handles is Silent, # we will be missing most non-coding types such as Intron, UTR, Flank + \dontrun{ +library(GAMBLR.predict) + sample_metadata = get_sample_metadata() \%>\% filter(seq_type!= "mrna") maf_data = get_all_coding_ssm(sample_metadata) diff --git a/man/weighted_knn_predict_with_conf.Rd b/man/weighted_knn_predict_with_conf.Rd index 1ac48a0..4c1afc5 100644 --- a/man/weighted_knn_predict_with_conf.Rd +++ b/man/weighted_knn_predict_with_conf.Rd @@ -11,9 +11,10 @@ weighted_knn_predict_with_conf( k, epsilon = 0.1, conf_threshold = NULL, - na_label = "Other", + other_class = "Other", verbose = FALSE, use_weights = TRUE, + ignore_self = TRUE, track_neighbors = TRUE, separate_other = TRUE, max_neighbors = 500 @@ -37,9 +38,6 @@ when use_weights is TRUE. Default: 1e-5} \item{conf_threshold}{Minimum confidence for classifying a sample based on it's neighbors. Below this value the sample will be assigned na_label instead} -\item{na_label}{Class to assign all samples that are not confidently assigned. -Default: Other} - \item{verbose}{Whether to print verbose outputs to console} \item{use_weights}{Set to FALSE for all neigbors to have equal weight when @@ -51,9 +49,26 @@ calculating the confidence} when calculating the confidence.} \item{max_neighbors}{Maximum number of neighbors to consider for each sample. Default 500.} + +\item{na_label}{Class to assign all samples that are not confidently assigned. +Default: Other} } \value{ -data frame with labels and confidence values for rows in test_coords +Data frame with rows = test samples and columns: +\item{predicted_label}{the predicted class} +\item{confidence}{predicted class weight / total weight} +If \code{track_neighbors = TRUE}, additional columns: +\item{other_score}{relative weight of outgroup vs predicted class} +\item{neighbor_id}{comma-separated neighbor sample IDs} +\item{neighbor}{comma-separated neighbor indices (in train order)} +\item{distance}{comma-separated neighbor distances} +\item{label}{comma-separated neighbor labels (in-group only if separate_other=TRUE)} +\item{vote_labels}{comma-separated unique labels contributing to weights} +\item{weighted_votes}{comma-separated weights per \code{vote_labels}} +\item{neighbors_other}{count of outgroup neighbors closer than the farthest in-group neighbor} +\item{other_weighted_votes}{sum of outgroup weights closer than the farthest in-group neighbor} +\item{total_w}{sum of weights for in-group neighbors used} +\item{pred_w}{weight supporting the predicted class} } \description{ Weighted k-nearest neighbor with confidence estimate From 251611fdbf490257ae9f8c8d0949c5247ff12f26 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Sat, 6 Sep 2025 13:18:12 -0700 Subject: [PATCH 78/79] final touches --- R/umap.R | 73 +++++++++++++++++++++++---- man/DLBCLone_KNN.Rd | 5 +- man/DLBCLone_KNN_predict.Rd | 5 ++ man/DLBCLone_load_optimized.Rd | 2 + man/DLBCLone_optimize_params.Rd | 5 ++ man/DLBCLone_predict_mixture_model.Rd | 17 ++++++- man/DLBCLone_save_optimized.Rd | 2 + man/DLBCLone_train_mixture_model.Rd | 12 ++++- man/DLBCLone_train_test_plot.Rd | 2 + man/make_and_annotate_umap.Rd | 2 +- man/optimize_purity.Rd | 2 + man/predict_single_sample_DLBCLone.Rd | 15 ++++++ man/process_votes.Rd | 4 +- 13 files changed, 128 insertions(+), 18 deletions(-) diff --git a/R/umap.R b/R/umap.R index 543c2ce..04db1e5 100644 --- a/R/umap.R +++ b/R/umap.R @@ -477,6 +477,7 @@ optimize_outgroup <- function(predicted_labels, #' @examples #' #add the dataset name to the metadata if it's not already there (required for the plot work) #' +#' \dontrun{ #' library(GAMBLR.predict) #' #' lymphgen_A53_DLBCLone$df$dataset = "GAMBL" @@ -489,6 +490,7 @@ optimize_outgroup <- function(predicted_labels, #' details = lymphgen_A53_DLBCLone$best_params, #' classes = c("MCD","EZB","BN2","ST2","N1","A53","Other"), #' annotate_accuracy=TRUE,label_offset = 1) +#' } #' DLBCLone_train_test_plot = function(test_df, train_df, @@ -619,7 +621,7 @@ DLBCLone_train_test_plot = function(test_df, #' #' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) #' -#' my_umap <- make_and_annotate_umap( +#' make_umap <- make_and_annotate_umap( #' df=all_full_status, #' metadata=dlbcl_meta #' ) @@ -882,11 +884,11 @@ make_and_annotate_umap = function(df, #' @export #' #' @examples -#' +#' /dontrun{ #' library(GAMBLR.predict) #' #' result <- process_votes(knn_output_df, k = 7) -#' +#' } #' process_votes <- function(df, raw_col = "label", @@ -1028,9 +1030,11 @@ process_votes <- function(df, #' #' @examples #' +#' \dontrun{ #' library(GAMBLR.predict) #' #' result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") +#' } #' optimize_purity <- function(optimized_model_object, vote_df, @@ -1220,6 +1224,11 @@ optimize_purity <- function(optimized_model_object, #' tibble::column_to_rownames("sample_id") #' #' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' +#' dlbcl_knn <- DLBCLone_KNN( +#' features_df = all_full_status, +#' metadata = dlbcl_meta +#' ) #' #' predictions <- DLBCLone_KNN_predict( #' train_df = all_full_status, @@ -1349,10 +1358,7 @@ DLBCLone_KNN_predict <- function(train_df, #' #' dlbcl_knn <- DLBCLone_KNN( #' features_df = all_full_status, -#' metadata = dlbcl_meta, -#' min_k = 5, -#' max_k = 21, -#' optimize_for_other = TRUE +#' metadata = dlbcl_meta #' ) #' DLBCLone_KNN <- function(features_df, @@ -2004,6 +2010,11 @@ DLBCLone_KNN <- function(features_df, #' tibble::column_to_rownames("sample_id") #' #' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) #' #' # Aim to maximize classification of samples into non-Other class #' optimize_params_F <- DLBCLone_optimize_params( @@ -2783,6 +2794,21 @@ prepare_single_sample_DLBCLone <- function(optimized_model,seed=12345){ #' #' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) #' +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) +#' +#' optimize_params <- DLBCLone_optimize_params( +#' all_full_status, +#' dlbcl_meta, +#' umap_out = make_umap, +#' truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), +#' optimize_for_other = T, +#' min_k=5, +#' max_k=23 +#' ) +#' #' predict_single <- predict_single_sample_DLBCLone( #' test_df = optimize_params$features[1,], #' train_metadata = dlbcl_meta, @@ -3018,6 +3044,7 @@ predict_single_sample_DLBCLone <- function( #' #' @examples #' +#' \dontrun{ #' library(GAMBLR.predict) #' #' DLBCLone_save_optimized( @@ -3025,6 +3052,7 @@ predict_single_sample_DLBCLone <- function( #' path="/save_optimized/trial_folder", #' name_prefix="test_A" #' ) +#' } #' DLBCLone_save_optimized = function( optimized_params=NULL, @@ -3074,12 +3102,14 @@ DLBCLone_save_optimized = function( #' #' @examples #' +#' \dontrun{ #' library(GAMBLR.predict) #' #' load_optimized <- DLBCLone_load_optimized( #' path="/save_optimized/trial_folder", #' name_prefix="test_A" #' ) +#' } #' DLBCLone_load_optimized <- function( path="models/", @@ -3160,7 +3190,17 @@ DLBCLone_load_optimized <- function( #' #' library(GAMBLR.predict) #' -#' result <- DLBCLone_train_mixture_model(umap_out) +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) +#' +#' result <- DLBCLone_train_mixture_model(umap_out = make_umap) #' head(result$predictions) #' DLBCLone_train_mixture_model = function(umap_out, @@ -3267,7 +3307,22 @@ return(to_return) #' #' library(GAMBLR.predict) #' -#' result <- DLBCLone_predict_mixture_model(model, umap_out) +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' +#' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +#' +#' make_umap <- make_and_annotate_umap( +#' df=all_full_status, +#' metadata=dlbcl_meta +#' ) +#' +#' mixture_model <- DLBCLone_train_mixture_model(umap_out = make_umap) +#' +#' result <- DLBCLone_predict_mixture_model( +#' model=mixture_model, +#' umap_out=make_umap +#' ) #' head(result$predictions) #' #' diff --git a/man/DLBCLone_KNN.Rd b/man/DLBCLone_KNN.Rd index 2beee40..76c2048 100644 --- a/man/DLBCLone_KNN.Rd +++ b/man/DLBCLone_KNN.Rd @@ -100,10 +100,7 @@ dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv", dlbcl_knn <- DLBCLone_KNN( features_df = all_full_status, - metadata = dlbcl_meta, - min_k = 5, - max_k = 21, - optimize_for_other = TRUE + metadata = dlbcl_meta ) } diff --git a/man/DLBCLone_KNN_predict.Rd b/man/DLBCLone_KNN_predict.Rd index 9e03b6a..1f7b9a6 100644 --- a/man/DLBCLone_KNN_predict.Rd +++ b/man/DLBCLone_KNN_predict.Rd @@ -60,6 +60,11 @@ all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",pack dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +dlbcl_knn <- DLBCLone_KNN( + features_df = all_full_status, + metadata = dlbcl_meta +) + predictions <- DLBCLone_KNN_predict( train_df = all_full_status, test_df = lyseq_validation_data, diff --git a/man/DLBCLone_load_optimized.Rd b/man/DLBCLone_load_optimized.Rd index 6fc4f65..084efdf 100644 --- a/man/DLBCLone_load_optimized.Rd +++ b/man/DLBCLone_load_optimized.Rd @@ -19,11 +19,13 @@ load previously saved DLBCLone model and parameters } \examples{ +\dontrun{ library(GAMBLR.predict) load_optimized <- DLBCLone_load_optimized( path="/save_optimized/trial_folder", name_prefix="test_A" ) +} } diff --git a/man/DLBCLone_optimize_params.Rd b/man/DLBCLone_optimize_params.Rd index 20301b3..9cb9ac3 100644 --- a/man/DLBCLone_optimize_params.Rd +++ b/man/DLBCLone_optimize_params.Rd @@ -90,6 +90,11 @@ all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",pack dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) + # Aim to maximize classification of samples into non-Other class optimize_params_F <- DLBCLone_optimize_params( all_full_status, diff --git a/man/DLBCLone_predict_mixture_model.Rd b/man/DLBCLone_predict_mixture_model.Rd index 77c8e0c..b3b2db3 100644 --- a/man/DLBCLone_predict_mixture_model.Rd +++ b/man/DLBCLone_predict_mixture_model.Rd @@ -48,7 +48,22 @@ Assigns class predictions and optionally reclassifies samples as "Other" based o library(GAMBLR.predict) -result <- DLBCLone_predict_mixture_model(model, umap_out) +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) + +mixture_model <- DLBCLone_train_mixture_model(umap_out = make_umap) + +result <- DLBCLone_predict_mixture_model( + model=mixture_model, + umap_out=make_umap +) head(result$predictions) diff --git a/man/DLBCLone_save_optimized.Rd b/man/DLBCLone_save_optimized.Rd index 90fef41..ef50b4d 100644 --- a/man/DLBCLone_save_optimized.Rd +++ b/man/DLBCLone_save_optimized.Rd @@ -25,6 +25,7 @@ model storage for DLBCLone outputs } \examples{ +\dontrun{ library(GAMBLR.predict) DLBCLone_save_optimized( @@ -32,5 +33,6 @@ DLBCLone_save_optimized( path="/save_optimized/trial_folder", name_prefix="test_A" ) +} } diff --git a/man/DLBCLone_train_mixture_model.Rd b/man/DLBCLone_train_mixture_model.Rd index 68c8ebc..d103272 100644 --- a/man/DLBCLone_train_mixture_model.Rd +++ b/man/DLBCLone_train_mixture_model.Rd @@ -48,7 +48,17 @@ Assigns class predictions and optionally reclassifies samples as "Other" based o library(GAMBLR.predict) -result <- DLBCLone_train_mixture_model(umap_out) +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + +dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) + +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) + +result <- DLBCLone_train_mixture_model(umap_out = make_umap) head(result$predictions) } diff --git a/man/DLBCLone_train_test_plot.Rd b/man/DLBCLone_train_test_plot.Rd index b0d2e48..b28d581 100644 --- a/man/DLBCLone_train_test_plot.Rd +++ b/man/DLBCLone_train_test_plot.Rd @@ -51,6 +51,7 @@ Plot the result of a DLBCLone classification \examples{ #add the dataset name to the metadata if it's not already there (required for the plot work) +\dontrun{ library(GAMBLR.predict) lymphgen_A53_DLBCLone$df$dataset = "GAMBL" @@ -63,5 +64,6 @@ DLBCLone_train_test_plot( details = lymphgen_A53_DLBCLone$best_params, classes = c("MCD","EZB","BN2","ST2","N1","A53","Other"), annotate_accuracy=TRUE,label_offset = 1) +} } diff --git a/man/make_and_annotate_umap.Rd b/man/make_and_annotate_umap.Rd index 0fdb29c..a205a6c 100644 --- a/man/make_and_annotate_umap.Rd +++ b/man/make_and_annotate_umap.Rd @@ -93,7 +93,7 @@ all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",pack dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) -my_umap <- make_and_annotate_umap( +make_umap <- make_and_annotate_umap( df=all_full_status, metadata=dlbcl_meta ) diff --git a/man/optimize_purity.Rd b/man/optimize_purity.Rd index b90af3e..f453d39 100644 --- a/man/optimize_purity.Rd +++ b/man/optimize_purity.Rd @@ -51,8 +51,10 @@ The function returns the best accuracy achieved and the corresponding purity thr } \examples{ +\dontrun{ library(GAMBLR.predict) result <- optimize_purity(processed_votes, prediction_column = "pred_label", truth_column = "true_label") +} } diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index fd47d83..65261da 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -53,6 +53,21 @@ library(GAMBLR.predict) dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +make_umap <- make_and_annotate_umap( + df=all_full_status, + metadata=dlbcl_meta +) + +optimize_params <- DLBCLone_optimize_params( + all_full_status, + dlbcl_meta, + umap_out = make_umap, + truth_classes = c("MCD","EZB","BN2","N1","ST2","Other"), + optimize_for_other = T, + min_k=5, + max_k=23 +) + predict_single <- predict_single_sample_DLBCLone( test_df = optimize_params$features[1,], train_metadata = dlbcl_meta, diff --git a/man/process_votes.Rd b/man/process_votes.Rd index da0cdb4..7a91863 100644 --- a/man/process_votes.Rd +++ b/man/process_votes.Rd @@ -50,10 +50,10 @@ The function also supports custom logic for handling the "Other" class, includin } } \examples{ - +/dontrun{ library(GAMBLR.predict) result <- process_votes(knn_output_df, k = 7) - +} } From 04e852405216da431f00179be90dd92770a47838 Mon Sep 17 00:00:00 2001 From: Atlantis-Real Date: Sun, 7 Sep 2025 00:31:47 -0700 Subject: [PATCH 79/79] predict_songle_sample fix --- R/umap.R | 347 ++++++++++++-------------- man/predict_single_sample_DLBCLone.Rd | 19 +- 2 files changed, 163 insertions(+), 203 deletions(-) diff --git a/R/umap.R b/R/umap.R index 04db1e5..03c6575 100644 --- a/R/umap.R +++ b/R/umap.R @@ -2794,6 +2794,9 @@ prepare_single_sample_DLBCLone <- function(optimized_model,seed=12345){ #' #' dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) #' +#' all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) %>% +#' tibble::column_to_rownames("sample_id") +#' #' make_umap <- make_and_annotate_umap( #' df=all_full_status, #' metadata=dlbcl_meta @@ -2816,218 +2819,186 @@ prepare_single_sample_DLBCLone <- function(optimized_model,seed=12345){ #' ) #' predict_single_sample_DLBCLone <- function( - test_df, - train_df, - train_metadata, - projection, - umap_out, - best_params, - optimized_model = NULL, - other_df, - ignore_self = FALSE, - truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), - drop_unlabeled_from_training=TRUE, - make_plot = TRUE, - annotate_accuracy = FALSE, - label_offset = 2, - title1="GAMBL", - title2="predicted_class_for_HighConf", - title3 ="predicted_class_for_Other", - seed = 12345, - max_neighbors = 500, - other_class = "Other" + test_df, + train_metadata, + optimized_model = NULL, + truth_classes = c("EZB","MCD","ST2","N1","BN2","Other"), + seed = 12345, + max_neighbors = 500 ){ - set.seed(seed) - if(is.null(optimized_model)){ - warning("optimized_model will become a required argument in the future. Please update your code accordingly") - } - if(ignore_self){ - # Allow overlapping samples: rename test duplicates temporarily - dupes <- intersect(train_df$sample_id, test_df$sample_id) - if(length(dupes) > 0){ - test_df <- test_df %>% - mutate(sample_id = ifelse( - sample_id %in% dupes, - paste0(sample_id, "_test"), - sample_id - )) - } - } else { - # Drop overlaps to prevent rowname collisions - dupes <- intersect(train_df$sample_id, test_df$sample_id) - - } - - - train_df = train_df %>% - column_to_rownames("sample_id") %>% + if(is.null(optimized_model)){ + stop("optimized_model the output of DLBCLone_optimize_params is a required argument. Please update your code accordingly") + } - rownames_to_column("sample_id") - train_id <- train_df$sample_id + if(any(!colnames(test_df) %in% colnames(optimized_model$features))){ + stop( + "test_df should not contain any features that are not in training data. ", + "Please check the column names of test_df and optimized_model$features." + ) + } - test_df = test_df %>% - column_to_rownames("sample_id") %>% + bad_samps <- rowSums(test_df) == 0 + bad_test_df <- test_df[bad_samps, ] %>% + rownames_to_column("sample_id") %>% + select(sample_id) + test_df <- test_df[!bad_samps, ] - rownames_to_column("sample_id") - test_id <- test_df$sample_id - - combined_df <- bind_rows(train_df, test_df) - - if(missing(projection)){ - projection <- make_and_annotate_umap( - df = combined_df, - umap_out = umap_out, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = best_params$na_option - ) - }else{ - message("Using provided projection for prediction") - } + test_id <- test_df %>% + rownames_to_column("sample_id") %>% + pull(sample_id) - - train_coords = dplyr::filter( - projection$df, - sample_id %in% train_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - print("TRAIN:") - print(dim(train_coords)) - train_df_proj = dplyr::filter( - projection$df, - sample_id %in% train_id - ) %>% + train_coords = dplyr::filter( + optimized_model$df + ) %>% select(sample_id,V1,V2) %>% - left_join( #Join to the incoming metadata rather than trusting the metadata in the projection - train_metadata %>% select( - sample_id, - lymphgen - ), - by = "sample_id" - ) + column_to_rownames("sample_id") - train_labels = train_df_proj %>% - pull(lymphgen) + train_df_proj = dplyr::filter( + optimized_model$df + ) %>% + select(sample_id,V1,V2) %>% + left_join( #Join to the incoming metadata rather than trusting the metadata in the projection + train_metadata %>% + select( + sample_id, + lymphgen + ), + by = "sample_id" + ) - #Obtain UMAP coordinates for the test sample(s) - print(paste("num rows:",nrow(test_df))) - test_projection <- make_and_annotate_umap( - df = test_df, - umap_out = umap_out, - ret_model = FALSE, - seed = seed, - join_column = "sample_id", - na_vals = best_params$na_option - ) - test_coords = dplyr::filter( - test_projection$df, - sample_id %in% test_id - ) %>% - select(sample_id,V1,V2) %>% - column_to_rownames("sample_id") - - predict_training = FALSE - if(predict_training){ - train_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = train_coords, # <- predicitng training on self - k = best_params$k, - conf_threshold = best_params$threshold, - other_class = other_class, - use_weights = best_params$use_weights, - ignore_self = ignore_self - ) + train_labels = train_df_proj %>% + pull(lymphgen) + + #Obtain UMAP coordinates for the test sample(s) + print(paste("num rows:",nrow(test_df))) + test_projection <- make_and_annotate_umap( + df = test_df, + umap_out = optimized_model, + ret_model = FALSE, + seed = seed, + join_column = "sample_id", + na_vals = optimized_model$best_params$na_option + ) - train_pred = rownames_to_column(train_pred, var = "sample_id") - } - + test_coords = dplyr::filter( + test_projection$df, + sample_id %in% test_id + ) %>% + select(sample_id,V1,V2) %>% + column_to_rownames("sample_id") - test_pred = weighted_knn_predict_with_conf( - train_coords = train_coords, - train_labels = train_labels, - test_coords = test_coords, - k = best_params$k, - conf_threshold = best_params$threshold, - other_class = other_class, - use_weights = best_params$use_weights, - ignore_self = ignore_self, - max_neighbors = max_neighbors - ) + test_pred = weighted_knn_predict_with_conf( + train_coords = train_coords, + train_labels = train_labels, + test_coords = test_coords, + k = optimized_model$best_params$k, + conf_threshold = optimized_model$best_params$threshold, + na_label = "Other", + use_weights = optimized_model$best_params$use_weights, + max_neighbors = max_neighbors + ) - test_pred = rownames_to_column(test_pred, var = "sample_id") - #test_pred = bind_cols(test_pred, test_coords) - if(!is.null(optimized_model)){ - test_pred = mutate(test_pred, - DLBCLone_i = predicted_label, - DLBCLone_io = ifelse(other_score > best_params$threshold_outgroup, - other_class, - predicted_label)) + test_pred = rownames_to_column(test_pred, var = "sample_id") + if(!is.null(optimized_model)){ + test_pred = mutate( + test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse( + other_score > optimized_model$best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) - }else{ - test_pred = mutate(test_pred, - DLBCLone_i = predicted_label, - DLBCLone_io = ifelse(other_score > best_params$threshold_outgroup, - other_class, - predicted_label)) - } + }else{ + test_pred = mutate( + test_pred, + DLBCLone_i = predicted_label, + DLBCLone_io = ifelse( + other_score > optimized_model$best_params$threshold_outgroup, + "Other", + predicted_label + ) + ) + } - anno_umap = select(test_projection$df, sample_id, V1, V2) + anno_umap = select(test_projection$df, sample_id, V1, V2) - anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% - mutate(label = paste(sample_id,predicted_label,round(confidence,3))) + anno_out = left_join(test_pred,anno_umap,by="sample_id") %>% + mutate(label = paste(sample_id,predicted_label,round(confidence,3))) - anno_out = anno_out %>% + anno_out = anno_out %>% mutate( - V1 = as.numeric(V1), - V2 = as.numeric(V2), - label = as.character(label) - ) - if(predict_training){ - predictions_train_df = left_join(train_pred, projection$df, by = "sample_id") - }else{ - predictions_train_df = filter(projection$df, sample_id %in% train_id) %>% - select(sample_id, V1, V2) - } - #This had assumed that projection$df contains the test samples! - #predictions_test_df = left_join(test_pred, projection$df, by = "sample_id") - predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") + V1 = as.numeric(V1), + V2 = as.numeric(V2), + label = as.character(label) + ) + + predictions_train_df = filter( + optimized_model$df + ) %>% + select(sample_id, V1, V2) - predictions_df = bind_rows(predictions_train_df %>% select(sample_id, V1, V2), - predictions_test_df %>% select(sample_id, V1, V2)) + predictions_test_df = left_join(test_pred, test_projection$df, by = "sample_id") + + predictions_df = bind_rows( + predictions_train_df %>% select(sample_id, V1, V2), + predictions_test_df %>% select(sample_id, V1, V2) + ) - if(!is.null(optimized_model)){ - best_w_purity = optimized_model$best_w_purity - best_w_score_thresh = optimized_model$best_w_score_thresh + if(!is.null(optimized_model)){ + best_w_purity = optimized_model$best_w_purity + best_w_score_thresh = optimized_model$best_w_score_thresh - predictions_test_df = process_votes( - df=predictions_test_df, - group_labels=optimized_model$truth_classes, - k=optimized_model$k_DLBCLone_w) + predictions_test_df = process_votes( + df=predictions_test_df, + group_labels=optimized_model$truth_classes, + k=optimized_model$k_DLBCLone_w + ) - predictions_test_df = predictions_test_df %>% - mutate(DLBCLone_w = by_score, - DLBCLone_wo = ifelse(score_ratio >= optimized_model$purity_DLBCLone_w | - top_group_score > optimized_model$score_thresh_DLBCLone_w, - by_score, - other_class)) + predictions_test_df = predictions_test_df %>% + mutate( + DLBCLone_w = by_score, + DLBCLone_wo = ifelse( + score_ratio >= optimized_model$purity_DLBCLone_w | top_group_score > optimized_model$score_thresh_DLBCLone_w, + by_score, + "Other" + ) + ) + } + + predictions_test_df = predictions_test_df %>% + bind_rows( + bad_test_df %>% + mutate( + predicted_label = "Other", + confidence = 1 + ) + ) + + combined_df <- bind_rows( + optimized_model$features %>% rownames_to_column("sample_id"), + test_df %>% rownames_to_column("sample_id") + ) %>% + distinct(sample_id, .keep_all = TRUE) %>% # keep first occurrence + column_to_rownames("sample_id") + if(any(!colnames(optimized_model$features) %in% colnames(test_df))){ + message("filling in missing features in test_df with zeros") + combined_df[is.na(combined_df)] = 0 } + to_return = list( - prediction = predictions_test_df, - projection = projection$df, - umap_input = umap_out$features, - model=umap_out$model, - features_df = combined_df %>% column_to_rownames("sample_id"), - df = predictions_df, - anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), - anno_out = anno_out, - type = "predict_single_sample_DLBCLone" - ) + predictions = predictions_test_df, + df = predictions_df, + anno_df = predictions_df %>% left_join(.,train_metadata,by="sample_id"), + anno_out = anno_out, + features_df = combined_df, + type = "predict_single_sample_DLBCLone", + optimized_predictions = optimized_model$predictions + ) - return(to_return) + return(to_return) } #' model storage for DLBCLone outputs diff --git a/man/predict_single_sample_DLBCLone.Rd b/man/predict_single_sample_DLBCLone.Rd index 65261da..b0fa4b4 100644 --- a/man/predict_single_sample_DLBCLone.Rd +++ b/man/predict_single_sample_DLBCLone.Rd @@ -6,25 +6,11 @@ \usage{ predict_single_sample_DLBCLone( test_df, - train_df, train_metadata, - projection, - umap_out, - best_params, optimized_model = NULL, - other_df, - ignore_self = FALSE, truth_classes = c("EZB", "MCD", "ST2", "N1", "BN2", "Other"), - drop_unlabeled_from_training = TRUE, - make_plot = TRUE, - annotate_accuracy = FALSE, - label_offset = 2, - title1 = "GAMBL", - title2 = "predicted_class_for_HighConf", - title3 = "predicted_class_for_Other", seed = 12345, - max_neighbors = 500, - other_class = "Other" + max_neighbors = 500 ) } \arguments{ @@ -53,6 +39,9 @@ library(GAMBLR.predict) dlbcl_meta = readr::read_tsv(system.file("extdata/dlbcl_meta_with_dlbclass.tsv",package = "GAMBLR.predict")) +all_full_status = readr::read_tsv(system.file("extdata/all_full_status.tsv",package = "GAMBLR.predict")) \%>\% + tibble::column_to_rownames("sample_id") + make_umap <- make_and_annotate_umap( df=all_full_status, metadata=dlbcl_meta