From 3bdc2b1987d36b738d9a23208304e1dd52fcb7d8 Mon Sep 17 00:00:00 2001 From: Givanna Putri Date: Mon, 22 Sep 2025 15:09:12 +1000 Subject: [PATCH] update vignettes --- DESCRIPTION | 6 +- R/runSuperCellCyto.R | 7 +- inst/README.md | 21 ++ .../Data23_Panel3_base_NR4_Patient9.fcs | Bin .../Data23_Panel3_base_R5_Patient15.fcs | Bin inst/extdata/Levine_32dim_H1_sub.csv | 201 ++++++++++++++++++ inst/extdata/Levine_32dim_H2_sub.csv | 201 ++++++++++++++++++ inst/extdata/Levine_32dim_sce_sub.qs2 | Bin 0 -> 262361 bytes inst/extdata/Levine_32dim_seurat_sub.qs2 | Bin 0 -> 281478 bytes inst/scripts/make_levine_32dim.R | 66 ++++++ ...reate_supercells.Rmd => SuperCellCyto.Rmd} | 36 ++++ vignettes/data/Levine_32dim_H1_sub.csv | 200 ----------------- vignettes/data/Levine_32dim_H2_sub.csv | 200 ----------------- vignettes/data/Levine_32dim_sce_sub.qs | Bin 207316 -> 0 bytes vignettes/data/Levine_32dim_seurat_sub.qs | Bin 346343 -> 0 bytes vignettes/how_to_prepare_data.Rmd | 28 ++- vignettes/interoperability_with_sce.Rmd | 14 +- vignettes/interoperability_with_seurat.Rmd | 19 +- ...percellcyto_for_stratified_summarising.Rmd | 2 +- 19 files changed, 571 insertions(+), 430 deletions(-) create mode 100644 inst/README.md rename {vignettes/data => inst/extdata}/Data23_Panel3_base_NR4_Patient9.fcs (100%) rename {vignettes/data => inst/extdata}/Data23_Panel3_base_R5_Patient15.fcs (100%) create mode 100644 inst/extdata/Levine_32dim_H1_sub.csv create mode 100644 inst/extdata/Levine_32dim_H2_sub.csv create mode 100644 inst/extdata/Levine_32dim_sce_sub.qs2 create mode 100644 inst/extdata/Levine_32dim_seurat_sub.qs2 create mode 100644 inst/scripts/make_levine_32dim.R rename vignettes/{how_to_create_supercells.Rmd => SuperCellCyto.Rmd} (92%) delete mode 100644 vignettes/data/Levine_32dim_H1_sub.csv delete mode 100644 vignettes/data/Levine_32dim_H2_sub.csv delete mode 100644 vignettes/data/Levine_32dim_sce_sub.qs delete mode 100644 vignettes/data/Levine_32dim_seurat_sub.qs diff --git a/DESCRIPTION b/DESCRIPTION index 7454d94..9992f81 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,14 +33,14 @@ Suggests: testthat (>= 3.0.0), BiocSingular, bluster, - qs, scater, scran, Seurat, SingleCellExperiment, - BiocStyle + BiocStyle, + magick, + qs2 VignetteBuilder: knitr Config/testthat/edition: 3 URL: https://phipsonlab.github.io/SuperCellCyto/ BugReports: https://github.com/phipsonlab/SuperCellCyto/issues - diff --git a/R/runSuperCellCyto.R b/R/runSuperCellCyto.R index f2b2b7f..f075d52 100644 --- a/R/runSuperCellCyto.R +++ b/R/runSuperCellCyto.R @@ -256,10 +256,9 @@ runSuperCellCyto <- function( .adjust_n_pc <- function(n_pc, markers) { if (n_pc > length(markers)) { warning( - paste0( - "Requested n_pc (", n_pc, - ") is greater than the number of markers (", length(markers), - "). Setting n_pc to ", length(markers), "." + sprintf( + "Requested n_pc (%d) > markers (%d). Setting n_pc to %d.", + n_pc, length(markers), length(markers) ) ) n_pc <- length(markers) diff --git a/inst/README.md b/inst/README.md new file mode 100644 index 0000000..0a47da8 --- /dev/null +++ b/inst/README.md @@ -0,0 +1,21 @@ +# README + +Data in `extdata` folder are obtained from the following sources: + +* `Data23_Panel3_base_NR4_Patient9.fcs` and +`Data23_Panel3_base_R5_Patient15.fcs` are mass cytometry data from anti-PD1 + immunotherapy study by [Krieg et. al](https://doi.org/10.1038/nm.4466). + FCS files are available from + [FlowRepository]( + http://flowrepository.org/public_experiment_representations/1124). +* `Levine_32dim_H1_sub.csv` and `Levine_32dim_H2_sub.csv` are mass cytometry + data from AML study by + [Levine et. al](https://doi.org/10.1016/j.cell.2015.05.047). +* `Levine_32dim_seurat_sub.qs2` and `Levine_32dim_sce_sub.qs2` are + Seurat object and SingleCellExperiment object containing subsampled + mass cytometry data from AML study by + [Levine et. al](https://doi.org/10.1016/j.cell.2015.05.047). + +Scripts to generate the different formats of `Levine_32dim` dataset is stored +in the `inst/scripts` directory of +[SuperCellCyto](https://github.com/phipsonlab/SuperCellCyto) repo. diff --git a/vignettes/data/Data23_Panel3_base_NR4_Patient9.fcs b/inst/extdata/Data23_Panel3_base_NR4_Patient9.fcs similarity index 100% rename from vignettes/data/Data23_Panel3_base_NR4_Patient9.fcs rename to inst/extdata/Data23_Panel3_base_NR4_Patient9.fcs diff --git a/vignettes/data/Data23_Panel3_base_R5_Patient15.fcs b/inst/extdata/Data23_Panel3_base_R5_Patient15.fcs similarity index 100% rename from vignettes/data/Data23_Panel3_base_R5_Patient15.fcs rename to inst/extdata/Data23_Panel3_base_R5_Patient15.fcs diff --git a/inst/extdata/Levine_32dim_H1_sub.csv b/inst/extdata/Levine_32dim_H1_sub.csv new file mode 100644 index 0000000..10ff4f6 --- /dev/null +++ b/inst/extdata/Levine_32dim_H1_sub.csv @@ -0,0 +1,201 @@ 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zTf7|%XY+)37klL(FUAlfIp7n z?$t#NX(yFwVJgv5CuWw!6D5H8!OLw#g5Zy z{$H|!7E1F|BLk`BQ2Ss_V}BKvQUI~VR6Zhze?*Y@Xyuj|c*S&^HV?}^U1jAX<@pUw zgXhr_d*cVJT|6T2|!VjC1s=;lNkho;3D5kO(NOp3&WBHvit{g>lL3(#VRgzmD(Iix-%i!4F=-_ zNoz$6V;;Dha!q}Dw7^71O_o9BsXXaJ^!~)Sqvy!zrtbGgt~dAOnNtX z(5K(m-{0@wSN;6{y|8t^|D4O;|2eInuRs5H{?_$&{%`;GeE-(<{{H-4{r>v?-_hFp g`TO(R`@P?L*ZcLq?fu!WYyJK0zdOCWV|~Q`KN{?_Bme*a literal 0 HcmV?d00001 diff --git a/inst/scripts/make_levine_32dim.R b/inst/scripts/make_levine_32dim.R new file mode 100644 index 0000000..d461756 --- /dev/null +++ b/inst/scripts/make_levine_32dim.R @@ -0,0 +1,66 @@ +library(HDCytoData) +library(data.table) +library(qs2) + + +sce <- Levine_32dim_SE() + +# isolate data from H1 and H2 patients +sce_H1 <- sce[rowData(sce)$patient_id == "H1", ] +sce_H2 <- sce[rowData(sce)$patient_id == "H2", ] + +# remove unassigned +sce_H1 <- sce_H1[rowData(sce_H1)$population_id != "unassigned", ] +sce_H2 <- sce_H2[rowData(sce_H2)$population_id != "unassigned", ] + +# randomly select 200 cells from each patient +set.seed(42) +sce_H1_sub <- sce_H1[sample(nrow(sce_H1), 200), ] +sce_H2_sub <- sce_H2[sample(nrow(sce_H2), 200), ] + +# convert to data.table +dt_H1_sub <- data.table(assay(sce_H1_sub, "exprs")) +dt_H2_sub <- data.table(assay(sce_H2_sub, "exprs")) + +# save them as csv files +fwrite(dt_H1_sub, "inst/extdata/Levine_32dim_H1_sub.csv") +fwrite(dt_H2_sub, "inst/extdata/Levine_32dim_H2_sub.csv") + + +# create the sce object +dt <- data.table(assay(sce, "exprs")) +dt[, population_id := rowData(sce)$population_id] +dt[, sample := rowData(sce)$patient_id] + +# get 50 cells from each population and sample +set.seed(42) +n_cells <- 50 +sampled_dt <- dt[, .SD[sample(.N, min(.N, n_cells))], by = c("population_id", "sample")] + +# assign cell id +sampled_dt[, cell_id := paste0("cell_", .I)] + +# create SCE object +exprs_mat <- t(as.matrix(sampled_dt[, !c("population_id", "sample", "cell_id"), with = FALSE])) +sce_sampled <- SingleCellExperiment::SingleCellExperiment(assays = list(counts = exprs_mat)) +SummarizedExperiment::colData(sce_sampled)$population <- sampled_dt$population_id +SummarizedExperiment::colData(sce_sampled)$sample <- sampled_dt$sample +SummarizedExperiment::colData(sce_sampled)$cell_id <- sampled_dt$cell_id +colnames(sce_sampled) <- sampled_dt$cell_id + +qs_save(sce_sampled, "inst/extdata/Levine_32dim_sce_sub.qs2") + + +# create Seurat object +seurat_obj <- Seurat::CreateSeuratObject( + counts = exprs_mat, + assay = "originalexp", +) +seurat_obj <- Seurat::AddMetaData(seurat_obj, metadata = sampled_dt[, .(population_id, sample, cell_id)]) +# rename patient_id to sample + + +qs_save(seurat_obj, "inst/extdata/Levine_32dim_seurat_sub.qs2") + + + diff --git a/vignettes/how_to_create_supercells.Rmd b/vignettes/SuperCellCyto.Rmd similarity index 92% rename from vignettes/how_to_create_supercells.Rmd rename to vignettes/SuperCellCyto.Rmd index 5e2b129..4198a57 100644 --- a/vignettes/how_to_create_supercells.Rmd +++ b/vignettes/SuperCellCyto.Rmd @@ -27,6 +27,42 @@ library(BiocParallel) This vignette describes the steps to generate supercells for cytometry data using SuperCellCyto R package. +Briefly, supercells are "mini" clusters of cells that are similar in their +marker expressions. +The motivation behind supercells is that instead of analysing millions of +individual cells, you can analyse thousands of supercells, +making downstream analysis much faster while maintaining +biological interpretability. + + +See other vignettes for how to: + +* [Prepare your data for SuperCellCyto](how_to_prepare_data.html) +* [Use SuperCellCyto for downsampling]( + using_supercellcyto_for_stratified_summarising.html) +* [Integrate SuperCellCyto output with SingleCellExperiment objects]( + interoperability_with_sce.html) +* [Integrate SuperCellCyto output with Seurat objects]( + interoperability_with_seurat.html) + +## Installation + +You can install stable version of SuperCellCyto from Bioconductor using: + +```{r, eval=FALSE} +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") +BiocManager::install("SuperCellCyto") +``` + +For the latest development version, you can install it from GitHub using `pak`: + +```{r, eval=FALSE} +if (!requireNamespace("pak", quietly = TRUE)) + install.packages("pak") +pak::install_github("phipsonlab/SuperCellCyto") +``` + ## Preparing your dataset The function which creates supercells is called `runSuperCellCyto`, and it diff --git a/vignettes/data/Levine_32dim_H1_sub.csv b/vignettes/data/Levine_32dim_H1_sub.csv deleted file mode 100644 index d28d7e7..0000000 --- a/vignettes/data/Levine_32dim_H1_sub.csv +++ /dev/null @@ -1,200 +0,0 @@ -Time,Cell_length,DNA1,DNA2,CD45RA,CD133,CD19,CD22,CD11b,CD4,CD8,CD34,Flt3,CD20,CXCR4,CD235ab,CD45,CD123,CD321,CD14,CD33,CD47,CD11c,CD7,CD15,CD16,CD44,CD38,CD13,CD3,CD61,CD117,CD49d,HLA-DR,CD64,CD41,Viability,file_number,event_number 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