Coding-agent fixes to get RcisTarget passing Bioc build/check/test

.github/workflows/check-bioc.yml

@@ -0,0 +1,13 @@
+name: R-CMD-check-bioc
+
+on:
+  push:
+    branches:
+      - main
+      - master
+      - devel
+  pull_request:
+
+jobs:
+  run-check:
+    uses: bioc-package-rescue/workflows/.github/workflows/check-bioc.yml@main

DESCRIPTION

@@ -4,9 +4,12 @@ Title: RcisTarget Identify transcription factor binding motifs enriched on a
     list of genes or genomic regions
 Version: 1.23.1
 Date: 2024-03-02
-Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. 
-    VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium
-Maintainer: Gert Hulselmans <Gert.Hulselmans@kuleuven.be>
+Authors@R: c(
+    person("Sara", "Aibar", role = "aut"),
+    person("Gert", "Hulselmans", email = "Gert.Hulselmans@kuleuven.be", role = c("aut", "cre")),
+    person("Stein", "Aerts", role = "aut"),
+    person("Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium", role = "cph")
+  )
 Description: RcisTarget identifies transcription factor binding
     motifs (TFBS) over-represented on a gene list.
     In a first step, RcisTarget selects DNA motifs that are significantly 
@@ -31,6 +34,7 @@ Imports:
     dplyr,
     tibble,
     GSEABase,
+    igraph,
     methods,
     R.utils,
     stats,
@@ -48,7 +52,6 @@ Suggests:
     foreach,
     gplots,
     rtracklayer,
-    igraph,
     knitr,
     RcisTarget.hg19.motifDBs.cisbpOnly.500bp, 
     rmarkdown,

R/02_addMotifAnnotation.R

@@ -186,7 +186,7 @@ addMotifAnnotation <- function(auc, nesThreshold=3.0, digits=3,
 
     if(!is.null(motifAnnot)) {
       motifAnnot_subset <- motifAnnot[(motifAnnot[[idColumn]] %in% aucTable[[idColumn]]) 
-                                      & (motifAnnot$TF %in% TFs), ][,c(idColumn, "TF", "annotationSource"),with=F]
+                                      & (motifAnnot$TF %in% TFs), ][,c(idColumn, "TF", "annotationSource"),with=FALSE]
       motifAnnot_subset <- split(motifAnnot_subset, motifAnnot_subset[[idColumn]])
       for(motifName in names(motifAnnot_subset))
       {

R/aux_addLogo.R

@@ -94,7 +94,7 @@ showLogo <- function(motifEnrDT,
       stop("Please indicate the column containing the motif id (argument 'motifCol') or set it to NULL.")
     }
     # TODO 
-    met <- met[grep("transfac_pro__", met[[motifCol]], invert = T),]
+    met <- met[grep("transfac_pro__", met[[motifCol]], invert = TRUE),]
   }
 
   # For numeric columns, show only the number of significant digits...
@@ -106,7 +106,7 @@ showLogo <- function(motifEnrDT,
   if(!is.null(colsToShow)) {
     colsToShow <- unique(unname(unlist(colsToShow)))
     colsToShow <- colsToShow[which(colsToShow %in% colnames(met))]
-    met <- met[, colsToShow, with=F]
+    met <- met[, colsToShow, with=FALSE]
   }
 
   # Show...

R/aux_convertToTargetRegions.R

@@ -14,11 +14,13 @@
 #' \code{vignette("RcisTarget")}
 #'
 #' @examples
-#' \dontrun{
-#'  ## To apply on a list of regionSets:
-#'  regionSets_db <- lapply(regionSets, function(x) 
-#'     convertToTargetRegions(queryRegions=x, targetRegions=dbRegionsLoc))
-#'  }
+#' # Create mock query and target regions
+#' library(GenomicRanges)
+#' queryRegions <- GRanges("chr1", IRanges(start = c(10, 100), end = c(50, 150)))
+#' targetRegions <- GRanges("chr1", IRanges(start = c(5, 80, 200), end = c(45, 120, 250)))
+#' 
+#' # Convert query regions to target regions
+#' convertToTargetRegions(queryRegions = queryRegions, targetRegions = targetRegions)
 #' @rdname convertToTargetRegions
 #' @importFrom methods isClass
 #' @importFrom GenomeInfoDb keepSeqlevels

R/aux_getDbRegionsLoc.R

@@ -15,10 +15,24 @@
 #' \code{vignette("RcisTarget")}
 #'
 #' @examples
-#' \dontrun{
-#' featherFilePath <- "~/databases/dm6-regions-11species.mc9nr.feather"
-#' dbRegionsLoc <- getDbRegionsLoc(featherFilePath)
-#' }
+#' library(arrow)
+#' # Create a dummy data frame with drosophila-style region column names
+#' df <- data.frame(
+#'   features = c("motif1", "motif2"),
+#'   `motif1__chr3L:1000-2000` = c(1, 2),
+#'   `motif2__chr3L:3000-4000` = c(3, 4),
+#'   check.names = FALSE
+#' )
+#' 
+#' # Write to a temporary feather file
+#' tmpFile <- tempfile(fileext = ".feather")
+#' write_feather(df, tmpFile)
+#' 
+#' # Get the region locations
+#' getDbRegionsLoc(tmpFile)
+#' 
+#' # Clean up
+#' file.remove(tmpFile)
 #' @rdname getDbRegionsLoc
 #' @importFrom GenomeInfoDb sortSeqlevels seqlevels 
 #' @importFrom GenomicRanges GRanges

R/priv_calcErn.R

@@ -44,7 +44,7 @@
   maxRankExtra <- maxRank+nMean
   gsRankings.asMat <- as.matrix(gsRankings) # Much faster!
   gsRankings.asMat[gsRankings.asMat>maxRankExtra] <- NA
-  globalMat <- matrix(0, nrow=max(nrow(gsRankings),max(gsRankings.asMat, na.rm=T)), ncol=maxRankExtra) #  nrow=nrow(gsRankings): can be out of range, add extra rows and subset at the end
+  globalMat <- matrix(0, nrow=max(nrow(gsRankings),max(gsRankings.asMat, na.rm=TRUE)), ncol=maxRankExtra) #  nrow=nrow(gsRankings): can be out of range, add extra rows and subset at the end
 
   # x <- x[seq_len(min(length(x), nrow(globalMat)))] # or: # if(nrow(globalMat) < length(x)) x <- x[seq_len(nrow(globalMat))] # cannot be in the loop... too slow!
   for(i in 1:nrow(gsRankings)) # (TO DO: Paralellize?)

inst/examples/example_addSignificantGenes.R

@@ -61,16 +61,17 @@ motifEnr_wIncidMat <- addSignificantGenes(motifEnrichmentTable,
                 genesFormat = "incidMatrix")
 
 motifEnr_wIncidMat <- as.data.frame(motifEnr_wIncidMat)
-which(colnames(motifEnr_wIncidMat) == "rankAtMax")
+rankAtMaxIdx <- match("rankAtMax", colnames(motifEnr_wIncidMat))
+if(is.na(rankAtMaxIdx)) stop("Column 'rankAtMax' not found in results.")
 
-incidMat <- motifEnr_wIncidMat[,8:ncol(motifEnr_wIncidMat)]
+incidMat <- as.matrix(motifEnr_wIncidMat[, (rankAtMaxIdx + 1):ncol(motifEnr_wIncidMat)])
 rownames(incidMat) <- motifEnr_wIncidMat[,"motif"]
 incidMat <- incidMat[, colSums(incidMat)>0, drop=FALSE]
 
 # Plot as network
 par(mfrow=c(1,1))
 library(igraph)
-plot(graph.incidence(incidMat))
+plot(graph_from_incidence_matrix(incidMat))
 
 ###############################################################
 # Alternative method: getSignificantGenes()

inst/examples/example_workflow.R

@@ -1,38 +1,33 @@
 # RcisTarget workflow for advanced users:
 # Running the workflow steps individually
 
-\dontrun{
-
-##################################################
-#### Load your gene sets
 # As example, the package includes an Hypoxia gene set:
 txtFile <- paste(file.path(system.file('examples', package='RcisTarget')),
                "hypoxiaGeneSet.txt", sep="/")
 geneLists <- list(hypoxia=read.table(txtFile, stringsAsFactors=FALSE)[,1])
-
-#### Load databases
-## Motif rankings: Select according to organism and distance around TSS
-## (See the vignette for URLs to download)
-motifRankings <- importRankings("~/databases/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather")
 
-## Motif - TF annotation:
-data("motifAnnotations_hgnc") # human TFs (for motif collection 10)
-##################################################
-
-#### Run RcisTarget
+# Since the suggested database package is in Suggests, 
+# we guard the example to avoid hard failures if it is not installed:
+if (requireNamespace("RcisTarget.hg19.motifDBs.cisbpOnly.500bp", quietly = TRUE)) {
+
+  # Load rankings database
+  data(hg19_500bpUpstream_motifRanking_cispbOnly, package="RcisTarget.hg19.motifDBs.cisbpOnly.500bp")
+  motifRankings <- hg19_500bpUpstream_motifRanking_cispbOnly
 
-# Step 1. Calculate AUC
-motifs_AUC <- calcAUC(geneLists, motifRankings)
+  # Load motif annotations
+  data(motifAnnotations_hgnc_v9) # human TFs (for motif collection 9)
+  motifAnnotation <- motifAnnotations_hgnc_v9
 
-# Step 2. Select significant motifs, add TF annotation & format as table
-motifEnrichmentTable <- addMotifAnnotation(motifs_AUC,
-                         motifAnnot=motifAnnotations)
+  # Step 1. Calculate AUC
+  motifs_AUC <- calcAUC(geneLists, motifRankings)
 
-# Step 3 (optional). Identify genes that have the motif significantly enriched
-# (i.e. genes from the gene set in the top of the ranking)
-motifEnrichmentTable_wGenes <- addSignificantGenes(motifEnrichmentTable,
-                                                   geneSets=geneLists,
-                                                   rankings=motifRankings,
-                                                   method="aprox")
+  # Step 2. Select significant motifs, add TF annotation & format as table
+  motifEnrichmentTable <- addMotifAnnotation(motifs_AUC,
+                           motifAnnot=motifAnnotation)
 
+  # Step 3 (optional). Identify genes that have the motif significantly enriched
+  motifEnrichmentTable_wGenes <- addSignificantGenes(motifEnrichmentTable,
+                                                     geneSets=geneLists,
+                                                     rankings=motifRankings,
+                                                     method="aprox")
 }