Smaller test dataset; change default for chunk.size=100

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: UCell
 Type: Package
 Title: Rank-based signature enrichment analysis for single-cell data
-Version: 2.7.3
+Version: 2.7.4
 Authors@R: c(
   person('Massimo', 'Andreatta', 
      email = '[email protected]',

R/AddModuleScore_UCell.R

@@ -66,7 +66,7 @@
 #'
 #' @export
 AddModuleScore_UCell <- function(obj, features, maxRank=1500,
-        chunk.size=1000, BPPARAM=NULL, ncores=1, storeRanks=FALSE,
+        chunk.size=100, BPPARAM=NULL, ncores=1, storeRanks=FALSE,
         w_neg=1, assay=NULL, slot="counts", ties.method="average",
         force.gc=FALSE, name="_UCell") {
     if (!requireNamespace("Seurat", quietly = TRUE)) {
@@ -89,6 +89,7 @@ AddModuleScore_UCell <- function(obj, features, maxRank=1500,
         if (is.null(layers)) {
           stop(sprintf("Cannot find layer %s in assay %s", slot, assay))
         }
+
         meta.list <- lapply(layers, function(x) {
           calculate_Uscore(
             Seurat::GetAssayData(obj, layer=x, assay=assay),
@@ -97,6 +98,7 @@ AddModuleScore_UCell <- function(obj, features, maxRank=1500,
             ncores=ncores, BPPARAM=BPPARAM, ties.method=ties.method,
             force.gc=force.gc, storeRanks=storeRanks, name=name)
         })
+
         meta.list <- unlist(meta.list, recursive = FALSE)
         #store ranks matrix?
         if (storeRanks==TRUE){

R/HelperFunctions.R

@@ -83,7 +83,7 @@ u_stat_signature_list <- function(sig_list, ranks_matrix, maxRank=1000,
 #' @param   matrix        Input data matrix 
 #' @param   features      List of signatures
 #' @param   maxRank       Rank cutoff (1500) 
-#' @param   chunk.size    Cells per sub-matrix (1000)
+#' @param   chunk.size    Cells per sub-matrix (100)
 #' @param   BPPARAM       A BioParallel object to instruct UCell how to
 #'    parallelize  
 #' @param   ncores        Number of cores to use for parallelization
@@ -99,7 +99,7 @@ u_stat_signature_list <- function(sig_list, ranks_matrix, maxRank=1000,
 #' @import  Matrix
 #' @import  BiocParallel
 calculate_Uscore <- function(
-        matrix, features,  maxRank=1500, chunk.size=1000,
+        matrix, features,  maxRank=1500, chunk.size=100,
         BPPARAM = NULL, ncores=1, w_neg=1, ties.method="average",
         storeRanks=FALSE, force.gc=FALSE, name="_UCell"){
 
@@ -185,7 +185,7 @@ calculate_Uscore <- function(
 #' 
 #' @return                    A list of signature scores
 #' @import    data.table
-rankings2Uscore <- function(ranks_matrix, features, chunk.size=1000, w_neg=1,
+rankings2Uscore <- function(ranks_matrix, features, chunk.size=100, w_neg=1,
                             BPPARAM = NULL,ncores=1, force.gc=FALSE,
                             name="_UCell") {
 
@@ -313,7 +313,7 @@ data_to_ranks_data_table <- function(data, ties.method="average") {
 #' @param   chunk.size  How many cells to include in each sub-matrix
 #' 
 #' @return  A list of sub-matrices, each with size {n_features x chunk_size}
-split_data.matrix <- function(matrix, chunk.size=1000) {
+split_data.matrix <- function(matrix, chunk.size=100) {
     ncols <- dim(matrix)[2]
     nchunks <- (ncols-1) %/% chunk.size + 1
 

R/ScoreSignatures_UCell.R

@@ -65,7 +65,7 @@
 ScoreSignatures_UCell <- function(
         matrix=NULL,features, precalc.ranks=NULL,
         maxRank=1500, w_neg=1, name="_UCell",
-        assay="counts", chunk.size=1000,
+        assay="counts", chunk.size=100,
         BPPARAM=NULL, ncores=1,
         ties.method="average", force.gc=FALSE) {
 

R/StoreRankings_UCell.R

@@ -43,7 +43,7 @@
 #' @importFrom SummarizedExperiment assay
 #' @import Matrix
 #' @export
-StoreRankings_UCell <- function(matrix, maxRank=1500, chunk.size=1000,
+StoreRankings_UCell <- function(matrix, maxRank=1500, chunk.size=100,
         BPPARAM=NULL, ncores=1, assay='counts',
         ties.method="average", force.gc=FALSE) {
 

R/datasets.R

@@ -1,7 +1,7 @@
 #' Sample dataset to test UCell installation
 #'
 #' A sparse matrix (class "dgCMatrix") of single-cell transcriptomes
-#' (scRNA-seq) for 600 cells and 20729 genes.
+#' (scRNA-seq) for 30 cells and 20729 genes.
 #' Single-cell UMI counts were normalized using a standard log-normalization:
 #' counts for each cell were divided by the total counts for that cell and
 #' multiplied by 10,000, then natural-log transformed using `log1p`. \cr\cr
@@ -11,7 +11,7 @@
 #' available as UMI counts at
 #' \url{https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat}
 #' 
-#' @format A sparse matrix of 600 cells and 20729 genes.
+#' @format A sparse matrix of 30 cells and 20729 genes.
 #' @source \url{https://doi.org/10.1016/j.cell.2021.04.048}
 "sample.matrix"
 

inst/NEWS.Rd

@@ -1,6 +1,13 @@
 \name{NEWS}
 \title{News for package \pkg{UCell}}
 
+\section{New in version 2.7.4}{
+\itemize{
+    \item Add support for Seurat v5 assay and datasets in multiple layers. Added dependency on Seurat >= 5.0.
+    \item Change default for \code{chunk.size} to 100. On parallelized jobs, this can improve up to 2-fold execution time.
+    \item Smaller test dataset (30 cells), to speed up package function checks.  
+}}
+
 \section{New in version 2.1.2}{
 \itemize{
     \item New function \code{SmoothKNN()} for k-nearest neighbor smoothing of UCell scores. It can be applied both on SingleCellExperiment and Seurat objects (S3 method).