Resync the package with the most recent developments in packages

SparseArray and DelayedArray

DESCRIPTION

@@ -12,8 +12,8 @@ Authors@R: c(
         person("Leo", "Lahti", role=c("ctb"), email="[email protected]", comment = c(ORCID = "0000-0001-5537-637X")),
         person("Tuomas", "Borman", role = c("ctb"), comment = c(ORCID = "0000-0002-8563-8884"))
     )
-Version: 1.33.2
-Date: 2024-06-21
+Version: 1.33.3
+Date: 2024-07-16
 License: GPL-3
 Title: Single-Cell Analysis Toolkit for Gene Expression Data in R
 Description: A collection of tools for doing various analyses of
@@ -31,8 +31,9 @@ Imports:
     BiocGenerics,
     S4Vectors,
     SummarizedExperiment,
-    DelayedArray,
     MatrixGenerics,
+    SparseArray,
+    DelayedArray,
     beachmat,
     BiocNeighbors,
     BiocSingular,
@@ -49,6 +50,7 @@ Imports:
     ggrastr
 Suggests: 
     BiocStyle,
+    DelayedMatrixStats,
     snifter,
     densvis,
     cowplot,
@@ -66,6 +68,6 @@ biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, No
         GeneExpression, Sequencing, Software, DataImport,
         DataRepresentation, Infrastructure, Coverage
 Encoding: UTF-8
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 URL: http://bioconductor.org/packages/scater/
 BugReports: https://support.bioconductor.org/

NAMESPACE

@@ -104,6 +104,8 @@ import(scuttle)
 importClassesFrom(BiocParallel,MulticoreParam)
 importClassesFrom(S4Vectors,DataFrame)
 importClassesFrom(SingleCellExperiment,SingleCellExperiment)
+importClassesFrom(SparseArray,COO_SparseMatrix)
+importClassesFrom(SparseArray,SVT_SparseMatrix)
 importClassesFrom(SummarizedExperiment,SummarizedExperiment)
 importFrom(BiocGenerics,"counts<-")
 importFrom(BiocGenerics,colnames)
@@ -123,15 +125,15 @@ importFrom(BiocSingular,runPCA)
 importFrom(DelayedArray,DelayedArray)
 importFrom(DelayedArray,getAutoBPPARAM)
 importFrom(DelayedArray,setAutoBPPARAM)
-importFrom(Matrix,colSums)
-importFrom(Matrix,rowMeans)
-importFrom(Matrix,rowSums)
 importFrom(Matrix,t)
 importFrom(MatrixGenerics,colQuantiles)
+importFrom(MatrixGenerics,colSums)
 importFrom(MatrixGenerics,colSums2)
 importFrom(MatrixGenerics,colVars)
+importFrom(MatrixGenerics,rowMeans)
 importFrom(MatrixGenerics,rowMeans2)
 importFrom(MatrixGenerics,rowMedians)
+importFrom(MatrixGenerics,rowSums)
 importFrom(MatrixGenerics,rowSums2)
 importFrom(MatrixGenerics,rowVars)
 importFrom(S4Vectors,DataFrame)

R/getVarianceExplained.R

@@ -55,10 +55,11 @@ NULL
 
 #' @importFrom MatrixGenerics rowVars
 #' @importFrom stats model.matrix
+#' @importClassesFrom SparseArray COO_SparseMatrix SVT_SparseMatrix
 #' @importFrom scuttle fitLinearModel
 .get_variance_explained_internal <- function(block, variables) {
-    if (is(block, "SparseArraySeed")) {
-        block <- as(block, "dgCMatrix")
+    if (is(block, "COO_SparseMatrix")) {
+        block <- as(block, "SVT_SparseMatrix")
     }
 
     rsquared_mat <- matrix(NA_real_, nrow(block), ncol(variables), dimnames=list(NULL, colnames(variables))) 

R/nexprs.R

@@ -36,7 +36,7 @@
 NULL
 
 #' @importFrom BiocParallel SerialParam
-#' @importFrom Matrix rowSums colSums
+#' @importFrom MatrixGenerics rowSums colSums
 #' @importFrom DelayedArray getAutoBPPARAM setAutoBPPARAM
 .nexprs <- function(x, byrow=FALSE, detection_limit=0, subset_row=NULL, subset_col=NULL, BPPARAM=SerialParam()) {
     if (!is.null(subset_row)) {

R/plotGroupedHeatmap.R

@@ -50,7 +50,7 @@
 #'
 #' @export
 #' @importFrom SummarizedExperiment assay 
-#' @importFrom Matrix rowMeans
+#' @importFrom MatrixGenerics rowMeans
 #' @importFrom scuttle summarizeAssayByGroup
 plotGroupedHeatmap <- function(object, features, group, block = NULL, 
     columns=NULL, exprs_values = "logcounts", center = FALSE, scale = FALSE,

R/plotHeatmap.R

@@ -47,7 +47,7 @@
 #' \code{column_annotation_colors}, \code{color_rows_by}, 
 #' \code{row_annotation_colors}.
 #' @param exprs_values Alias to \code{assay.type}.
-#' @param by_exprs_values Alias to {by.assay.type}.
+#' @param by_exprs_values Alias to \code{by.assay.type}.
 #'
 #' @details 
 #' Setting \code{center=TRUE} is useful for examining log-fold changes of each cell's expression profile from the average across all cells.