Merge pull request #760 from satijalab/fix/foldchange_counts

Fix/foldchange counts

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 4.3.0.9007
-Date: 2023-06-02
+Version: 4.3.0.9008
+Date: 2023-07-06
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(

NAMESPACE

@@ -32,6 +32,7 @@ S3method(FindVariableFeatures,Seurat)
 S3method(FindVariableFeatures,default)
 S3method(FoldChange,Assay)
 S3method(FoldChange,DimReduc)
+S3method(FoldChange,SCTAssay)
 S3method(FoldChange,Seurat)
 S3method(FoldChange,default)
 S3method(GetAssay,Seurat)

R/differential_expression.R

@@ -723,6 +723,13 @@ FindMarkers.SCTAssay <- function(
     yes = 'counts',
     no = slot
   )
+  if (test.use %in% DEmethods_counts()){
+    # set slot to counts
+    if (slot !="counts") {
+      message(paste0("Setting slot to counts for ", test.use, " (counts based test: "))
+      slot <- "counts"
+    }
+  }
   if (recorrect_umi && length(x = levels(x = object)) > 1) {
     cell_attributes <- SCTResults(object = object, slot = "cell.attributes")
     observed_median_umis <- lapply(

R/preprocessing.R

@@ -3130,7 +3130,7 @@ SampleUMI <- function(
 #' Use regularized negative binomial regression to normalize UMI count data
 #'
 #' This function calls sctransform::vst. The sctransform package is available at
-#' https://github.com/ChristophH/sctransform.
+#' https://github.com/satijalab/sctransform.
 #' Use this function as an alternative to the NormalizeData,
 #' FindVariableFeatures, ScaleData workflow. Results are saved in a new assay
 #' (named SCT by default) with counts being (corrected) counts, data being log1p(counts),