Merge pull request #6652 from satijalab/release/4.2.1

Seurat v4.2.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 4.2.0
-Date: 2022-09-21
+Version: 4.2.1
+Date: 2022-11-07
 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(
@@ -48,7 +48,7 @@ Imports:
     leiden (>= 0.3.1),
     lmtest,
     MASS,
-    Matrix (>= 1.5.0),
+    Matrix (>= 1.5-0),
     matrixStats,
     miniUI,
     patchwork,
@@ -65,10 +65,10 @@ Imports:
     Rtsne,
     scales,
     scattermore (>= 0.7),
-    sctransform (>= 0.3.4),
-    SeuratObject (>= 4.1.2),
+    sctransform (>= 0.3.5),
+    SeuratObject (>= 4.1.3),
     shiny,
-    spatstat.core,
+    spatstat.explore,
     spatstat.geom,
     stats,
     tibble,

NAMESPACE

@@ -666,7 +666,7 @@ importFrom(shiny,sliderInput)
 importFrom(shiny,stopApp)
 importFrom(shiny,updateSelectInput)
 importFrom(shiny,verbatimTextOutput)
-importFrom(spatstat.core,markvario)
+importFrom(spatstat.explore,markvario)
 importFrom(spatstat.geom,ppp)
 importFrom(stats,aggregate)
 importFrom(stats,anova)

NEWS.md

@@ -1,3 +1,9 @@
+# Seurat 4.2.1 (2022-11-07)
+
+## Changes
+- Replaced import from `spatstat.core` with `spatstat.explore`
+- Fix bug in `FindMarkers()` when using `SCT` assay ([#6856](https://github.com/satijalab/seurat/issues/6586))
+
 # Seurat 4.2.0 (2022-09-21)
 
 ## Changes

R/differential_expression.R

@@ -752,6 +752,12 @@ FindMarkers.SCTAssay <- function(
     'scale.data' = GetAssayData(object = object, slot = "counts"),
     numeric()
   )
+  if (is.null(x = mean.fxn)){
+    mean.fxn <- function(x) {
+      return(log(x = rowMeans(x = expm1(x = x)) + pseudocount.use, base = base))
+
+    }
+  }
   fc.results <- FoldChange(
     object = object,
     slot = data.slot,
@@ -762,7 +768,7 @@ FindMarkers.SCTAssay <- function(
     mean.fxn = mean.fxn,
     fc.name = fc.name,
     base = base
-  )
+    )
   de.results <- FindMarkers(
     object = data.use,
     slot = data.slot,

R/preprocessing.R

@@ -1338,7 +1338,7 @@ RelativeCounts <- function(data, scale.factor = 1, verbose = TRUE) {
 #' @param data Matrix containing the data used as "marks" (e.g. gene expression)
 #' @param ... Arguments passed to markvario
 #'
-#' @importFrom spatstat.core markvario
+#' @importFrom spatstat.explore markvario
 #' @importFrom spatstat.geom ppp
 #'
 #' @export

R/visualization.R

@@ -7097,6 +7097,10 @@ SingleDimPlot <- function(
     dims <- colnames(x = data)[dims]
   }
   if (!is.null(x = cells.highlight)) {
+    if (inherits(x = cells.highlight, what = "data.frame")) {
+      stop("cells.highlight cannot be a dataframe. ",
+           "Please supply a vector or list")
+    }
     highlight.info <- SetHighlight(
       cells.highlight = cells.highlight,
       cells.all = rownames(x = data),

README.md

@@ -3,7 +3,7 @@
 [![CRAN Version](https://www.r-pkg.org/badges/version/Seurat)](https://cran.r-project.org/package=Seurat)
 [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/Seurat)](https://cran.r-project.org/package=Seurat)
 
-# Seurat v4.1
+# Seurat v4.2.1
 
 Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
 

cran-comments.md

@@ -1,4 +1,4 @@
-# Seurat v4.2.0
+# Seurat v4.2.1
 
 ## Test environments
 * local Ubuntu 20.04 install, R 4.1.3

tests/testthat/test_differential_expression.R

@@ -46,11 +46,11 @@ test_that("Default settings work as expected", {
 
   # SCT normalization
   expect_equal(results.sct[1, "p_val"], 6.225491e-11)
-  expect_equal(results.sct[1, "avg_logFC"], -0.6768721, tolerance = 1e-6)
+  expect_equal(results.sct[1, "avg_logFC"], -1.081321, tolerance = 1e-6)
   expect_equal(results.sct[1, "pct.1"], 0.111)
   expect_equal(results.sct[1, "pct.2"], 0.96)
   expect_equal(results.sct[1, "p_val_adj"], 1.369608e-08)
-  expect_equal(nrow(x = results.sct), 92)
+  expect_equal(nrow(x = results.sct), 158)
   expect_equal(rownames(x = results.sct)[1], "TYMP")
 })
 
@@ -96,8 +96,8 @@ test_that("setting pseudocount.use works", {
   expect_equal(results[1, "avg_logFC"], -2.630395, tolerance = 1e-6)
   expect_equal(nrow(x = results.clr), 182)
   expect_equal(results.clr[1, "avg_logFC"], -2.317338, tolerance = 1e-6)
-  expect_equal(nrow(results.sct), 185)
-  expect_equal(results.sct[1, "avg_logFC"], -1.845681, tolerance = 1e-6)
+  expect_equal(nrow(results.sct), 194)
+  expect_equal(results.sct[1, "avg_logFC"], -2.253920, tolerance = 1e-6)
 })
 
 results <- suppressWarnings(FindMarkers(object = pbmc_small, ident.1 = 0, ident.2 = 1, verbose = FALSE, base = exp(1), mean.fxn = rowMeans))
@@ -308,12 +308,12 @@ test_that("FindAllMarkers works as expected", {
   expect_equal(rownames(x = results.clr)[1], "HLA-DPB1")
 
   # SCT normalization
-  expect_equal(results.sct[1, "p_val"], 6.225491e-11)
-  expect_equal(results.sct[1, "avg_log2FC"], -1.265307, tolerance = 1e-6)
+  expect_equal(results.sct[1, "p_val"], 4.25861e-12)
+  expect_equal(results.sct[1, "avg_log2FC"], -2.70188, tolerance = 1e-6)
   expect_equal(results.sct[1, "pct.1"], 0.167)
   expect_equal(results.sct[1, "pct.2"], 0.909)
-  expect_equal(results.sct[1, "p_val_adj"], 1.369608e-08)
-  expect_equal(nrow(x = results.sct), 201)
+  expect_equal(results.sct[1, "p_val_adj"], 9.368941e-10)
+  expect_equal(nrow(x = results.sct), 210)
   expect_equal(rownames(x = results.sct)[1], "HLA-DPB1")
 
   # pseudocount.use = 0.1