Merge branch 'develop' into fix/FeatureScatter_cells

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 3.1.4.9025
-Date: 2020-04-09
+Version: 3.1.4.9027
+Date: 2020-04-10
 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>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details. Please note:  SDMTools is available is available from the CRAN archives with install.packages("https://cran.rstudio.com//src/contrib/Archive/SDMTools/SDMTools_1.1-221.2.tar.gz", repos = NULL); it is not in the standard repositories.
 Authors@R: c(

R/objects.R

@@ -1309,6 +1309,12 @@ RenameAssays <- function(object, ...) {
       DefaultAssay(object = object) <- new
     }
     Key(object = object[[new]]) <- old.key
+    # change assay used in any dimreduc object
+    for (i in Reductions(object = object)) {
+      if (DefaultAssay(object = object[[i]]) == old) {
+        DefaultAssay(object = object[[i]]) <- new
+      }
+    }
     object[[old]] <- NULL
   }
   return(object)

R/preprocessing.R

@@ -832,6 +832,7 @@ ReadAlevin <- function(base.path) {
 #' will be prefixed with the name.
 #' @param gene.column Specify which column of genes.tsv or features.tsv to use for gene names; default is 2
 #' @param unique.features Make feature names unique (default TRUE)
+#' @param strip.suffix Remove trailing "-1" if present in all cell barcodes.
 #'
 #' @return If features.csv indicates the data has multiple data types, a list
 #'   containing a sparse matrix of the data from each type will be returned.
@@ -857,7 +858,12 @@ ReadAlevin <- function(base.path) {
 #' seurat_object[['Protein']] = CreateAssayObject(counts = data$`Antibody Capture`)
 #' }
 #'
-Read10X <- function(data.dir = NULL, gene.column = 2, unique.features = TRUE) {
+Read10X <- function(
+  data.dir = NULL,
+  gene.column = 2,
+  unique.features = TRUE,
+  strip.suffix = FALSE
+) {
   full.data <- list()
   for (i in seq_along(along.with = data.dir)) {
     run <- data.dir[i]
@@ -888,7 +894,7 @@ Read10X <- function(data.dir = NULL, gene.column = 2, unique.features = TRUE) {
     }
     data <- readMM(file = matrix.loc)
     cell.names <- readLines(barcode.loc)
-    if (all(grepl(pattern = "\\-1$", x = cell.names))) {
+    if (all(grepl(pattern = "\\-1$", x = cell.names)) & strip.suffix) {
       cell.names <- as.vector(x = as.character(x = sapply(
         X = cell.names,
         FUN = ExtractField,

tests/testthat/test_load_10X.R

@@ -9,15 +9,15 @@ test_that("Cell Ranger 3.0 Data Parsing", {
   expect_is(test.data, "list")
   expect_equal(ncol(test.data$`Gene Expression`), .5 * ncol(test.data2$`Gene Expression`))
   expect_equal(ncol(test.data$`Antibody Capture`), .5 * ncol(test.data2$`Antibody Capture`))
-  expect_equal(colnames(test.data2[[1]])[6], "2_AAAGTAGCACAGTCGC")
+  expect_equal(colnames(test.data2[[1]])[6], "2_AAAGTAGCACAGTCGC-1")
   expect_equal(test.data$`Gene Expression`[2,2], 1000)
 })
 
 # Tests of Pre-3.0 Data
 test.data3 <- Read10X("../testdata/")
 test_that("Read10X creates sparse matrix", {
   expect_is(test.data3, "dgCMatrix")
-  expect_equal(colnames(test.data3)[1], "ATGCCAGAACGACT")
+  expect_equal(colnames(test.data3)[1], "ATGCCAGAACGACT-1")
   expect_equal(rownames(test.data3)[1], "MS4A1")
 })