Merge pull request satijalab#412 from satijalab/fix/dimnamecheck

Convert to matrix before dimname checks in CreateAssayObject

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 3.2.1.9002
-Date: 2020-09-11
+Version: 3.2.1.9003
+Date: 2020-09-17
 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.
 Authors@R: c(

NEWS.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 ### Changes
 - Set the seed in `WhichCells` regardless of whether or not `idents` is passed
 - Retain Graph and Neighbor objects when subsetting only on features
+- Fix data.frame input to `CreateAssayObject()` when data.frame has no rownames.
 
 ## [3.2.1] - 2020-09-04
 ### Added

R/objects.R

@@ -649,6 +649,9 @@ CreateAssayObject <- function(
   } else if (!missing(x = counts) && !missing(x = data)) {
     stop("Either 'counts' or 'data' must be missing; both cannot be provided")
   } else if (!missing(x = counts)) {
+    if (!inherits(x = counts, what = 'dgCMatrix')) {
+      counts <- as(object = as.matrix(x = counts), Class = 'dgCMatrix')
+    }
     # check that dimnames of input counts are unique
     if (anyDuplicated(rownames(x = counts))) {
       warning(
@@ -675,9 +678,6 @@ CreateAssayObject <- function(
     if (nrow(x = counts) > 0 && is.null(x = rownames(x = counts))) {
       stop("No feature names (rownames) names present in the input matrix")
     }
-    if (!inherits(x = counts, what = 'dgCMatrix')) {
-      counts <- as(object = as.matrix(x = counts), Class = 'dgCMatrix')
-    }
     # Filter based on min.features
     if (min.features > 0) {
       nfeatures <- Matrix::colSums(x = counts > 0)