Merge pull request satijalab#337 from satijalab/fix/FeatureScatter_pr…

…efix

add prefix to feature 1 2

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: Seurat
-Version: 3.1.5.9013
+Version: 3.1.5.9014
 Date: 2020-07-09
 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.

NEWS.md

@@ -16,6 +16,7 @@ object inheriting from the Assay class
 - Efficiency improvements in anchor scoring (`ScoreAnchors`)
 - Fix bug in `FindClusters()` when the last node has no edges 
 - Default to weighted = TRUE when constructing igraph objects in `RunLeiden`. Remove corresponding weights parameter from `FindClusters()`.
+- Fix handling of keys in `FeatureScatter()` 
 
 ## [3.1.5] - 2020-04-14
 ### Added

R/visualization.R

@@ -1487,13 +1487,15 @@ FeatureScatter <- function(
     cells = cells,
     slot = slot
   )
-  if (isFALSE(x = feature1 %in% colnames(x = data))) {
+  if (!grepl(pattern = feature1, x = colnames(x = data)[1])) {
     stop("Feature 1 (", feature1, ") not found.", call. = FALSE)
   }
-  if (isFALSE(x = feature2 %in% colnames(x = data))) {
+  if (!grepl(pattern = feature2, x = colnames(x = data)[2])) {
     stop("Feature 2 (", feature2, ") not found.", call. = FALSE)
   }
   data <- as.data.frame(x = data)
+  feature1 <-  colnames(x = data)[1]
+  feature2 <-  colnames(x = data)[2]
   for (group in group.by) {
     if (!is.factor(x = data[, group])) {
       data[, group] <- factor(x = data[, group])