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compatibility with spaceranger 2.0 #6208

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merged 5 commits into from
Jul 19, 2022
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compatibility with spaceranger 2.0 #6208

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f2bc08e
compatibility with spaceranger 2.0
stephenwilliams22 Jul 18, 2022
0168ea5
Style fixes
mojaveazure Jul 19, 2022
a01063e
Update changelog
mojaveazure Jul 19, 2022
023bb86
Merge branch 'develop' into stephen/spaceranger2.0
mojaveazure Jul 19, 2022
857fdf1
Update DESCRIPTION
mojaveazure Jul 19, 2022
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4 changes: 2 additions & 2 deletions DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
Package: Seurat
Version: 4.1.1.9002
Date: 2022-06-17
Version: 4.1.1.9003
Date: 2022-07-19
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(
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1 change: 1 addition & 0 deletions NEWS.md
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Expand Up @@ -4,6 +4,7 @@
- Fix legend color in `DoHeatmap()` ([#5783](https://github.com/satijalab/seurat/issues/5783))
- Fix bug in `ScaleData()` when regressing out one gene ([#5970](https://github.com/satijalab/seurat/pull/5970))
- Fix name pulling in `PlotPerturbScore()` ([#6081](https://github.com/satijalab/seurat/pull/6081))
- Support spaceranger 2.0 ([#6208](https://github.com/satijalab/seurat/pull/6208))

# Seurat 4.1.1 (2022-05-01)

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9 changes: 7 additions & 2 deletions R/preprocessing.R
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Expand Up @@ -1015,10 +1015,15 @@ Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
Read10X_Image <- function(image.dir, image.name = "tissue_lowres_image.png", filter.matrix = TRUE, ...) {
image <- readPNG(source = file.path(image.dir, image.name))
scale.factors <- fromJSON(txt = file.path(image.dir, 'scalefactors_json.json'))
tissue.positions.path <- Sys.glob(paths = file.path(image.dir, 'tissue_positions*'))
tissue.positions <- read.csv(
file = file.path(image.dir, 'tissue_positions_list.csv'),
file = tissue.positions.path,
col.names = c('barcodes', 'tissue', 'row', 'col', 'imagerow', 'imagecol'),
header = FALSE,
header = ifelse(
test = basename(tissue.positions.path) == "tissue_positions.csv",
yes = TRUE,
no = FALSE
),
as.is = TRUE,
row.names = 1
)
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