In drisso/scRNAseq: Collection of Public Single-Cell RNA-Seq Datasets

library(BiocStyle)
knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)

Downloading the data (RNA-seq)

We obtain a single-cell RNA sequencing dataset of haematopoietic stem cells from @giladi2018singlecell. Counts for endogenous genes are available from the Gene Expression Omnibus using the accession number GSE92575. We download and cache them using the r Biocpkg("BiocFileCache") package.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
collected <- bfcrpath(bfc, "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE92575&format=file")

dir <- tempfile()
untar(collected, exdir=dir)
all.files <- list.files(dir, full=TRUE)
length(all.files)

Loading them all into memory.

library(scuttle)
library(BiocParallel)
out <- bplapply(all.files, readSparseCounts)

# Sanity check:
stopifnot(length(unique(lapply(out, rownames)))==1)

# Checking that the names are unique after combining.
all.cn <- unlist(lapply(out, colnames))
stopifnot(anyDuplicated(all.cn)==0)

combined <- do.call(cbind, out)

Constructing a SingleCellExperiment object.

sce <- SingleCellExperiment(list(counts=combined))
sce

Filling in the metadata as well.

meta.path <- bfcrpath(bfc, 
    "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE92575&format=file&file=GSE92575%5Fmetadata%2Etxt%2Egz")
meta <- read.delim(meta.path, skip=14)

# Expanding to cover all cells in the SCE.
true.samples <- sub(".*_", "", sub(".txt.gz", "", basename(all.files)))
true.samples <- rep(true.samples, vapply(out, ncol, 0L))
ref <- DataFrame(well=colnames(combined), sample=true.samples)
obs <- DataFrame(well=meta$well, sample=meta$Amp_batch_ID)
stopifnot(all(obs %in% ref))

m <- match(ref, obs)
meta <- meta[m,]
meta$retained <- !is.na(m)
meta$sample <- true.samples

stopifnot(all(meta$well == colnames(combined) | !meta$retained))
meta$well <- colnames(combined)
rownames(meta) <- meta$well

colData(sce) <- DataFrame(meta)
colData(sce)

Downloading the data (CRISPR-seq)

Counts for CRISPR barcodes are also available from the Gene Expression Omnibus using the accession number GSE113494. We download and cache them using the r Biocpkg("BiocFileCache") package.

crisp.path <- bfcrpath(bfc, 
    "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE113494&format=file&file=GSE113494%5Fcrispseq%5Fcount%2Etxt%2Egz")
crisp.df <- read.delim(crisp.path, skip=9)
head(crisp.df)

For some bizarre reason, this is not actually a count matrix, but rather, a molecule information file. So we convert this into a count matrix in the simplest way - namely, counting the number of unique UMIs for each cell in each barcode.

library(DropletUtils)
crisp.mat <- makeCountMatrix(crisp.df$grna, crisp.df$well)
dim(crisp.mat)

crisp.cd <- DataFrame(crisp.df[,c("well", "amplification.batch")])
crisp.cd <- crisp.cd[match(colnames(crisp.mat), crisp.cd$well),]
rownames(crisp.cd) <- crisp.cd$well
crisp.cd <- crisp.cd[,2,drop=FALSE]
head(crisp.cd)

crisp.rd <- DataFrame(crisp.df[,c("grna", "ugi")])
crisp.rd <- crisp.rd[match(rownames(crisp.mat), crisp.rd$grna),]
rownames(crisp.rd) <- crisp.rd$grna
crisp.rd <- crisp.rd[,2,drop=FALSE]
head(crisp.rd)

Saving to file

We now save all of the relevant components to file for upload to r Biocpkg("ExperimentHub").

path <- file.path("scRNAseq", "giladi-hsc", "2.6.0")
dir.create(path, showWarnings=FALSE, recursive=TRUE)
saveRDS(assay(sce), file=file.path(path, "counts-rna.rds"))
saveRDS(colData(sce), file=file.path(path, "coldata-rna.rds"))

saveRDS(crisp.mat, file=file.path(path, "counts-crispr.rds"))
saveRDS(crisp.cd, file=file.path(path, "coldata-crispr.rds"))
saveRDS(crisp.rd, file=file.path(path, "rowdata-crispr.rds"))