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

We obtain a single-cell RNA sequencing dataset of human radial glial cells from @pollen2015molecular. Counts for endogenous genes are available from Dropbox (why would you do that!?) via https://www.pollenlab.org/datasets.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
exprs.path <- bfcrpath(bfc, "https://www.dropbox.com/s/rjrkq96li4j4rvn/oRG%20paper%20-%20counts.txt?dl=1")

library(scuttle)
mat <- readSparseCounts(exprs.path, row.names=1)
dim(mat)
str(dimnames(mat))

We also read in the per-cell metadata from an Excel file (why!?).

meta.path <- bfcrpath(bfc, "https://www.dropbox.com/s/rb9tl4gjswrxfy9/Pollen%20et%20al%202015%20updated%20metadata.xlsx?dl=1")

library(readxl)
meta <- DataFrame(read_excel(meta.path), check.names=FALSE)
meta$`AlignmentRate, Pairs` <- as.numeric(sub("%$", "", meta$`AlignmentRate, Pairs`))/100
rownames(meta) <- meta$Cell
stopifnot(all(colnames(mat) %in% sort(meta$Cell)))

meta <- meta[colnames(mat),]
meta

Making sure we can assemble the final SCE:

sce <- SingleCellExperiment(list(counts=mat), colData=meta)
sce

Saving to file

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

path <- file.path("scRNAseq", "pollen-glia", "2.6.0")
dir.create(path, showWarnings=FALSE, recursive=TRUE)
saveRDS(mat, file=file.path(path, "counts.rds"))
saveRDS(meta, file=file.path(path, "coldata.rds"))