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

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

Overview

Here, we re-process datasets that were previously stored inside r Biocpkg("scRNAseq") itself, in preparation for relocating them into r Biocpkg("ExperimentHub"). This aims to reduce the size of the package and improve consistency with the other ExperimentHub-hosted datasets.

Downloading the data

We download and cache an old version of the r Biocpkg("scRNAseq") package using the r Biocpkg("BiocFileCache") package. We choose the latest version that still contains the serialized SingleCellExperiment objects.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
tarpath <- bfcrpath(bfc, file.path("http://bioconductor.org/packages",
    "3.9/data/experiment/src/contrib/scRNAseq_1.10.0.tar.gz"))

We unpack this in preparation for extraction of the serialized objects.

tmploc <- tempfile()
dir.create(tmploc, showWarnings=FALSE)
untar(tarpath, exdir=tmploc)
path <- file.path(tmploc, "scRNAseq", "data")
list.files(path)

Extracting objects

We set up a function to extract the SingleCellExperiment object from each Rdata file.

library(SingleCellExperiment)
extractor <- function(fname) {
    env <- new.env()
    load(fname, envir=env)
    env[[ls(env)[1]]]
}

Using this function, we read in the counts for the endogenous genes, ERCC spike-in transcripts and mitochondrial genes.

allen <- extractor(file.path(path, "allen.rda"))
allen
th2 <- extractor(file.path(path, "th2.rda"))
th2
fluidigm <- extractor(file.path(path, "fluidigm.rda"))
fluidigm

Saving objects

We set up another function that peels apart the SingleCellExperiment object and saves its components. Components are used to reconstruct a SingleCellExperiment object on the client side, which provides some robustness against changes to the class structure.

# No rowData for any of these objects.
saver <- function(sce, target) {
    dir.create(target, recursive=TRUE, showWarnings=FALSE)
    saveRDS(colData(sce), file=file.path(target, "coldata.rds"))
    saveRDS(metadata(sce), file=file.path(target, "metadata.rds"))
    for (x in assayNames(sce)) {
        saveRDS(assay(sce, x), file=file.path(target, paste0(x, ".rds")))
    }
}

We then apply this function to all of the objects.

saver(allen, file.path("scRNAseq", "legacy-allen", "1.10.0"))
saver(th2, file.path("scRNAseq", "legacy-th2", "1.10.0"))
saver(fluidigm, file.path("scRNAseq", "legacy-fluidigm", "1.10.0"))