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

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

Downloading the count data

We obtain a single-cell RNA sequencing dataset of the mouse brain from Chen et al. (2017). Counts for endogenous genes are available from the Gene Expression Omnibus using the accession number GSE87544. We download and cache them using the r Biocpkg("BiocFileCache") package.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
base.url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87544/suppl/"
count.file <- bfcrpath(bfc, paste0(base.url, "GSE87544_Merged_17samples_14437cells_count.txt.gz"))

Reading them in as a sparse matrix.

library(scuttle)
counts <- readSparseCounts(count.file)
dim(counts)

Downloading the metadata

We also download the cluster identities.

cluster.file <- bfcrpath(bfc, paste0(base.url, "GSE87544_1443737Cells.SVM.cluster.identity.renamed.csv.gz"))
coldata <- read.csv(cluster.file, stringsAsFactors=FALSE, row.names=1)
coldata <- as(coldata, "DataFrame")
coldata

We check that the columns are in the same order.

m <- match(colnames(counts), rownames(coldata))
coldata <- coldata[m,]
stopifnot(identical(colnames(counts), rownames(coldata)))

Looks like the first column isn't really necessary:

stopifnot(identical(colnames(counts), coldata[,1]))
coldata <- coldata[,-1,drop=FALSE]

We hack out some of the sample attributes from the cell names:

coldata$batch <- as.integer(sub("^B([0-9]+).*", "\\1", rownames(coldata)))
coldata$treatment <- sub(".*_([a-zA-Z]+)[^a-zA-Z]*$", "\\1", rownames(coldata))
table(coldata$batch, coldata$treatment, useNA="always")

Saving to file

We slap together a SingleCellExperiment object:

library(scRNAseq)
sce <- SingleCellExperiment(list(counts=counts), colData=coldata)
sce <- polishDataset(sce)
sce

Now, saving to disk:

meta <- list(
    title="Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity",
    description="The hypothalamus is one of the most complex brain structures involved in homeostatic regulation. Defining cell composition and identifying cell-type-specific transcriptional features of the hypothalamus is essential for understanding its functions and related disorders. Here, we report single-cell RNA sequencing results of adult mouse hypothalamus, which defines 11 non-neuronal and 34 neuronal cell clusters with distinct transcriptional signatures. Analyses of cell-type-specific transcriptomes reveal gene expression dynamics underlying oligodendrocyte differentiation and tanycyte subtypes. Additionally, data analysis provides a comprehensive view of neuropeptide expression across hypothalamic neuronal subtypes and uncover Crabp1+ and Pax6+ neuronal populations in specific hypothalamic sub-regions. Furthermore, we found food deprivation exhibited differential transcriptional effects among the different neuronal subtypes, suggesting functional specification of various neuronal subtypes. Thus, the work provides a comprehensive transcriptional perspective of adult hypothalamus, which serves as a valuable resource for dissecting cell-type-specific functions of this complex brain region.",
    taxonomy_id="10090",
    genome="GRCm38",
    sources=list(
        list(provider="GEO", id='GSE87544'),
        list(provider="PubMed", id='28355573')
    ),
    maintainer_name="Aaron Lun",
    maintainer_email="[e-mail]"
)

saveDataset(sce, "2023-12-14_output", meta)

Session information {-}

sessionInfo()

LTLA/scRNAseq documentation built on June 28, 2024, 7:31 p.m.