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 Xenopus tail cells from Aztekin et al. (2019). We download and cache the count matrix and metadata using the r Biocpkg("BiocFileCache") package.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
url <- "https://www.ebi.ac.uk/biostudies/files/E-MTAB-7716/arrayExpressUpload.zip"
contents <- bfcrpath(bfc, url)

cache <- tempfile()
unzip(contents, exdir=cache)
unzip(file.path(cache, "ArrayExpressV2.zip"), exdir=cache)

Processing the read counts

We load the counts into memory.

library(Matrix)
path <- file.path(cache, "ArrayExpress")
counts <- as(readMM(file.path(path, "countsMatrix.mtx")), "dgCMatrix")

gene.info <- read.csv(file.path(path, "genes.csv"), stringsAsFactors=FALSE, header=FALSE)
head(gene.info)
rownames(counts) <- gene.info[,1]

cell.info <- read.csv(file.path(path, "cells.csv"), stringsAsFactors=FALSE, header=FALSE)
head(cell.info)
colnames(counts) <- cell.info[,1]

dim(counts)

Processing the metadata

Processing the per-cell metadata, and merging in the per-sample metadata along with it.

library(S4Vectors)
meta <- read.csv(file.path(path, "meta.csv"), stringsAsFactors=FALSE)
labels <- read.csv(file.path(path, "labels.csv"), stringsAsFactors=FALSE)
meta <- cbind(meta, labels[match(meta$sample, labels$Sample),-1])
meta <- DataFrame(meta)
rownames(meta) <- NULL
meta

Now enforcing consistency checks with the column names of counts. We remove cell afterwards to save some space, given that it's redundant with the column names.

stopifnot(identical(colnames(counts), meta$cell))
meta$cell <- NULL

Saving for upload

Let's put together a SingleCellExperiment object:

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

# Adding reduced dimensions.
reducedDim(sce, "UMAP") <- as.matrix(colData(sce)[,c("X", "Y")])
colData(sce) <- colData(sce)[,setdiff(colnames(colData(sce)), c("X", "Y"))]

# Performing some polishing.
library(scRNAseq)
sce <- polishDataset(sce)
sce

We save this to disk in preparation for upload.

meta <- list(
    title="Identification of a regeneration-organizing cell in the Xenopus tail",
    description="Unlike mammals, Xenopus laevis tadpoles have a high regenerative potential. To characterize this regenerative response, we performed single-cell RNA sequencing after tail amputation. By comparing naturally occurring regeneration-competent and -incompetent tadpoles, we identified a previously unrecognized cell type, which we term the regeneration-organizing cell (ROC). ROCs are present in the epidermis during normal tail development and specifically relocalize to the amputation plane of regeneration-competent tadpoles, forming the wound epidermis. Genetic ablation or manual removal of ROCs blocks regeneration, whereas transplantation of ROC-containing grafts induces ectopic outgrowths in early embryos. Transcriptional profiling revealed that ROCs secrete ligands associated with key regenerative pathways, signaling to progenitors to reconstitute lost tissue. These findings reveal the cellular mechanism through which ROCs form the wound epidermis and ensure successful regeneration.",
    sources=list(
        list(provider="ArrayExpress", id="E-MTAB-7716"),
        list(provider="PubMed", id="31097661")
    ),
    taxonomy_id="8355",
    genome="Xenla9.1",
    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.