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

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

Download the data

We obtain a single-cell RNA sequencing dataset of the human PBMCs from @kotliarov2020citeseq. Counts for endogenous genes and antibody-derived tags (ADTs) are available from figshare.

Code used to analyze the data is available in the same link.

Of particular interest is the H1_day0_demultilexed_singlets.RDS file, which is a Seurat object that contains demultiplexed counts (filtered for singlets). Demultiplexing was performed with HTO counts and verified with demuxlet. Counts available have not been filtered by QC or normalized.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
contents <- bfcrpath(bfc,
    "https://nih.figshare.com/ndownloader/files/20706642")

seuratObj <- readRDS(contents)

Extract elements from Seurat object

First we extract the RNA counts:

library(S4Vectors)
rna.mat <- seuratObj@data
dim(rna.mat)

coldata <- DataFrame(seuratObj@meta.data)
nrow(coldata)
colnames(coldata)

Then the ADT counts:

adt.mat <- seuratObj@assay$CITE@raw.data
dim(adt.mat)

Save for upload

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

repath <- file.path("scRNAseq", "kotliarov-pbmc", "2.4.0")
dir.create(repath, showWarnings=FALSE, recursive=TRUE)
saveRDS(coldata, file=file.path(repath, "coldata.rds"))
saveRDS(rna.mat, file=file.path(repath, "counts-rna.rds"))
saveRDS(adt.mat, file=file.path(repath, "counts-adt.rds"))