data | R Documentation |
A SingleCellExperiment
containing
10x droplet-based scRNA-seq PBCM data from 8 Lupus patients befor and after
6h-treatment with INF-beta (16 samples in total).
The original data has been filtered to
remove unassigned cells & cell multiplets
retain only 4 out of 8 samples per experimental group
retain only 5 out of 8 subpopulations (clusters)
retain genes with a count > 1 in > 50 cells
retain cells with > 200 detected genes
retain at most 100 cells per cluster-sample instance
Assay logcounts
corresponds to log-normalized values
obtained from logNormCounts
with default parameters.
The original measurement data, as well as gene and cell metadata is available through the NCBI GEO accession number GSE96583; code to reproduce this example dataset from the original data is provided in the examples section.
a SingleCellExperiment
.
Helena L Crowell
Kang et al. (2018). Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nature Biotechnology, 36(1): 89-94. DOI: 10.1038/nbt.4042.
# set random seed for cell sampling set.seed(2929) # load data library(ExperimentHub) eh <- ExperimentHub() sce <- eh[["EH2259"]] # drop unassigned cells & multiplets sce <- sce[, !is.na(sce$cell)] sce <- sce[, sce$multiplets == "singlet"] # keep 4 samples per group sce$id <- paste0(sce$stim, sce$ind) inds <- sample(sce$ind, 4) ids <- paste0(levels(sce$stim), rep(inds, each = 2)) sce <- sce[, sce$id %in% ids] # keep 5 clusters kids <- c("B cells", "CD4 T cells", "CD8 T cells", "CD14+ Monocytes", "FCGR3A+ Monocytes") sce <- sce[, sce$cell %in% kids] sce$cell <- droplevels(sce$cell) # basic filtering on genes & cells gs <- rowSums(counts(sce) > 1) > 50 cs <- colSums(counts(sce) > 0) > 200 sce <- sce[gs, cs] # sample max. 100 cells per cluster-sample cs_by_ks <- split(colnames(sce), list(sce$cell, sce$id)) cs <- sapply(cs_by_ks, function(u) sample(u, min(length(u), 100))) sce <- sce[, unlist(cs)] # compute logcounts library(scater) sce <- computeLibraryFactors(sce) sce <- logNormCounts(sce) # re-format for 'muscat' sce <- prepSCE(sce, kid = "cell", sid = "id", gid = "stim", drop = TRUE)
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