data: Example datasets

In HelenaLC/ddSingleCell: Multi-sample multi-group scRNA-seq data analysis tools

Example datasets

Description

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.

Value

a SingleCellExperiment.

Author(s)

Helena L Crowell

References

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.

Examples

# 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)
 

HelenaLC/ddSingleCell documentation built on Feb. 21, 2023, 4:31 p.m.