Here we perform analysis of PBMCs from 8 individuals stimulated with interferon-β Kang, et al, 2018, Nature Biotech. We perform standard processing with dreamlet to compute pseudobulk before applying crumblr.
Here, single cell RNA-seq data is downloaded from ExperimentHub.
library(dreamlet)
library(muscat)
library(ExperimentHub)
library(scater)
# Download data, specifying EH2259 for the Kang, et al study
eh <- ExperimentHub()
sce <- eh[["EH2259"]]
sce$ind <- as.character(sce$ind)
# only keep singlet cells with sufficient reads
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
sce <- sce[, colData(sce)$multiplets == "singlet"]
# compute QC metrics
qc <- perCellQCMetrics(sce)
# remove cells with few or many detected genes
ol <- isOutlier(metric = qc$detected, nmads = 2, log = TRUE)
sce <- sce[, !ol]
# set variable indicating stimulated (stim) or control (ctrl)
sce$StimStatus <- sce$stimDreamlet creates the pseudobulk dataset:
# Since 'ind' is the individual and 'StimStatus' is the stimulus status,
# create unique identifier for each sample
sce$id <- paste0(sce$StimStatus, sce$ind)
# Create pseudobulk data by specifying cluster_id and sample_id for aggregating cells
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)Here we evaluate whether the observed cell proportions change in response to interferon-β.
library(crumblr)
# use dreamlet::cellCounts() to extract data
cellCounts(pb)[1:3, 1:3]## B cells CD14+ Monocytes CD4 T cells
## ctrl101 101 136 288
## ctrl1015 424 644 819
## ctrl1016 119 315 413# Apply crumblr transformation
# cobj is an EList object compatable with limma workflow
# cobj$E stores transformed values
# cobj$weights stores precision weights
cobj <- crumblr(cellCounts(pb))Now continue on with the downstream analysis
library(variancePartition)
fit <- dream(cobj, ~ StimStatus + ind, colData(pb))
fit <- eBayes(fit)
topTable(fit, coef = "StimStatusstim", number = Inf)## logFC AveExpr t P.Value adj.P.Val B
## CD8 T cells -0.25085170 0.0857175 -4.0787416 0.002436375 0.01949100 -1.279815
## Dendritic cells 0.37386979 -2.1849234 3.1619195 0.010692544 0.02738587 -2.638507
## CD14+ Monocytes -0.10525402 1.2698117 -3.1226341 0.011413912 0.02738587 -2.709377
## B cells -0.10478652 0.5516882 -3.0134349 0.013692935 0.02738587 -2.940542
## CD4 T cells -0.07840101 2.0201947 -2.2318104 0.050869691 0.08139151 -4.128069
## FCGR3A+ Monocytes 0.07425165 -0.2567492 1.6647681 0.128337022 0.17111603 -4.935304
## NK cells 0.10270672 0.3797777 1.5181860 0.161321761 0.18436773 -5.247806
## Megakaryocytes 0.01377768 -1.8655172 0.1555131 0.879651456 0.87965146 -6.198336Given the results here, we see that CD8 T cells at others change relative abundance following treatment with interferon-β.
## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
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## attached base packages:
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## other attached packages:
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