Integration with dreamlet / SingleCellExperiment
Developed by Gabriel Hoffman
Run on 2025-08-11 14:19:51.385562
Source:vignettes/integration.Rmd
integration.RmdLoad and process single cell data
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$StimStatus <- sce$stimAggregate to pseudobulk
Dreamlet 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$ind)
# Create pseudobulk data by specifying cluster_id and sample_id for aggregating cells
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)Process data
Here we evaluate whether the observed cell proportions change in response to interferon-β.
library(crumblr)
# use dreamlet::cellCounts() to extract data
cellCounts(pb)[1:3, 1:3]## B cells CD14+ Monocytes CD4 T cells
## ctrl101 101 136 288
## ctrl1015 424 644 819
## ctrl1016 119 315 413
# Apply crumblr transformation
# cobj is an EList object compatable with limma workflow
# cobj$E stores transformed values
# cobj$weights stores precision weights
cobj <- crumblr(cellCounts(pb))Analysis
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.
Multivariate testing along a tree
ere we construct a hierarchical clustering between cell types based
on gene expression from pseudobulk, and perform a multivariate test for
each internal node of the tree based on its leaf nodes. The results for
the leaves are the same as from topTable() above.
# hierarchical cluster based on pseudobulked gene expression
hcl <- buildClusterTreeFromPB(pb)
# Perform multivariate test across the hierarchy
res <- treeTest(fit, cobj, hcl, coef = "StimStatusstim")
# Plot hierarchy and testing results
plotTreeTest(res)
Session Info
## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin23.6.0
## Running under: macOS Sonoma 14.7.1
##
## Matrix products: default
## BLAS/LAPACK: /opt/homebrew/Cellar/openblas/0.3.30/lib/libopenblasp-r0.3.30.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crumblr_0.99.22 muscData_1.22.0 scater_1.36.0
## [4] scuttle_1.18.0 ExperimentHub_2.16.1 AnnotationHub_3.16.1
## [7] BiocFileCache_2.16.1 dbplyr_2.5.0 muscat_1.22.0
## [10] dreamlet_1.6.0 SingleCellExperiment_1.30.1 SummarizedExperiment_1.38.1
## [13] Biobase_2.68.0 GenomicRanges_1.60.0 GenomeInfoDb_1.44.1
## [16] IRanges_2.42.0 S4Vectors_0.46.0 BiocGenerics_0.54.0
## [19] generics_0.1.4 MatrixGenerics_1.20.0 matrixStats_1.5.0
## [22] variancePartition_1.37.4 BiocParallel_1.42.1 limma_3.64.3
## [25] ggplot2_3.5.2 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.6 bitops_1.0-9 httr_1.4.7
## [4] RColorBrewer_1.1-3 doParallel_1.0.17 Rgraphviz_2.52.0
## [7] numDeriv_2016.8-1.1 tools_4.5.1 sctransform_0.4.2
## [10] backports_1.5.0 R6_2.6.1 metafor_4.8-0
## [13] lazyeval_0.2.2 mgcv_1.9-3 GetoptLong_1.0.5
## [16] withr_3.0.2 prettyunits_1.2.0 gridExtra_2.3
## [19] cli_3.6.5 textshaping_1.0.1 labeling_0.4.3
## [22] sass_0.4.10 KEGGgraph_1.68.0 SQUAREM_2021.1
## [25] mvtnorm_1.3-3 blme_1.0-6 pkgdown_2.1.3
## [28] mixsqp_0.3-54 yulab.utils_0.2.0 systemfonts_1.2.3
## [31] zenith_1.10.0 parallelly_1.45.1 invgamma_1.2
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## [61] boot_1.3-31 corpcor_1.6.10 future.apply_1.20.0
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