Integration with dreamlet / SingleCellExperiment

Load 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$StimStatus <- sce$stim

Aggregate 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 -6.198336

Given the results here, we see that CD8 T cells at others change relative abundance following treatment with interferon-β.

Session Info

## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] scater_1.40.0               scuttle_1.22.0              ExperimentHub_3.2.0        
##  [4] AnnotationHub_4.2.0         BiocFileCache_3.2.0         dbplyr_2.5.2               
##  [7] muscat_1.26.0               dreamlet_1.8.0              SingleCellExperiment_1.34.0
## [10] SummarizedExperiment_1.42.0 Biobase_2.72.0              GenomicRanges_1.64.0       
## [13] Seqinfo_1.2.0               IRanges_2.46.0              S4Vectors_0.50.0           
## [16] BiocGenerics_0.58.0         generics_0.1.4              MatrixGenerics_1.24.0      
## [19] matrixStats_1.5.0           variancePartition_1.42.0    BiocParallel_1.46.0        
## [22] limma_3.68.0                lubridate_1.9.5             forcats_1.0.1              
## [25] stringr_1.6.0               dplyr_1.2.1                 purrr_1.2.2                
## [28] readr_2.2.0                 tidyr_1.3.2                 tibble_3.3.1               
## [31] tidyverse_2.0.0             glue_1.8.1                  HMP_2.0.1                  
## [34] dirmult_0.1.3-5             crumblr_1.4.0               ggplot2_4.0.3              
## [37] BiocStyle_2.40.0           
## 
## loaded via a namespace (and not attached):
##   [1] fs_2.1.0                  bitops_1.0-9              doParallel_1.0.17        
##   [4] httr_1.4.8                RColorBrewer_1.1-3        Rgraphviz_2.56.0         
##   [7] numDeriv_2016.8-1.1       tools_4.6.0               backports_1.5.1          
##  [10] R6_2.6.1                  vegan_2.7-3               metafor_5.0-1            
##  [13] lazyeval_0.2.3            mgcv_1.9-4                GetoptLong_1.1.1         
##  [16] permute_0.9-10            withr_3.0.2               prettyunits_1.2.0        
##  [19] gridExtra_2.3             cli_3.6.6                 sandwich_3.1-1           
##  [22] labeling_0.4.3            sass_0.4.10               KEGGgraph_1.72.0         
##  [25] SQUAREM_2026.1            mvtnorm_1.3-7             S7_0.2.2                 
##  [28] blme_1.0-7                mixsqp_0.3-54             systemfonts_1.3.2        
##  [31] yulab.utils_0.2.4         zenith_1.12.0             invgamma_1.2             
##  [34] RSQLite_2.4.6             shape_1.4.6.1             gridGraphics_0.5-1       
##  [37] gtools_3.9.5              Matrix_1.7-5              metadat_1.4-0            
##  [40] ggbeeswarm_0.7.3          abind_1.4-8               lifecycle_1.0.5          
##  [43] yaml_2.3.12               edgeR_4.10.0              mathjaxr_2.0-0           
##  [46] gplots_3.3.0              SparseArray_1.12.0        grid_4.6.0               
##  [49] blob_1.3.0                crayon_1.5.3              lattice_0.22-9           
##  [52] beachmat_2.28.0           msigdbr_26.1.0            annotate_1.90.0          
##  [55] KEGGREST_1.52.0           sys_3.4.3                 maketools_1.3.2          
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##  [97] rpart_4.1.27              colorspace_2.1-2          rmeta_3.0                
## [100] DBI_1.3.0                 tidyselect_1.2.1          curl_7.1.0               
## [103] bit_4.6.0                 compiler_4.6.0            httr2_1.2.2              
## [106] graph_1.90.0              BiocNeighbors_2.6.0       fontBitstreamVera_0.1.1  
## [109] DelayedArray_0.38.0       scales_1.4.0              caTools_1.18.3           
## [112] remaCor_0.0.20            rappdirs_0.3.4            digest_0.6.39            
## [115] minqa_1.2.8               rmarkdown_2.31            aod_1.3.3                
## [118] XVector_0.52.0            RhpcBLASctl_0.23-42       htmltools_0.5.9          
## [121] pkgconfig_2.0.3           lme4_2.0-1                sparseMatrixStats_1.24.0 
## [124] mashr_0.2.79              fastmap_1.2.0             GlobalOptions_0.1.4      
## [127] rlang_1.2.0               htmlwidgets_1.6.4         DelayedMatrixStats_1.34.0
## [130] farver_2.1.2              jquerylib_0.1.4           zoo_1.8-15               
## [133] jsonlite_2.0.0            BiocSingular_1.28.0       RCurl_1.98-1.18          
## [136] magrittr_2.0.5            ggplotify_0.1.3           patchwork_1.3.2          
## [139] Rcpp_1.1.1-1.1            ape_5.8-1                 babelgene_22.9           
## [142] viridis_0.6.5             gdtools_0.5.0             EnrichmentBrowser_2.42.0 
## [145] stringi_1.8.7             MASS_7.3-65               plyr_1.8.9               
## [148] ggrepel_0.9.8             Biostrings_2.80.0         splines_4.6.0            
## [151] circlize_0.4.18           hms_1.1.4                 locfit_1.5-9.12          
## [154] buildtools_1.0.0          ScaledMatrix_1.20.0       reshape2_1.4.5           
## [157] BiocVersion_3.23.1        XML_3.99-0.23             evaluate_1.0.5           
## [160] RcppParallel_5.1.11-2     rpart.plot_3.1.4          BiocManager_1.30.27      
## [163] nloptr_2.2.1              tzdb_0.5.0                foreach_1.5.2            
## [166] clue_0.3-68               scattermore_1.2           ashr_2.2-63              
## [169] rsvd_1.0.5                broom_1.0.12              xtable_1.8-8             
## [172] fANCOVA_0.6-1             tidytree_0.4.7            viridisLite_0.4.3        
## [175] truncnorm_1.0-9           glmmTMB_1.1.14            lmerTest_3.2-1           
## [178] aplot_0.2.9               memoise_2.0.1             beeswarm_0.4.0           
## [181] AnnotationDbi_1.74.0      cluster_2.1.8.2           timechange_0.4.0         
## [184] GSEABase_1.74.0