1 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

1.1 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
)

1.2 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))

1.3 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.

1.4 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)

2 Session Info

## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB             
##  [4] LC_COLLATE=C               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: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] muscData_1.27.1             scater_1.41.2               scuttle_1.23.1             
##  [4] ExperimentHub_3.3.1         AnnotationHub_4.3.2         BiocFileCache_3.3.0        
##  [7] dbplyr_2.6.0                muscat_1.27.4               dreamlet_1.11.0            
## [10] SingleCellExperiment_1.35.1 SummarizedExperiment_1.43.0 Biobase_2.73.1             
## [13] GenomicRanges_1.65.1        Seqinfo_1.3.0               IRanges_2.47.2             
## [16] S4Vectors_0.51.5            BiocGenerics_0.59.10        generics_0.1.4             
## [19] MatrixGenerics_1.25.0       matrixStats_1.5.0           lubridate_1.9.5            
## [22] forcats_1.0.1               stringr_1.6.0               dplyr_1.2.1                
## [25] purrr_1.2.2                 readr_2.2.0                 tidyr_1.3.2                
## [28] tibble_3.3.1                tidyverse_2.0.0             glue_1.8.1                 
## [31] variancePartition_1.43.1    BiocParallel_1.47.0         limma_3.69.2               
## [34] crumblr_1.5.3               ggplot2_4.0.3               BiocStyle_2.41.0           
## 
## loaded via a namespace (and not attached):
##   [1] fs_2.1.0                  bitops_1.0-9              httr_1.4.8               
##   [4] RColorBrewer_1.1-3        doParallel_1.0.17         Rgraphviz_2.57.0         
##   [7] numDeriv_2016.8-1.1       tools_4.6.1               backports_1.5.1          
##  [10] R6_2.6.1                  metafor_5.0-1             mgcv_1.9-4               
##  [13] lazyeval_0.2.3            GetoptLong_1.1.1          withr_3.0.3              
##  [16] prettyunits_1.2.0         gridExtra_2.3.1           cli_3.6.6                
##  [19] sandwich_3.1-2            labeling_0.4.3            sass_0.4.10              
##  [22] KEGGgraph_1.73.0          SQUAREM_2026.1            mvtnorm_1.4-2            
##  [25] S7_0.2.2                  blme_1.0-7                mixsqp_0.3-54            
##  [28] systemfonts_1.3.2         yulab.utils_0.2.4         zenith_1.15.0            
##  [31] dichromat_2.0-0.1         invgamma_1.2              RSQLite_3.53.3           
##  [34] shape_1.4.6.1             gridGraphics_0.5-1        gtools_3.9.5             
##  [37] scrapper_1.7.3            Matrix_1.7-5              metadat_1.6-0            
##  [40] ggbeeswarm_0.7.3          abind_1.4-8               lifecycle_1.0.5          
##  [43] multcomp_1.4-31           yaml_2.3.12               edgeR_4.11.4             
##  [46] mathjaxr_2.0-0            gplots_3.3.0              SparseArray_1.13.2       
##  [49] grid_4.6.1                blob_1.3.0                crayon_1.5.3             
##  [52] lattice_0.22-9            beachmat_2.29.0           msigdbr_26.1.0           
##  [55] annotate_1.91.0           KEGGREST_1.53.5           magick_2.9.1             
##  [58] pillar_1.11.1             knitr_1.51                ComplexHeatmap_2.29.0    
##  [61] rjson_0.2.23              boot_1.3-32               estimability_2.0.0       
##  [64] corpcor_1.6.10            codetools_0.2-20          ggiraph_0.9.6            
##  [67] ggfun_0.2.1               fontLiberation_0.1.0      data.table_1.18.4        
##  [70] vctrs_0.7.3               png_0.1-9                 treeio_1.37.0            
##  [73] Rdpack_2.6.6              gtable_0.3.6              assertthat_0.2.1         
##  [76] cachem_1.1.0              zigg_0.0.2                xfun_0.60                
##  [79] rbibutils_2.4.1           S4Arrays_1.13.0           Rfast_2.1.5.2            
##  [82] coda_0.19-4.1             reformulas_0.4.4          survival_3.8-9           
##  [85] iterators_1.0.14          tinytex_0.60              statmod_1.5.2            
##  [88] TH.data_1.1-5             dirmult_0.1.3-5           nlme_3.1-169             
##  [91] pbkrtest_0.5.5            ggtree_4.3.0              bit64_4.8.2              
##  [94] fontquiver_0.2.1          filelock_1.0.3            progress_1.2.3           
##  [97] EnvStats_3.1.0            TMB_1.9.21                bslib_0.11.0             
## [100] irlba_2.3.7               vipor_0.4.7               KernSmooth_2.23-26       
## [103] otel_0.2.0                colorspace_2.1-3          rmeta_3.0                
## [106] DBI_1.3.0                 tidyselect_1.2.1          emmeans_2.0.3            
## [109] bit_4.6.0                 compiler_4.6.1            curl_7.1.0               
## [112] httr2_1.2.3               graph_1.91.0              BiocNeighbors_2.7.2      
## [115] fontBitstreamVera_0.1.1   DelayedArray_0.39.3       bookdown_0.47            
## [118] scales_1.4.0              caTools_1.18.3            remaCor_0.0.20           
## [121] rappdirs_0.3.4            digest_0.6.39             minqa_1.2.8              
## [124] rmarkdown_2.31            aod_1.3.3                 XVector_0.53.0           
## [127] RhpcBLASctl_0.23-42       htmltools_0.5.9           pkgconfig_2.0.3          
## [130] lme4_2.0-1                sparseMatrixStats_1.25.0  mashr_0.2.79             
## [133] fastmap_1.2.0             GlobalOptions_0.1.4       rlang_1.3.0              
## [136] htmlwidgets_1.6.4         DelayedMatrixStats_1.35.0 farver_2.1.2             
## [139] jquerylib_0.1.4           zoo_1.8-15                jsonlite_2.0.0           
## [142] BiocSingular_1.29.0       RCurl_1.98-1.19           magrittr_2.0.5           
## [145] ggplotify_0.1.3           patchwork_1.3.2           Rcpp_1.1.2               
## [148] ape_5.8-1                 babelgene_22.9            viridis_0.6.5            
## [151] gdtools_0.5.1             EnrichmentBrowser_2.43.0  stringi_1.8.7            
## [154] MASS_7.3-65               plyr_1.8.9                ggrepel_0.9.8            
## [157] Biostrings_2.81.5         splines_4.6.1             circlize_0.4.18          
## [160] hms_1.1.4                 locfit_1.5-9.12           ScaledMatrix_1.21.0      
## [163] reshape2_1.4.5            BiocVersion_3.24.0        XML_3.99-0.23            
## [166] evaluate_1.0.5            RcppParallel_5.1.11-2     BiocManager_1.30.27      
## [169] foreach_1.5.2             nloptr_2.2.1              tzdb_0.5.0               
## [172] clue_0.3-68               scattermore_1.2           ashr_2.2-63              
## [175] rsvd_1.0.5                broom_1.0.13              xtable_1.8-8             
## [178] fANCOVA_0.6-1             tidytree_0.4.8            viridisLite_0.4.3        
## [181] truncnorm_1.0-9           glmmTMB_1.1.14            lmerTest_3.2-1           
## [184] aplot_0.3.1               memoise_2.0.1             beeswarm_0.4.0           
## [187] AnnotationDbi_1.75.0      cluster_2.1.8.2           timechange_0.4.0         
## [190] GSEABase_1.75.0