Author: Developed by Gabriel Hoffman

# 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
    ##  [58] ComplexHeatmap_2.28.0     pillar_1.11.1             knitr_1.51
    ##  [61] rjson_0.2.23              boot_1.3-32               corpcor_1.6.10
    ##  [64] codetools_0.2-20          ggiraph_0.9.6             ggfun_0.2.0
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    ##  [76] zigg_0.0.2                xfun_0.57                 rbibutils_2.4.1
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    ##  [82] iterators_1.0.14          statmod_1.5.1             nlme_3.1-169
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    ##  [88] fontquiver_0.2.1          filelock_1.0.3            progress_1.2.3
    ##  [91] EnvStats_3.1.0            TMB_1.9.21                bslib_0.10.0
    ##  [94] irlba_2.3.7               vipor_0.4.7               KernSmooth_2.23-26
    ##  [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
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    ## [121] pkgconfig_2.0.3           lme4_2.0-1                sparseMatrixStats_1.24.0
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    ## [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
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    ## [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