# 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 4.6.1 (2026-06-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 26.04 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-r0.3.32.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_1.40.2               scuttle_1.22.0              ExperimentHub_3.2.1
    ##  [4] AnnotationHub_4.2.2         BiocFileCache_3.2.0         dbplyr_2.6.0
    ##  [7] muscat_1.26.0               dreamlet_1.10.0             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_0.50.1
    ## [16] BiocGenerics_0.58.1         generics_0.1.4              MatrixGenerics_1.24.0
    ## [19] matrixStats_1.5.0           variancePartition_1.42.0    BiocParallel_1.46.0
    ## [22] limma_3.68.4                dirmult_0.1.3-5             lubridate_1.9.5
    ## [25] forcats_1.0.1               stringr_1.6.0               dplyr_1.2.1
    ## [28] purrr_1.2.2                 readr_2.2.0                 tidyr_1.3.2
    ## [31] tibble_3.3.1                tidyverse_2.0.0             glue_1.8.1
    ## [34] crumblr_1.4.5               ggplot2_4.0.3               BiocStyle_2.40.0
    ##
    ## 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_2.56.0
    ##   [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_1.72.0          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_1.14.0
    ##  [31] invgamma_1.2              RSQLite_3.53.3            shape_1.4.6.1
    ##  [34] gridGraphics_0.5-1        gtools_3.9.5              Matrix_1.7-5
    ##  [37] metadat_1.6-0             ggbeeswarm_0.7.3          abind_1.4-8
    ##  [40] lifecycle_1.0.5           yaml_2.3.12               edgeR_4.10.1
    ##  [43] mathjaxr_2.0-0            gplots_3.3.0              SparseArray_1.12.2
    ##  [46] grid_4.6.1                blob_1.3.0                crayon_1.5.3
    ##  [49] lattice_0.22-9            beachmat_2.28.0           msigdbr_26.1.0
    ##  [52] annotate_1.90.0           KEGGREST_1.52.2           sys_3.4.3
    ##  [55] maketools_1.3.2           pillar_1.11.1             knitr_1.51
    ##  [58] ComplexHeatmap_2.28.0     rjson_0.2.23              boot_1.3-32
    ##  [61] corpcor_1.6.10            codetools_0.2-20          ggiraph_0.9.6
    ##  [64] ggfun_0.2.1               fontLiberation_0.1.0      data.table_1.18.4
    ##  [67] vctrs_0.7.3               png_0.1-9                 treeio_1.36.1
    ##  [70] Rdpack_2.6.6              gtable_0.3.6              assertthat_0.2.1
    ##  [73] cachem_1.1.0              zigg_0.0.2                xfun_0.60
    ##  [76] rbibutils_2.4.1           S4Arrays_1.12.0           Rfast_2.1.5.2
    ##  [79] reformulas_0.4.4          iterators_1.0.14          statmod_1.5.2
    ##  [82] nlme_3.1-170              pbkrtest_0.5.5            ggtree_4.2.0
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    ##  [88] progress_1.2.3            EnvStats_3.1.0            TMB_1.9.21
    ##  [91] bslib_0.11.0              irlba_2.3.7               vipor_0.4.7
    ##  [94] KernSmooth_2.23-26        otel_0.2.0                colorspace_2.1-3
    ##  [97] rmeta_3.0                 DBI_1.3.0                 tidyselect_1.2.1
    ## [100] bit_4.6.0                 compiler_4.6.1            curl_7.1.0
    ## [103] httr2_1.3.0               graph_1.90.0              BiocNeighbors_2.6.0
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    ## [118] htmltools_0.5.9           pkgconfig_2.0.3           lme4_2.0-6
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    ## [124] GlobalOptions_0.1.4       rlang_1.3.0               htmlwidgets_1.6.4
    ## [127] DelayedMatrixStats_1.34.0 farver_2.1.2              jquerylib_0.1.4
    ## [130] zoo_1.8-15                jsonlite_2.0.0            BiocSingular_1.28.0
    ## [133] RCurl_1.98-1.19           magrittr_2.0.5            ggplotify_0.1.3
    ## [136] patchwork_1.3.2           Rcpp_1.1.2                ape_5.8-1
    ## [139] babelgene_22.9            viridis_0.6.5             gdtools_0.5.1
    ## [142] EnrichmentBrowser_2.42.0  stringi_1.8.7             MASS_7.3-66
    ## [145] plyr_1.8.9                ggrepel_0.9.8             Biostrings_2.80.1
    ## [148] splines_4.6.1             circlize_0.4.18           hms_1.1.4
    ## [151] locfit_1.5-9.12           buildtools_1.0.0          ScaledMatrix_1.20.0
    ## [154] reshape2_1.4.5            BiocVersion_3.23.1        XML_3.99-0.23
    ## [157] evaluate_1.0.5            RcppParallel_5.1.11-2     BiocManager_1.30.27
    ## [160] nloptr_2.2.1              tzdb_0.5.0                foreach_1.5.2
    ## [163] clue_0.3-68               scattermore_1.2           ashr_2.2-63
    ## [166] rsvd_1.0.5                broom_1.0.13              xtable_1.8-8
    ## [169] fANCOVA_0.6-1             tidytree_0.4.8            viridisLite_0.4.3
    ## [172] truncnorm_1.0-9           glmmTMB_1.1.14            lmerTest_3.2-1
    ## [175] aplot_0.3.1               memoise_2.0.1             beeswarm_0.4.0
    ## [178] AnnotationDbi_1.74.0      cluster_2.1.8.2           timechange_0.4.0
    ## [181] GSEABase_1.74.0