Modeling continuous cell-level covariates
Collapse using mean value for pseudobulk data
Run on 2025-04-27 17:01:19
1 Introduction
Since read counts are summed across cells in a pseudobulk approach, modeling continuous cell-level covariates also requires a collapsing step. Here we summarize the values of a variable from a set of cells using the mean, and store the value for each cell type. Including these variables in a regression formula uses the summarized values from the corresponding cell type.
We demonstrate this feature on a lightly modified analysis of PBMCs from 8 individuals stimulated with interferon-β (Kang, et al, 2018, Nature Biotech).
2 Standard processing
Here is the code from the main vignette:
library(dreamlet)
library(muscat)
library(ExperimentHub)
library(scater)
# Download data, specifying EH2259 for the Kang, et al study
eh <- ExperimentHub()
sce <- eh[["EH2259"]]
# 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$StimStatus <- sce$stimIn many datasets, continuous cell-level variables could be mapped reads, gene count, mitochondrial rate, etc. There are no continuous cell-level variables in this dataset, so we can simulate two from a normal distribution:
sce$value1 <- rnorm(ncol(sce))
sce$value2 <- rnorm(ncol(sce))3 Pseudobulk
Now compute the pseudobulk using standard code:
sce$id <- paste0(sce$StimStatus, sce$ind)
# Create pseudobulk
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)The means per variable, cell type, and sample are stored in the pseudobulk SingleCellExperiment object:
metadata(pb)$aggr_means## # A tibble: 128 × 5
## # Groups: cell [8]
## cell id cluster value1 value2
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 B cells ctrl101 3.96 0.176 -0.0463
## 2 B cells ctrl1015 4.00 -0.0144 -0.0274
## 3 B cells ctrl1016 4 0.00912 0.200
## 4 B cells ctrl1039 4.04 0.141 0.185
## 5 B cells ctrl107 4 0.0927 -0.0197
## 6 B cells ctrl1244 4 0.00735 0.106
## 7 B cells ctrl1256 4.01 0.116 -0.0602
## 8 B cells ctrl1488 4.02 -0.0521 -0.0856
## 9 B cells stim101 4.09 0.0595 0.156
## 10 B cells stim1015 4.06 -0.0205 0.0973
## # ℹ 118 more rows4 Analysis
Including these variables in a regression formula uses the summarized values from the corresponding cell type. This happens behind the scenes, so the user doesn’t need to distinguish bewteen sample-level variables stored in colData(pb) and cell-level variables stored in metadata(pb)$aggr_means.
Variance partition and hypothesis testing proceeds as ususal:
form <- ~ StimStatus + value1 + value2
# Normalize and apply voom/voomWithDreamWeights
res.proc <- processAssays(pb, form, min.count = 5)
# run variance partitioning analysis
vp.lst <- fitVarPart(res.proc, form)
# Summarize variance fractions genome-wide for each cell type
plotVarPart(vp.lst, label.angle = 60)
# Differential expression analysis within each assay
res.dl <- dreamlet(res.proc, form)
# dreamlet results include coefficients for value1 and value2
res.dl## class: dreamletResult
## assays(8): B cells CD14+ Monocytes ... Megakaryocytes NK cells
## Genes:
## min: 164
## max: 5262
## details(7): assay n_retain ... n_errors error_initial
## coefNames(4): (Intercept) StimStatusstim value1 value25 Details
A variable in colData(sce) is handled according to if the variable is
- continuous: the mean per donor/cell type is stored in
metadata(pb)$aggr_means - discrete
- [constant within each donor/cell type] it is stored in
colData(pb) - [varies within each donor/cell type] there is no good way to summarize it. The variable is dropped.
- [constant within each donor/cell type] it is stored in
6 Session Info
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-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
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] muscData_1.23.0 scater_1.37.0
## [3] scuttle_1.19.0 ExperimentHub_2.99.0
## [5] AnnotationHub_3.99.0 BiocFileCache_2.99.0
## [7] dbplyr_2.5.0 muscat_1.23.0
## [9] dreamlet_1.7.1 SingleCellExperiment_1.31.0
## [11] SummarizedExperiment_1.39.0 Biobase_2.69.0
## [13] GenomicRanges_1.61.0 GenomeInfoDb_1.45.0
## [15] IRanges_2.43.0 S4Vectors_0.47.0
## [17] BiocGenerics_0.55.0 generics_0.1.3
## [19] MatrixGenerics_1.21.0 matrixStats_1.5.0
## [21] variancePartition_1.39.0 BiocParallel_1.43.0
## [23] limma_3.65.0 ggplot2_3.5.2
## [25] BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 httr_1.4.7
## [3] RColorBrewer_1.1-3 doParallel_1.0.17
## [5] Rgraphviz_2.53.0 numDeriv_2016.8-1.1
## [7] tools_4.5.0 sctransform_0.4.1
## [9] backports_1.5.0 utf8_1.2.4
## [11] R6_2.6.1 metafor_4.8-0
## [13] mgcv_1.9-3 GetoptLong_1.0.5
## [15] withr_3.0.2 prettyunits_1.2.0
## [17] gridExtra_2.3 cli_3.6.5
## [19] sandwich_3.1-1 labeling_0.4.3
## [21] sass_0.4.10 KEGGgraph_1.69.0
## [23] SQUAREM_2021.1 mvtnorm_1.3-3
## [25] blme_1.0-6 mixsqp_0.3-54
## [27] zenith_1.11.0 dichromat_2.0-0.1
## [29] parallelly_1.43.0 invgamma_1.1
## [31] RSQLite_2.3.9 shape_1.4.6.1
## [33] gtools_3.9.5 dplyr_1.1.4
## [35] Matrix_1.7-3 metadat_1.4-0
## [37] ggbeeswarm_0.7.2 abind_1.4-8
## [39] lifecycle_1.0.4 multcomp_1.4-28
## [41] yaml_2.3.10 edgeR_4.7.0
## [43] mathjaxr_1.6-0 gplots_3.2.0
## [45] SparseArray_1.9.0 grid_4.5.0
## [47] blob_1.2.4 crayon_1.5.3
## [49] lattice_0.22-7 beachmat_2.25.0
## [51] msigdbr_10.0.2 annotate_1.87.0
## [53] KEGGREST_1.49.0 magick_2.8.6
## [55] pillar_1.10.2 knitr_1.50
## [57] ComplexHeatmap_2.25.0 rjson_0.2.23
## [59] boot_1.3-31 estimability_1.5.1
## [61] corpcor_1.6.10 future.apply_1.11.3
## [63] codetools_0.2-20 glue_1.8.0
## [65] data.table_1.17.0 vctrs_0.6.5
## [67] png_0.1-8 Rdpack_2.6.4
## [69] gtable_0.3.6 assertthat_0.2.1
## [71] cachem_1.1.0 zigg_0.0.2
## [73] xfun_0.52 rbibutils_2.3
## [75] S4Arrays_1.9.0 Rfast_2.1.5.1
## [77] coda_0.19-4.1 reformulas_0.4.0
## [79] survival_3.8-3 iterators_1.0.14
## [81] tinytex_0.57 statmod_1.5.0
## [83] TH.data_1.1-3 nlme_3.1-168
## [85] pbkrtest_0.5.3 bit64_4.6.0-1
## [87] filelock_1.0.3 progress_1.2.3
## [89] EnvStats_3.1.0 bslib_0.9.0
## [91] TMB_1.9.17 irlba_2.3.5.1
## [93] vipor_0.4.7 KernSmooth_2.23-26
## [95] colorspace_2.1-1 rmeta_3.0
## [97] DBI_1.2.3 DESeq2_1.49.0
## [99] tidyselect_1.2.1 emmeans_1.11.0
## [101] curl_6.2.2 bit_4.6.0
## [103] compiler_4.5.0 httr2_1.1.2
## [105] graph_1.87.0 BiocNeighbors_2.3.0
## [107] DelayedArray_0.35.1 bookdown_0.43
## [109] scales_1.4.0 caTools_1.18.3
## [111] remaCor_0.0.18 rappdirs_0.3.3
## [113] stringr_1.5.1 digest_0.6.37
## [115] minqa_1.2.8 rmarkdown_2.29
## [117] aod_1.3.3 XVector_0.49.0
## [119] RhpcBLASctl_0.23-42 htmltools_0.5.8.1
## [121] pkgconfig_2.0.3 lme4_1.1-37
## [123] sparseMatrixStats_1.21.0 mashr_0.2.79
## [125] fastmap_1.2.0 rlang_1.1.6
## [127] GlobalOptions_0.1.2 UCSC.utils_1.5.0
## [129] DelayedMatrixStats_1.31.0 farver_2.1.2
## [131] jquerylib_0.1.4 zoo_1.8-14
## [133] jsonlite_2.0.0 BiocSingular_1.25.0
## [135] RCurl_1.98-1.17 magrittr_2.0.3
## [137] GenomeInfoDbData_1.2.14 Rcpp_1.0.14
## [139] babelgene_22.9 viridis_0.6.5
## [141] EnrichmentBrowser_2.39.0 stringi_1.8.7
## [143] MASS_7.3-65 plyr_1.8.9
## [145] listenv_0.9.1 parallel_4.5.0
## [147] ggrepel_0.9.6 Biostrings_2.77.0
## [149] splines_4.5.0 hms_1.1.3
## [151] circlize_0.4.16 locfit_1.5-9.12
## [153] reshape2_1.4.4 ScaledMatrix_1.17.0
## [155] BiocVersion_3.22.0 XML_3.99-0.18
## [157] evaluate_1.0.3 RcppParallel_5.1.10
## [159] BiocManager_1.30.25 nloptr_2.2.1
## [161] foreach_1.5.2 tidyr_1.3.1
## [163] purrr_1.0.4 future_1.40.0
## [165] clue_0.3-66 scattermore_1.2
## [167] ashr_2.2-63 rsvd_1.0.5
## [169] broom_1.0.8 xtable_1.8-4
## [171] fANCOVA_0.6-1 viridisLite_0.4.2
## [173] truncnorm_1.0-9 tibble_3.2.1
## [175] lmerTest_3.1-3 glmmTMB_1.1.11
## [177] memoise_2.0.1 beeswarm_0.4.0
## [179] AnnotationDbi_1.71.0 cluster_2.1.8.1
## [181] globals_0.17.0 GSEABase_1.71.0