Modeling continuous cell-level covariates
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).
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:
Pseudobulk
Now compute the pseudobulk using standard code:
sce$id <- paste0(sce$StimStatus, sce$ind)
# Create pseudobulk
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)The means per variable, cell type, and sample are stored in the
pseudobulk SingleCellExperiment object:
## # A tibble: 128 × 5
## # Groups: cell [8]
## cell id cluster value1 value2
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 B cells ctrl101 3.96 -0.00527 -0.0582
## 2 B cells ctrl1015 4.00 -0.0254 0.00754
## 3 B cells ctrl1016 4 -0.0549 -0.0574
## 4 B cells ctrl1039 4.04 0.0666 -0.252
## 5 B cells ctrl107 4 0.0262 0.0339
## 6 B cells ctrl1244 4 0.263 -0.0129
## 7 B cells ctrl1256 4.01 -0.00363 0.163
## 8 B cells ctrl1488 4.02 -0.00540 0.0221
## 9 B cells stim101 4.09 0.0205 0.0174
## 10 B cells stim1015 4.06 0.147 0.00369
## # ℹ 118 more rowsAnalysis
Including these variables in a regression formula uses the summarized
values from the corresponding cell type. This happens behind the scenes,
so the user doesn’t need to distinguish bewteen sample-level variables
stored in colData(pb) and cell-level variables stored in
metadata(pb)$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 value2Session Info
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] muscData_1.20.0 scater_1.35.1
## [3] scuttle_1.17.0 ExperimentHub_2.15.0
## [5] AnnotationHub_3.15.0 BiocFileCache_2.15.1
## [7] dbplyr_2.5.0 muscat_1.21.0
## [9] dreamlet_1.5.0 SingleCellExperiment_1.29.1
## [11] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [13] GenomicRanges_1.59.1 GenomeInfoDb_1.43.4
## [15] IRanges_2.41.3 S4Vectors_0.45.4
## [17] BiocGenerics_0.53.6 generics_0.1.3
## [19] MatrixGenerics_1.19.1 matrixStats_1.5.0
## [21] variancePartition_1.37.2 BiocParallel_1.41.1
## [23] limma_3.63.3 ggplot2_3.5.1
## [25] BiocStyle_2.35.0
##
## 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_2.51.0 numDeriv_2016.8-1.1
## [7] tools_4.4.2 sctransform_0.4.1
## [9] backports_1.5.0 utf8_1.2.4
## [11] R6_2.6.1 metafor_4.8-0
## [13] mgcv_1.9-1 GetoptLong_1.0.5
## [15] withr_3.0.2 prettyunits_1.2.0
## [17] gridExtra_2.3 cli_3.6.4
## [19] labeling_0.4.3 sass_0.4.9
## [21] KEGGgraph_1.67.0 SQUAREM_2021.1
## [23] mvtnorm_1.3-3 blme_1.0-6
## [25] mixsqp_0.3-54 zenith_1.9.0
## [27] parallelly_1.42.0 invgamma_1.1
## [29] RSQLite_2.3.9 shape_1.4.6.1
## [31] gtools_3.9.5 dplyr_1.1.4
## [33] Matrix_1.7-2 metadat_1.4-0
## [35] ggbeeswarm_0.7.2 abind_1.4-8
## [37] lifecycle_1.0.4 yaml_2.3.10
## [39] edgeR_4.5.2 mathjaxr_1.6-0
## [41] gplots_3.2.0 SparseArray_1.7.5
## [43] grid_4.4.2 blob_1.2.4
## [45] crayon_1.5.3 lattice_0.22-6
## [47] beachmat_2.23.6 msigdbr_7.5.1
## [49] annotate_1.85.0 KEGGREST_1.47.0
## [51] sys_3.4.3 maketools_1.3.2
## [53] pillar_1.10.1 knitr_1.49
## [55] ComplexHeatmap_2.23.0 rjson_0.2.23
## [57] boot_1.3-31 corpcor_1.6.10
## [59] future.apply_1.11.3 codetools_0.2-20
## [61] glue_1.8.0 data.table_1.16.4
## [63] vctrs_0.6.5 png_0.1-8
## [65] Rdpack_2.6.2 gtable_0.3.6
## [67] assertthat_0.2.1 cachem_1.1.0
## [69] xfun_0.50 mime_0.12
## [71] rbibutils_2.3 S4Arrays_1.7.3
## [73] Rfast_2.1.4 reformulas_0.4.0
## [75] iterators_1.0.14 statmod_1.5.0
## [77] nlme_3.1-167 pbkrtest_0.5.3
## [79] bit64_4.6.0-1 filelock_1.0.3
## [81] progress_1.2.3 EnvStats_3.0.0
## [83] bslib_0.9.0 TMB_1.9.16
## [85] irlba_2.3.5.1 vipor_0.4.7
## [87] KernSmooth_2.23-26 colorspace_2.1-1
## [89] rmeta_3.0 DBI_1.2.3
## [91] DESeq2_1.47.3 tidyselect_1.2.1
## [93] curl_6.2.0 bit_4.5.0.1
## [95] compiler_4.4.2 graph_1.85.1
## [97] BiocNeighbors_2.1.2 DelayedArray_0.33.6
## [99] scales_1.3.0 caTools_1.18.3
## [101] remaCor_0.0.18 rappdirs_0.3.3
## [103] stringr_1.5.1 digest_0.6.37
## [105] minqa_1.2.8 rmarkdown_2.29
## [107] aod_1.3.3 XVector_0.47.2
## [109] RhpcBLASctl_0.23-42 htmltools_0.5.8.1
## [111] pkgconfig_2.0.3 lme4_1.1-36
## [113] sparseMatrixStats_1.19.0 mashr_0.2.79
## [115] fastmap_1.2.0 rlang_1.1.5
## [117] GlobalOptions_0.1.2 UCSC.utils_1.3.1
## [119] DelayedMatrixStats_1.29.1 farver_2.1.2
## [121] jquerylib_0.1.4 jsonlite_1.8.9
## [123] BiocSingular_1.23.0 RCurl_1.98-1.16
## [125] magrittr_2.0.3 GenomeInfoDbData_1.2.13
## [127] munsell_0.5.1 Rcpp_1.0.14
## [129] babelgene_22.9 viridis_0.6.5
## [131] EnrichmentBrowser_2.37.0 RcppZiggurat_0.1.6
## [133] stringi_1.8.4 MASS_7.3-64
## [135] plyr_1.8.9 parallel_4.4.2
## [137] listenv_0.9.1 ggrepel_0.9.6
## [139] Biostrings_2.75.3 splines_4.4.2
## [141] hms_1.1.3 circlize_0.4.16
## [143] locfit_1.5-9.11 buildtools_1.0.0
## [145] reshape2_1.4.4 ScaledMatrix_1.15.0
## [147] BiocVersion_3.21.1 XML_3.99-0.18
## [149] evaluate_1.0.3 RcppParallel_5.1.10
## [151] BiocManager_1.30.25 nloptr_2.1.1
## [153] foreach_1.5.2 tidyr_1.3.1
## [155] purrr_1.0.4 future_1.34.0
## [157] clue_0.3-66 scattermore_1.2
## [159] ashr_2.2-63 rsvd_1.0.5
## [161] broom_1.0.7 xtable_1.8-4
## [163] fANCOVA_0.6-1 viridisLite_0.4.2
## [165] truncnorm_1.0-9 tibble_3.2.1
## [167] lmerTest_3.1-3 glmmTMB_1.1.10
## [169] memoise_2.0.1 beeswarm_0.4.0
## [171] AnnotationDbi_1.69.0 cluster_2.1.8
## [173] globals_0.16.3 GSEABase_1.69.1