Modeling continuous cell-level covariates
Collapse using mean value for pseudobulk data
Run on 2024-05-01 17:08:46
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 -0.0471 -0.0129
## 2 B cells ctrl1015 4.00 -0.0674 -0.0359
## 3 B cells ctrl1016 4 0.0282 0.0214
## 4 B cells ctrl1039 4.04 0.162 -0.0638
## 5 B cells ctrl107 4 0.0815 0.0425
## 6 B cells ctrl1244 4 0.0337 -0.0375
## 7 B cells ctrl1256 4.01 0.0543 0.0585
## 8 B cells ctrl1488 4.02 0.0372 0.0731
## 9 B cells stim101 4.09 0.140 0.00817
## 10 B cells stim1015 4.06 -0.0179 -0.0579
## # 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 Session Info
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_1.17.0 scater_1.33.0
## [3] scuttle_1.15.0 ExperimentHub_2.13.0
## [5] AnnotationHub_3.13.0 BiocFileCache_2.13.0
## [7] dbplyr_2.5.0 muscat_1.19.0
## [9] dreamlet_1.3.0 SingleCellExperiment_1.27.0
## [11] SummarizedExperiment_1.35.0 Biobase_2.65.0
## [13] GenomicRanges_1.57.0 GenomeInfoDb_1.41.0
## [15] IRanges_2.39.0 S4Vectors_0.43.0
## [17] BiocGenerics_0.51.0 MatrixGenerics_1.17.0
## [19] matrixStats_1.3.0 variancePartition_1.35.0
## [21] BiocParallel_1.39.0 limma_3.61.0
## [23] ggplot2_3.5.1 BiocStyle_2.33.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 httr_1.4.7
## [3] RColorBrewer_1.1-3 doParallel_1.0.17
## [5] Rgraphviz_2.49.0 numDeriv_2016.8-1.1
## [7] tools_4.4.0 sctransform_0.4.1
## [9] backports_1.4.1 utf8_1.2.4
## [11] R6_2.5.1 metafor_4.6-0
## [13] mgcv_1.9-1 GetoptLong_1.0.5
## [15] withr_3.0.0 prettyunits_1.2.0
## [17] gridExtra_2.3 cli_3.6.2
## [19] sandwich_3.1-0 labeling_0.4.3
## [21] sass_0.4.9 KEGGgraph_1.65.0
## [23] SQUAREM_2021.1 mvtnorm_1.2-4
## [25] blme_1.0-5 mixsqp_0.3-54
## [27] zenith_1.7.0 parallelly_1.37.1
## [29] invgamma_1.1 RSQLite_2.3.6
## [31] generics_0.1.3 shape_1.4.6.1
## [33] gtools_3.9.5 dplyr_1.1.4
## [35] Matrix_1.7-0 metadat_1.2-0
## [37] ggbeeswarm_0.7.2 fansi_1.0.6
## [39] abind_1.4-5 lifecycle_1.0.4
## [41] multcomp_1.4-25 yaml_2.3.8
## [43] edgeR_4.3.0 mathjaxr_1.6-0
## [45] gplots_3.1.3.1 SparseArray_1.5.0
## [47] grid_4.4.0 blob_1.2.4
## [49] crayon_1.5.2 lattice_0.22-6
## [51] beachmat_2.21.0 msigdbr_7.5.1
## [53] annotate_1.83.0 KEGGREST_1.45.0
## [55] magick_2.8.3 pillar_1.9.0
## [57] knitr_1.46 ComplexHeatmap_2.21.0
## [59] rjson_0.2.21 boot_1.3-30
## [61] estimability_1.5 corpcor_1.6.10
## [63] future.apply_1.11.2 codetools_0.2-20
## [65] glue_1.7.0 data.table_1.15.4
## [67] vctrs_0.6.5 png_0.1-8
## [69] Rdpack_2.6 gtable_0.3.5
## [71] assertthat_0.2.1 cachem_1.0.8
## [73] xfun_0.43 mime_0.12
## [75] rbibutils_2.2.16 S4Arrays_1.5.0
## [77] Rfast_2.1.0 coda_0.19-4.1
## [79] survival_3.6-4 iterators_1.0.14
## [81] tinytex_0.50 statmod_1.5.0
## [83] TH.data_1.1-2 nlme_3.1-164
## [85] pbkrtest_0.5.2 bit64_4.0.5
## [87] filelock_1.0.3 progress_1.2.3
## [89] EnvStats_2.8.1 bslib_0.7.0
## [91] TMB_1.9.11 irlba_2.3.5.1
## [93] vipor_0.4.7 KernSmooth_2.23-22
## [95] colorspace_2.1-0 rmeta_3.0
## [97] DBI_1.2.2 DESeq2_1.45.0
## [99] tidyselect_1.2.1 emmeans_1.10.1
## [101] curl_5.2.1 bit_4.0.5
## [103] compiler_4.4.0 graph_1.83.0
## [105] BiocNeighbors_1.23.0 DelayedArray_0.31.0
## [107] bookdown_0.39 scales_1.3.0
## [109] caTools_1.18.2 remaCor_0.0.18
## [111] rappdirs_0.3.3 stringr_1.5.1
## [113] digest_0.6.35 minqa_1.2.6
## [115] rmarkdown_2.26 aod_1.3.3
## [117] XVector_0.45.0 RhpcBLASctl_0.23-42
## [119] htmltools_0.5.8.1 pkgconfig_2.0.3
## [121] lme4_1.1-35.3 sparseMatrixStats_1.17.0
## [123] highr_0.10 mashr_0.2.79
## [125] fastmap_1.1.1 rlang_1.1.3
## [127] GlobalOptions_0.1.2 UCSC.utils_1.1.0
## [129] DelayedMatrixStats_1.27.0 farver_2.1.1
## [131] jquerylib_0.1.4 zoo_1.8-12
## [133] jsonlite_1.8.8 BiocSingular_1.21.0
## [135] RCurl_1.98-1.14 magrittr_2.0.3
## [137] GenomeInfoDbData_1.2.12 munsell_0.5.1
## [139] Rcpp_1.0.12 babelgene_22.9
## [141] viridis_0.6.5 EnrichmentBrowser_2.35.0
## [143] RcppZiggurat_0.1.6 stringi_1.8.3
## [145] zlibbioc_1.51.0 MASS_7.3-60.2
## [147] plyr_1.8.9 listenv_0.9.1
## [149] parallel_4.4.0 ggrepel_0.9.5
## [151] Biostrings_2.73.0 splines_4.4.0
## [153] hms_1.1.3 circlize_0.4.16
## [155] locfit_1.5-9.9 reshape2_1.4.4
## [157] ScaledMatrix_1.13.0 BiocVersion_3.20.0
## [159] XML_3.99-0.16.1 evaluate_0.23
## [161] RcppParallel_5.1.7 BiocManager_1.30.22
## [163] nloptr_2.0.3 foreach_1.5.2
## [165] tidyr_1.3.1 purrr_1.0.2
## [167] future_1.33.2 clue_0.3-65
## [169] scattermore_1.2 ashr_2.2-63
## [171] rsvd_1.0.5 broom_1.0.5
## [173] xtable_1.8-4 fANCOVA_0.6-1
## [175] viridisLite_0.4.2 truncnorm_1.0-9
## [177] tibble_3.2.1 lmerTest_3.1-3
## [179] glmmTMB_1.1.9 memoise_2.0.1
## [181] beeswarm_0.4.0 AnnotationDbi_1.67.0
## [183] cluster_2.1.6 globals_0.16.3
## [185] GSEABase_1.67.0