Modeling continuous cell-level covariates
Collapse using mean value for pseudobulk data
Run on 2024-11-12 16:53:58
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 0.112 -0.148
## 2 B cells ctrl1015 4.00 0.0326 0.00382
## 3 B cells ctrl1016 4 -0.0660 0.00360
## 4 B cells ctrl1039 4.04 -0.196 -0.0287
## 5 B cells ctrl107 4 -0.134 0.0329
## 6 B cells ctrl1244 4 0.113 0.168
## 7 B cells ctrl1256 4.01 -0.0115 -0.0565
## 8 B cells ctrl1488 4.02 -0.0746 -0.0664
## 9 B cells stim101 4.09 0.0180 0.0484
## 10 B cells stim1015 4.06 -0.0647 -0.0361
## # 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 Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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_1.21.0 scater_1.35.0
## [3] scuttle_1.17.0 ExperimentHub_2.15.0
## [5] AnnotationHub_3.15.0 BiocFileCache_2.15.0
## [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_1.59.0 GenomeInfoDb_1.43.0
## [15] IRanges_2.41.0 S4Vectors_0.45.1
## [17] BiocGenerics_0.53.2 generics_0.1.3
## [19] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [21] variancePartition_1.37.1 BiocParallel_1.41.0
## [23] limma_3.63.2 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] sctransform_0.4.1 tools_4.5.0
## [9] backports_1.5.0 utf8_1.2.4
## [11] R6_2.5.1 metafor_4.6-0
## [13] mgcv_1.9-1 GetoptLong_1.0.5
## [15] withr_3.0.2 prettyunits_1.2.0
## [17] gridExtra_2.3 cli_3.6.3
## [19] sandwich_3.1-1 labeling_0.4.3
## [21] sass_0.4.9 KEGGgraph_1.67.0
## [23] SQUAREM_2021.1 mvtnorm_1.3-2
## [25] blme_1.0-6 mixsqp_0.3-54
## [27] zenith_1.9.0 parallelly_1.39.0
## [29] invgamma_1.1 RSQLite_2.3.7
## [31] shape_1.4.6.1 gtools_3.9.5
## [33] dplyr_1.1.4 Matrix_1.7-1
## [35] metadat_1.2-0 ggbeeswarm_0.7.2
## [37] fansi_1.0.6 abind_1.4-8
## [39] lifecycle_1.0.4 multcomp_1.4-26
## [41] yaml_2.3.10 edgeR_4.5.0
## [43] mathjaxr_1.6-0 gplots_3.2.0
## [45] SparseArray_1.7.1 grid_4.5.0
## [47] blob_1.2.4 crayon_1.5.3
## [49] lattice_0.22-6 beachmat_2.23.1
## [51] msigdbr_7.5.1 annotate_1.85.0
## [53] KEGGREST_1.47.0 magick_2.8.5
## [55] pillar_1.9.0 knitr_1.49
## [57] ComplexHeatmap_2.23.0 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_1.16.2 vctrs_0.6.5
## [67] png_0.1-8 Rdpack_2.6.1
## [69] gtable_0.3.6 assertthat_0.2.1
## [71] cachem_1.1.0 xfun_0.49
## [73] mime_0.12 rbibutils_2.3
## [75] S4Arrays_1.7.1 Rfast_2.1.0
## [77] coda_0.19-4.1 reformulas_0.4.0
## [79] survival_3.7-0 iterators_1.0.14
## [81] tinytex_0.54 statmod_1.5.0
## [83] TH.data_1.1-2 nlme_3.1-166
## [85] pbkrtest_0.5.3 bit64_4.5.2
## [87] filelock_1.0.3 progress_1.2.3
## [89] EnvStats_3.0.0 bslib_0.8.0
## [91] TMB_1.9.15 irlba_2.3.5.1
## [93] vipor_0.4.7 KernSmooth_2.23-24
## [95] colorspace_2.1-1 rmeta_3.0
## [97] DBI_1.2.3 DESeq2_1.47.0
## [99] tidyselect_1.2.1 emmeans_1.10.5
## [101] curl_6.0.0 bit_4.5.0
## [103] compiler_4.5.0 graph_1.85.0
## [105] BiocNeighbors_2.1.0 DelayedArray_0.33.1
## [107] bookdown_0.41 scales_1.3.0
## [109] caTools_1.18.3 remaCor_0.0.18
## [111] rappdirs_0.3.3 stringr_1.5.1
## [113] digest_0.6.37 minqa_1.2.8
## [115] rmarkdown_2.29 aod_1.3.3
## [117] XVector_0.47.0 RhpcBLASctl_0.23-42
## [119] htmltools_0.5.8.1 pkgconfig_2.0.3
## [121] lme4_1.1-35.5 sparseMatrixStats_1.19.0
## [123] mashr_0.2.79 fastmap_1.2.0
## [125] rlang_1.1.4 GlobalOptions_0.1.2
## [127] UCSC.utils_1.3.0 DelayedMatrixStats_1.29.0
## [129] farver_2.1.2 jquerylib_0.1.4
## [131] zoo_1.8-12 jsonlite_1.8.9
## [133] BiocSingular_1.23.0 RCurl_1.98-1.16
## [135] magrittr_2.0.3 GenomeInfoDbData_1.2.13
## [137] munsell_0.5.1 Rcpp_1.0.13-1
## [139] babelgene_22.9 viridis_0.6.5
## [141] EnrichmentBrowser_2.37.0 RcppZiggurat_0.1.6
## [143] stringi_1.8.4 zlibbioc_1.53.0
## [145] MASS_7.3-61 plyr_1.8.9
## [147] listenv_0.9.1 parallel_4.5.0
## [149] ggrepel_0.9.6 Biostrings_2.75.1
## [151] splines_4.5.0 hms_1.1.3
## [153] circlize_0.4.16 locfit_1.5-9.10
## [155] reshape2_1.4.4 ScaledMatrix_1.15.0
## [157] BiocVersion_3.21.1 XML_3.99-0.17
## [159] evaluate_1.0.1 RcppParallel_5.1.9
## [161] BiocManager_1.30.25 nloptr_2.1.1
## [163] foreach_1.5.2 tidyr_1.3.1
## [165] purrr_1.0.2 future_1.34.0
## [167] clue_0.3-65 scattermore_1.2
## [169] ashr_2.2-63 rsvd_1.0.5
## [171] broom_1.0.7 xtable_1.8-4
## [173] fANCOVA_0.6-1 viridisLite_0.4.2
## [175] truncnorm_1.0-9 tibble_3.2.1
## [177] lmerTest_3.1-3 glmmTMB_1.1.10
## [179] memoise_2.0.1 beeswarm_0.4.0
## [181] AnnotationDbi_1.69.0 cluster_2.1.6
## [183] globals_0.16.3 GSEABase_1.69.0