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).
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$StimStatus <- sce$stim
In 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:
Now compute the pseudobulk using standard code:
sce$id <- paste0(sce$StimStatus, sce$ind)
# Create pseudobulk
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)
The means per variable, cell type, and sample are stored in the
pseudobulk SingleCellExperiment
object:
## # A tibble: 128 × 5
## # Groups: cell [8]
## cell id cluster value1 value2
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 B cells ctrl101 3.96 -0.00527 -0.0582
## 2 B cells ctrl1015 4.00 -0.0254 0.00754
## 3 B cells ctrl1016 4 -0.0549 -0.0574
## 4 B cells ctrl1039 4.04 0.0666 -0.252
## 5 B cells ctrl107 4 0.0262 0.0339
## 6 B cells ctrl1244 4 0.263 -0.0129
## 7 B cells ctrl1256 4.01 -0.00363 0.163
## 8 B cells ctrl1488 4.02 -0.00540 0.0221
## 9 B cells stim101 4.09 0.0205 0.0174
## 10 B cells stim1015 4.06 0.147 0.00369
## # ℹ 118 more rows
Including these variables in a regression formula uses the summarized
values from the corresponding cell type. This happens behind the scenes,
so the user doesn’t need to distinguish bewteen sample-level variables
stored in colData(pb)
and cell-level variables stored in
metadata(pb)$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 value2
## 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
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## [95] compiler_4.4.2 graph_1.85.1
## [97] BiocNeighbors_2.1.2 DelayedArray_0.33.6
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