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()# 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$ind)
# Create pseudobulk
pb <- aggregateToPseudoBulk(sce,
assay = "counts",
cluster_id = "cell",
sample_id = "id",
verbose = FALSE
)The means per variable, cell type, and sample are stored in the
pseudobulk SingleCellExperiment object:
## # A tibble: 128 × 5
## # Groups: cell [8]
## cell id cluster value1 value2
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 B cells ctrl101 3.96 -0.0165 -0.0391
## 2 B cells ctrl1015 4.00 0.0177 0.0228
## 3 B cells ctrl1016 4 -0.0332 -0.00106
## 4 B cells ctrl1039 4.04 -0.175 -0.267
## 5 B cells ctrl107 4 0.162 0.120
## 6 B cells ctrl1244 4 0.0890 -0.0366
## 7 B cells ctrl1256 4.01 0.0272 0.121
## 8 B cells ctrl1488 4.02 0.0663 0.0294
## 9 B cells stim101 4.09 0.0172 0.224
## 10 B cells stim1015 4.06 0.0270 0.0192
## # ℹ 118 more rowsAnalysis
Including these variables in a regression formula uses the summarized
values from the corresponding cell type. This happens behind the scenes,
so the user doesn’t need to distinguish bewteen sample-level variables
stored in colData(pb) and cell-level variables stored in
metadata(pb)$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 value2Details
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
Session Info
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 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
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## time zone: Etc/UTC
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## attached base packages:
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## other attached packages:
## [1] muscData_1.25.0 scater_1.39.2
## [3] scuttle_1.21.0 ExperimentHub_3.1.0
## [5] AnnotationHub_4.1.0 BiocFileCache_3.1.0
## [7] dbplyr_2.5.2 muscat_1.25.2
## [9] dreamlet_1.9.1 SingleCellExperiment_1.33.0
## [11] SummarizedExperiment_1.41.1 Biobase_2.71.0
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## [17] BiocGenerics_0.57.0 generics_0.1.4
## [19] MatrixGenerics_1.23.0 matrixStats_1.5.0
## [21] variancePartition_1.41.3 BiocParallel_1.45.0
## [23] limma_3.67.0 ggplot2_4.0.2
## [25] BiocStyle_2.39.0
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