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$StimStatus <- sce$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:

sce$value1 <- rnorm(ncol(sce))
sce$value2 <- rnorm(ncol(sce))

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 -0.00527 -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

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 value2

Session 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           
## 
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