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[rowSums2(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))

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 -0.102   -0.0989 
##  2 B cells ctrl1015    4.00  0.0305  -0.00961
##  3 B cells ctrl1016    4     0.146    0.115  
##  4 B cells ctrl1039    4.04 -0.0196   0.149  
##  5 B cells ctrl107     4     0.173    0.0838 
##  6 B cells ctrl1244    4     0.0573  -0.0564 
##  7 B cells ctrl1256    4.01  0.125    0.0516 
##  8 B cells ctrl1488    4.02  0.0351   0.0560 
##  9 B cells stim101     4.09  0.00692  0.109  
## 10 B cells stim1015    4.06  0.00291 -0.00965
## # ℹ 118 more rows

4 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

5 Session Info

## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] muscData_1.16.0             scater_1.30.1              
##  [3] scuttle_1.12.0              ExperimentHub_2.10.0       
##  [5] AnnotationHub_3.10.0        BiocFileCache_2.10.1       
##  [7] dbplyr_2.4.0                muscat_1.16.0              
##  [9] dreamlet_1.0.3              SingleCellExperiment_1.24.0
## [11] SummarizedExperiment_1.32.0 Biobase_2.62.0             
## [13] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
## [15] IRanges_2.36.0              S4Vectors_0.40.2           
## [17] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
## [19] matrixStats_1.0.0           variancePartition_1.32.5   
## [21] BiocParallel_1.36.0         limma_3.58.1               
## [23] ggplot2_3.4.4               BiocStyle_2.30.0           
## 
## 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_2.46.0              numDeriv_2016.8-1.1          
##   [7] tools_4.3.2                   sctransform_0.4.1            
##   [9] backports_1.4.1               utf8_1.2.4                   
##  [11] R6_2.5.1                      metafor_4.4-0                
##  [13] mgcv_1.9-0                    GetoptLong_1.0.5             
##  [15] withr_2.5.2                   prettyunits_1.2.0            
##  [17] gridExtra_2.3                 cli_3.6.1                    
##  [19] sandwich_3.0-2                labeling_0.4.3               
##  [21] sass_0.4.7                    KEGGgraph_1.62.0             
##  [23] SQUAREM_2021.1                mvtnorm_1.2-3                
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##  [47] grid_4.3.2                    blob_1.2.4                   
##  [49] promises_1.2.1                crayon_1.5.2                 
##  [51] lattice_0.22-5                beachmat_2.18.1              
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## [105] compiler_4.3.2                graph_1.80.0                 
## [107] BiocNeighbors_1.20.2          DelayedArray_0.28.0          
## [109] bookdown_0.36                 scales_1.2.1                 
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## [129] GlobalOptions_0.1.2           shiny_1.7.5.1                
## [131] DelayedMatrixStats_1.24.0     farver_2.1.1                 
## [133] jquerylib_0.1.4               zoo_1.8-12                   
## [135] jsonlite_1.8.7                BiocSingular_1.18.0          
## [137] RCurl_1.98-1.13               magrittr_2.0.3               
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## [147] zlibbioc_1.48.0               MASS_7.3-60                  
## [149] plyr_1.8.9                    listenv_0.9.0                
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## [159] ScaledMatrix_1.10.0           BiocVersion_3.18.1           
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## [163] RcppParallel_5.1.7            BiocManager_1.30.22          
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## [173] ashr_2.2-63                   rsvd_1.0.5                   
## [175] broom_1.0.5                   xtable_1.8-4                 
## [177] fANCOVA_0.6-1                 later_1.3.1                  
## [179] viridisLite_0.4.2             truncnorm_1.0-9              
## [181] tibble_3.2.1                  lmerTest_3.1-3               
## [183] glmmTMB_1.1.8                 memoise_2.0.1                
## [185] beeswarm_0.4.0                AnnotationDbi_1.64.1         
## [187] cluster_2.1.4                 globals_0.16.2               
## [189] GSEABase_1.64.0