• 1 Introduction
  • 2 Standard processing
  • 3 Pseudobulk
  • 4 Analysis
  • 5 Session Info

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$StimStatus <- sce$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  0.0206  -0.0180 
##  2 B cells ctrl1015    4.00  0.0326   0.120  
##  3 B cells ctrl1016    4    -0.0108   0.0313 
##  4 B cells ctrl1039    4.04 -0.325   -0.0359 
##  5 B cells ctrl107     4     0.104    0.0169 
##  6 B cells ctrl1244    4     0.0209  -0.0651 
##  7 B cells ctrl1256    4.01  0.00764  0.0522 
##  8 B cells ctrl1488    4.02 -0.0427   0.00181
##  9 B cells stim101     4.09 -0.0385   0.107  
## 10 B cells stim1015    4.06  0.0507   0.0327 
## # 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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /media/volume/teran2_disk/biocbuild/bbs-3.20-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_1.20.0             scater_1.34.0              
##  [3] scuttle_1.16.0              ExperimentHub_2.14.0       
##  [5] AnnotationHub_3.14.0        BiocFileCache_2.14.0       
##  [7] dbplyr_2.5.0                muscat_1.20.0              
##  [9] dreamlet_1.4.1              SingleCellExperiment_1.28.0
## [11] SummarizedExperiment_1.36.0 Biobase_2.66.0             
## [13] GenomicRanges_1.58.0        GenomeInfoDb_1.42.0        
## [15] IRanges_2.40.0              S4Vectors_0.44.0           
## [17] BiocGenerics_0.52.0         MatrixGenerics_1.18.0      
## [19] matrixStats_1.4.1           variancePartition_1.36.1   
## [21] BiocParallel_1.40.0         limma_3.62.1               
## [23] ggplot2_3.5.1               BiocStyle_2.34.0           
## 
## 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_2.50.0          numDeriv_2016.8-1.1      
##   [7] sctransform_0.4.1         tools_4.4.1              
##   [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_1.66.0         
##  [23] SQUAREM_2021.1            mvtnorm_1.3-2            
##  [25] blme_1.0-6                mixsqp_0.3-54            
##  [27] zenith_1.8.0              parallelly_1.38.0        
##  [29] invgamma_1.1              RSQLite_2.3.7            
##  [31] generics_0.1.3            shape_1.4.6.1            
##  [33] gtools_3.9.5              dplyr_1.1.4              
##  [35] Matrix_1.7-1              metadat_1.2-0            
##  [37] ggbeeswarm_0.7.2          fansi_1.0.6              
##  [39] abind_1.4-8               lifecycle_1.0.4          
##  [41] multcomp_1.4-26           yaml_2.3.10              
##  [43] edgeR_4.4.0               mathjaxr_1.6-0           
##  [45] gplots_3.2.0              SparseArray_1.6.0        
##  [47] grid_4.4.1                blob_1.2.4               
##  [49] crayon_1.5.3              lattice_0.22-6           
##  [51] beachmat_2.22.0           msigdbr_7.5.1            
##  [53] annotate_1.84.0           KEGGREST_1.46.0          
##  [55] magick_2.8.5              pillar_1.9.0             
##  [57] knitr_1.48                ComplexHeatmap_2.22.0    
##  [59] rjson_0.2.23              boot_1.3-31              
##  [61] estimability_1.5.1        corpcor_1.6.10           
##  [63] future.apply_1.11.3       codetools_0.2-20         
##  [65] glue_1.8.0                data.table_1.16.2        
##  [67] vctrs_0.6.5               png_0.1-8                
##  [69] Rdpack_2.6.1              gtable_0.3.6             
##  [71] assertthat_0.2.1          cachem_1.1.0             
##  [73] xfun_0.49                 mime_0.12                
##  [75] rbibutils_2.3             S4Arrays_1.6.0           
##  [77] Rfast_2.1.0               coda_0.19-4.1            
##  [79] reformulas_0.4.0          survival_3.7-0           
##  [81] iterators_1.0.14          tinytex_0.54             
##  [83] statmod_1.5.0             TH.data_1.1-2            
##  [85] nlme_3.1-166              pbkrtest_0.5.3           
##  [87] bit64_4.5.2               filelock_1.0.3           
##  [89] progress_1.2.3            EnvStats_3.0.0           
##  [91] bslib_0.8.0               TMB_1.9.15               
##  [93] irlba_2.3.5.1             vipor_0.4.7              
##  [95] KernSmooth_2.23-24        colorspace_2.1-1         
##  [97] rmeta_3.0                 DBI_1.2.3                
##  [99] DESeq2_1.46.0             tidyselect_1.2.1         
## [101] emmeans_1.10.5            curl_6.0.0               
## [103] bit_4.5.0                 compiler_4.4.1           
## [105] graph_1.84.0              BiocNeighbors_2.0.0      
## [107] DelayedArray_0.32.0       bookdown_0.41            
## [109] scales_1.3.0              caTools_1.18.3           
## [111] remaCor_0.0.18            rappdirs_0.3.3           
## [113] stringr_1.5.1             digest_0.6.37            
## [115] minqa_1.2.8               rmarkdown_2.29           
## [117] aod_1.3.3                 XVector_0.46.0           
## [119] RhpcBLASctl_0.23-42       htmltools_0.5.8.1        
## [121] pkgconfig_2.0.3           lme4_1.1-35.5            
## [123] sparseMatrixStats_1.18.0  highr_0.11               
## [125] mashr_0.2.79              fastmap_1.2.0            
## [127] rlang_1.1.4               GlobalOptions_0.1.2      
## [129] UCSC.utils_1.2.0          DelayedMatrixStats_1.28.0
## [131] farver_2.1.2              jquerylib_0.1.4          
## [133] zoo_1.8-12                jsonlite_1.8.9           
## [135] BiocSingular_1.22.0       RCurl_1.98-1.16          
## [137] magrittr_2.0.3            GenomeInfoDbData_1.2.13  
## [139] munsell_0.5.1             Rcpp_1.0.13-1            
## [141] babelgene_22.9            viridis_0.6.5            
## [143] EnrichmentBrowser_2.36.0  RcppZiggurat_0.1.6       
## [145] stringi_1.8.4             zlibbioc_1.52.0          
## [147] MASS_7.3-61               plyr_1.8.9               
## [149] listenv_0.9.1             parallel_4.4.1           
## [151] ggrepel_0.9.6             Biostrings_2.74.0        
## [153] splines_4.4.1             hms_1.1.3                
## [155] circlize_0.4.16           locfit_1.5-9.10          
## [157] reshape2_1.4.4            ScaledMatrix_1.14.0      
## [159] BiocVersion_3.20.0        XML_3.99-0.17            
## [161] evaluate_1.0.1            RcppParallel_5.1.9       
## [163] BiocManager_1.30.25       nloptr_2.1.1             
## [165] foreach_1.5.2             tidyr_1.3.1              
## [167] purrr_1.0.2               future_1.34.0            
## [169] clue_0.3-65               scattermore_1.2          
## [171] ashr_2.2-63               rsvd_1.0.5               
## [173] broom_1.0.7               xtable_1.8-4             
## [175] fANCOVA_0.6-1             viridisLite_0.4.2        
## [177] truncnorm_1.0-9           tibble_3.2.1             
## [179] lmerTest_3.1-3            glmmTMB_1.1.10           
## [181] memoise_2.0.1             beeswarm_0.4.0           
## [183] AnnotationDbi_1.68.0      cluster_2.1.6            
## [185] globals_0.16.3            GSEABase_1.68.0