# Modeling continuous cell-level covariates

### Collapse using mean value for pseudobulk data

Developed by [Gabriel Hoffman](http://gabrielhoffman.github.io/)

#### Run on 2024-11-06 18:17:29

# 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 -0.0370  0.0764
    ##  2 B cells ctrl1015    4.00  0.0840  0.00895
    ##  3 B cells ctrl1016    4    -0.121  -0.0269
    ##  4 B cells ctrl1039    4.04  0.171   0.0209
    ##  5 B cells ctrl107     4     0.150   0.100
    ##  6 B cells ctrl1244    4     0.100  -0.0947
    ##  7 B cells ctrl1256    4.01  0.0172 -0.0500
    ##  8 B cells ctrl1488    4.02  0.0377 -0.0702
    ##  9 B cells stim101     4.09 -0.0373 -0.110
    ## 10 B cells stim1015    4.06 -0.0621  0.0480
    ## # 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 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## 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_1.20.0             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
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