pbDS {muscat} | R Documentation

## pseudobulk DS analysis

### Description

`pbDS` tests for DS after aggregating single-cell measurements to pseudobulk data, by applying bulk RNA-seq DE methods, such as `edgeR`, `DESeq2` and `limma`.

### Usage

    pbDS(
      pb,
      method = c("edgeR", "DESeq2", "limma-trend", "limma-voom"),
      design = NULL,
      coef = NULL,
      contrast = NULL,
      min_cells = 10,
      filter = c("both", "genes", "samples", "none"),
      treat = FALSE,
      verbose = TRUE
    )

### Arguments

`pb` | a `SingleCellExperiment` containing pseudobulks as returned by `aggregateData`.
`method` | a character string.
`design` | For methods `"edegR"` and `"limma"`, a design matrix with row & column names(!) created with `model.matrix`; For `"DESeq2"`, a formula with variables in `colData(pb)`. Defaults to `~ group_id` or the corresponding `model.matrix`.
`coef` | passed to `glmQLFTest`, `contrasts.fit`, `results` for `method = "edgeR", "limma-x", "DESeq2"`, respectively.
`contrast` | a matrix of contrasts to test for created with `makeContrasts`.
`min_cells` | a numeric. Specifies the minimum number of cells in a given cluster-sample required to consider the sample for differential testing.
`filter` | characterstring specifying whether to filter on genes, samples, both or neither.
`treat` | logical specifying whether empirical Bayes moderated-t p-values should be computed relative to a minimum fold-change threshold. Only applicable for methods `"limma-x"` (`treat`) and `"edgeR"` (`glmTreat`), and ignored otherwise.
`verbose` | logical. Should information on progress be reported?

### Value

a list containing

- a data.frame with differential testing results,

- a `DGEList` object of length nb.-clusters, and

- the `design` matrix, and `contrast` or `coef` used.

### Author(s)

Helena L Crowell & Mark D Robinson

### References

Crowell, HL, Soneson, C, Germain, P-L, Calini, D, Collin, L, Raposo, C, Malhotra, D & Robinson, MD: On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. bioRxiv 713412 (2018). doi: https://doi.org/10.1101/713412

### Examples

    # simulate 5 clusters, 20% of DE genes
    data(sce)

    # compute pseudobulk sum-counts & run DS analysis
    pb <- aggregateData(sce)
    res <- pbDS(pb, method = "limma-trend")

    names(res)
    names(res$table)
    head(res$table$`stim-ctrl`$`B cells`)

    # count nb. of DE genes by cluster
    vapply(res$table$`stim-ctrl`, function(u)
      sum(u$p_adj.loc < 0.05), numeric(1))

    # get top 5 hits for ea. cluster w/ abs(logFC) > 1
    library(dplyr)
    lapply(res$table$`stim-ctrl`, function(u)
      filter(u, abs(logFC) > 1) %>%
        arrange(p_adj.loc) %>%
        slice(seq_len(5)))

[Package muscat version 1.1.6 Index]