Collapse a single-cell count matrix into pseudobulks

Sums (or averages) the columns of a feature-by-cell count matrix according to one or more cell-metadata columns. The result is a feature-by-pseudobulk matrix where each column pools all cells that share the same combination of metadata values (e.g. all cells from a given donor in a given cluster). The resulting matrix is suitable for bulk-RNA-seq tools such as DESeq2, edgeR, or limma. See the pseudobulk vignette for an end-to-end walkthrough.

Usage

collapse_counts(
  counts_mat,
  meta_data,
  varnames,
  min_cells_per_group = 0,
  keep_n = FALSE,
  how = c("sum", "mean")[1]
)

Arguments

counts_mat

Counts matrix. Rows are features (genes), columns are cells. Sparse (dgCMatrix) or dense.

meta_data

data.frame of cell metadata. Must have one row per column of counts_mat.

varnames

Character vector of column names in meta_data that together define the pseudobulk grouping. Each unique combination of values across these columns becomes one pseudobulk sample.

min_cells_per_group

Drop pseudobulks containing fewer than this many cells. Default 0 (keep all).

keep_n

If TRUE, retain the per-pseudobulk cell count as column N in the returned meta_data. Default FALSE.

how

"sum" (default) sums counts across cells in each pseudobulk; "mean" divides by N to give the mean expression per cell. DESeq2 and other count-based regressions expect sums.

Value

A list with two elements:

• counts_mat - feature-by-pseudobulk numeric matrix.

• meta_data - data.frame with one row per pseudobulk containing the columns named in varnames (and N if keep_n = TRUE).

See also

pseudobulk_deseq2(), compute_hash()

Examples

m <- matrix(sample.int(8, 100*500, replace=TRUE), nrow=100, ncol=500)
rownames(m) <- paste0("G", 1:100)
colnames(m) <- paste0("C", 1:500)
md1 <- sample(c("a", "b"), 500, replace=TRUE)
md2 <- sample(c("c", "d"), 500, replace=TRUE)
df <- data.frame(md1, md2)
data_collapsed <- collapse_counts(m, df, c("md1", "md2"))
head(data_collapsed$counts_mat)
#>    sample_1 sample_0 sample_2 sample_3
#> G1      627      523      550      573
#> G2      663      498      559      554
#> G3      574      517      575      502
#> G4      605      511      551      520
#> G5      639      548      577      546
#> G6      582      554      596      521
head(data_collapsed$meta_data)
#>   md1 md2
#> 1   b   c
#> 2   a   c
#> 3   a   d
#> 4   b   d