Aggregate single-cell data into pseudobulk samples based on read coverage

This function aggregates single-cell RNA-seq data into pseudobulk samples by sequentially accumulating cells until a cumulative read coverage threshold is reached; the next cell starts a new sample. Every pseudobulk sample holds at least coverage_threshold reads, except the last one, made of the cells left over, which is dropped with drop_incomplete = TRUE. The rule is the one of aggregate_on_obs_columns and of the Python package's tage.pp.aggregate.

Usage

aggregate_pseudobulk(
  seurat_obj,
  coverage_threshold = 1e+06,
  assay = "RNA",
  layer = "counts",
  shuffle = FALSE,
  seed = NULL,
  new_sample_prefix = "",
  drop_incomplete = FALSE,
  verbose = TRUE
)

Arguments

seurat_obj: A Seurat object containing single-cell RNA-seq data.
coverage_threshold: Integer specifying the minimum cumulative read count per pseudobulk sample. Cells are sequentially added to a group until this threshold is met, then a new group begins. Default is 1e6 (1 million reads).
assay: Character string specifying which assay to use. Default is "RNA".
layer: Character string specifying which layer to use. Default is "counts".
shuffle: Logical indicating whether to randomly shuffle cells before aggregation. Shuffling breaks any ordering present in the data. Default is FALSE.
seed: Integer seed for reproducibility when shuffle = TRUE. Default is NULL.
new_sample_prefix: Character string prefix for pseudobulk sample names. Default is "".
drop_incomplete: Logical. Drop the last pseudobulk sample when its leftover cells do not reach coverage_threshold. Default FALSE.
verbose: Logical indicating whether to print progress messages. Default is TRUE.

Value

An ExpressionSet object with pseudobulk expression data and metadata including cumulative_coverage (total reads) and n_cells per pseudobulk sample.

Examples

if (FALSE) { # \dontrun{
# Basic coverage-based aggregation
eset <- aggregate_pseudobulk(seurat_obj, coverage_threshold = 1e6)

# With shuffling for randomized grouping
eset <- aggregate_pseudobulk(seurat_obj, coverage_threshold = 5e5,
                             shuffle = TRUE, seed = 42)
} # }