cinaR (version 0.2.6)

prep_scATAC_cinaR: prep_scATAC_cinaR

Description

Prepare 10x scATAC peak-by-cell matrices for cinaR by pseudobulking per sample.

Usage

prep_scATAC_cinaR(
  counts,
  cell.meta,
  sample.col,
  group.col,
  cluster.col = NULL,
  peak.bed = NULL,
  min.cells = 20,
  verbose = TRUE
)

Value

list with elements `bed`, `contrasts`, and `group.info`, or a named list of such lists when cluster.col is provided.

Arguments

counts

peak-by-cell count matrix (dense matrix or sparse dgCMatrix).

cell.meta

data.frame with rownames as cell barcodes.

sample.col

column name in cell.meta indicating biological replicate.

group.col

column name in cell.meta indicating condition/group.

cluster.col

optional column name for cell type/cluster. If provided, output is a named list per cluster.

peak.bed

optional data.frame with CHR/START/STOP columns for peaks. If not provided, rownames(counts) are parsed as "chr:start-end" or "chr_start_end".

min.cells

minimum number of cells required per sample (and per cluster if used).

verbose

logical, prints informative messages.

Examples

# \donttest{
counts <- matrix(c(1, 0, 2, 1, 0, 1, 3, 0, 0, 2, 1, 0),
                 nrow = 2, byrow = TRUE)
rownames(counts) <- c("chr1:1-100", "chr1:101-200")
colnames(counts) <- paste0("cell", 1:6)
meta <- data.frame(sample = c("S1", "S1", "S2", "S2", "S3", "S3"),
                   group = c("A", "A", "B", "B", "B", "B"),
                   row.names = colnames(counts))
prep <- prep_scATAC_cinaR(counts, meta, sample.col = "sample", group.col = "group",
                          min.cells = 2)
# }