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