Pseudo-bulk counts of the bins

pb_counts() adds up, or averages, the counts of the cells of each bin into the pseudo-bulk counts (bulk.counts) of the Dense slot. sc.squeeze() and sc.restruct() call it; call it after changing the bins with cDense().

Usage

pb_counts(
  scmpObj,
  bin_mem_col = scmpObj@Parameters@bin_mem_col,
  bin_col = scmpObj@Parameters@bin_col,
  assay_name = "counts",
  cluster_count_by = "sum"
)

Arguments

scmpObj: A ScMaSigPro object.
bin_mem_col: The name of the column of the bin metadata that holds the cells of each bin, separated by |.
bin_col: The name of the column of the bin metadata that holds the name of each bin.
assay_name: The name of the assay of the Sparse slot that holds the counts of the cells.
cluster_count_by: How the counts of the cells of a bin are combined: "sum" or "mean" (rounded to whole numbers).

Value

The object whose Dense slot holds the pseudo-bulk counts (bulk.counts, genes by bins) and the bin metadata.

Details

The bins are the rows of the bin metadata (cDense()): their row names, or the bin_col column when there are none, name the columns of the counts, and bin_mem_col gives their cells.

References

Srivastava P, Benegas Coll M, Götz S, Nueda MJ, Conesa A (2024). scMaSigPro: differential expression analysis along single-cell trajectories. Bioinformatics 40(7), btae443. doi:10.1093/bioinformatics/btae443

Author

Priyansh Srivastava spriyansh29@gmail.com

Examples

data("scmp.ob", package = "scMaSigPro")
# The mean instead of the sum of the counts of the cells of each bin
scmp <- pb_counts(scmp.ob, cluster_count_by = "mean")
eDense(scmp)[1:3, 1:4]
#>       Path1_bin_1 Path1_bin_2 Path1_bin_3 Path1_bin_4
#> Gene1           1           2           2           2
#> Gene2         452         489         482         539
#> Gene3          14          16          15          13