filter genes locally in the pairwise analysis (de9b094a) · Commits · github_fork / Presto 20250701

R/pseudobulk.R

+12 −3

Original line number	Diff line number	Diff line
		@@ -54,13 +54,20 @@ collapse_counts <- function(counts_mat, meta_data, varnames, min_cells_per_group
		}

		#' @export
		pseudobulk_pairwise <- function(dge_formula, counts_df, meta_data, contrast_var, vals_test, verbose) {
		pseudobulk_pairwise <- function(dge_formula, counts_df, meta_data, contrast_var, vals_test, verbose,
		min_counts_per_sample, present_in_min_samples) {
		lapply(vals_test, function(foreground_id) {
		background_ids <- as.character(unique(meta_data[[contrast_var]])) %>% setdiff(foreground_id)
		lapply(background_ids, function(background_id) {
		if (verbose) {
		message(sprintf('%s vs %s', foreground_id, background_id))
		}

		## genes could be absent in these two groups
		genes_keep <- which(Matrix::rowSums(counts_df >= min_counts_per_sample) >= present_in_min_samples)
		## filter locally
		counts_df <- counts_df[genes_keep, ]

		suppressMessages({suppressWarnings({
		idx_use <- which(meta_data[[contrast_var]] %in% c(foreground_id, background_id))
		design <- meta_data[idx_use, ]
		@@ -169,10 +176,12 @@ pseudobulk_deseq2 <- function(dge_formula, meta_data, counts_df, verbose=TRUE,
		}

		if (mode == 'one_vs_all') {
		res <- pseudobulk_one_vs_all(dge_formula, counts_df, meta_data, contrast_var, vals_test, collapse_background, verbose)
		res <- pseudobulk_one_vs_all(dge_formula, counts_df, meta_data,
		contrast_var, vals_test, collapse_background, verbose)

		} else if (mode == 'pairwise') {
		res <- pseudobulk_pairwise(dge_formula, counts_df, meta_data, contrast_var, vals_test, verbose)
		res <- pseudobulk_pairwise(dge_formula, counts_df, meta_data, contrast_var, vals_test, verbose,
		min_counts_per_sample, present_in_min_samples)
		}
		return (res)
		}

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