filter genes locally in the pairwise analysis (b5a71b4f) · Commits · github_fork / Presto 20250701

R/pseudobulk.R

+4 −4

Original line number	Diff line number	Diff line
		@@ -63,13 +63,13 @@ pseudobulk_pairwise <- function(dge_formula, counts_df, meta_data, contrast_var,
		message(sprintf('%s vs %s', foreground_id, background_id))
		}

		idx_use <- which(meta_data[[contrast_var]] %in% c(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)
		genes_keep <- which(Matrix::rowSums(counts_df[, idx_use] >= min_counts_per_sample) >= present_in_min_samples)
		## filter locally
		counts_df <- counts_df[genes_keep, ]
		counts_df <- counts_df[genes_keep, idx_use]

		suppressMessages({suppressWarnings({
		idx_use <- which(meta_data[[contrast_var]] %in% c(foreground_id, background_id))
		design <- meta_data[idx_use, ]

		design[[contrast_var]] <- factor(ifelse(design[[contrast_var]] == foreground_id,
		@@ -77,7 +77,7 @@ pseudobulk_pairwise <- function(dge_formula, counts_df, meta_data, contrast_var,
		'background'))
		## Do DGE with DESeq2
		dds <- DESeqDataSetFromMatrix(
		countData = counts_df[, idx_use],
		countData = counts_df,#[, idx_use],
		colData = design,
		design = dge_formula) %>%
		DESeq2::DESeq()