pseudobulk_deseq2 | R Documentation |
Pseudobulk DESeq2
pseudobulk_deseq2( dge_formula, meta_data, counts_df, verbose = TRUE, min_counts_per_sample = 10, present_in_min_samples = 5, collapse_background = TRUE, vals_test = NULL, mode = c("one_vs_all", "pairwise", "within")[1] )
dge_formula |
differential gene expression formula for DESeq2 |
meta_data |
data.frame of cell metadata |
counts_df |
A feature-by-sample matrix |
verbose |
verbose |
min_counts_per_sample |
minimum counts per sample to include in differential gene expression |
present_in_min_samples |
minimum samples with gene counts to include in differential gene expression |
collapse_background |
collapse background. Default is 'TRUE' |
vals_test |
cell metadata columns |
mode |
kind of pseudobulk testing to perform. One of 'one_vs_all', 'pairwise', or 'within' |
## Not run: m <- matrix(sample.int(8, 100*500, replace=TRUE),nrow=100, ncol=500) rownames(m) <- paste0("G", 1:100) colnames(m) <- paste0("C", 1:500) md1 <- sample(c("a", "b"), 500, replace=TRUE) md2 <- sample(c("c", "d"), 500, replace=TRUE) df <- data.frame(md1, md2) data_collapsed <- collapse_counts(m, df, c("md1", "md2")) res_mat <- pseudobulk_deseq2( ~md1 + md1, data_collapsed$meta_data, data_collapsed$counts_mat, verbose = TRUE, present_in_min_samples = 1 ) head(res_mat) ## End(Not run)
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