pseudobulk_deseq2: Pseudobulk DESeq2

In immunogenomics/presto: Fast Functions for Differential Expression using Wilcox and AUC

Pseudobulk DESeq2

Description

Pseudobulk DESeq2

Usage

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]
)

Arguments

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'

Examples

## 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)

immunogenomics/presto documentation built on Nov. 12, 2022, 5:13 p.m.