Differential Expression with limma-voom — bb_limma

Runs limma-voom differential expression analysis. Requires the limma and edgeR packages.

Usage

bb_limma_voom(counts, design, contrast = NULL)

Arguments

counts: Numeric matrix. Raw count matrix.
design: A design matrix (e.g., from stats::model.matrix()).
contrast: A contrast vector or matrix. If NULL, the last coefficient is tested.

Value

A data.frame with columns gene, log2fc, pvalue, padj.

Examples

# \donttest{
if (requireNamespace("limma", quietly = TRUE) &&
    requireNamespace("edgeR", quietly = TRUE)) {
  counts <- matrix(rpois(600, 100), nrow = 100, ncol = 6,
    dimnames = list(paste0("gene", 1:100), paste0("S", 1:6)))
  group <- factor(rep(c("ctrl", "treat"), each = 3))
  design <- model.matrix(~ group)
  result <- bb_limma_voom(counts, design)
  head(result)
}
#> calcNormFactors has been renamed to normLibSizes
#>    gene      log2fc     pvalue      padj
#> 1 gene1 -0.07229408 0.52407853 0.9092510
#> 2 gene2 -0.04190298 0.71594977 0.9549223
#> 3 gene3  0.04908073 0.60525976 0.9315685
#> 4 gene4  0.08188223 0.56818642 0.9164297
#> 5 gene5 -0.05009243 0.60471181 0.9315685
#> 6 gene6  0.22960595 0.02025713 0.6498947
# }