voom_polar: Convert RNA-Seq count data to a volcano3d object using 'limma voom'

Description

This function is used instead of polar_coords if you have raw RNA-Seq count data. The function takes a design formula, metadata and raw RNA-Seq count data and uses 'limma voom' to analyse the data. The results are converted to a 'volc3d' object ready for plotting a 3d volcano plot or polar plot.

Usage

voom_polar(
  formula,
  metadata,
  counts,
  pcutoff = 0.05,
  padj.method = "BH",
  filter_pairwise = TRUE,
  ...
)

Value

Calls polar_coords to return an S4 'volc3d' object

Arguments

formula: Design formula which must be of the form ~ 0 + outcome + .... The 3-way outcome variable must be the first variable after the '0', and this variable must be a factor with exactly 3 levels.
metadata: Matrix or dataframe containing metadata as referenced by formula
counts: Matrix containing raw gene expression count data
pcutoff: Cut-off for p-value significance
padj.method: Can be any method available in p.adjust or "qvalue". The option "none" is a pass-through.
filter_pairwise: Logical whether adjusted p-value pairwise statistical tests are only conducted on genes which reach significant adjusted p-value cut-off on the group likelihood ratio test
...: Optional arguments passed to polar_coords

Details

Statistical results for the group and pairwise comparisons are calculated using the 'limma voom' pipeline and the results passed to polar_coords to generate a 'volc3d' object ready for plotting a 3d volcano plot or polar plot.

Examples

Run this code

if (requireNamespace("limma", quietly = TRUE) & 
    requireNamespace("edgeR", quietly = TRUE)) {
  library(limma)
  library(edgeR)
  
  counts <- matrix(rnbinom(n=1500, mu=100, size=1/0.5), ncol=15)
  cond <- factor(rep(1:3, each=5), labels = c('A', 'B', 'C'))
  cond <- data.frame(cond)
  
  polar <- voom_polar(~0 + cond, cond, counts)
  
  volcano3D(polar)
  radial_ggplot(polar)
}