PCAPlot: PCA of samples (if use of DESeq2)

In PF2-pasteur-fr/SARTools: Statistical Analysis of RNA-Seq Tools

PCA of samples (if use of DESeq2)

Description

Principal Component Analysis of samples based on the 500 most variant features on VST- or rlog-counts (if use of DESeq2)

Usage

PCAPlot(
  counts.trans,
  group,
  n = min(500, nrow(counts.trans)),
  col = c("lightblue", "orange", "MediumVioletRed", "SpringGreen"),
  outfile = TRUE,
  ggplot_theme = theme_gray()
)

Arguments

counts.trans	a matrix a transformed counts (VST- or rlog-counts)
group	factor vector of the condition from which each sample belongs
n	number of features to keep among the most variant
col	colors to use (one per biological condition)
outfile	TRUE to export the figure in a png file
ggplot_theme	ggplot2 theme function (theme_gray() by default)

Value

A file named PCA.png in the figures directory with a pairwise plot of the three first principal components

Author(s)

Marie-Agnes Dillies and Hugo Varet

PF2-pasteur-fr/SARTools documentation built on April 6, 2022, 2:24 a.m.