smart PCA from count matrix data

nice plot using ggplot2 from prcomp function

degPCA(counts, metadata = NULL, condition = NULL, pc1 = "PC1",
  pc2 = "PC2", name = NULL, shape = NULL, data = FALSE)

Arguments

counts	matrix with count data
metadata	dara.frame with sample information
condition	character column in metadata to use to color samples
pc1	character PC to plot on x-axis
pc2	character PC to plot on y-axis
name	character if given, column in metadata to print label
shape	character if given, column in metadata to shape points
data	Whether return PCA data or just plot the PCA.

Value

if results <- used, the function return the output of prcomp().

Examples

data(humanGender)
library(DESeq2)
idx <- c(1:10, 75:85)
dse <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx],
colData(humanGender)[idx,], design=~group)
degPCA(log2(counts(dse)+0.5), colData(dse),
  condition="group", name="group", shape="group")