signaturePCA: PCA based on a given gene-signature

In sidiropoulos/PSigA: A gene-signature ranking method based on sample density in PCA space

Description

signaturePCA performs principal component analysis on the given data matrix and gene-signature and returns the results as an object of class prcomp.

Usage

signaturePCA(data, signature, center = TRUE, scale = FALSE, ...)

Arguments

data	Gene expression matrix where rownames correspond to unique gene identifiers in signature format and columns correspond to samples.
signature	character vector with the signature's gene identifiers.
center	a logical value indicating whether the variables should be shifted to be zero centered. See prcomp for more details.
scale	a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. See prcomp for more details.
...	arguments passed to prcomp.

Value

A list with class prcomp.

Examples

dummyData <- do.call(rbind, lapply(seq(0.1, 1, by = 0.1),
                     rnorm, n = 100, m = 6))
rownames(dummyData) <- paste(rep("gene", nrow(dummyData)),
                             seq(1, nrow(dummyData)), sep = "")
dummySig <- c("gene1", "gene8", "gene9", "gene10")

dummyPca <- signaturePCA(dummyData, dummySig)

sidiropoulos/PSigA documentation built on May 29, 2019, 9:58 p.m.