computePca {AffiXcan}R Documentation

Perform a PCA on a matrix where columns are variables

Description

Perform a PCA on a matrix where columns are variables

Usage

computePca(data, varExplained, scale)

Arguments

data

A matrix containing the TBA values for a certain genomic region; columns are PWMs, rows are individuals IIDs

varExplained

An integer between 0 and 100; varExplained=80 means that the principal components selected to fit the models must explain at least 80 percent of variation of TBA values

scale

A logical; if scale=FALSE the TBA values will be only centered, not scaled before performing PCA

Value

A list containing two objects:

eigenvectors: a matrix containing eigenvectors for those principal components selected according to the param varExplained

pcs: a matrix containing the principal components values selected according to the param varExplained

Examples

if (interactive()) {
tbaMatrixMAE <- readRDS(system.file("extdata","training.tba.toydata.rds",
package="AffiXcan"))

tbaMatrix <- MultiAssayExperiment::experiments(tbaMatrixMAE)

tba <- tbaMatrix$ENSG00000256377.1

pca <- computePca(data=tba, varExplained=80, scale=TRUE)
}

[Package AffiXcan version 1.2.0 Index]