computePca: Perform a PCA on a matrix where columns are variables

In alussana/AffiXcan: A Functional Approach To Impute Genetically Regulated Expression

Description

Perform a PCA on a matrix where columns are variables

Usage

computePca(data, varExplained = 80, scale = TRUE)

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; default is 80
scale	A logical; if scale=FALSE the TBA values will be only centered, not scaled before performing PCA; default is TRUE

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

• eigenvalues: A vector containing eigenvalues for those principal components selected according to the param varExplained

Examples

if (interactive()) {
data(exprMatrix)
sampleNames <- colnames(exprMatrix)
nSamples <- length(sampleNames)
sampGroups <- subsetKFold(k=3, n=nSamples)
for (i in seq(length(sampGroups))) {
     sampGroups[[i]] <- colnames(exprMatrix)[sampGroups[[i]]]
}
testingSamples <- sampGroups[[i]]
trainingSamples <- sampleNames[!sampleNames %in% testingSamples]

tbaMatrixMAE <- readRDS(system.file("extdata","training.tba.toydata.rds",
package="AffiXcan"))
tbaMatrixMAE <- MultiAssayExperiment::subsetByRow(tbaMatrixMAE,
                                                  trainingSamples)
tbaMatrix <- MultiAssayExperiment::experiments(tbaMatrixMAE)
tba <- tbaMatrix$ENSG00000256377.1

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

}

alussana/AffiXcan documentation built on Oct. 20, 2020, 12:08 a.m.