computePca {AffiXcan} | R Documentation |
Perform a PCA on a matrix where columns are variables
computePca(data, varExplained, scale)
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 |
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
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) }