make_PCs_irlba: Run Principal Component Analysis (PCA) using the irlba...

In OmicKriging: Poly-Omic Prediction of Complex TRaits

Description

A simple wrapper around the irlba() function which computes a partial SVD efficiently. This function's run time depends on the number of eigenvectors requested but scales well. Use this function to generate covariates for use with the okriging or krigr_cross_validation functions.

Usage

make_PCs_irlba(X, n.top = 2)

Arguments

X	A correlation matrix.
n.top	Number of top principal compenents to return

Value

A matrix of Principal Components of dimension (# of samples) x (n.top). As expected, eigenvectors are ordered by eigenvalue. Rownames are given as sample IDs.

References

library(irlba)

Examples

 ## compute PC's using the  gene expression correlation matrix from vignette
 ## load gene expression values from vignette
 expressionFile <- system.file(package = "OmicKriging",
                     "doc/vignette_data/ig_gene_subset.txt.gz")
 ## compute correlation matrix
 geneCorrelationMatrix <- make_GXM(expressionFile)
 ## find top ten PC's of this matrix using SVD
 topPcs <- make_PCs_irlba(geneCorrelationMatrix, n.top=10) 

OmicKriging documentation built on May 1, 2019, 9:16 p.m.