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
make_PCs_irlba(X, n.top = 2)
A correlation matrix.
Number of top principal compenents to return
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.
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## 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)
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