getPCA {singleCellTK} | R Documentation |
Perform PCA on a SingleCellExperiment Object A wrapper to runPCA function to compute principal component analysis (PCA) from a given SingleCellExperiment object.
getPCA( inSCE, useAssay = "logcounts", useAltExp = NULL, reducedDimName = "PCA", ndim = 50, scale = TRUE, ntop = NULL )
inSCE |
Input SingleCellExperiment object. |
useAssay |
Assay to use for PCA computation. If |
useAltExp |
The subset to use for PCA computation, usually for the
selected.variable features. Default |
reducedDimName |
Name to use for the reduced output assay. Default
|
ndim |
Number of principal components to obtain from the PCA
computation. Default |
scale |
Logical scalar, whether to standardize the expression values.
Default |
ntop |
Number of top features to use as a further variable feature
selection. Default |
A SingleCellExperiment object with PCA computation
updated in reducedDim(inSCE, reducedDimName)
.
data(scExample, package = "singleCellTK") sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'") logcounts(sce) <- log(counts(sce) + 1) sce <- getPCA(sce, ntop = 500)