getExplanatoryPCs: Per-PC variance explained by a variable

In davismcc/scater: Single-Cell Analysis Toolkit for Gene Expression Data in R

Per-PC variance explained by a variable

Description

Compute, for each principal component, the percentage of variance that is explained by one or more variables of interest.

Usage

getExplanatoryPCs(x, dimred = "PCA", n_dimred = 10, ...)

Arguments

x	A SingleCellExperiment object containing dimensionality reduction results.
dimred	String or integer scalar specifying the field in reducedDims(x) that contains the PCA results.
n_dimred	Integer scalar specifying the number of the top principal components to use.
...	Additional arguments passed to getVarianceExplained.

Details

This function computes the percentage of variance in PC scores that is explained by variables in the sample-level metadata. It allows identification of important PCs that are driven by known experimental conditions, e.g., treatment, disease. PCs correlated with technical factors (e.g., batch effects, library size) can also be detected and removed prior to further analysis.

By default, the function will attempt to use pre-computed PCA results in object. This is done by taking the top n_dimred PCs from the matrix specified by dimred. If these are not available or if rerun=TRUE, the function will rerun the PCA using runPCA; however, this mode is deprecated and users are advised to explicitly call runPCA themselves.

Value

A matrix containing the percentage of variance explained by each factor (column) and for each PC (row).

Author(s)

Aaron Lun

See Also

plotExplanatoryPCs, to plot the results.

getVarianceExplained, to compute the variance explained.

Examples

example_sce <- mockSCE()
example_sce <- logNormCounts(example_sce)
example_sce <- runPCA(example_sce)

r2mat <- getExplanatoryPCs(example_sce)

davismcc/scater documentation built on July 19, 2024, 2:01 a.m.