pcaExplorer (version 1.0.2)

correlatePCs: Principal components (cor)relation with experimental covariates

Description

Computes the significance of (cor)relations between PCA scores and the sample experimental covariates, using Kruskal-Wallis test for categorial variables and the cor.test based on Spearman's correlation for continuous variables

Usage

correlatePCs(pcaobj, coldata, pcs = 1:4)

Arguments

pcaobj

A prcomp object

coldata

A data.frame object containing the experimental covariates

pcs

A numeric vector, containing the corresponding PC number

Value

• A data.frame object with computed p values for each covariate and for each principal component

Examples

library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 1)
rlt <- rlogTransformation(dds)
pcaobj <- prcomp(t(assay(rlt)))
correlatePCs(pcaobj,colData(dds))