correlatePCs {pcaExplorer} | R Documentation

## 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 <- DESeq2::rlogTransformation(dds)
    pcaobj <- prcomp(t(assay(rlt)))
    correlatePCs(pcaobj, colData(dds))

[Package pcaExplorer version 2.20.2 Index]