plotPCcorrs {pcaExplorer} | R Documentation

## Plot significance of (cor)relations of covariates VS principal components

### Description

Plots the significance of the (cor)relation of each covariate vs a principal component

### Usage

    plotPCcorrs(pccorrs, pc = 1, logp = TRUE)

### Arguments

`pccorrs` | A `data.frame` object generated by correlatePCs
`pc` | An integer number, corresponding to the principal component of interest
`logp` | Logical, defaults to `TRUE`, displays the -`log10` of the pvalue instead of the p value itself

### Value

A base plot object

### Examples

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

[Package pcaExplorer version 2.20.2 Index]