plotPCcorrs: Plot significance of (cor)relations of covariates VS...

In federicomarini/pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

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)

federicomarini/pcaExplorer documentation built on Sept. 29, 2024, 9:31 p.m.