pcrplot: Principal component regression plot

In USCbiostats/ENmixUSC: Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip (USC version)

Description

First, principal component analysis will be performed in the standadized input data matrix (standadized for each row/CpG), and then the specified number of top principal components (that explain most data variation) will be used to perform linear regression with each specified variables. Regression P values will be plotted for exploration of methylation data variance structure or identification of possible confounding variables for association analysis.

Usage

pcrplot(beta, cov,npc=50)

Arguments

beta	A methylation beta value matrix with row for probes and column for samples.
cov	A data frame of covariates. Categorical variables should be converted to factors.
npc	The number of top principal components to plot

Value

A jpeg figure "svdscreeplot.jpg" to show the variations explained by each principal component.

A jpeg figure "pcr_diag.jpg" to show association strength between principal components and covariates with cell colors indicating different levels of association P values.

Author(s)

Zongli Xu

References

Zongli Xu, Liang Niu, Leping Li and Jack A. Taylor, ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Research 2015

Examples

if(FALSE){
if (require(minfiData)) {
mdat <- preprocessRaw(RGsetEx)
beta=getBeta(mdat, "Illumina")
group=pData(mdat)$Sample_Group
slide=factor(pData(mdat)$Slide)
cov=data.frame(group,slide)
pcrplot(beta,cov,npc=6)
}}

USCbiostats/ENmixUSC documentation built on June 1, 2019, 3:55 a.m.