rcp: Regression on Correlated Probes(RCP)

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

Description

Probe design type bias correction using Regression on Correlated Probes (RCP) method

Usage

        rcp(mdat, dist=25, quantile.grid=seq(0.001,0.999,by=0.001), qcscore = NULL, nbthre=3, detPthre=0.000001)

Arguments

mdat	An object of class MethylSet.
dist	Maximum distance in base pair between type I and type II probe pairs for regression calibration
quantile.grid	Quantile grid used in linear regression
qcscore	If the data quality infomation (the output from function QCinfo) is provied, low quality data points as defined by detection p value threshold (detPthre=0.000001) or number of bead threshold (nbthre=3) will be set to missing.
detPthre	Detection P value threshold to define low qualitye data points, detPthre=0.000001 in default.
nbthre	Number of beads threshold to define low qualitye data points, nbthre=3 in default.

Details

The function will first identify type I and type II probe pairs within specified distance, and then perform linear regression between the probe types to estimate regression coefficients. With the estimates the function will then calibrates type II data using type I data as references.

Value

A beta value matrix

Author(s)

Liang Niu, Zongli Xu

References

Liang Niu, Zongli Xu and Jack A. Taylor RCP: a novel probe design bias correction method for Illumina Methylation BeadChip, Bioinformatics 2016

Examples

if(FALSE){
if (require(minfiData)) {
mdat=preprocessENmix(RGsetEx,bgParaEst="oob",nCores=6)
mdatq1=norm.quantile(mdat,method="quantile1")
beta=rcp(mdatq1)
}}

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