rcp {ENmix} | R Documentation |
Probe design type bias correction using Regression on Correlated Probes (RCP) method
rcp(mdat, dist=25, quantile.grid=seq(0.001,0.999,by=0.001), qcscore = NULL, nbthre=3, detPthre=0.000001)
mdat |
An object of class |
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 |
Data quality infomation object, the output from function QCinfo. If the object is provied, low quality data points as defined by detection p value threshold (detPthre) or number of bead threshold (nbthre) will be set as missing values. |
detPthre |
Detection P value threshold to define low qualitye data points |
nbthre |
Number of beads threshold to define low qualitye data points, nbthre=3 in default. |
The function will first identify type I and type II probe pairs within a specified distance, and then perform linear regression calibration between the probe types. With the estimates the function will then adjust type II data using type I data as references.
A beta value matrix
Liang Niu, Zongli Xu
Liang Niu, Zongli Xu and Jack A. Taylor RCP: a novel probe design bias correction method for Illumina Methylation BeadChip, Bioinformatics 2016
if (require(minfiData)) { #methDataSet as input path <- file.path(find.package("minfiData"),"extdata") rgSet <- readidat(path = path,recursive = TRUE) qc=QCinfo(rgSet) mdat=preprocessENmix(rgSet,QCinfo=qc,nCores=6) mdat=norm.quantile(mdat,method="quantile1") beta=rcp(mdat) #methylset as input sheet <- read.metharray.sheet(file.path(find.package("minfiData"),"extdata"), pattern = "csv$") rgSet <- read.metharray.exp(targets = sheet,extended = TRUE) qc=QCinfo(rgSet) mdat=preprocessENmix(rgSet,QCinfo=qc,nCores=6) mdat=norm.quantile(mdat,method="quantile1") beta=rcp(mdat) }