## ----style, echo=FALSE, results="asis", message=FALSE-------------------- knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE) ## ----library, echo=FALSE, results='hide', message=FALSE------------------ require(minfi) ## ------------------------------------------------------------------------ require(funtooNorm) require(minfiData) # We randomly assign cell types for the purpose of this example. pData(RGsetEx)$cell_type <- rep(c("type1","type2"),3) mySampleSet=fromRGChannelSet(RGsetEx) ## ------------------------------------------------------------------------ origBeta <- getRawBeta(mySampleSet) origBeta[1:3,1:3] ## ------------------------------------------------------------------------ plotValidationGraph(mySampleSet, type.fits="PCR") ## ----normalize data------------------------------------------------------ mySampleSet=funtooNorm(mySampleSet,type.fits="PCR",ncmp=3) mySampleSet normBeta <- getNormBeta(mySampleSet) normBeta[1:3,1:3] ## ------------------------------------------------------------------------ #technical replicates are fictional, just for demonstration purposes. agreement(origBeta, c(1:5,5)) # M for data before the normalization agreement(normBeta, c(1:5,5)) # M for data after normalization ## ------------------------------------------------------------------------ library(minfi) age=pData(RGsetEx)$age dmp=dmpFinder(getNormM(mySampleSet), age, type="continuous") dmp[1:2,] ## ------------------------------------------------------------------------ phenoData <- pData(RGsetEx)[,c("age","sex","status")] genomerange <- getGRanges(mySampleSet) grs <- GenomicRatioSet(gr=genomerange, Beta=normBeta, preprocessMethod="funtooNorm", metadata=list(pData=phenoData)) grs ## ----echo=FALSE---------------------------------------------------------- sessionInfo()