DNA Methylation (IlluminaHumanMethylation450k) analysis

# 1: Reading Raw Files (.idat)

1. Create Target/csv/sample file
• TCGA (not a sample.txt or csv file)(https://sagebionetworks.jira.com/wiki/pages/viewpage.action?pageId=11206919&src=search)
• Download the data, Use .sdrf file provided with the downloaded data to make the targets file that facilitates data reading using minfi functions into R;
• For target file, the only important column is Basename that indicates where the files are for every patients if _Grn.idat or _Red.idat can be attached (find creat_target.pl)
• GEO
• ...
2. Read .idat files

• library(minfi)
• targets <- read.table("target.txt", header=T, sep="\t")
• RGSet<-read.450k.exp(targets=targets)
  • #Get the methylation matrix that will allow to extract methylated and unmethylated probes:
• Mset.raw<-preprocessRaw(RGSet) 
  • #this doesn't apply any normalization although the package has a few methods available including the methods implemented in GenomeStudio

# 2: DNA methylation data normalization

• Easy way:
• • m<-getMeth(Mset.raw)
• u<-getUnmeth(Mset.raw)
• mval<-log2((m+0.01)/(u+0.01)) #alpha=0.01
• x<-apply(mval,2,function(x) which(is.na(x)))
• beta_val<-m/(m+u) #alpha=0
• Complicated way (minfi + shinyMethyl)
• require(minfi)
• require(minfiData)
• phenoData <- pData(RGSet)
• phenoData[,1:6]
• manifest <- getManifest(RGSet)
• manifest
• head(getProbeInfo(manifest))
• typeIProbes <- getProbeInfo(manifest, type = "I")$Name
• head(typeIProbes)
• snpProbesI <- getProbeInfo(manifest, type = "SnpI")$Name
• snpProbesII <- getProbeInfo(manifest, type = "SnpI")$Name
• head(snpProbesI)
• Quality Control
• detP <- detectionP(RGSet)
• failed <- detP > 0.01
• head(failed, n=3)
• colMeans(failed)
• sum(rowMeans(failed)>0.5)
• MSet <- preprocessRaw(RGSet)
• MSet
• head(getMeth(MSet)[,1:3])
• head(getUnmeth(MSet)[,1:3])
• qc <- getQC(MSet)
• head(qc)
• plotQC(qc)
• densityPlot(RGSet, sampGroups = phenoData$Sample_Group, main= "Beta", xlab = "Beta")
• densityBeanPlot(RGSet, sampGroups = phenoData$Sample_Group)
• controlStripPlot(RGSet, controls="BISULFITE CONVERSION II")
• qcReport(RGSet, pdf= "qcReport.pdf")
• Starting with ShinyMethyl
• source("E:/AML/shinyMethyl-master/R/extractFromRGSet450k.R")
• extractedDataMinfiLab <- extractFromRGSet450k(RGSet,file = "extractedDataMinfiLab.Rda")
• source("/data/minfiLab/myRunApp.R")
• myRunApp(appDir = "/data/minfiLab/shinyMethyl")
• Proprocessing and normalization
• MSet.illumina <- preprocessIllumina(RGSet, bg.correct = TRUE,normalize = "controls")
• MSet.swan <- preprocessSWAN(RGSet)
• MethylSet and RatioSet
• getBeta(MSet, type = "Illumina")[1:4,1:3]
• getM(MSet)[1:4,1:3]
• ratioSet <- ratioConvert(MSet, what = "both", keepCN = TRUE)
• ratioSet
• Map to Genome
• gRatioSet <- mapToGenome(ratioSet, genomeBuild = "hg19", mergeManifest = TRUE)
• gRatioSet
• showClass("GenomicRatioSet")
• getBeta(gRatioSet)
• getM(gRatioSet)
• getCN(gRatioSet)
• sampleNames <- sampleNames(gRatioSet)
• probeNames <- featureNames(gRatioSet)
• pheno <- pData(gRatioSet)
• gRanges <- rowData(gRatioSet)
• head(gRanges, n= 3)