## ----，回声=假--------------------------------------------------------------- knitr :: Opts_chunk $ set（cache = true）## ----，回声=假---------------------------------------------------- suppressPackageStartUpMessages（{Required（Minfi）要求（minfidata）}）## ----，eval = false ------------------------------------------------------------------中需要（minfi）＃要求（minfidata）＃browsevignettes（“minfi”）## ---------------------------------------------------------------------------------------已加入< - system.file（“extdata”，package =“minfidata”）基于dir（pashedir）dir（file.path（绑定）5723646052“））##--------------------------------------------------------------------------------------- ##'PDATA'目标< - READ.450K.SHEET（PResideS）头（目标）##'RAW'探测级别数据RGSET < - READ.450K.EXP（基本=与基本，目标=目标）## --------------------------------------------------------------------------------------------------- ##基本QA - 可比密度跨样品？ densityPlot(RGset, sampGroups = RGset$Sample_Group, main = "Beta", xlab = "Beta") ## ------------------------------------------------------------------------ ## background correction and normalization ## like Illumina Genome Studio (other approaches available) MSet.norm <- preprocessIllumina(RGset, bg.correct = TRUE, normalize = "controls", reference = 2) ## ------------------------------------------------------------------------ ## How similar are the samples to one another? mdsPlot(MSet.norm, numPositions = 1000, sampGroups = MSet.norm$Sample_Group, sampNames = MSet.norm$Sample_Name) ## ------------------------------------------------------------------------ ## Identify probes with methylation status differing between groups mset <- MSet.norm[1:100000,] ## logit(beta) M <- getM(mset, type = "beta", betaThreshold = 0.001) dmp <- dmpFinder(M, pheno=mset$Sample_Group, type="categorical") head(dmp) ## ------------------------------------------------------------------------ plotCpg(mset, cpg=rownames(dmp)[1], pheno=mset$Sample_Group) ## ------------------------------------------------------------------------ ## Genomic locations mset <- mset[rownames(dmp),] mse <- mapToGenome(mset) # 'SummarizedExperiment' rowData(mse) mcols(rowData(mse)) <- cbind(mcols(rowData(mse)), dmp)