# #——风格,回声= FALSE,结果=“黑名单”,消息= FALSE -------------------- 美元knitr: opts_chunk集(整洁= FALSE,警告= FALSE,消息= FALSE) # #——图书馆,呼应= FALSE,结果=“隐藏”,消息= FALSE ------------------ 要求(minfi ) ## ------------------------------------------------------------------------ 需要(funtooNorm)要求(minfiData) #我们随机分配为这个例子的目的细胞类型。pData (RGsetEx)美元cell_type < -代表(c(“类型1”、“type2”),3)mySampleSet = fromRGChannelSet (RGsetEx ) ## ------------------------------------------------------------------------ origBeta < - getRawBeta (mySampleSet) origBeta [1:3, 1:3 ] ## ------------------------------------------------------------------------ plotValidationGraph (mySampleSettype.fits = " PCR ") ## ---- 规范化的数据 ------------------------------------------------------ mySampleSet = funtooNorm (mySampleSet type.fits =“PCR ncmp = 3) mySampleSet normBeta < - getNormBeta (mySampleSet) normBeta [1:3, 1:3 ] ## ------------------------------------------------------------------------ # 技术复制都是虚构的,只是出于演示目的。协议(origBeta c(1:5, 5) #米之前对数据标准化协议(normBeta c(1:5, 5)) #米标准化后的数据 ## ------------------------------------------------------------------------ 图书馆(minfi)年龄= pData (RGsetEx)年龄dmp = dmpFinder美元(getNormM (mySampleSet),年龄,类型=“连续”)dmp (1:2 ,] ## ------------------------------------------------------------------------ phenoData < - pData (RGsetEx) [c(“年龄”、“性”、“地位”)]genomerange < - getGRanges (mySampleSet) grs < - GenomicRatioSet (gr = genomerange,β= normBeta preprocessMethod =“funtooNorm”,元数据=列表(pData = phenoData)) grs # #——回声= FALSE ---------------------------------------------------------- sessionInfo ()