# #设置,回声= FALSE,结果=“隐藏 "---------------------------------------- knitr: opts_chunk美元集(整洁= FALSE,缓存= FALSE, dev =“png”,消息= FALSE,错误= FALSE,警告= TRUE) # #——快速入门,eval = FALSE --------------------------------------------------- # b < - BSseq(=对应,从而向pos = pos # M = M x = x, # sampleNames = sampleNames) # pData (bs)美元条件< - <泰爱泰党# #区域dmrseq (b = b,testCovariate = "条件 ") ## ---- bismarkinput ------------------------------------------------------------- 库(dmrseq) infile < -执行(“extdata / test_data.fastq_bismark.bismark.cov.gz”、包= bsseq) bismarkBSseq < -阅读。俾斯麦(文件= infile rmZeroCov = TRUE, strandCollapse = FALSE, verbose = TRUE) bismarkBSseq # #,解剖结果=“隐藏”,回声= FALSE -------------------------------------- 数据(“BS.chr21”)M < - getCoverage (BS。chr21, type="M") Cov <- getCoverage(BS.;chr21, type = " x ")所对应< - as.character (seqnames (BS.chr21)) pos < -开始(BS.chr21) celltype < pData (BS.chr21)美元celltype sampleNames < - sampleNames (BS.chr21) # #——fromScratch -------------------------------------------------------------- 头(M)头(浸)头(科)负责人(pos)暗(M)的(x)长(科)长(pos)打印(sampleNames)打印(celltype) b <——BSseq(=对应,从而向pos = pos, M = M x = x, sampleNames = sampleNames)显示(bs) # #——清理,结果=“隐藏”,回声= FALSE -------------------------------------- rm (M、浸、pos、空空bismarkBSseq) # #——元 --------------------------------------------------------------------- pData (bs)美元CellType < - CellType pData (bs)复制< - substr (sampleNames, nchar (sampleNames) nchar (sampleNames)) pData (bs ) ## ---- 过滤器 ------------------------------------------------------------------ # 位点和样本指标保持位点。idx <- which(DelayedMatrixStats::rowSums2(getCoverage(bs, type="Cov")==0) ==0)样本。idx <- where (pData(bs)$CellType %in% c("imr90"， "h1"))过滤<- bs[位点。idx,样本。idx ] ## ---- 结果=“隐藏”,回声= FALSE ---------------------------------------------- rm (bs.filtered) # #——mainfunction消息= TRUE,警告= TRUE --------------------------------- testCovariate < - <“CellType”地区dmrseq (b = b(240001:260000,),截止= 0.05,testCovariate = testCovariate ) ## ---- showresults ------------------------------------------------------------- 显示(地区)# #——平行,eval = FALSE ----------------------------------------------------- # 库(BiocParallel) #注册(MulticoreParam (4 )) ## ---- 块,消息= TRUE,警告= TRUE --------------------------------------- testCovariate < - <“CellType”块dmrseq (b = b(120001:125000,),截止= 0.05,testCovariate = testCovariate块= TRUE, minInSpan = 500, bpSpan = 5 e4, maxGapSmooth = 1 e6,maxGap = 5 e3)头(块 ) ## ----------------------------------------------------------------------------- 总和(地区qval < 0.05美元)#选择下面的地区罗斯福0.05和地点在一个新的data.frame sigRegions < -区域(区域qval < 0.05美元 ,] ## ---- 超 -------------------------------------------------------------------- 总和(sigRegions统计> 0美元)/长度(sigRegions) # #——情节,。width='\\textwidth'， figure .height = 2.5, warning='hide'---------- # get注释for hg18 annoTrack <- getAnnot("hg18") plotDMRs(bs, regions=regions[1，]， testCovariate="CellType"， annoTrack=annoTrack) ## ----plotblock, out。宽度= ' \ \ textwidth ' fig.height = 2.5, =警告“隐藏”——plotDMRs (bs、地区=块[1],testCovariate =“CellType annoTrack = annoTrack) # #——plot2 fig.height = 3 ------------------------------------------------------ plotEmpiricalDistribution (b, testCovariate = " CellType ") ## ---- plot3 fig.height = 3 ------------------------------------------------------ plotEmpiricalDistribution (b, testCovariate =“CellType”,类型=“浸”,bySample = TRUE) # #——出口,eval = FALSE ------------------------------------------------------- # write.csv (as.data.frame(地区),#文件= " h1_imr90_results.csv ") ## ---- meandiff ---------------------------------------------------------------- rawDiff < - meanDiff(废话,dmr = sigRegions testCovariate =“CellType”)str (rawDiff ) ## ---- sim卡 --------------------------------------------------------------------- 样本数据(BS.chr21) #重新排序来创建一个空比较BS。null <- BS.chr21[1:2000,c(1,3,2,4)] #添加100 DMRs BS.chr21。sim <- simDMRs(bs= bs。空,num.dmrs = 100) # bsseq对象与原空+模拟dmr显示(BS.chr21.sim bs美元)#在飙升的坐标dmr显示(BS.chr21.sim gr.dmrs美元)#效应大小头(BS.chr21.sim三角洲美元)# #——sessionInfo -------------------------------------------------------------- sessionInfo ()