# #——数据,缓存= TRUE --------------------------------------------------------- 库(DMRScan)数据(DMRScan.methylationData)从染色体22 # #负载甲基化数据,与52018年论文认定测量数据(DMRScan.phenotypes) # #负载表型(甲基化数据端点)# #——奥林匹克广播服务公司,缓存= TRUE, depenson = '数据 '--------------------------------------- # 观察< -应用(DMRScan.methylationData 1函数(x, y){#总结(glm (y ~ x, #家庭=二项(链接=“分对数”)))$系数[2、3]},# y = DMRScan.phenotypes)观察< -应用(DMRScan.methylationData 1函数(x, y){总结(lm (x - y)) $系数[2、3]},y = DMRScan.phenotypes)头(观测)# #——pos,缓存= TRUE, depenson = '奥林匹克广播服务公司 '-------------------------------------- pos < -矩阵(as.integer (unlist (strsplit(名字(观察),分= "空空的 |[.]"))), ncol = 3, byrow = TRUE (pos)[1]的头 ) ## ---- 缓存= TRUE,依赖= pos-------------------------------------------- ##测试集群中cpg的最小数量min.cpg <- 3 ##在被分解为两个独立的集群之前，集群内的最大距离(以碱基对为单位)##差距< - 750 # #,reg缓存= TRUE, depenson = ' pos '-------------------------------------- 地区< - makeCpGregions(观察=观察,空空的= pos [1], pos = pos [2], maxGap = 750, minCpG = 3) # #——用力推,缓存= TRUE, depenson = '注册 '------------------------------------ 窗口。n.CpG <- 3:7 ##滑动窗口中的cpg数量##(可以是单个数字或序列)n.CpG <- sum(sapply(regions, length)) ##要测试的cpg数量##根据cpg的数量和窗口大小估算窗口阈值##使用重要的采样窗口.thresholds. importancsampling <- estimateWindowThreshold(nProbe = n.CpG, windowSize = window. size)##使用闭窗体表达式window.thresholds.siegmund <- estimateWindowThreshold(nProbe = n.c cpg, windowSize = window. cpg)大小、方法= " siegmund ") ## ---- res,缓存= TRUE, depenson = '用力推 '------------------------------------ window.thresholds.importancSampling < estimateWindowThreshold (nProbe = n.CpG windowSize =窗口。尺寸，方法= "sampling"， MCMC = 10000) dmrscan。结果<- dmrscan(观察=区域，windowSize =窗口。##打印结果Print (dmrscan.results) ## ----res2, cache = TRUE, dependson = 'thres'----------------------------------- dmrscan.results)结果<- dmrscan(观察=区域，windowSize =窗口。大小,windowThreshold = window.thresholds.siegmund) # #打印结果打印(dmrscan.results ) ## ---- eval = FALSE ------------------------------------------------------------ # # 由于时间限制没有运行。# window.threshold.mcmc <- estimateWindowThreshold(nProbe = n.CpG, windowSize = window. cpg)sizes, # method = "mcmc", mcmc = 1000, nCPU = 1, submethod = "arima", # model = list(ar = c(0.1,0.03), ma = c(0.04), order = c(2,0,1))) # # dmrscan.results <- dmrscan(observations = regions, windowSize = window.sizes, # windowThreshold = window.thresholds.mcmc) # # Print the result # print(dmrscan.results) ## ----------------------------------------------------------------------------- sessionInfo()