## ----数据，缓存= true ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------库（DMRSCAN）数据（dmrscan.methylationdata）##染色体22的负载甲基化数据，具有52018 CPG的测量数据（DMRSCAN.PHENOTYPES）## LOAD表型（甲基化数据的终点）## ---- obs，cache = true，depenson ='data'------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- #Observations < - appl（dmrscan.methylationdata，1，函数（x，y）{＃summary（y〜x，＃family = birnemial = binomial（link = = = =“ logit”）））$系数[2,3]}，＃y = dmrscan.phenotypes）观察值< - applation（dmrscan.methylationdata，1，function（x，y）系数[2,3]}，y = dmrscan.phenotypes）头（观测）## ------------------------------------------------------------------------------------------------------------------------矩阵（as.integer（unlist（strsplit（names（观察）），split =“ chr | [。]”）），ncol = 3，byrow = true）[， - 1] head（pos）## -------------- cache = true，depenson = pos ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ##测试群集中的最小CPG数量.cpg <-3 ## maxium距离（以碱基对为基础）##在将其分解为两个分离群集max.gap <-750 ## ---- reg，reg，cache = true，depenson ='之前，depenson ='pos'-------------------------------------- regions <- makeCpGregions(observations = observations, chr= pos [，1]，pos = pos [，2]，maxgap = 750，mincpg = 3）## ---------------------------------------------------------------------------------------------------------尺寸<-3：7 ##滑动窗口中的CPG数（可以是一个单个数字或序列）n.cpg < - sum（sapply（区域，长度））##要测试的CpG数量##估计窗口阈值，基于CPGS和窗口大小的数量##使用重要采样窗口。nprobe = n.cpg，windowsize = window。大小，方法 = "siegmund") ## ----res, cache = TRUE, depenson = 'thres'------------------------------------ window.thresholds.importancSampling <- estimateWindowThreshold(nProbe = n.CpG, windowSize = window.sizes, method = "sampling", mcmc = 10000) dmrscan.results <- dmrscan(observations = regions, windowSize = window.sizes, windowThreshold = window.thresholds.importancSampling) ## Print the result print(dmrscan.results) ## ----res2, cache = TRUE, depenson = 'thres'----------------------------------- dmrscan.results <- dmrscan(observations = regions, windowSize = window.sizes, windowThreshold = window.thresholds.siegmund) ## Print the result print(dmrscan.results) ## ---- eval = FALSE------------------------------------------------------------ # # Not run due to time constraints. # window.threshold.mcmc <- estimateWindowThreshold(nProbe = n.CpG, windowSize = window.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()