## ----echo = FALSE,hide=TRUE, message=FALSE, warning=FALSE--------------------- library(ELMER.data) library(ELMER) library(DT) library(dplyr) library(BiocStyle) ## ----eval = TRUE, message = FALSE, warning = FALSE, results = "hide"---------- # Load results from previous sections mae <- get(load("mae.rda")) sig.diff <- read.csv("result/getMethdiff.hypo.probes.significant.csv") nearGenes <- GetNearGenes( data = mae, probes = sig.diff$probe, numFlankingGenes = 20 ) # 10 upstream and 10 dowstream genes Hypo.pair <- get.pair( data = mae, group.col = "definition", group1 = "Primary solid Tumor", group2 = "Solid Tissue Normal", nearGenes = nearGenes, mode = "unsupervised", permu.dir = "result/permu", permu.size = 100, # Please set to 100000 to get significant results raw.pvalue = 0.05, Pe = 0.01, # Please set to 0.001 to get significant results filter.probes = TRUE, # See preAssociationProbeFiltering function filter.percentage = 0.05, filter.portion = 0.3, dir.out = "result", cores = 1, label = "hypo" ) ## ----eval = TRUE, message = FALSE, warning = FALSE---------------------------- Hypo.pair %>% datatable(options = list(scrollX = TRUE)) # get.pair automatically save output files. # getPair.hypo.all.pairs.statistic.csv contains statistics for all the probe-gene pairs. # getPair.hypo.pairs.significant.csv contains only the significant probes which is # same with Hypo.pair. dir(path = "result", pattern = "getPair")