## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install(MethylMix) ## ----eval=FALSE--------------------------------------------------------------- # cancerSite <- "OV" # targetDirectory <- paste0(getwd(), "/") # GetData(cancerSite, targetDirectory) ## ----eval=FALSE--------------------------------------------------------------- # cancerSite <- "OV" # targetDirectory <- paste0(getwd(), "/") # # library(doParallel) # cl <- makeCluster(5) # registerDoParallel(cl) # GetData(cancerSite, targetDirectory) # stopCluster(cl) ## ----eval=FALSE, tidy=TRUE---------------------------------------------------- # cancerSite <- "OV" # targetDirectory <- paste0(getwd(), "/") # # cl <- makeCluster(5) # registerDoParallel(cl) # # # Downloading methylation data # METdirectories <- Download_DNAmethylation(cancerSite, targetDirectory) # # Processing methylation data # METProcessedData <- Preprocess_DNAmethylation(cancerSite, METdirectories) # # Saving methylation processed data # saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # # # Downloading gene expression data # GEdirectories <- Download_GeneExpression(cancerSite, targetDirectory) # # Processing gene expression data # GEProcessedData <- Preprocess_GeneExpression(cancerSite, GEdirectories) # # Saving gene expression processed data # saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds")) # # # Clustering probes to genes methylation data # METProcessedData <- readRDS(paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds")) # res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]]) # # # Putting everything together in one file # toSave <- list(METcancer = res[[1]], METnormal = res[[2]], GEcancer = GEProcessedData[[1]], GEnormal = GEProcessedData[[2]], ProbeMapping = res$ProbeMapping) # saveRDS(toSave, file = paste0(targetDirectory, "data_", cancerSite, ".rds")) # # stopCluster(cl) ## ----eval=FALSE, tidy=TRUE---------------------------------------------------- # METcancer = matrix(data = methylation_data, nrow = nb_of_genes, ncol = nb_of_samples) # METnormal = matrix(data = methylation_data, nrow = nb_of_genes, ncol = nb_of_samples) # GEcancer = matrix(data = expression_data, nrow = nb_of_genes, ncol = nb_of_samples) # ClusterProbes(MET_Cancer, MET_Normal, CorThreshold = 0.4) ## ----tidy=TRUE---------------------------------------------------------------- library(MethylMix) library(doParallel) data(METcancer) data(METnormal) data(GEcancer) head(METcancer[, 1:4]) head(METnormal) head(GEcancer[, 1:4]) ## ----tidy=TRUE, warning=F----------------------------------------------------- MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) ## ----tidy=TRUE, eval=FALSE---------------------------------------------------- # library(doParallel) # cl <- makeCluster(5) # registerDoParallel(cl) # MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal) # stopCluster(cl) ## ----tidy=TRUE---------------------------------------------------------------- MethylMixResults$MethylationDrivers MethylMixResults$NrComponents MethylMixResults$MixtureStates MethylMixResults$MethylationStates[, 1:5] MethylMixResults$Classifications[, 1:5] # MethylMixResults$Models ## ----tidy=TRUE, eval=F-------------------------------------------------------- # # Plot the most famous methylated gene for glioblastoma # plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer) # plots$MixtureModelPlot ## ----tidy=TRUE, eval=F-------------------------------------------------------- # # Plot MGMT also with its normal methylation variation # plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, METnormal = METnormal) # plots$MixtureModelPlot ## ----tidy=TRUE, eval=F-------------------------------------------------------- # # Plot a MethylMix model for another gene # plots <- MethylMix_PlotModel("ZNF217", MethylMixResults, METcancer, METnormal = METnormal) # plots$MixtureModelPlot ## ----tidy=TRUE, eval=F-------------------------------------------------------- # # Also plot the inverse correlation with gene expression (creates two separate plots) # plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, GEcancer, METnormal) # plots$MixtureModelPlot # plots$CorrelationPlot ## ----eval = FALSE, tidy=TRUE-------------------------------------------------- # # Plot all functional and differential genes # for (gene in MethylMixResults$MethylationDrivers) { # MethylMix_PlotModel(gene, MethylMixResults, METcancer, METnormal = METnormal) # } ## ----tidy=TRUE, echo = FALSE-------------------------------------------------- sessionInfo()