## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("coMethDMR") ## ----packLoad, message=FALSE-------------------------------------------------- library(coMethDMR) library(BiocParallel) ## ----ex_data------------------------------------------------------------------ data(betasChr22_df) ## ----------------------------------------------------------------------------- # Purge any probes or samples with excessive missing values markCells_ls <- MarkMissing(dnaM_df = betasChr22_df) betasChr22_df <- betasChr22_df[ markCells_ls$keepProbes, markCells_ls$keepSamples ] # Inspect betasChr22_df[1:5, 1:5] ## ----ex_pheno_data------------------------------------------------------------ data(pheno_df) head(pheno_df) ## ----list_of_cgis------------------------------------------------------------- closeByGeneAll_ls <- readRDS( system.file( "extdata", "450k_Gene_3_200.rds", package = 'coMethDMR', mustWork = TRUE ) ) ## ----------------------------------------------------------------------------- closeByGeneAll_ls[1] ## ----------------------------------------------------------------------------- indx <- grep("chr22:", names(closeByGeneAll_ls)) closeByGene_ls <- closeByGeneAll_ls[indx] rm(closeByGeneAll_ls) length(closeByGene_ls) ## ----------------------------------------------------------------------------- closeByGene_ls[1:10] ## ----subset_betasChr22-------------------------------------------------------- keepCpGs_char <- unique(unlist(closeByGene_ls[1:10])) betasChr22small_df <- betasChr22_df[rownames(betasChr22_df) %in% keepCpGs_char, ] dim(betasChr22small_df) ## ----results='hide'----------------------------------------------------------- resid_mat <- GetResiduals( dnam = betasChr22small_df, # converts to Mvalues for fitting linear model betaToM = TRUE, pheno_df = pheno_df, # Features in pheno_df used as covariates covariates_char = c("age.brain", "sex", "slide"), nCores_int = 2 ) ## ----BiocParallel_Gene, message=FALSE----------------------------------------- system.time( coMeth_ls <- CoMethAllRegions( dnam = resid_mat, betaToM = FALSE, method = "spearman", arrayType = "450k", CpGs_ls = closeByGene_ls[1:10], nCores_int = 2 ) ) # Windows: NA # Mac: ~14 seconds for 2 cores ## ----singleRegionType_lmmTest, warning=FALSE, results='hide', message = FALSE---- res_df <- lmmTestAllRegions( betas = betasChr22small_df, region_ls = coMeth_ls, pheno_df = pheno_df, contPheno_char = "stage", covariates_char = c("age.brain", "sex"), modelType = "randCoef", arrayType = "450k", nCores_int = 2 # outLogFile = "res_lmm_log.txt" ) ## ----------------------------------------------------------------------------- AnnotateResults(res_df) ## ----------------------------------------------------------------------------- closeByGene_ls <- readRDS( system.file( "extdata", "450k_Gene_3_200.rds", package = 'coMethDMR', mustWork = TRUE ) ) closeByInterGene_ls <- readRDS( system.file( "extdata", "450k_InterGene_3_200.rds", package = 'coMethDMR', mustWork = TRUE ) ) # put them together in one list closeBy_ls <- c(closeByInterGene_ls, closeByGene_ls) length(closeBy_ls) closeBy_ls[1:3] ## ----------------------------------------------------------------------------- utils::sessionInfo()