## ----style, echo=FALSE, results='asis'---------------------------------------- BiocStyle::markdown() ## ----BiocManager, eval=FALSE-------------------------------------------------- # if (!require("BiocManager")) # install.packages("BiocManager") # BiocManager::install("m6Aboost") ## ----initialize, results="hide", warning=FALSE, message=FALSE----------------- library(m6Aboost) ## ----echo=TRUE, warning=FALSE, message=FALSE---------------------------------- library(m6Aboost) ## Load the test data testpath <- system.file("extdata", package = "m6Aboost") test_gff3 <- file.path(testpath, "test_annotation.gff3") test <- readRDS(file.path(testpath, "test.rds")) test ## ----eval=TRUE, include=TRUE-------------------------------------------------- ## truncationAssignment allows to assign the number of truncation events ## The input should be a GRanges object with the peaks and bigWig files ## with the truncation events (separated per strand) truncationBw_p <- file.path(testpath, "truncation_positive.bw") truncationBw_n <- file.path(testpath, "truncation_negative.bw") test <- truncationAssignment(test, bw_positive=truncationBw_p, bw_negative=truncationBw_n, sampleName = "WT1") ## CtoTAssignment allows to assign the number of C-to-T transitions ctotBw_p <- file.path(testpath, "C2T_positive.bw") ctotBw_n <- file.path(testpath, "C2T_negative.bw") test <- CtoTAssignment(test, bw_positive=ctotBw_p, bw_negative=ctotBw_n, sampleName = "CtoT_WT1") ## ----eval=FALSE, include=TRUE------------------------------------------------- # ## E.g. for two replicates, this can be calculated as # peak$WTmean <- (peak$WT1 + peak$WT2)/2 ## ----------------------------------------------------------------------------- ## Extract the features for the m6Aboost prediction test <- preparingData(test, test_gff3, colname_reads="WTmean", colname_C2T="CtoTmean") test ## ----warning=FALSE, message=FALSE--------------------------------------------- ## Note that since the test data set contains only a tiny fraction of the real ## data, and a part of the test data belongs to the training set. Here for ## preventing the unnecessary value change, we set the normalization to FALSE. out <- m6Aboost(test, "BSgenome.Mmusculus.UCSC.mm10", normalization = FALSE) out ## ----echo=TRUE, message=FALSE------------------------------------------------- ## firstly user need to load the ExperimentHub library(ExperimentHub) eh <- ExperimentHub() ## "EH6021" is the retrieve record of the m6Aboost model <- eh[["EH6021"]] ## here shows more information about the stored model query(eh, "m6Aboost") ## ----sessionInfo-------------------------------------------------------------- sessionInfo()