## ----设置，包括= false -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ---- eval=FALSE----------------------------------------------------------------------------------＃New S4函数（注意大写w）＃？瀑布＃＃＃旧建立的功能＃？瀑布## ----消息= false，tidy = true -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------＃设置一个种子套装（426）＃加载genvisr到r库（genvisr）＃＃----消息= false，整理= true -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------＃获取MAF测试文件的磁盘位置testfiledir <-system.file（“ extdata”，package =“ genvisr”）testfile <-sys.glob（paste0（testfiledir，“/brca，”）.maf“））＃定义测试mafobject < - mutationAnnotationFormat（testfile）## ----消息= false，tidy = true --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------获取测试文件的磁盘位置testfiledir <-System.file（“ Extdata”，package = =“ genvisr”）testfile <-sys.glob（paste0（testfiledir，“/dep”））＃定义E测试VEPOBJECT <-VEP（testfile）## -----------------------------------------------------------------------------------------------------------------------------------------------------＃获取测试文件的磁盘位置testfiledir <-system.file（“ extdata）“，package =“ genvisr”）testfile <-sys.glob（paste0（testfiledir，“/fl.gms”））＃定义用于测试gmsobject <-gms（testfile）## ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------评估= false ----------------------------------------------------------------------------------------------------------------------------＃＃从VEP文件中查看样本＃getpample（vepobject）## ---------------- true，parning = false ----------------------------------------------------------------------- waterfallPlot <- Waterfall(vepObject, recurrence=.40) ## ----eval=FALSE----------------------------------------------------------------------------------------------------＃＃插槽名称提取数据＃getData（WaterfallPlot，name =“ priendardata”）＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃，index = 1）## ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # draw the plot # drawPlot(waterfallPlot) # # # draw the plot and save it to a pdf # pdf(file="waterfall.pdf", height=10, width=15) # drawPlot(waterfallPlot) # dev.off() ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE---- # draw a waterfall plot for the maf object drawPlot(Waterfall(vepObject, recurrence=.20)) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE---- # show those genes which recur in 50% of the cohort for these 4 samples drawPlot(Waterfall(vepObject, recurrence=.50, samples=c("FLX0040Naive", "FLX0070Naive", "FLX0050Naive", "FLX0030Naive"))) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE---- # define a coverage for each sample sampCov <- c("FLX0040Naive"=1.45e7, "FLX0070Naive"=1.39e7, "FLX0050Naive"=1.21e7, "FLX0030Naive"=1.3e7, "FLX0010Naive"=1.1e7) drawPlot(Waterfall(vepObject, recurrence=.50, coverage=sampCov, plotA="burden", plotATally="complex", drop=FALSE)) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE---- # find which mutations are in the input data mutations <- getMutation(vepObject)$Conse # define a new color and hierarchy for mutations library(data.table) newHierarchy <- data.table("mutation"=c("splice_region_variant", "splice_acceptor_variant", "splice_donor_variant", "missense_variant", "stop_gained"), "color"=c("tomato1", "tomato2", "tomato3", "purple", "cyan")) # draw the plot drawPlot(Waterfall(vepObject, recurrence=.50, mutationHierarchy = newHierarchy, plotATally="complex", drop=FALSE)) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE, message=FALSE---- # determine the appropriate BSgenome object to use from the vep header assembly <- getHeader(vepObject) assembly <- assembly[grepl("assembly", assembly$Info),] # load in the correct BSgenome object library(BSgenome.Hsapiens.UCSC.hg19) # create a MutSpectra plot drawPlot(MutSpectra(vepObject, BSgenome=BSgenome.Hsapiens.UCSC.hg19)) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE, message=FALSE---- # determine the appropriate BSgenome object to use from the vep header assembly <- getHeader(vepObject) assembly <- assembly[grepl("assembly", assembly$Info),] # load in the correct BSgenome object library(BSgenome.Hsapiens.UCSC.hg19) # create a Rainfall plot drawPlot(Rainfall(vepObject, BSgenome=BSgenome.Hsapiens.UCSC.hg19)) ## ---- fig.keep='last', fig.width=10, fig.height=6.5, tidy=TRUE, warning=FALSE, message=FALSE---- # create a Rainfall plot limiting the chromosomes and samples plotted drawPlot(Rainfall(vepObject, BSgenome=BSgenome.Hsapiens.UCSC.hg19, sample=c("FLX0010Naive"), chromosomes=c("chr1", "chr2", "chr3"))) ## ---- message=FALSE, tidy=TRUE------------------------------------------------ sessionInfo()