## ----kable 1, echo=FALSE, error=TRUE------------------------------------------ library(knitr) MGI <- c("nonsense", "frame_shift_del", "frame_shift_ins", "splice_site_del", "splice_site_ins", "splice_site", "nonstop", "in_frame_del", "in_frame_ins", "missense", "splice_region_del", "splice_region_ins", "splice_region", "5_prime_flanking_region", "3_prime_flanking_region", "3_prime_untranslated_region", "5_prime_untranslated_region", "rna", "intronic", "silent") MAF <- c("Nonsense_Mutation", "Frame_Shift_Ins", "Frame_Shift_Del", "Translation_Start_Site", "Splice_Site", "Nonstop_Mutation", "In_Frame_Ins", "In_Frame_Del", "Missense_Mutation", "5\'Flank", "3\'Flank", "5\'UTR", "3\'UTR", "RNA", "Intron", "IGR", "Silent", "Targeted_Region", "", "") kable(as.data.frame(cbind(MAF, MGI))) ## ----eval=FALSE, error=TRUE--------------------------------------------------- # # Plot the mutation landscape # waterfall(brcaMAF, fileType="MAF") ## ----fig.keep='last', fig.width=10, fig.height=7, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Load GenVisR and set seed library(GenVisR) set.seed(383) # Plot only genes with mutations in 6% or more of samples waterfall(brcaMAF, mainRecurCutoff=.06) ## ----fig.keep='last', fig.width=10, fig.height=7, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Plot only the specified genes waterfall(brcaMAF, plotGenes=c("PIK3CA", "TP53", "USH2A", "MLL3", "BRCA1")) ## ----kable, echo=FALSE, tidy=TRUE, error=TRUE--------------------------------- kable(as.data.frame(cbind(sample=as.character(brcaMAF[1:10,16]), mut_burden=as.numeric(rnorm(10, mean=2, sd=.5))))) ## ----fig.keep='last', fig.width=12, fig.height=8.5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Create clinical data subtype <- c('lumA', 'lumB', 'her2', 'basal', 'normal') subtype <- sample(subtype, 50, replace=TRUE) age <- c('20-30', '31-50', '51-60', '61+') age <- sample(age, 50, replace=TRUE) sample <- as.character(unique(brcaMAF$Tumor_Sample_Barcode)) clinical <- as.data.frame(cbind(sample, subtype, age)) # Melt the clinical data into "long" format. library(reshape2) clinical <- melt(clinical, id.vars=c('sample')) # Run waterfall waterfall(brcaMAF, clinDat=clinical, clinVarCol=c('lumA'='blue4', 'lumB'='deepskyblue', 'her2'='hotpink2', 'basal'='firebrick2', 'normal'='green4', '20-30'='#ddd1e7', '31-50'='#bba3d0', '51-60'='#9975b9', '61+'='#7647a2'), plotGenes=c("PIK3CA", "TP53", "USH2A", "MLL3", "BRCA1"), clinLegCol=2, clinVarOrder=c('lumA', 'lumB', 'her2', 'basal', 'normal', '20-30', '31-50', '51-60', '61+')) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Load transcript meta data library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene # Load BSgenome library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 # Define a region of interest gr <- GRanges(seqnames=c("chr10"), ranges=IRanges(start=c(89622195), end=c(89729532)), strand=strand(c("+"))) # Create Data for input start <- c(89622194:89729524) end <- c(89622195:89729525) chr <- 10 cov <- c(rnorm(100000, mean=40), rnorm(7331, mean=10)) cov_input_A <- as.data.frame(cbind(chr, start, end, cov)) start <- c(89622194:89729524) end <- c(89622195:89729525) chr <- 10 cov <- c(rnorm(50000, mean=40), rnorm(7331, mean=10), rnorm(50000, mean=40)) cov_input_B <- as.data.frame(cbind(chr, start, end, cov)) # Define the data as a list data <- list("Sample A"=cov_input_A, "Sample B"=cov_input_B) # Call genCov genCov(data, txdb, gr, genome, gene_labelTranscriptSize=2, transform=NULL, base=NULL) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Turn off feature compression and reduce gene transcripts genCov(data, txdb, gr, genome, transform=c("Intron", "CDS", "UTR"), base=c(10, 2, 2), reduce=TRUE) ## ----fig.keep='last', fig.width=11, fig.height=5.5, message=FALSE, warning=FALSE, results='hide', error=TRUE---- # Call TvTi TvTi(brcaMAF, lab_txtAngle=75, fileType="MAF") ## ----fig.keep='last', fig.width=11, fig.height=5.5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Plot the frequency with a different color pallete TvTi(brcaMAF, type='Frequency', palette=c("#77C55D", "#A461B4", "#C1524B", "#93B5BB", "#4F433F", "#BFA753"), lab_txtAngle=75, fileType="MAF") ## ----fig.keep='last', fig.width=11, fig.height=5.5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # Create a named vector of apriori expectations expec <- c("A->C or T->G (TV)"=.066, "A->G or T->C (TI)"=.217, "A->T or T->A (TV)"=.065, "G->A or C->T (TI)"=.4945, "G->C or C->G (TV)"=.0645, "G->T or C->A (TV)"=.093) # Call TvTi with the additional data TvTi(brcaMAF, y=expec, lab_txtAngle=45, fileType="MAF") ## ----fig.keep='last', fig.width=10, fig.height=4.5, message=TRUE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Call cnSpec with minimum required inputs cnSpec(LucCNseg, genome="hg19") ## ----eval=FALSE, tidy=TRUE, error=TRUE---------------------------------------- # # Call cnSpec with the y parameter # cnSpec(LucCNseg, y=hg19chr) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Create data chromosome <- 'chr14' coordinate <- sort(sample(0:106455000, size=2000, replace=FALSE)) cn <- c(rnorm(300, mean=3, sd=.2), rnorm(700, mean=2, sd=.2), rnorm(1000, mean=3, sd=.2)) data <- as.data.frame(cbind(chromosome, coordinate, cn)) # Call cnView with basic input cnView(data, chr='chr14', genome='hg19', ideogram_txtSize=4) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # create copy number data chromosome <- 'chr14' coordinate <- sort(sample(0:106455000, size=2000, replace=FALSE)) cn <- c(rnorm(300, mean=3, sd=.2), rnorm(700, mean=2, sd=.2), rnorm(1000, mean=3, sd=.2)) data <- as.data.frame(cbind(chromosome, coordinate, cn)) # create segment data dataSeg <- data.frame(chromosome=c(14, 14, 14), start=coordinate[c(1, 301, 1001)], end=coordinate[c(300, 1000, 2000)], segmean=c(3, 2, 3)) # call cnView with included segment data cnView(data, z=dataSeg, chr='chr14', genome='hg19', ideogram_txtSize=4) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=TRUE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Example input to x x <- matrix(sample(100000,500), nrow=50, ncol=10, dimnames=list(0:49,paste0("Sample",1:10))) covBars(x) ## ----fig.keep='last', fig.width=10, fig.height=6.5, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- covBars(x, colour=c("blue","grey","red")) ## ----eval=FALSE, tidy=TRUE---------------------------------------------------- # cnFreq(LucCNseg) ## ----fig.keep='last', fig.width=11, fig.height=3, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Obtain cytogenetic information for the genome of interest data <- cytoGeno[cytoGeno$genome == 'hg38',] # Call ideoView for chromosome 1 ideoView(data, chromosome='chr1', txtSize=4) ## ----fig.keep='last', fig.width=11, fig.height=3, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Call lohSpec with basic input lohSpec(x=HCC1395_Germline) ## ----fig.keep='last', fig.width=11, fig.height=5, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Call lohView with basic input, make sure input contains only Germline calls lohView(HCC1395_Germline, chr='chr5', genome='hg19', ideogram_txtSize=4) ## ----fig.keep='last', fig.width=11, fig.height=8, message=FALSE, warning=FALSE, results='asis', tidy=TRUE, error=TRUE---- # Read in BSgenome object (hg19) library(BSgenome.Hsapiens.UCSC.hg19) hg19 <- BSgenome.Hsapiens.UCSC.hg19 # Generate plot compIdent(genome=hg19, debug=TRUE) ## ----fig.keep='last', fig.width=10, fig.height=5, message=FALSE, warning=FALSE, results='hide', tidy=TRUE, error=TRUE---- # need transcript data for reference txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene # need a biostrings object for reference genome <- BSgenome.Hsapiens.UCSC.hg19 # need Granges object gr <- GRanges(seqnames=c("chr10"), ranges=IRanges(start=c(89622195), end=c(89729532)), strand=strand(c("+"))) # Plot and call the graphic p1 <- geneViz(txdb, gr, genome) p1[[1]] ## ----eval=FALSE, tidy=TRUE, error=TRUE---------------------------------------- # pdf(file="plot.pdf", height=8, width=14) # # Call a GenVisR function # waterfall(brcaMAF) # dev.off() ## ----eval=FALSE, tidy=TRUE, error=TRUE---------------------------------------- # library(ggplot2) # plot_theme <- theme(axis.text.x=element_blank(), # axis.title.x=element_blank(), # axis.ticks.x=element_blank()) # # cnFreq(LucCNseg, plotLayer=plot_theme) ## ----tidy=TRUE, error=TRUE---------------------------------------------------- sessionInfo()