## ---- echo=FALSE--------------------------------------------------------- library(knitr) opts_chunk$set(comment="", message=FALSE, warning = FALSE, tidy.opts=list(keep.blank.line=TRUE, width.cutoff=150),options(width=150), cache=TRUE, fig.width=10, fig.height=10, eval = FALSE) ## ------------------------------------------------------------------------ ## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("RTCGA.PANCAN12") # or try devel version require(devtools) if (!require(RTCGA.PANCAN12)) { install_github("RTCGA/RTCGA.PANCAN12") require(RTCGA.PANCAN12) } # or if you have RTCGA package then simpler code is RTCGA::installTCGA('RTCGA.PANCAN12') ## ------------------------------------------------------------------------ expression.cb <- rbind(expression.cb1, expression.cb2) ## ------------------------------------------------------------------------ grep(expression.cb[,1], pattern="MDM2") MDM2 <- expression.cb[8467,-1] MDM2v <- t(MDM2) ## ------------------------------------------------------------------------ grep(mutation.cb[,1], pattern="TP53$", value = FALSE) TP53 <- mutation.cb[18475,-1] TP53v <- t(TP53) ## ------------------------------------------------------------------------ dfC <- data.frame(names=gsub(clinical.cb[,1], pattern="-", replacement="."), clinical.cb[,c("X_cohort","X_TIME_TO_EVENT","X_EVENT","X_PANCAN_UNC_RNAseq_PANCAN_K16")]) dfT <- data.frame(names=rownames(TP53v), vT = TP53v) dfM <- data.frame(names=rownames(MDM2v), vM = MDM2v) dfTMC <- merge(merge(dfT, dfM), dfC) colnames(dfTMC) = c("names", "TP53", "MDM2", "cohort","TIME_TO_EVENT","EVENT","PANCAN_UNC_RNAseq_PANCAN_K16") dfTMC$TP53 <- factor(dfTMC$TP53) # only primary tumor # (removed because of Leukemia) # dfTMC <- dfTMC[grep(dfTMC$names, pattern="01$"),] ## ------------------------------------------------------------------------ library(ggplot2) quantile <- stats::quantile ggplot(dfTMC, aes(x=cohort, y=MDM2)) + geom_boxplot() + theme_bw() + coord_flip() + ylab("") ## ------------------------------------------------------------------------ ggplot(dfTMC, aes(x=cohort, fill=TP53)) + geom_bar() + theme_bw() + coord_flip() + ylab("") ## ------------------------------------------------------------------------ sort(table(dfTMC$cohort)) ## ------------------------------------------------------------------------ dfTMC$MDM2b <- cut(dfTMC$MDM2, c(-100,0,100), labels=c("low", "high")) ## ------------------------------------------------------------------------ library(dplyr) library(tidyr) dfTMC %>% group_by(MDM2b, TP53, cohort) %>% summarize(count=n()) %>% unite(TP53_MDM2, TP53, MDM2b) %>% spread(TP53_MDM2, count, fill = 0) ## ------------------------------------------------------------------------ library(survey) library(scales) library(survMisc) # cancer = "TCGA Breast Cancer" cancers <- names(sort(-table(dfTMC$cohort))) for (cancer in cancers[1:11]) { survp <- survfit(Surv(TIME_TO_EVENT/356,EVENT)~TP53+MDM2b, data=dfTMC, subset=cohort == cancer) pl <- autoplot(survp, title = "")$plot + theme_bw() + scale_x_continuous(limits=c(0,10), breaks=0:10) + ggtitle(cancer) + scale_y_continuous(labels = percent, limits=c(0,1)) cat(cancer,"\n") plot(pl) }