## ---- echo=FALSE------------------------------------------------------------------------------------------------------------------------------------ library(knitr) opts_chunk$set(comment="", message=FALSE, warning = FALSE, tidy.opts=list(keep.blank.line=TRUE, width.cutoff=150),options(width=150), cache=TRUE, fig.width=10, fig.height=10, eval = FALSE) ## --------------------------------------------------------------------------------------------------------------------------------------------------- # ## try http:// if https:// URLs are not supported # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("RTCGA.PANCAN12") # # or try devel version # require(devtools) # if (!require(RTCGA.PANCAN12)) { # install_github("RTCGA/RTCGA.PANCAN12") # require(RTCGA.PANCAN12) # } # # or if you have RTCGA package then simpler code is # RTCGA::installTCGA('RTCGA.PANCAN12') ## --------------------------------------------------------------------------------------------------------------------------------------------------- # expression.cb <- rbind(expression.cb1, expression.cb2) ## --------------------------------------------------------------------------------------------------------------------------------------------------- # grep(expression.cb[,1], pattern="MDM2") # # MDM2 <- expression.cb[8467,-1] # MDM2v <- t(MDM2) ## --------------------------------------------------------------------------------------------------------------------------------------------------- # grep(mutation.cb[,1], pattern="TP53$", 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) # } #