Adding preliminary version

README.md

+Author: Min Kim <MinS.Kim@UTSouthwestern.edu> and Spencer Barnes <Spencer.Barnes@UTSouthwestern.edu>
+Date: April 20, 2017    
+Description: Shiny app for analyzing TCGA data for frequently asked requests through the Help-Desk.    
+
+Project name     Cancer type
+ACC		 Adrenocortical Carcinoma 
+BLCA             Bladder Urothelial Carcinoma 
+BRCA             Breast Invasive Carcinoma
+CESC             Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma
+CHOL             Cholangiocarcinoma
+COAD		 Colon Adenocarcinoma
+DLBC		 Lymphoid Neoplasm Diffuse Large B-cell Lymphoma	
+GBM		 Glioblastoma Multiforme
+HNSC		 Head and Neck Squamous Cell Carcinoma
+KICH 		 Kidney Chromophobe
+KIRC	   	 Kidney Renal Clear Cell Carcinoma
+KIRP 		 Kidney Renal Papillary Cell Carcinoma
+LAML		 Acute Myeloid Leukemia
+LGG  		 Brain Lower Grade Glioma
+LIHC	         Liver Hepatocellular Carcinoma
+LUAD  		 Lung Adenocarcinoma
+LUSC             Lung Squamous Cell Carcinoma
+MESO		 Mesothelioma
+OV		 Ovarian Serous Cystadenocarcinoma
+PAAD		 Pancreatic Adenocarcinoma
+PCPG		 Pheochromocytoma and Paraganglioma
+PRAD   		 Prostate Adenocarcinoma
+READ		 Rectum Adenocarcinoma
+SARC		 Sarcoma
+SKCM		 Skin Cutaneous Melanoma
+TGCT	 	 Testicular Germ Cell Tumors
+THCA 		 Thyroid Carcinoma
+THYM	 	 Thymoma
+UCEC		 Uterine Corpus Endometrial Carcinoma
+UCS	 	 Uterine Carcinosarcoma
+UVM	 	 Uveal Melanoma
+

scripts/server.R

+library(ggplot2)
+library(shiny)
+library(shinyjs)
+library("survival")
+library("survminer")
+
+get.filt<-function(var){
+  if(var$operation=="Pancan"){
+    return(
+      list(
+        operation=var$operation,
+        gene=var$gene,
+        cancer.list=var$cancerType
+      )
+    )
+  }else{
+    return(
+      list(
+        operation=var$operation,
+        gene=var$gene2,
+        cancer=var$cancerType2,
+        cutoff=var$cut.off
+      )
+    )
+  }
+}
+
+shinyServer(
+  function(input,output,session){
+    
+    vars<-eventReactive(input$go,{
+      get.filt(input)
+    })
+    
+    output$myPlot <- renderPlot({
+      if(vars()$operation=="Pancan"){
+
+        gene<-as.character(vars()$gene)
+        cancer.list<-as.character(vars()$cancer.list)
+        
+        data <- data.frame(value=as.numeric(),type=as.character(),cancer=as.character(),pval=as.numeric())
+        pval <- NULL
+        for(i in cancer.list){
+          if(!file.exists(paste0("../Expression/out_genes/",gene,"/",i,".txt"))){
+            cat("File not present\n")
+            system(paste0("mkdir ../Expression/out_genes/",gene))
+            cat("Made directory/n")
+            system(paste("ls ../Expression/*txt | while read line ; do fname=$(basename $line); head -1 $line > ../Expression/out_genes/",
+                         gene,"/","$fname; grep -wi ",gene," $line >> ../Expression/out_genes/",gene,"/","$fname;done",sep=""))
+            cat("creating data\n")
+            
+          }
+          temp <- as.data.frame(t(read.table(paste("../Expression/out_genes/",gene,"/",i,".txt",sep=""),header=T,sep="\t",row.names=1)))
+          temp$type <- as.factor(ifelse(grepl("11",row.names(temp)),"Normal","Tumor"))
+          temp$cancer <- rep(i,nrow(temp))
+          if(ncol(temp)<3 | length(unique(temp$type))<2){ 
+            next 
+          }else{
+            colnames(temp) <- c("value","type","cancer")
+            print(i)
+            pval <- c(pval,signif(kruskal.test(value~type,temp)$p.value,2))
+            data <- rbind(data,temp)
+          }
+        }
+        
+        if(nrow(data)==0){
+          cat("Not enough data for this cancer type\n")
+        }else{
+          data$value <- 2^data$value
+          p <- ggplot(data = data, aes(x=type, y=value)) + geom_boxplot(aes(fill=type),outlier.size = 0.2,outlier.alpha=0.8) + scale_y_continuous(limits = c(0, 2000)) + geom_text(data=data.frame(x=1.5,y=1800,labels=pval,cancer=cancer.list),aes(x,y,label=labels),size=3 , inherit.aes=FALSE)
+          #cat("printing",gene,"\n")
+          print(p + facet_wrap( ~ cancer, scales="free") + ggtitle(gene) + theme(plot.title = element_text(hjust = 0.5)))
+
+        }
+      }else if(vars()$operation=="Survival"){
+        #Define Cancer & Gene
+        cat("Survival plot/n")
+        cancer <- vars()$cancer
+        gene <- vars()$gene
+        cat(paste0(cancer," ",gene,"\n"))
+        
+        #Import Expression and Clinical Data
+        cat(paste0("going to read expression ",cancer))
+        exp <- read.table(paste0("../Expression/",cancer,".txt"),header=T,row.names=1,check.names=F) #Instead of importing entire expression matrix should import single gene.
+        # exp <- read.table(paste("Expression/",cancer,".txt",sep=""),header=T,row.names=1,check.names=F)
+        cat("going to read clinical data")
+        clinical <- read.table(paste("../Clinical/",cancer,".txt",sep=""),header=T,row.names=1,sep="\t")
+        
+        
+        #Process Clinical
+        # get the columns that contain data we can use: days to death, new tumor event, last day contact to....
+        ind_keep <- grep("days_to_new_tumor_event_after_initial_treatment",colnames(clinical))
+        
+        # this is a bit tedious, since there are numerous follow ups, let's collapse them together and keep the first value (the higher one) if more than one is available
+        new_tum <- as.matrix(clinical[,ind_keep])
+        new_tum_collapsed <- c()
+        for (i in 1:dim(new_tum)[1]){
+          if(sum(is.na(new_tum[i,])) < dim(new_tum)[2]){
+            m <- min(new_tum[i,],na.rm=T)
+            new_tum_collapsed <- c(new_tum_collapsed,m)
+          } else {
+            new_tum_collapsed <- c(new_tum_collapsed,"NA")
+          }
+        }
+        
+        # do the same to death
+        ind_keep <- grep("days_to_death",colnames(clinical))
+        death <- as.matrix(clinical[,ind_keep])
+        death_collapsed <- c()
+        for (i in 1:dim(death)[1]){
+          if(sum(is.na(death[i,])) < dim(death)[2]){
+            m <- max(death[i,],na.rm=T)
+            death_collapsed <- c(death_collapsed,m)
+          } else {
+            death_collapsed <- c(death_collapsed,"NA")
+          }
+        }
+        
+        # and days last follow up here we take the most recent which is the max number
+        ind_keep <- grep("days_to_last_followup",colnames(clinical))
+        fl <- as.matrix(clinical[,ind_keep])
+        fl_collapsed <- c()
+        for (i in 1:dim(fl)[1]){
+          if(sum(is.na(fl[i,])) < dim(fl)[2]){
+            m <- max(fl[i,],na.rm=T)
+            fl_collapsed <- c(fl_collapsed,m)
+          } else {
+            fl_collapsed <- c(fl_collapsed,"NA")
+          }
+        }
+        
+        # and put everything together
+        all_clin <- data.frame(new_tum_collapsed,death_collapsed,fl_collapsed)
+        colnames(all_clin) <- c("new_tumor_days", "death_days", "followUp_days")
+        
+        # create vector with time to new tumor containing data to censor for new_tumor
+        all_clin$new_time <- c()
+        for (i in 1:length(as.numeric(as.character(all_clin$new_tumor_days)))){
+          all_clin$new_time[i] <- ifelse(is.na(as.numeric(as.character(all_clin$new_tumor_days))[i]),
+                                         as.numeric(as.character(all_clin$followUp_days))[i],as.numeric(as.character(all_clin$new_tumor_days))[i])
+        }
+        
+        # create vector time to death containing values to censor for death
+        all_clin$new_death <- c()
+        for (i in 1:length(as.numeric(as.character(all_clin$death_days)))){
+          all_clin$new_death[i] <- ifelse(is.na(as.numeric(as.character(all_clin$death_days))[i]),
+                                          as.numeric(as.character(all_clin$followUp_days))[i],as.numeric(as.character(all_clin$death_days))[i])
+        }
+        
+        all_clin$death_event <- ifelse(clinical$vital_status == "LIVING", 0,1)
+        row.names(all_clin) <- row.names(clinical)
+        
+        cutoff <- as.numeric(vars()$cutoff)
+        #Setup Data
+        ### Deprecated Code ###
+        # data <- all_clin[names[names %in% row.names(all_clin)],]#[which(substr(colnames(exp),14,14) == "0"),]
+        #all_clin$event_rna <- apply(exp[gene,], 1, function(x) ifelse(abs(x) > median(as.numeric(exp[gene,])),1,0))
+        # gene.exp <- apply(gene.exp,2,function(x) ifelse(x > quantile(as.numeric(x),1-cutoff),1,0))
+        # gene.exp <- t(as.data.frame(sapply(by(gene.exp,rownames(gene.exp),colSums),identity)))
+        colnames(exp) <- gsub("01A","01",colnames(exp))
+        gene.exp <- t(exp[gene,])
+        colnames(gene.exp) <- "gene"
+        data <- merge(all_clin,gene.exp,by="row.names")
+        data$event_rna <- ifelse(data$gene > quantile(as.numeric(data$gene),1-cutoff),1,0)
+        data <- data[which(data$gene > quantile(as.numeric(data$gene),1-cutoff) | data$gene < quantile(as.numeric(data$gene),cutoff)),]
+        # run survival analysis
+        s <- surv_fit(Surv(as.numeric(as.character(data$new_death)),data$death_event)~event_rna,data)
+        p<-ggsurvplot(s,conf.int=F,xlab="Time(days)",pval=T,pval.coord=c(1000,0.3),risk.table=TRUE,legend.title="",legend=c(0.8,0.8),legend.labs = c( paste(gene,"LOW",sep=" "), paste(gene,"HIGH",sep=" ")))
+        print(p+ggtitle(paste(cancer, gene,"cutoff: ", cutoff)))
+      }
+      
+    })
+    
+    
+  }
+)

scripts/ui.R

+shinyUI(
+  pageWithSidebar(
+    headerPanel("TGCA Analysis"),
+    sidebarPanel(
+      selectInput("operation","Select Plot Type",choices = c("Pancan","Survival")),
+      conditionalPanel(condition = "input.operation=='Pancan'",
+                       selectInput("cancerType","Select one or more cancer types",
+                                   choices = substr(list.files("../Expression/",pattern=".txt"),1,
+                                                    nchar(list.files("../Expression/",pattern=".txt"))-4),
+                                   multiple = TRUE),
+                       textInput("gene","Please input one gene of interest",placeholder = "Gene name ie 'TRIM21'")
+                       ),
+      conditionalPanel(condition="input.operation=='Survival'",
+                       selectInput("cancerType2","Select a cancer type",
+                                   choices = substr(list.files("../Expression/",pattern=".txt"),1,
+                                                    nchar(list.files("../Expression/",pattern=".txt"))-4)),
+                       textInput("gene2","Please input one gene of interest",placeholder = "Gene name ie 'TRIM21'",value="TRIM21"),
+                       sliderInput("cut.off","Select a cutoff value",min = .10,max=.50,step = .05,value = .25)
+      ),
+      actionButton("go","GO")
+      #conditionalPanel(condition = "input.operation=='Pancan'"
+                       
+      #                 )
+      #selectInput("Distribution", "Select dist type", 
+      #            choices=c("normal","expo")),
+      #sliderInput("sampleSize","Select sample size: ", 
+      #            min=100,max=5000,value=1000,step=100),
+      #conditionalPanel(condition = "input.Distribution=='normal'",
+      #                 textInput("Mean","Plase Select the mean",10),
+      #                 textInput("sd","Please Select Standard Deviation",3)),
+      #conditionalPanel(condition="input.Distribution == 'expo'",
+      #                 textInput("lambda","Pleas select Expo Lambda",1)
+      #)
+    ),
+    mainPanel(
+      plotOutput("myPlot")
+    )
+    
+  )
+)
+