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Spencer Barnes authoredac554efd
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server.R 26.17 KiB
library(ggplot2)
library(shiny)
library(shinyjs)
library("survival")
library("survminer")
cat("reading in gene list...")
all.genes<-read.table("tcga_genes.txt",sep = "\t",header = FALSE)
all.genes<-as.character(all.genes[,1])
cat("done\n")
get.filt<-function(var){
if(var$operation=="Boxplot"){
return(
list(
operation=var$operation,
gene=var$gene,
cancer.list=var$cancerType
)
)
}else{
if(var$operation2 == "Manual"){
return(
list(
operation=var$operation,
operation2=var$operation2,
gene=var$gene2,
cancer=var$cancerType2,
cutoff=var$cut.off
)
)
}else if(var$operation2 == "Automatic"){
return(
list(
operation=var$operation,
operation2=var$operation2,
gene=var$gene2,
cancer=var$cancerType2
)
)
}
}
}
shinyServer(
function(input,output,session){
vars<-eventReactive(input$go,{
get.filt(input)
})
output$myPlot <- renderPlot({
#PANCAN BOX PLOT
withProgress(message = 'Making plot', value = 0,{
if(vars()$operation=="Boxplot"){
gene<-toupper(gsub("\\s", "", as.character(vars()$gene)))
cancer.list<-as.character(vars()$cancer.list)
if(!(gene %in% all.genes)){
showNotification(paste0("SORRY! ", gene, " is not a valid gene!"), duration = 8, type = "error")
}else{
data <- data.frame(value=as.numeric(),type=as.character(),cancer=as.character(),pval=as.numeric())
pval <- NULL
count <- 1
n = length(cancer.list) + 1
for(i in cancer.list){
incProgress(1/n, detail = paste0("collating data ", count, " of ", length(cancer.list)))
if(!file.exists(paste0("../Expression/out_genes/",gene,"/",i,".txt"))){
cat("File not present\n")
if(!dir.exists(paste0("../Expression/out_genes/",gene))){
system(paste0("mkdir ../Expression/out_genes/",gene)) cat("Made directory\n")
}
system(paste0("ls ../Expression/*",i,"*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"))
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$type))<2){
if(length(cancer.list) > 1){
showNotification(paste0("WARNING! Project ", i, " does not have normal tissue data! Skipping..."), duration = 8, type = "warning")
next
}else{
showNotification(paste0("SORRY! Project ", i, " does not have normal tissue data! Choose another project."), duration = 8, type = "error")
break
}
}else{
colnames(temp) <- c("value","type","cancer")
print(i)
pval <- c(pval,signif(kruskal.test(value~type,temp)$p.value,2))
data <- rbind(data,temp)
}
count <- count + 1
}
if(nrow(data)==0){
return()
}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=unique(data$cancer)),aes(x,y,label=labels),size=3 , inherit.aes=FALSE) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
#cat("printing",gene,"\n")
print(p + facet_wrap( ~ cancer, scales="free") + ggtitle(gene) + theme(plot.title = element_text(hjust = 0.5)))
incProgress(1/n, detail = paste0("printing plot!"))
}
}
#SURVIVAL PLOTS
}else if(vars()$operation=="Survival"){
#Define Cancer & Gene
oldw <- getOption("warn")
options(warn = -1)
cat("Survival plot\n")
cancer <- vars()$cancer
gene<-toupper(gsub("\\s", "", as.character(vars()$gene)))
cat(paste0(cancer," ",gene,"\n"))
if(!(gene %in% all.genes)){
showNotification(paste0("SORRY! ", gene, " is not a valid gene!"), duration = 8, type = "error")
}else{
n <- 4
incProgress(1/n, detail = "Getting expression data")
#Import Expression and Clinical Data
cat(paste0("going to read expression ",cancer,"\n"))
if(!file.exists(paste0("../Expression/out_genes/",gene,"/",cancer,".txt"))){
cat("File not present\n")
if(!dir.exists(paste0("../Expression/out_genes/",gene))){
system(paste0("mkdir ../Expression/out_genes/",gene))
cat("Made directory\n")
}
system(paste0("ls ../Expression/*",cancer,"*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"))
cat("creating data\n")
}
exp <- read.table(paste0("../Expression/out_genes/",gene,"/",cancer,".txt"),header=T,row.names=1,check.names=F)
#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. cat("going to read clinical data\n")
incProgress(1/n, detail = "Getting 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)
colnames(exp) <- gsub("01A","01",colnames(exp))
gene.exp <- t(exp[gene,])
colnames(gene.exp) <- "gene"
#Fork for Manual vs Automatic selection
if(vars()$operation2 == "Manual"){
cutoff <- as.numeric(vars()$cutoff)
#Setup Data
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$death_event)~event_rna,data)
incProgress(1/n, detail = "Printing plot")
p<-ggsurvplot(s,conf.int=F,xlab="Time(days)",pval=T,pval.coord=c(1000,0.3),risk.table=TRUE,legend.title="",legend=c(0.8,0.8),legend.labs = c( paste(gene,"LOW",sep=" "), paste(gene,"HIGH",sep=" ")))
print(p+ggtitle(paste(cancer, gene,"cutoff: ", cutoff)))
options(warn = oldw)
}else{
cutoff.vals=c(.10, .15, .20, .25, .30, .35, .40, .45, .50)
incProgress(1/n, detail = "Checking Cutoffs")
for(cutoff in cutoff.vals){
cat("\tchecking cutoff value:",cutoff,"\n") ###add status update here
data <- merge(all_clin,gene.exp,by="row.names")
if(is.na(data$gene)[1]){
showNotification(paste0("SORRY! No data for gene ", gene,"!"), duration = 8, type = "error")
break
}
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$gene),cutoff)),]
# run survival analysis
if(length(unique(data$event_rna)) < 2){
next
}
cat("done!\n")
if(cutoff == 0.10){
cat("first run\n")
best.data <- data
best.res <- surv_fit(Surv(as.numeric(as.character(best.data$new_death)),best.data$death_event)~event_rna,best.data)
p<-ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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=" ")))
best.cutoff <- cutoff
}else{
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=TRUE,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=" ")))
if((as.numeric(surv_pvalue(s)[2]) < as.numeric(surv_pvalue(best.res)[2])) & (p$table$data$n.risk[1] > 10) & (p$table$data$n.risk[6] > 10)){
best.data <- data
best.res <- surv_fit(Surv(as.numeric(as.character(best.data$new_death)),best.data$death_event)~event_rna,best.data)
p<-ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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=" ")))
best.cutoff <- cutoff
}
}
}
incProgress(1/n, detail = "Printing plot")
p <- ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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, "cutoff: ", best.cutoff)))
}
}
}
})
})
plotInput = function(){ if(vars()$operation=="Boxplot"){
gene<-toupper(gsub("\\s", "", as.character(vars()$gene)))
cancer.list<-as.character(vars()$cancer.list)
if(!(gene %in% all.genes)){
#showNotification(paste0("SORRY! ", gene, " is not a valid gene!"), duration = 8, type = "error")
}else{
data <- data.frame(value=as.numeric(),type=as.character(),cancer=as.character(),pval=as.numeric())
pval <- NULL
count <- 1
n = length(cancer.list) + 1
for(i in cancer.list){
#incProgress(1/n, detail = paste0("collating data ", count, " of ", length(cancer.list)))
if(!file.exists(paste0("../Expression/out_genes/",gene,"/",i,".txt"))){
cat("File not present\n")
if(!dir.exists(paste0("../Expression/out_genes/",gene))){
system(paste0("mkdir ../Expression/out_genes/",gene))
cat("Made directory\n")
}
system(paste0("ls ../Expression/*",i,"*txt | while read line ; do fname=$(basename $line); head -1 $line > ../Expression/out_genes/",
gene,"/","$fname; grep -wi ",gene," $line >> ../Expression/out_genes/",gene,"/","$fname;done"))
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$type))<2){
if(length(cancer.list) > 1){
#showNotification(paste0("WARNING! Project ", i, " does not have normal tissue data! Skipping..."), duration = 8, type = "warning")
next
}else{
#showNotification(paste0("SORRY! Project ", i, " does not have normal tissue data! Choose another project."), duration = 8, type = "error")
break
}
}else{
colnames(temp) <- c("value","type","cancer")
print(i)
pval <- c(pval,signif(kruskal.test(value~type,temp)$p.value,2))
data <- rbind(data,temp)
}
count <- count + 1
}
if(nrow(data)==0){
return()
}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=unique(data$cancer)),aes(x,y,label=labels),size=3 , inherit.aes=FALSE) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
#cat("printing",gene,"\n")
print(p + facet_wrap( ~ cancer, scales="free") + ggtitle(gene) + theme(plot.title = element_text(hjust = 0.5)))
#incProgress(1/n, detail = paste0("printing plot!"))
}
}
#SURVIVAL PLOTS
}else if(vars()$operation=="Survival"){
#Define Cancer & Gene
oldw <- getOption("warn")
options(warn = -1)
cat("Survival plot\n")
cancer <- vars()$cancer
gene<-toupper(gsub("\\s", "", as.character(vars()$gene)))
cat(paste0(cancer," ",gene,"\n"))
if(!(gene %in% all.genes)){
#showNotification(paste0("SORRY! ", gene, " is not a valid gene!"), duration = 8, type = "error")
}else{ n <- 4
#incProgress(1/n, detail = "Getting expression data")
#Import Expression and Clinical Data
cat(paste0("going to read expression ",cancer,"\n"))
if(!file.exists(paste0("../Expression/out_genes/",gene,"/",cancer,".txt"))){
cat("File not present\n")
if(!dir.exists(paste0("../Expression/out_genes/",gene))){
system(paste0("mkdir ../Expression/out_genes/",gene))
cat("Made directory\n")
}
system(paste0("ls ../Expression/*",cancer,"*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"))
cat("creating data\n")
}
exp <- read.table(paste0("../Expression/out_genes/",gene,"/",cancer,".txt"),header=T,row.names=1,check.names=F)
#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.
cat("going to read clinical data\n")
#incProgress(1/n, detail = "Getting 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)
colnames(exp) <- gsub("01A","01",colnames(exp))
gene.exp <- t(exp[gene,])
colnames(gene.exp) <- "gene"
#Fork for Manual vs Automatic selection
if(vars()$operation2 == "Manual"){
cutoff <- as.numeric(vars()$cutoff)
#Setup Data
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$death_event)~event_rna,data)
#incProgress(1/n, detail = "Printing plot")
p<-ggsurvplot(s,conf.int=F,xlab="Time(days)",pval=T,pval.coord=c(1000,0.3),risk.table=TRUE,legend.title="",legend=c(0.8,0.8),legend.labs = c( paste(gene,"LOW",sep=" "), paste(gene,"HIGH",sep=" ")))
print(p+ggtitle(paste(cancer, gene,"cutoff: ", cutoff)))
options(warn = oldw)
}else{
cutoff.vals=c(.10, .15, .20, .25, .30, .35, .40, .45, .50)
#incProgress(1/n, detail = "Checking Cutoffs")
for(cutoff in cutoff.vals){
cat("\tchecking cutoff value:",cutoff,"\n") ###add status update here
data <- merge(all_clin,gene.exp,by="row.names")
if(is.na(data$gene)[1]){
showNotification(paste0("SORRY! No data for gene ", gene,"!"), duration = 8, type = "error")
break
}
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$gene),cutoff)),]
# run survival analysis
if(length(unique(data$event_rna)) < 2){
next
}
cat("done!\n")
if(cutoff == 0.10){
cat("first run\n")
best.data <- data
best.res <- surv_fit(Surv(as.numeric(as.character(best.data$new_death)),best.data$death_event)~event_rna,best.data)
p<-ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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=" ")))
best.cutoff <- cutoff
}else{
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=TRUE,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=" ")))
if((as.numeric(surv_pvalue(s)[2]) < as.numeric(surv_pvalue(best.res)[2])) & (p$table$data$n.risk[1] > 10) & (p$table$data$n.risk[6] > 10)){
best.data <- data
best.res <- surv_fit(Surv(as.numeric(as.character(best.data$new_death)),best.data$death_event)~event_rna,best.data)
p<-ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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=" ")))
best.cutoff <- cutoff }
}
}
#incProgress(1/n, detail = "Printing plot")
p <- ggsurvplot(best.res,conf.int=F,xlab="Time(days)",pval=TRUE,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, "cutoff: ", best.cutoff)))
}
}
}
}
output$downloadplot <- downloadHandler(
filename = function() { paste0("plot", '.pdf') },
content = function(file) {
#ggsave(file = file, plot = plotInput(), device = "pdf")
pdf(file)
plotInput()
dev.off()
}
)
}
)