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5.GenePanel.R
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5.GenePanel.R
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# loading packages
library(survival)
library(survminer)
library(forestplot)
library(survivalROC)
library(survMisc)
library(ggplot2)
library(GGally)
library(parallel)
library(glmnet)
library(caret)
library(dplyr)
library(limma)
library(tableone)
library(ggpubr)
# ---------------opt input-------------------
opt <- NULL
opt$train_prefix <- "SHH.train"
opt$outdir <- "PanelScreen"
opt$defFunctions <- TRUE
opt$ifuniv <- TRUE
# -----------------def functions-------------------
if(opt$defFunctions){
get_feature_survival_info <- function(exp,survival,time_column,outcome_column,dead_status,alive_status){
data.expr=read.delim(exp,row.names=1,stringsAsFactors=T,check.names=F,sep="\t")
data.p=read.delim(survival,row.names=1,stringsAsFactors=F,check.names=F,sep="\t")
common = intersect(colnames(data.expr), rownames(data.p))
m1 = match(common, colnames(data.expr))
m2 = match(common, rownames(data.p))
data.expr = data.expr[,m1]
data.p = data.p[m2,]
da=data.frame(cbind(data.p[,time_column-1],data.p[,outcome_column-1],t(data.expr)),check.names = F, stringsAsFactors = F)
colnames(da)[1:2]=c("Time","Dead")
da$Dead[da$Dead == dead_status]=1
da$Dead[da$Dead == alive_status]=0
da[1:dim(da)[2]]=lapply(da[1:dim(da)[2]],FUN=function(x){as.numeric(x)})
da = da[!(is.na(da$Time) | da$Time<=0 | is.na(da$Dead)),]
return(da)
}
univ_analysis <- function(features,da){
univ_formulas <- sapply(features,function(x) as.formula(paste('Surv(Time, Dead)~', paste0('`',x,'`'))))
univ_models <- lapply( univ_formulas, function(x){coxres <- try(coxph(x, data = da),silent = T);if(class(coxres) %in% "try-error"){return(NULL)}else{return(coxres)}})
univ_models <- univ_models[!sapply(univ_models,is.null)]
univ_results <- lapply(univ_models,
function(x){
suma <- summary(x)
res <- sapply(rownames(suma$coef),function(x){
p.value<-signif(suma$coef[x,'Pr(>|z|)'])
beta <- signif(suma$coef[x,'coef']);#coeficient beta
HR <- signif(suma$coef[x,'exp(coef)']);#exp(beta)
HR.confint.lower <- signif(suma$conf.int[x,"lower .95"])
HR.confint.upper <- signif(suma$conf.int[x,"upper .95"])
CI <- paste0(HR.confint.lower, "-", HR.confint.upper)
res<-c(beta, HR, CI, p.value)
names(res)<-c("univ_beta", "univ_HR", "univ_95% CI for HR", "univ_p.value")
return(res)
})
})
res <- as.data.frame(t(as.data.frame(univ_results, check.names = FALSE)))
res$univ_beta <- sapply(res$univ_beta,function(x) as.numeric(as.character(x)))
res$univ_HR <- sapply(res$univ_HR,function(x) as.numeric(as.character(x)))
res$`univ_95% CI for HR` <- sapply(res$`univ_95% CI for HR`,as.character)
res$`univ_p.value` <- sapply(res$`univ_p.value`,function(x) as.numeric(as.character(x)))
return(res)
}
lasso_screen <- function(da){
survInfo <- as.matrix(da[,1:2])
colnames(survInfo) <- c("time","status")
mat <- as.matrix(da[,3:ncol(da)])
cvfit <- cv.glmnet(x=mat,y=survInfo,family="cox")
cvfit_info <- coef(cvfit,s=cvfit$lambda.min)
cvfit_act <- names(cvfit_info[which(cvfit_info!=0),])
if(length(cvfit_act)>0){
return(cvfit_act)
}else{
return(NULL)
}
}
cross_screen <- function(da,split,seed){
set.seed(seed)
trainIndex <- createDataPartition(da$Dead, p=split, list=FALSE)
data_train <- da[trainIndex,]
res <- lasso_screen(data_train)
return(res)
}
plot.shiny.km <- function(time, death, x, cut, title = "", ids = NULL,
subset = rep(TRUE, length(time)),
col = NULL, xlab = NULL, ylab = NULL, hr.inverse = FALSE,
no.plot = FALSE, optimal.cut = FALSE, assign.val=NULL,dir=NULL,output=NULL,...) {
## filter out missing data ##
subset = subset & !is.na(time) & !is.na(death) & !is.na(x) & !is.nan(x)
x = x[subset]; time = time[subset]; death = death[subset];
if (!is.null(ids)) ids = ids[subset]
if (length(x) ==0 | length(time) == 0 | length(death) == 0
| length(unique(death)) < 2) {
return(invisible(NULL))
}
if (is.null(ids)) ids = 1:length(x)
km.data = data.frame(ID = ids, X = x, Group = NA, time = time, event = death)
## settings for median cutoff ##
#cut = median(x)
p.adj = NULL
if(is.numeric(x)){
#upper = "upper 50%"; lower = "lower 50%"
#upper = paste0("upper ", 100-cut, "%")
#lower = paste0("lower ", cut, "%")
upper = "high risk group"
lower = "low risk group"
cut = quantile(x,cut/100)
## find optimal cutoff if specified ##
if (optimal.cut) {
mod <- coxph(Surv(time, event) ~ X, data = km.data)
cc = try(cutp(mod), silent = TRUE)
if (class(cc) %in% "try-error") {
return(invisible(NULL))
}
cut = cc$X$X[1]
# assign(assign.val,cut,envir = .GlobalEnv)
p.adj = cc$X$p[1]
percentile = round(sum(x>=cut) / length(x) * 100,2)
#upper = paste0("upper ", percentile, "%")
#lower = paste0("lower ", 100-percentile, "%")
}
if(!is.null(assign.val)){
assign(assign.val,cut,envir = .GlobalEnv)
## split into high and low groups using appropriate cutoff ##
}
newX = x
newX[x > cut] = upper
newX[x <= cut] = lower
expression = factor(newX,levels=c("low risk group","high risk group"))
km.data$Group = expression
}else{
km.data$Group = as.factor(x)
}
if (no.plot) return(invisible(km.data))
n = length(levels(km.data$Group))
km.group = try(survdiff(Surv(time, death) ~ as.numeric(km.data$Group)), silent = TRUE)
km.group1 = try(coxph(Surv(time, death) ~ as.numeric(km.data$Group)), silent = TRUE)
if (class(km.group) %in% "try-error") return(invisible(NULL))
if (class(km.group1) %in% "try-error") return(invisible(NULL))
p.km = 1 - pchisq(km.group$chisq,n-1)
hr = exp(km.group1$coefficients)
conf = paste0("(",signif(summary(km.group1)$conf.int[,"lower .95"],2),"-",signif(summary(km.group1)$conf.int[,"upper .95"],2),")")
n = km.group1$n
if (hr.inverse) hr = 1/hr
hr.str = paste0("HR=", round(hr,2),conf,", ")
p.str = paste0("P=", round(p.km,4))
if (!is.null(p.adj)) {
p.str = paste0(p.str, ", P(cutoff) = ", round(p.adj,4))
}
if (title=="") {
title = paste(hr.str, p.str, sep = "")
} else {
title = paste0(title, "\n",hr.str, p.str)
}
if (is.null(xlab)) xlab = "Time"
if (is.null(ylab)) ylab = "Survival"
## plot graph ### ggplot2/GGally form
km.group2 = surv_fit(Surv(time, death) ~ Group,data=km.data)
#if(F){
#km.group = ggsurv(km.group2,
# main = title,
# xlab = xlab, ylab = ylab,...) +
# ggplot2::coord_cartesian(ylim = c(0, 1))
# theme(plot.title = element_text(vjust = 0.5, colour = "black"))
#}
km.group = ggsurvplot(km.group2,data=km.data,
pval = TRUE, conf.int = F,
risk.table = TRUE, # Add risk table
risk.table.col = "strata", # Change risk table color by groups
linetype = "strata", # Change line type by groups
surv.median.line = "none", # Specify median survival
ggtheme = theme_bw(), # Change ggplot2 theme
palette = c("#E7B800", "#2E9FDF"))
return(km.group)
#return(invisible(km.data))
}
plot.shiny.km1 <- function(time, death, x, cut, title = "", ids = NULL,
subset = rep(TRUE, length(time)),
col = NULL, xlab = NULL, ylab = NULL, hr.inverse = FALSE,
no.plot = FALSE, optimal.cut = FALSE, assign.val=NULL,dir=NULL,output=NULL,...) {
## filter out missing data ##
subset = subset & !is.na(time) & !is.na(death) & !is.na(x) & !is.nan(x)
x = x[subset]; time = time[subset]; death = death[subset];
if (!is.null(ids)) ids = ids[subset]
if (length(x) ==0 | length(time) == 0 | length(death) == 0
| length(unique(death)) < 2) {
return(invisible(NULL))
}
if (is.null(ids)) ids = 1:length(x)
km.data = data.frame(ID = ids, X = x, Group = NA, time = time, event = death)
## settings for median cutoff ##
#cut = median(x)
p.adj = NULL
if(is.numeric(x)){
#upper = "upper 50%"; lower = "lower 50%"
#upper = paste0("upper ", 100-cut, "%")
#lower = paste0("lower ", cut, "%")
upper = "high risk group"
lower = "low risk group"
#cut = quantile(x,cut/100)
## find optimal cutoff if specified ##
if (optimal.cut) {
mod <- coxph(Surv(time, event) ~ X, data = km.data)
cc = try(cutp(mod), silent = TRUE)
if (class(cc) %in% "try-error") {
return(invisible(NULL))
}
cut = cc$X$X[1]
# assign(assign.val,cut,envir = .GlobalEnv)
p.adj = cc$X$p[1]
percentile = round(sum(x>=cut) / length(x) * 100,2)
#upper = paste0("upper ", percentile, "%")
#lower = paste0("lower ", 100-percentile, "%")
}
if(!is.null(assign.val)){
assign(assign.val,cut,envir = .GlobalEnv)
## split into high and low groups using appropriate cutoff ##
}
newX = x
newX[x > cut] = upper
newX[x <= cut] = lower
expression = factor(newX,levels=c("low risk group","high risk group"))
km.data$Group = expression
}else{
km.data$Group = as.factor(x)
}
if (no.plot) return(invisible(km.data))
n = length(levels(km.data$Group))
km.group = try(survdiff(Surv(time, death) ~ as.numeric(km.data$Group)), silent = TRUE)
km.group1 = try(coxph(Surv(time, death) ~ as.numeric(km.data$Group)), silent = TRUE)
if (class(km.group) %in% "try-error") return(invisible(NULL))
if (class(km.group1) %in% "try-error") return(invisible(NULL))
p.km = 1 - pchisq(km.group$chisq,n-1)
hr = exp(km.group1$coefficients)
conf = paste0("(",signif(summary(km.group1)$conf.int[,"lower .95"],2),"-",signif(summary(km.group1)$conf.int[,"upper .95"],2),")")
n = km.group1$n
if (hr.inverse) hr = 1/hr
hr.str = paste0("HR=", round(hr,2),conf,", ")
p.str = paste0("P=", round(p.km,4))
if (!is.null(p.adj)) {
p.str = paste0(p.str, ", P(cutoff) = ", round(p.adj,4))
}
if (title=="") {
title = paste(hr.str, p.str, sep = "")
} else {
title = paste0(title, "\n",hr.str, p.str)
}
if (is.null(xlab)) xlab = "Time"
if (is.null(ylab)) ylab = "Survival"
## plot graph ### ggplot2/GGally form
km.group2 = surv_fit(Surv(time, death) ~ Group,data=km.data)
#if(F){
#km.group = ggsurv(km.group2,
# main = title,
# xlab = xlab, ylab = ylab,...) +
# ggplot2::coord_cartesian(ylim = c(0, 1))
# theme(plot.title = element_text(vjust = 0.5, colour = "black"))
#}
km.group = ggsurvplot(km.group2,data=km.data,
pval = TRUE, conf.int = F,
risk.table = TRUE, # Add risk table
risk.table.col = "strata", # Change risk table color by groups
linetype = "strata", # Change line type by groups
surv.median.line = "none", # Specify median survival
ggtheme = theme_bw(), # Change ggplot2 theme
palette = c("#E7B800", "#2E9FDF"))
#plot(km.group)
return(km.group)
#return(invisible(km.data))
}
multi_cox <- function(sig_features,data){
#surv_da <- data[,c("Time","Dead")]
#data <- data[,sig_features]
#indx <- sapply(data, is.factor)
#data[indx] <- lapply(data[indx],function(x) as.numeric(x))
#data <- cbind(surv_da,data)
sig_features_formula <- unlist(sapply(sig_features,function(x){paste0('`',x,'`')}))
multi_formula <- as.formula(paste('Surv(Time, Dead) ~',paste(sig_features_formula,collapse='+')))
multi_models <- coxph(multi_formula, data = data)
suma <- summary(multi_models)
#rownames(suma$coef) <- sig_features
#rownames(suma$conf.int) <- sig_features
multi_res <- sapply(rownames(suma$coef),function(x){
p.value <- signif(suma$coef[x,'Pr(>|z|)'])
beta <- signif(suma$coef[x,'coef'])
HR <- signif(suma$coef[x,'exp(coef)'])
HR.confint.lower <- signif(suma$conf.int[x,"lower .95"])
HR.confint.upper <- signif(suma$conf.int[x,"upper .95"])
CI <- paste0(HR.confint.lower, "-", HR.confint.upper)
res<-c(beta, HR, CI, p.value)
names(res)<-c("multi_beta", "multi_HR", "multi_95% CI for HR", "multi_p.value")
return(res)
})
multi_res <- as.data.frame(t(multi_res))
multi_res$multi_beta <- sapply(multi_res$multi_beta,function(x) as.numeric(as.character(x)))
multi_res$multi_HR <- sapply(multi_res$multi_HR,function(x) as.numeric(as.character(x)))
multi_res$`multi_95% CI for HR` <- sapply(multi_res$`multi_95% CI for HR`,as.character)
multi_res$`multi_p.value` <- sapply(multi_res$`multi_p.value`,function(x) as.numeric(as.character(x)))
return(multi_res)
}
forest_plot_multi <- function(res,prefix){
result <- cbind(rownames(res),res)
colnames(result)[1] <- "features"
write.table(result,paste0(opt$outdir,'/',prefix,'_res.txt'),sep="\t",quote=F,row.names=F)
res$multi_beta <- signif(res$multi_beta,2)
res$multi_HR <- signif(res$multi_HR,2)
res$`multi_95% CI for HR` <- sapply(res$`multi_95% CI for HR`,function(x){paste0(signif(as.numeric(unlist(strsplit(x,'-'))),2),collapse = "-")})
res$multi_p.value <- signif(res$multi_p.value,4)
res$mean <- as.numeric(as.character(res$multi_HR))
res$lower <- as.numeric(as.character(sapply(res$`multi_95% CI for HR`,function(x){unlist(strsplit(as.character(x),'-'))[1]})))
res$upper <- as.numeric(as.character(sapply(res$`multi_95% CI for HR`,function(x){unlist(strsplit(as.character(x),'-'))[2]})))
text <- rbind(colnames(res)[1:4],as.matrix(res[1:4]))
rownames(text)[1] <- 'features'
text <- cbind(rownames(text),text)
plotHR(text,res,prefix)
}
forest_plot <- function(res,prefix){
result <- cbind(rownames(res),res)
colnames(result)[1] <- "features"
write.table(result,paste0(opt$outdir,'/',opt$prefix,'.',prefix,'_res.txt'),sep="\t",quote=F,row.names=F)
res$mean <- as.numeric(as.character(res[,2]))
res$lower <- as.numeric(as.character(sapply(res[,3],function(x){unlist(strsplit(as.character(x),'-'))[1]})))
res$upper <- as.numeric(as.character(sapply(res[,3],function(x){unlist(strsplit(as.character(x),'-'))[2]})))
text <- rbind(colnames(res)[1:4],as.matrix(res[1:4]))
rownames(text)[1] <- 'features'
text <- cbind(rownames(text),text)
plotHR(text,res,prefix)
}
risk_cal <- function(da,res){
risk_score <- NULL
for(i in 1:dim(da)[1]){
score <- sum(res$multi_beta * da[i,rownames(res)])
names(score) <- rownames(da)[i]
risk_score <- c(risk_score,score)
}
return(risk_score)
}
plot_roc <- function(predict_obj,dir,prefix){
pdf(paste0(opt$outdir,"/",prefix,"_risk_score_roc.pdf"))
plot(predict_obj[[1]]$FP, predict_obj[[1]]$TP, type="l", xlim=c(0,1), ylim=c(0,1),
xlab="False positive rate",ylab="True positive rate",main="Time-dependent ROC curve",col="red",lwd=2,lty=1)
lines(predict_obj[[2]]$FP, predict_obj[[2]]$TP,type="l",col="blue",lwd=2,lty=2)
lines(predict_obj[[3]]$FP, predict_obj[[3]]$TP,type="l",col="black",lwd=2,lty=5)
lg1 = paste0("AUC at 1 year","=",format(predict_obj[[1]]$AUC,dig=3))
lg2 = paste0("AUC at 3 year","=",format(predict_obj[[2]]$AUC,dig=3))
lg3 = paste0("AUC at 5 year","=",format(predict_obj[[3]]$AUC,dig=3))
legend("bottomright",legend=c(lg1,lg2,lg3),bty="n",col=c("red","blue","black"),lty=c(1,2,5))
abline(0,1,lwd=1)
dev.off()
}
plotHR <- function(text,res,prefix){
text[1,] <- gsub("univ_|multi_","",colnames(text))
png(paste0(opt$outdir,'/',prefix,'_forestplot.png'),width=960, height=(1+dim(res)[1])*50)
colinfo <- lapply(1:nrow(res),function(x){ifelse(x %% 2==1,
return(gpar(col="gray",lineend="butt",columns=c(2:6))),return(gpar(col='gray',lineend="butt",columns=c(2:6))))})
names(colinfo) <- 1:nrow(res)+1
forestplot(as.matrix(text),mean=c(NA,res$mean),lower = c(NA,res$lower),upper = c(NA,res$upper),
graph.pos = 4,graphwidth = unit(60,'mm'),lineheight = unit(12,'mm'),line.margin = unit(5,'mm'),
colgap = unit(4,'mm'),zero = 1,cex=0.9,col=fpColors(box="black",lines="black",zero="gray50"),
txt_gp = fpTxtGp(label=gpar(cex=1.25),ticks=gpar(cex=1.1),xlab=gpar(cex=1.2),title = gpar(cex=1.2)),
lwd.ci=1.5,ci.vertices = T,title="Hazard Ratio",boxsize = 0.3,hrzl_lines = colinfo)
dev.off()
}
cross_validation <- function(data_trian,data_validation,Group3,Group4,WNT,gene_panel,seed){
set.seed(seed)
train_data <- data_train
test_data <- data_validation
train_multi_cox_res <- multi_cox(gene_panel,train_data)
forest_plot_multi(train_multi_cox_res,"SHH_train_data_multicox")
train_rs <- risk_cal(train_data,train_multi_cox_res)
train_auc_1year_r1 <- survivalROC(Stime=train_data$Time,status=train_data$Dead,marker=train_rs,predict.time=1,span = 0.25*length(train_rs)^(-0.20))
train_auc_3year_r1 <- survivalROC(Stime=train_data$Time,status=train_data$Dead,marker=train_rs,predict.time=3,span = 0.25*length(train_rs)^(-0.20))
train_auc_5year_r1 <- survivalROC(Stime=train_data$Time,status=train_data$Dead,marker=train_rs,predict.time=5,span = 0.25*length(train_rs)^(-0.20))
train_auc_obj_r1 <- list(train_auc_1year=train_auc_1year_r1,train_auc_3year=train_auc_3year_r1,train_auc_5year=train_auc_5year_r1)
plot_roc(train_auc_obj_r1,opt$outdir,"SHH_train_data")
pdf(paste0(opt$outdir,'/SHH_train_data_kmplot.pdf'))
train_data_km <- plot.shiny.km(train_data$Time,train_data$Dead,train_rs,cut=50,optimal.cut = T,assign.val="Train_cutoff",xlab="time (years)",ylab="OS probability")
print(train_data_km,newpage=F)
dev.off()
test_rs <- risk_cal(test_data,train_multi_cox_res)
test_auc_1year_r1 <- survivalROC(Stime=test_data$Time,status=test_data$Dead,marker=test_rs,predict.time=1,span = 0.25*length(test_rs)^(-0.20))
test_auc_3year_r1 <- survivalROC(Stime=test_data$Time,status=test_data$Dead,marker=test_rs,predict.time=3,span = 0.25*length(test_rs)^(-0.20))
test_auc_5year_r1 <- survivalROC(Stime=test_data$Time,status=test_data$Dead,marker=test_rs,predict.time=5,span = 0.25*length(test_rs)^(-0.20))
test_auc_obj_r1 <- list(test_auc_1year=test_auc_1year_r1,test_auc_3year=test_auc_3year_r1,test_auc_5year=test_auc_5year_r1)
plot_roc(test_auc_obj_r1,opt$outdir,"SHH_test_data")
pdf(paste0(opt$outdir,'/SHH_test_data_kmplot.pdf'))
test_data_km <- plot.shiny.km1(test_data$Time,test_data$Dead,test_rs,cut=Train_cutoff,optimal.cut = F,assign.val=NULL,xlab="time (years)",ylab="OS probability")
print(test_data_km,newpage=F)
dev.off()
Group3_rs <- risk_cal(Group3,train_multi_cox_res)
Group3_auc_1year_r1 <- survivalROC(Stime=Group3$Time,status=Group3$Dead,marker=Group3_rs,predict.time=1,span = 0.25*length(Group3_rs)^(-0.20))
Group3_auc_3year_r1 <- survivalROC(Stime=Group3$Time,status=Group3$Dead,marker=Group3_rs,predict.time=3,span = 0.25*length(Group3_rs)^(-0.20))
Group3_auc_5year_r1 <- survivalROC(Stime=Group3$Time,status=Group3$Dead,marker=Group3_rs,predict.time=5,span = 0.25*length(Group3_rs)^(-0.20))
Group3_auc_obj_r1 <- list(Group3_auc_1year=Group3_auc_1year_r1,Group3_auc_3year=Group3_auc_3year_r1,Group3_auc_5year=Group3_auc_5year_r1)
plot_roc(Group3_auc_obj_r1,opt$outdir,"Group3")
pdf(paste0(opt$outdir,'/Group3_kmplot.pdf'))
Group3_km <- plot.shiny.km1(Group3$Time,Group3$Dead,Group3_rs,cut=Train_cutoff,optimal.cut = F,assign.val=NULL,xlab="time (years)",ylab="OS probability")
print(Group3_km,newpage=F)
dev.off()
Group4_rs <- risk_cal(Group4,train_multi_cox_res)
Group4_auc_1year_r1 <- survivalROC(Stime=Group4$Time,status=Group4$Dead,marker=Group4_rs,predict.time=1,span = 0.25*length(Group4_rs)^(-0.20))
Group4_auc_3year_r1 <- survivalROC(Stime=Group4$Time,status=Group4$Dead,marker=Group4_rs,predict.time=3,span = 0.25*length(Group4_rs)^(-0.20))
Group4_auc_5year_r1 <- survivalROC(Stime=Group4$Time,status=Group4$Dead,marker=Group4_rs,predict.time=5,span = 0.25*length(Group4_rs)^(-0.20))
Group4_auc_obj_r1 <- list(Group4_auc_1year=Group4_auc_1year_r1,Group4_auc_3year=Group4_auc_3year_r1,Group4_auc_5year=Group4_auc_5year_r1)
plot_roc(Group4_auc_obj_r1,opt$outdir,"Group4")
pdf(paste0(opt$outdir,'/Group4_kmplot.pdf'))
Group4_km <- plot.shiny.km1(Group4$Time,Group4$Dead,Group4_rs,cut=Train_cutoff,optimal.cut = F,assign.val=NULL,xlab="time (years)",ylab="OS probability")
print(Group4_km,newpage=F)
dev.off()
WNT_rs <- risk_cal(WNT,train_multi_cox_res)
WNT_auc_1year <- survivalROC(Stime=WNT$Time,status=WNT$Dead,marker=WNT_rs,predict.time=1,method="KM")
WNT_auc_3year <- survivalROC(Stime=WNT$Time,status=WNT$Dead,marker=WNT_rs,predict.time=3,method="KM")
WNT_auc_5year <- survivalROC(Stime=WNT$Time,status=WNT$Dead,marker=WNT_rs,predict.time=5,method="KM")
#WNT_auc_1year_r1 <- survivalROC(Stime=WNT$OS.years,status=WNT$Dead,marker=WNT_rs,predict.time=1,span = 0.25*length(WNT_rs)^(-0.20))
#WNT_auc_3year_r1 <- survivalROC(Stime=WNT$OS.years,status=WNT$Dead,marker=WNT_rs,predict.time=3,span = 0.25*length(WNT_rs)^(-0.20))
#WNT_auc_5year_r1 <- survivalROC(Stime=WNT$OS.years,status=WNT$Dead,marker=WNT_rs,predict.time=5,span = 0.25*length(WNT_rs)^(-0.20))
WNT_auc_obj <- list(WNT_auc_1year=WNT_auc_1year,WNT_auc_3year=WNT_auc_3year,WNT_auc_5year=WNT_auc_5year)
#WNT_auc_obj_r1 <- list(WNT_auc_1year=WNT_auc_1year_r1,WNT_auc_3year=WNT_auc_3year_r1,WNT_auc_5year=WNT_auc_5year_r1)
plot_roc(WNT_auc_obj,opt$outdir,"WNT")
#plot_roc(WNT_auc_obj_r1,opt$outdir,"WNT")
print(WNT_rs)
pdf(paste0(opt$outdir,'/WNT_kmplot.pdf'))
WNT_km <- plot.shiny.km1(WNT$Time,WNT$Dead,WNT_rs,cut=Train_cutoff,optimal.cut = F,assign.val=NULL,xlab="time (years)",ylab="OS probability")
print(WNT_km,newpage=F)
dev.off()
print(Train_cutoff)
riskScore <- as.data.frame(c(train_rs,test_rs))
return(riskScore)
}
customPDF <- function(tMat,lightgrey,title){
colorPDF1 <- ifelse(c(1:nrow(tMat)) %in% lightgrey,"#F2F2F2","#FFFFFF")
pdftab <- NULL
pdftab <- ggtexttable(tMat, rows = NULL,cols = NULL, theme = ttheme(padding = unit(c(12, 4), "mm"),
tbody.style = tbody_style(fill = colorPDF1, hjust=0, x=0.1)))
for (iv in 1:ncol(tMat)) {
pdftab <- table_cell_font(pdftab, row = 1, column = iv,face = "bold")
pdftab <- table_cell_bg(pdftab, row = 1, column = iv,fill="#A9A9A9",color = "#A9A9A9",linewidth = NULL)
pdftab <- table_cell_font(pdftab, row = 2, column = iv,face = "bold")
pdftab <- table_cell_bg(pdftab, row = 2, column = iv,fill="#A9A9A9",color = "#A9A9A9",linewidth = NULL)
}
for (v in 3:nrow(tMat)) {
pdftab <- table_cell_font(pdftab, row = v, column = 1,face = "bold")
# pdftab <- table_cell_bg(pdftab, row = v, column = 1,fill="#D9D9D9",color = "#FFFFFF",linewidth = NULL)
pdftab <- table_cell_font(pdftab, row = v, column = 2,face = "bold")
# pdftab <- table_cell_bg(pdftab, row = v, column = 2,fill="#D9D9D9",color = "#FFFFFF",linewidth = NULL)
}
for (vii in which(tMat[2,]=="p")) {
noblankcp <- which(tMat[,1]!="")[-1]
for (vi in noblankcp) {
pvalue1 <- as.numeric(tMat[vi,vii])
colorpdfp <- ifelse(pvalue1<=0.05,"#FFCC66","#D3D3D3")
pdftab <- table_cell_bg(pdftab, row = vi, column = vii,fill=colorpdfp,color = "#FFFFFF",linewidth = NULL)
}
}
pdf(paste0(opt$outdir,'/ClinicStat/',title,'.pdf'),width=ncol(tMat)*2.5,height=ceiling(nrow(tMat)/3))
print(pdftab)
dev.off()
}
makeTab <- function(x){
tab <- CreateTableOne(vars = clinicVars, strata = x , data = data, factorVars = factorVars)
xDim <- max(length(tab$ContTable),length(tab$CatTable))
varlist <- tab$MetaData$vars
varFactors <- tab$MetaData$varFactors
varNumerics <- tab$MetaData$varNumerics
getFreqm <- function(y){
freq <- NULL
for (i in 1:xDim) {
freq1 <- as.integer(tab$CatTable[[i]][[y]]$freq)
freq <- c(freq,freq1)
}
freqm <- min(freq)
return(freqm)
}
nonnormal <- varNumerics
tMat <- print(tab, showAllLevels = TRUE, quote = FALSE, noSpaces = TRUE, printToggle = FALSE,pDigits = 30,exact = NULL, nonnormal = nonnormal)
pvalue <- as.numeric(tMat[,"p"])
tMat[,"p"] <- gsub(" NA","", format(pvalue,digits = 4,scientific = TRUE))
# tMat <- gsub("exact","fisher_exact",tMat)
gettesttext <- function(tMatrow){
n <- tMatrow
coln <- ncol(tMat)
if(tMat[n,coln-1]==" "){
return("")
}else{
if(tMat[n,coln]=="nonnorm"){
return("Kruskal-Wallis Test")
}else if(tMat[n,coln]=="exact"){
return("Fisher’s exact test")
}else if(tMat[n,coln]==""){
if(tMat[n,1]==""){
return("One-Way Anova")
}else{
return("Chisq Test")
}
}else{return("")}
}
}
testtext <- sapply(1:nrow(tMat),gettesttext)
tMat[,ncol(tMat)] <- testtext
n <- length(tMat[1,])-2
tMat <- rbind(c("",x,rep("", n)),colnames(tMat), tMat)
tMat <- cbind(row.names(tMat), tMat)
row.names(tMat) <- NULL
colnames(tMat) <- NULL
noblankcp <- which(tMat[,which(tMat[2,]=="p")]!="")
noblankc <- which(tMat[,1]!="")
ii <- 2
lightgrey<- NULL
while(ii<length(noblankc)){
lightgrey1 <- seq(noblankc[ii],noblankc[ii+1]-1,1)
lightgrey <- c(lightgrey,lightgrey1)
ii <- ii+2
}
if(length(noblankc)==ii){
lightgrey1 <- seq(noblankc[ii],nrow(tMat),1)
lightgrey <- c(lightgrey,lightgrey1)
}
customPDF(tMat,lightgrey,x)
return(tMat)
}
}
# -------------------create dir--------------------
if(!dir.exists(opt$outdir)){
dir.create(opt$outdir)
}
if(!dir.exists(paste0(opt$outdir,"/ClinicStat"))){
dir.create(paste0(opt$outdir,"/ClinicStat"))
}
# --------------------run train--------------------
load("data.Rdata")
set.seed(10086)
#-------------------Clinc Table---------------------
#ALL
data <- ALL.clindata
factorVars <- NULL
clinicVars <- colnames(ALL.clindata)[-c(1,12)]
strata <- colnames(ALL.clindata)[12]
tMats <- makeTab(strata)
#SHH
data <- SHH.clindata
clinicVars <- colnames(SHH.clindata)[c(2:11)]
strata <- colnames(SHH.clindata)[12]
tMats <- makeTab(strata)
#--------------------difgene Analysis----------------
exprSet <- SHH.expdata[-1]
rownames(exprSet) <- SHH.expdata[,1]
pheno <- SHH.sample.group
pheno <- pheno[match(colnames(exprSet),pheno$Samples),]
Group<-factor(pheno$Group,levels=c("mRNAsi_lower","mRNAsi_higher"))
design <- model.matrix(~0+Group)
colnames(design)=c("mRNAsi_lower","mRNAsi_higher")
rownames(design)=pheno$Samples
contrast.matrix<-makeContrasts(paste0(unique(pheno$Group),collapse = "-"),levels = design)
fit <- lmFit(exprSet,design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
tempOutput = topTable(fit2, coef=1, n=Inf)
nrDEG = na.omit(tempOutput)
features <-rownames(nrDEG[nrDEG$adj.P.Val<0.05,])
#----------------------univ cox----------------------
if(opt$ifuniv){
univ_res <- univ_analysis(features,da=da)
univ_res <- univ_res[!is.na(univ_res$univ_beta),]
rownames(univ_res) <- gsub("`","",rownames(univ_res))
univ_sig_res <- univ_res[univ_res$univ_p.value<=0.001,]
#****************************************************************************
newDF1 <- cbind(rownames(univ_res),univ_res)
newDF1 <- as.data.frame(lapply(newDF1,unlist),stringsAsFactors = F,check.names = F)
colnames(newDF1)[1] <- "features"
write.table(newDF1,paste0(opt$outdir,"/",opt$train_prefix,".univ_res.txt"),row.names = F,col.names = T,quote=F,sep="\t")
newDF <- dplyr::filter(newDF1,univ_p.value<=0.001)
write.table(newDF,paste0(opt$outdir,"/",opt$train_prefix,".univ_sig_res.txt"),row.names = F,col.names = T,quote=F,sep="\t")
#****************************************************************************
univ_sig_genes <- rownames(univ_sig_res)
features <- univ_sig_genes
}
do_da <- cbind(da[,1:2],da[,features]) # select univ cox sig gene for lesso
lasso_gene_list <- lapply(1:1000,FUN=function(x){print(x);res <- cross_screen(do_da,0.7,x);return(res)}) # 1000 times lesso with 70% sampling
lasso_gene_list <- lasso_gene_list[!sapply(lasso_gene_list,is.null)]
lasso_gene_freq <- table(unlist(lasso_gene_list))
lasso_gene_freq <- lasso_gene_freq[order(lasso_gene_freq,decreasing = T)]
lasso_gene_freq <- lasso_gene_freq[which(lasso_gene_freq>200)] # select gene by freq
gene_panel <- names(lasso_gene_freq)
# ----------------select gene panel data----------------------
data_train <- dplyr::select(da,c("Time","Dead",gene_panel))
# ------------------------run validation----------------------------
riskScore <- cross_validation(data_train,data_validations[[1]],data_validations[[2]],data_validations[[3]],data_validations[[4]],gene_panel,1)
SHH <- SHH.clindata[-1]
rownames(SHH) <- SHH.clindata[,1]
riskScore <- riskScore[rownames(SHH),,drop=F]
new_SHH <- cbind(SHH,riskScore)
colnames(new_SHH)[ncol(new_SHH)] <- "prognostic model"
new_SHH$Gender <- factor(new_SHH$Gender,levels = c("F","M"))
new_SHH$Histology <- factor(new_SHH$Histology,levels=c("Classic","Desmoplastic","LCA","MBEN"))
new_SHH$MetStatus <- factor(new_SHH$MetStatus,levels=c("no","yes"))
colnames(new_SHH) <- gsub("OS.years","Time",colnames(new_SHH))
new_SHH$Dead <- ifelse(new_SHH$Dead=="dead",1,0)
features <- c("Age","Gender","Histology","MetStatus","prognostic model")
prognostic_model_mutiCox_res <- multi_cox(features,new_SHH)
save(list=ls(),file="supplementary_append.Rdata")