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SFigure3.R
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SFigure3.R
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library(survival)
library(survminer)
library(patchwork)
load("H:/Project/Smart/MMP/data/BAL_IPF_cohort.Rdata")
lasso_fea <- readRDS("H:/Project/Smart/MMP-1/Figure2/lasso_fea.Rds")
#全局变量
methon <- 'best' #median mean best
GSE_num <- 'all' #"LEUVEN" "SIENA" "Freiburg"
for (gene in lasso_fea) {
#构建数据框
dat <- data.frame(t(exp[[GSE_num]][gene,]),
meta[[GSE_num]]['State'],
meta[[GSE_num]]['Days'])
dat$State <- as.numeric(dat$State)
dat$Days <- as.numeric(dat$Days)
#正式流程
#判断methon
res.cut <- surv_cutpoint(dat,
time ="Days",
event = "State",
variables = gene,
minprop = 0.3)
risk <- surv_categorize(res.cut)
#生存曲线
dat$risk <- risk[,gene]
surv_object = Surv(dat$Days, dat$State)
fit1 <- survfit(surv_object ~ risk, data = dat)
summary(fit1)
p <- ggsurvplot(fit1,palette = c("#E7B800", "#2E9FDF"),
risk.table =TRUE,pval =TRUE,xlab ="Time in Days",
ggtheme =theme_light(),title = gene,
ncensor.plot = F)
p1 <- p$plot/p$table + plot_layout(heights = c(3, 1))
ggsave(plot = p1, filename = paste0(GSE_num, '_', gene, '_Survival.pdf'), width = 6, height = 5)
}
#单因素COX
load("H:/Project/Smart/MMP/data/BAL_IPF_cohort.Rdata")
meta$all <- rbind(meta$LEUVEN,meta$Freiburg,meta$SIENA)
meta$all$group <- c(rep('LEUVEN',nrow(meta$LEUVEN)),
rep('Freiburg',nrow(meta$Freiburg)),
rep('SIENA',nrow(meta$SIENA)))
exp$all_un <- cbind(exp$LEUVEN,exp$Freiburg,exp$SIENA)
colnames(exp$all_un) == rownames(meta$all)
library(sva)
exp$all <- ComBat(dat = exp$all_un, batch = meta$all$group)
exp$all <- as.data.frame(exp$all)
GSE_num <- 'all' #"LEUVEN" "SIENA" "Freiburg"
#构建数据框
dat <- data.frame(t(exp[[GSE_num]][lasso_fea,]),
meta[[GSE_num]]['State'],
meta[[GSE_num]]['Days'])
dat$State <- as.numeric(dat$State)
dat$Days <- as.numeric(dat$Days)
res.cox <- coxph(Surv(Days, State) ~ MMP19, data = dat)
res.cox
#对所有特征做单因素cox回归分析
#分别对每一个变量,构建生存分析的公式
covariates <- colnames(dat)[-c(9,10)]
univ_formulas <- sapply(covariates,
function(x) as.formula(paste('Surv(Days, State)~', x)))
univ_models <- lapply( univ_formulas, function(x){coxph(x, data = dat)})
univ_results <- lapply(univ_models,
function(x){
x <- summary(x)
#获取p值
p.value<-signif(x$coefficients[,5], digits=2)
#获取HR
HR <-signif(x$conf.int[,1], digits=2);
#获取95%置信区间
HR.confint.lower <- signif(x$conf.int[,3], 2)
HR.confint.upper <- signif(x$conf.int[,4], 2)
HR_all <- paste0(HR, " (",
HR.confint.lower, "-", HR.confint.upper, ")")
cof <- signif(x$coef[1], 2)
res<-c(cof,p.value,HR_all,HR,HR.confint.lower,HR.confint.upper)
names(res)<-c("coef","p.value","HR (95% CI for HR)","HR","HR.confint.lower","HR.confint.upper")
return(res)
})
#转换成数据框,并转置
res <- as.data.frame(t(as.data.frame(univ_results, check.names = FALSE)))
write.table(file=paste0(GSE_num,"_univariate_cox_result.txt"),res,quote=F,sep="\t")
#作图森林图
library(grid)
library(magrittr)
library(checkmate)
library(forestplot)
res$coef <- round(as.numeric(res$coef),3)
res$p.value <- as.numeric(res$p.value)
res$HR <- as.numeric(res$HR)
res$HR.confint.lower <- as.numeric(res$HR.confint.lower)
res$HR.confint.upper <- as.numeric(res$HR.confint.upper)
mydata <- res
mydata$Gene <- rownames(mydata)
# mydata <- mydata[mydata$p.value < 0.05,]
# write.csv(mydata,'univariate_cox_p0.05.csv')
mydata <- mydata[order(mydata$p.value),]
mydata <- mydata[c(7,1:6)]
pdf(paste0(GSE_num,'_forestplot.pdf'),width = 5,height = 3.5, onefile=FALSE)
forestplot(labeltext=as.matrix(mydata[,1:4]),#只展示前面的三列
mean= mydata$HR,#HR值
lower= mydata$HR.confint.lower,#95%CI下限
upper= mydata$HR.confint.upper,#95%CI上限
zero=1,#HR值的位置,如果是线性回归则写0
boxsize=0.1,#中间方框的大小
xticks=seq(-1,5,1),#x轴刻度
lwd.zero=2,#中间竖线的宽度
lwd.ci=2,
col=fpColors(box='orange',lines = 'orange',zero = 'gray'),#颜色,box,lines和zero分别是方框,线条,中间竖线的颜色
xlab="HR",#x轴标签
lwd.xaxis =1,
txt_gp = fpTxtGp(ticks = gpar(cex = 0.85),xlab = gpar(cex = 0.8),
cex = 0.9),#设置字体大小
lty.ci = "solid",
title = GSE_num, #标题
graph.pos=2#中间竖线的位置
)
dev.off()
#模型构建
library(glmnet)
library(survival)
GSE_num <- 'all' #"LEUVEN" "SIENA" "Freiburg"
set.seed(100)
unicox <- read.table('all_univariate_cox_result.txt',header = T,sep = '\t')
unicox <- unicox[unicox$p.value < 0.05,]
gene <- rownames(unicox)
dat <- data.frame(t(exp[[GSE_num]][gene,]),
meta[[GSE_num]]['State'],
meta[[GSE_num]]['Days'])
dat$Days <- as.numeric(dat$Days)
dat$State <- as.numeric(dat$State)
y <- data.matrix(Surv(time = dat$Days,event = dat$State))
marker.exp <- dat[,gene]
#构建模型
fit <- glmnet(x = as.matrix(marker.exp),y,family = 'cox',alpha = 0)
pdf(paste0(GSE_num, '_ridge1.pdf'))
plot(fit,xvar = 'lambda',label = T)
dev.off()
ridge_fit <- cv.glmnet(x = as.matrix(marker.exp),y,family = 'cox',type.measure = 'deviance',alpha = 0)
pdf(paste0(GSE_num, '_ridge2.pdf'))
plot(ridge_fit,label = T)
dev.off()
save(fit,ridge_fit,file = paste0(GSE_num,'_ridge_gene.Rdata'))
fit <- glmnet(x = as.matrix(marker.exp),y,family = 'cox',alpha = 0, lambda = ridge_fit$lambda.min)
#saveRDS(fit,'fit_model.Rds')
dat$score <- as.numeric(predict(fit,newx = as.matrix(marker.exp)))
res.cut <- surv_cutpoint(dat,
time ="Days",
event = "State",
variables = "score",
minprop = 0.3)
risk <- surv_categorize(res.cut)
dat$risk <- risk$score
surv_object = Surv(dat$Days, dat$State)
fit1 <- survfit(surv_object ~ risk, data = dat)
summary(fit1)
pdf('merge_survive.pdf',onefile = F, width = 6, height = 5)
ggsurvplot(fit1,palette = c("#E7B800", "#2E9FDF"),
risk.table =TRUE,pval =TRUE,
conf.int =TRUE,xlab ="Time in months",
ggtheme =theme_light(),
ncensor.plot = F)
dev.off()
#单因素
rownames(dat) == rownames(meta$all)
dat$Age <- as.numeric(meta$all$`age:ch1`)
dat$Sex <- as.numeric(meta$all$`sex (0=female, 1=male):ch1`)
covariates <- colnames(dat)[-c(1:7,9)]
univ_formulas <- sapply(covariates,
function(x) as.formula(paste('Surv(Days, State)~', x)))
univ_models <- lapply( univ_formulas, function(x){coxph(x, data = dat)})
univ_results <- lapply(univ_models,
function(x){
x <- summary(x)
#获取p值
p.value<-signif(x$coefficients[,5], digits=2)
#获取HR
HR <-signif(x$conf.int[,1], digits=2);
#获取95%置信区间
HR.confint.lower <- signif(x$conf.int[,3], 2)
HR.confint.upper <- signif(x$conf.int[,4], 2)
HR_all <- paste0(HR, " (",
HR.confint.lower, "-", HR.confint.upper, ")")
cof <- signif(x$coef[1], 2)
res<-c(cof,p.value,HR_all,HR,HR.confint.lower,HR.confint.upper)
names(res)<-c("coef","p.value","HR (95% CI for HR)","HR","HR.confint.lower","HR.confint.upper")
return(res)
})
#转换成数据框,并转置
res <- as.data.frame(t(as.data.frame(univ_results, check.names = FALSE)))
write.table(file=paste0(GSE_num,"score_univariate_cox_result.txt"),res,quote=F,sep="\t")
#作图森林图
library(grid)
library(magrittr)
library(checkmate)
library(forestplot)
res$coef <- round(as.numeric(res$coef),3)
res$p.value <- as.numeric(res$p.value)
res$HR <- as.numeric(res$HR)
res$HR.confint.lower <- as.numeric(res$HR.confint.lower)
res$HR.confint.upper <- as.numeric(res$HR.confint.upper)
mydata <- res
mydata$Gene <- rownames(mydata)
# mydata <- mydata[mydata$p.value < 0.05,]
# write.csv(mydata,'univariate_cox_p0.05.csv')
mydata <- mydata[order(mydata$p.value),]
mydata <- mydata[c(3,2,1),c(7,1:6)]
rownames(mydata)[3] <- 'Score'
pdf(paste0(GSE_num,'score_forestplot.pdf'),width = 5,height = 3.5, onefile=FALSE)
forestplot(labeltext=as.matrix(mydata[,1:4]),#只展示前面的三列
mean= mydata$HR,#HR值
lower= mydata$HR.confint.lower,#95%CI下限
upper= mydata$HR.confint.upper,#95%CI上限
zero=1,#HR值的位置,如果是线性回归则写0
boxsize=0.1,#中间方框的大小
xticks=seq(-1,5,1),#x轴刻度
lwd.zero=2,#中间竖线的宽度
lwd.ci=2,
col=fpColors(box='orange',lines = 'orange',zero = 'gray'),#颜色,box,lines和zero分别是方框,线条,中间竖线的颜色
xlab="HR",#x轴标签
lwd.xaxis =1,
txt_gp = fpTxtGp(ticks = gpar(cex = 0.85),xlab = gpar(cex = 0.8),
cex = 0.9),#设置字体大小
lty.ci = "solid",
title = GSE_num, #标题
graph.pos=2#中间竖线的位置
)
dev.off()
fit1 <- coxph(Surv(Days, State)~ Age+Sex+score, data = dat)
x <- summary(fit1)
#获取p值
p.value<-signif(x$coefficients[,5], digits=2)
#获取HR
HR <-signif(x$conf.int[,1], digits=2);
#获取95%置信区间
HR.confint.lower <- signif(x$conf.int[,3], 2)
HR.confint.upper <- signif(x$conf.int[,4], 2)
HR_all <- paste0(HR, " (",
HR.confint.lower, "-", HR.confint.upper, ")")
cof <- signif(x$coef[,1], 2)
res<-data.frame(cof,p.value,HR_all,HR,HR.confint.lower,HR.confint.upper)
names(res)<-c("coef","p.value","HR (95% CI for HR)","HR","HR.confint.lower","HR.confint.upper")
res$coef <- round(as.numeric(res$coef),3)
res$p.value <- as.numeric(res$p.value)
res$HR <- as.numeric(res$HR)
res$HR.confint.lower <- as.numeric(res$HR.confint.lower)
res$HR.confint.upper <- as.numeric(res$HR.confint.upper)
mydata <- res
mydata$Gene <- rownames(mydata)
# mydata <- mydata[mydata$p.value < 0.05,]
# write.csv(mydata,'univariate_cox_p0.05.csv')
mydata <- mydata[,c(7,1:6)]
rownames(mydata)[3] <- 'Score'
pdf(paste0(GSE_num,'_muiltscore_forestplot.pdf'),width = 5,height = 3.5, onefile=FALSE)
forestplot(labeltext=as.matrix(mydata[,1:4]),#只展示前面的三列
mean= mydata$HR,#HR值
lower= mydata$HR.confint.lower,#95%CI下限
upper= mydata$HR.confint.upper,#95%CI上限
zero=1,#HR值的位置,如果是线性回归则写0
boxsize=0.1,#中间方框的大小
xticks=seq(-1,5,1),#x轴刻度
lwd.zero=2,#中间竖线的宽度
lwd.ci=2,
col=fpColors(box='orange',lines = 'orange',zero = 'gray'),#颜色,box,lines和zero分别是方框,线条,中间竖线的颜色
xlab="HR",#x轴标签
lwd.xaxis =1,
txt_gp = fpTxtGp(ticks = gpar(cex = 0.85),xlab = gpar(cex = 0.8),
cex = 0.9),#设置字体大小
lty.ci = "solid",
title = GSE_num, #标题
graph.pos=2#中间竖线的位置
)
dev.off()
library(timeROC)
result <-with(dat, timeROC(T=Days,
delta=State,
marker=score,
cause=1,
times=c(365,730,1095),
iid = TRUE))
#identical(c(result$TP[,1],result$TP[,2],result$TP[,3]),as.numeric(result$TP))
dat = data.frame(fpr = as.numeric(result$FP),
tpr = as.numeric(result$TP),
time = rep(as.factor(c(365,730,1095)),each = nrow(result$TP)))
library(ggplot2)
pdf(paste0(GSE_num, '_timeroc.pdf'),onefile = F)
ggplot() +
geom_line(data = dat,aes(x = fpr, y = tpr,color = time),size = 1) +
scale_color_manual(name = NULL,values = c("#92C5DE", "#F4A582", "#66C2A5"),
labels = paste0("AUC of ",c(1,2,3),"-y survival: ",
format(round(result$AUC,2),nsmall = 2)))+
geom_line(aes(x=c(0,1),y=c(0,1)),color = "grey")+
theme_bw()+
theme(panel.grid = element_blank(),
legend.background = element_rect(linetype = 1, size = 0.2, colour = "black"),
legend.position = c(0.765,0.125))+
scale_x_continuous(expand = c(0.005,0.005))+
scale_y_continuous(expand = c(0.005,0.005))+
labs(x = "1 - Specificity",
y = "Sensitivity")+
coord_fixed()
dev.off()