## 安装pROC
install.packages("pROC")

## 读取数据
data = read.table("示例数据.txt",sep = "\t",header = T, stringsAsFactors = F)

## 加载pROC包
library(pROC)

## 单个ROC分析
roc = roc(data$status, data$gene1, percent = TRUE, ci = TRUE) ## ROC分析
best = coords(roc, "best", ret = "all", transpose = F) ## 最佳分割点
outTab = data.frame(
  Gene = "gene1",
  AUC = round(roc$auc,3),
  "95% CI" = paste(round(roc$ci[c(1,3)],3),collapse = "-"),
  "Cutoff value" = round(best$threshold,3),
  Spensitivity = round(best$sensitivity,3),
  Specificity = round(best$specificity,3),
  check.names = FALSE
)
write.table(outTab,"ROC.result.txt", row.names = F, sep = "\t", quote = F) ## 输出ROC分析结果

## 输出ROC曲线
pdf("ROC.pdf",width = 5,height = 5)
plot.roc(roc,col="red", identity.col = "blue", legacy.axes = TRUE)
legend("bottomright", 
       c(paste("AUC = ",round(roc$auc,3),sep = ""), ## AUC表示曲线下面积
         paste("Cutoff value = ",round(best$threshold,3),sep = ""), ## Cutoff value表示最佳分割点
         paste("Spensitivity = ",round(best$sensitivity,3),sep = ""), ## Spensitivity 表示最佳分割点对应的敏感性
         paste("Specificity = ",round(best$specificity,3), sep = "")),## Specificity 表示最佳分割点对应的特异性
       bty = "n"
)
dev.off()