{"id":25146,"date":"2024-01-18T16:54:17","date_gmt":"2024-01-18T08:54:17","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25146"},"modified":"2024-01-18T16:54:22","modified_gmt":"2024-01-18T08:54:22","slug":"%e5%9f%ba%e5%9b%a0%e8%a1%a8%e8%be%be%e6%a3%ae%e6%9e%97%e4%b9%8b%e8%b0%9c%ef%bc%9a%e9%a2%84%e6%b5%8b%e7%94%9f%e5%ad%98%e9%a3%8e%e9%99%a9%e7%9a%84%e5%85%b3%e9%94%ae%e5%9b%a0%e7%b4%a0%e7%9a%84%e5%88%86","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25146","title":{"rendered":"\u57fa\u56e0\u8868\u8fbe\u68ee\u6797\u4e4b\u8c1c\uff1a\u9884\u6d4b\u751f\u5b58\u98ce\u9669\u7684\u5173\u952e\u56e0\u7d20\u7684\u5206\u6790"},"content":{"rendered":"

\u72ec\u7acb\u9884\u540e\u5206\u6790\uff1a\u901a\u8fc7\u4f7f\u7528\u751f\u5b58\u5206\u6790\u4e2d\u7684Cox\u6bd4\u4f8b\u98ce\u9669\u6a21\u578b\uff08Cox proportional hazards model\uff09\uff0c\u8bc4\u4f30\u57fa\u56e0\u8868\u8fbe\u4e0e\u751f\u5b58\u65f6\u95f4\u4e4b\u95f4\u7684\u5173\u8054\u3002\u8be5\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u786e\u5b9a\u54ea\u4e9b\u57fa\u56e0\u5bf9\u751f\u5b58\u65f6\u95f4\u5177\u6709\u663e\u8457\u7684\u5f71\u54cd\u3002<\/p>\n

\u68ee\u6797\u56fe\u7ed8\u5236\uff1a\u68ee\u6797\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u5f62\u8868\u793a\u65b9\u5f0f\uff0c\u7528\u4e8e\u5c55\u793a\u591a\u4e2a\u9884\u540e\u56e0\u7d20\u7684\u6548\u5e94\u91cf\uff08\u5982\u98ce\u9669\u6bd4\u6216\u5371\u9669\u6bd4\uff09\u548c\u663e\u8457\u6027\u6c34\u5e73\uff08\u5982p\u503c\uff09\u3002\u901a\u8fc7\u7ed8\u5236\u68ee\u6797\u56fe\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u6bd4\u8f83\u4e0d\u540c\u57fa\u56e0\u6216\u9884\u540e\u56e0\u7d20\u5bf9\u751f\u5b58\u65f6\u95f4\u7684\u5f71\u54cd\u3002<\/p>\n

\u5355\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff1a\u4ee3\u7801\u4f7f\u7528coxph\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\uff08\u6216\u9884\u540e\u56e0\u7d20\uff09\u4e0e\u751f\u5b58\u65f6\u95f4\u7684\u5173\u8054\uff0c\u5f97\u5230\u6548\u5e94\u91cf\uff08HR\uff09\u548c\u663e\u8457\u6027\u6c34\u5e73\uff08p\u503c\uff09\u3002\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u5355\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\uff08uniOutFile\uff09\uff0c\u7136\u540e\u8c03\u7528bioForest\u51fd\u6570\u7ed8\u5236\u5bf9\u5e94\u7684\u5355\u56e0\u7d20\u68ee\u6797\u56fe\u3002 \u591a\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff1a\u6839\u636e\u5355\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\uff0c\u9009\u62e9p\u503c\u5c0f\u4e8e1\u7684\u57fa\u56e0\uff0c\u5c06\u5b83\u4eec\u4f5c\u4e3a\u9884\u540e\u56e0\u7d20\u8fdb\u884c\u591a\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\u3002\u8fd9\u6837\u53ef\u4ee5\u8003\u8651\u591a\u4e2a\u56e0\u7d20\u5bf9\u751f\u5b58\u65f6\u95f4\u7684\u7efc\u5408\u5f71\u54cd\u3002\u591a\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u4fdd\u5b58\u5230\u591a\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\uff08multiOutFile\uff09\uff0c\u5e76\u7ed8\u5236\u591a\u56e0\u7d20\u68ee\u6797\u56fe\uff08\u8c03\u7528bioForest\u51fd\u6570\uff09\u3002<\/p>\n

#install.packages(‘survival’)<\/p>\n

library(survival) #\u5f15\u7528\u5305<\/p>\n

setwd(“C:\\\\biowolf\\\\Gene\\\\16.indep”) #\u8bbe\u7f6e\u5de5\u4f5c\u76ee\u5f55<\/p>\n

\u5b9a\u4e49\u68ee\u6797\u56fe\u51fd\u6570\uff08bioForest\uff09\uff1a\u7528\u4e8e\u7ed8\u5236\u68ee\u6797\u56fe\uff0c\u5c55\u793a\u72ec\u7acb\u9884\u540e\u5206\u6790\u7ed3\u679c\u7684\u6548\u5e94\u91cf\uff08Hazard Ratio\uff09\u548c\u663e\u8457\u6027\u6c34\u5e73\uff08p-value\uff09\u3002\u51fd\u6570\u63a5\u53d7\u4e09\u4e2a\u53c2\u6570\uff1acoxFile\uff08\u72ec\u7acb\u9884\u540e\u5206\u6790\u7ed3\u679c\u6587\u4ef6\uff09\uff0cforestFile\uff08\u68ee\u6797\u56fe\u8f93\u51fa\u6587\u4ef6\uff09\uff0cforestCol\uff08\u68ee\u6797\u56fe\u989c\u8272\uff09\u3002\u51fd\u6570\u8bfb\u53d6\u72ec\u7acb\u9884\u540e\u5206\u6790\u7ed3\u679c\u6587\u4ef6\uff0c\u63d0\u53d6\u76f8\u5173\u4fe1\u606f\uff0c\u5e76\u7ed8\u5236\u68ee\u6797\u56fe\u3002<\/p>\n

<\/p>\n

bioForest=function(coxFile=null, forestFile=null, forestCol=null){<\/p>\n

#\u8bfb\u53d6\u8f93\u5165\u6587\u4ef6<\/p>\n

rt <- read.table(coxFile, header=T, sep=”\\t”, check.names=F, row.names=1)<\/p>\n

gene <- rownames(rt)<\/p>\n

hr <- sprintf(“%.3f”,rt$”HR”)<\/p>\n

hrLow <- sprintf(“%.3f”,rt$”HR.95L”)<\/p>\n

hrHigh <- sprintf(“%.3f”,rt$”HR.95H”)<\/p>\n

Hazard.ratio <- paste0(hr,”(“,hrLow,”-“,hrHigh,”)”)<\/p>\n

pVal <- ifelse(rt$pvalue<0.001, “<0.001”, sprintf(“%.3f”, rt$pvalue))<\/p>\n

#\u8f93\u51fa\u56fe\u5f62<\/p>\n

pdf(file=forestFile, width=6.5, height=4.5)<\/p>\n

n <- nrow(rt)<\/p>\n

nRow <- n+1<\/p>\n

ylim <- c(1,nRow)<\/p>\n

layout(matrix(c(1,2),nc=2),width=c(3,2.5))<\/p>\n

#\u7ed8\u5236\u68ee\u6797\u56fe\u5de6\u8fb9\u7684\u4e34\u5e8a\u4fe1\u606f<\/p>\n

xlim = c(0,3)<\/p>\n

par(mar=c(4,2.5,2,1))<\/p>\n

plot(1,xlim=xlim,ylim=ylim,type=”n”,axes=F,xlab=””,ylab=””)<\/p>\n

text.cex=0.8<\/p>\n

text(0,n:1,gene,adj=0,cex=text.cex)<\/p>\n

text(1.5-0.5*0.2,n:1,pVal,adj=1,cex=text.cex);text(1.5-0.5*0.2,n+1,’pvalue’,cex=text.cex,font=2,adj=1)<\/p>\n

text(3.1,n:1,Hazard.ratio,adj=1,cex=text.cex);text(3.1,n+1,’Hazard ratio’,cex=text.cex,font=2,adj=1)<\/p>\n

#\u7ed8\u5236\u53f3\u8fb9\u7684\u68ee\u6797\u56fe<\/p>\n

par(mar=c(4,1,2,1),mgp=c(2,0.5,0))<\/p>\n

xlim = c(0,max(as.numeric(hrLow),as.numeric(hrHigh)))<\/p>\n

plot(1,xlim=xlim,ylim=ylim,type=”n”,axes=F,ylab=””,xaxs=”i”,xlab=”Hazard ratio”)<\/p>\n

arrows(as.numeric(hrLow),n:1,as.numeric(hrHigh),n:1,angle=90,code=3,length=0.05,col=”darkblue”,lwd=3)<\/p>\n

abline(v=1, col=”black”, lty=2, lwd=2)<\/p>\n

boxcolor = ifelse(as.numeric(hr) > 1, forestCol, forestCol)<\/p>\n

points(as.numeric(hr), n:1, pch = 15, col = boxcolor, cex=2)<\/p>\n

axis(1)<\/p>\n

dev.off()<\/p>\n

}<\/p>\n

############\u5b9a\u4e49\u68ee\u6797\u56fe\u51fd\u6570############<\/p>\n

\u5b9a\u4e49\u72ec\u7acb\u9884\u540e\u5206\u6790\u51fd\u6570\uff08indep\uff09\uff1a\u8be5\u51fd\u6570\u7528\u4e8e\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u72ec\u7acb\u9884\u540e\u5206\u6790\u3002\u51fd\u6570\u63a5\u53d7\u516d\u4e2a\u53c2\u6570\uff1aexpFile\uff08\u8868\u8fbe\u6570\u636e\u6587\u4ef6\uff09\uff0ccliFile\uff08\u4e34\u5e8a\u6570\u636e\u6587\u4ef6\uff09\uff0cuniOutFile\uff08\u5355\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\uff09\uff0cmultiOutFile\uff08\u591a\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\uff09\uff0cuniForest\uff08\u5355\u56e0\u7d20\u68ee\u6797\u56fe\u8f93\u51fa\u6587\u4ef6\uff09\uff0cmultiForest\uff08\u591a\u56e0\u7d20\u68ee\u6797\u56fe\u8f93\u51fa\u6587\u4ef6\uff09\u3002<\/p>\n

#\u5b9a\u4e49\u72ec\u7acb\u9884\u540e\u5206\u6790\u51fd\u6570<\/p>\n

indep=function(expFile=null,cliFile=null,uniOutFile=null,multiOutFile=null,uniForest=null,multiForest=null){<\/p>\n

exp=read.table(expFile, header=T, sep=”\\t”, check.names=F, row.names=1) #\u8bfb\u53d6\u8868\u8fbe\u6587\u4ef6<\/p>\n

<\/p>\n

cli=read.table(cliFile, header=T, sep=”\\t”, check.names=F, row.names=1) #\u8bfb\u53d6\u4e34\u5e8a\u6587\u4ef6<\/p>\n

<\/p>\n

#\u6570\u636e\u5408\u5e76\uff0c\u8bfb\u53d6\u8868\u8fbe\u6570\u636e\u6587\u4ef6\u548c\u4e34\u5e8a\u6570\u636e\u6587\u4ef6\uff0c\u7136\u540e\u5c06\u5b83\u4eec\u5408\u5e76\u4e3a\u4e00\u4e2a\u6570\u636e\u6846\u3002<\/p>\n

sameSample=intersect(row.names(cli),row.names(exp))<\/p>\n

exp=exp[sameSample,]<\/p>\n

cli=cli[sameSample,]<\/p>\n

rt=cbind(exp, cli)<\/p>\n

#\u8fdb\u884c\u5355\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u4e0e\u751f\u5b58\u98ce\u9669\u7684\u76f8\u5173\u6027\uff0c\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u5355\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\u3002<\/p>\n

uniTab=data.frame()<\/p>\n

for(i in colnames(rt[,3:ncol(rt)])){<\/p>\n

cox <- coxph(Surv(futime, fustat) ~ rt[,i], data = rt)<\/p>\n

coxSummary = summary(cox)<\/p>\n

uniTab=rbind(uniTab,<\/p>\n

cbind(id=i,<\/p>\n

HR=coxSummary$conf.int[,”exp(coef)”],<\/p>\n

HR.95L=coxSummary$conf.int[,”lower .95″],<\/p>\n

HR.95H=coxSummary$conf.int[,”upper .95″],<\/p>\n

pvalue=coxSummary$coefficients[,”Pr(>|z|)”])<\/p>\n

)<\/p>\n

}<\/p>\n

write.table(uniTab,file=uniOutFile,sep=”\\t”,row.names=F,quote=F)<\/p>\n

#\u51fd\u6570\u8c03\u7528bioForest\u51fd\u6570\u7ed8\u5236\u5355\u56e0\u7d20\u68ee\u6797\u56fe\uff0c\u5c06\u9884\u540e\u5206\u6790\u7ed3\u679c\u53ef\u89c6\u5316\u3002<\/p>\n

bioForest(coxFile=uniOutFile, forestFile=uniForest, forestCol=”green”)<\/p>\n

#\u591a\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff0c\u51fd\u6570\u8fdb\u884c\u591a\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff0c\u8ba1\u7b97\u591a\u4e2a\u56e0\u7d20\u5bf9\u751f\u5b58\u98ce\u9669\u7684\u7efc\u5408\u5f71\u54cd\uff0c\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u591a\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u6587\u4ef6\u3002<\/p>\n

uniTab=uniTab[as.numeric(uniTab[,”pvalue”])<1,]<\/p>\n

rt1=rt[,c(“futime”, “fustat”, as.vector(uniTab[,”id”]))]<\/p>\n

multiCox=coxph(Surv(futime, fustat) ~ ., data = rt1)<\/p>\n

multiCoxSum=summary(multiCox)<\/p>\n

multiTab=data.frame()<\/p>\n

multiTab=cbind(<\/p>\n

HR=multiCoxSum$conf.int[,”exp(coef)”],<\/p>\n

HR.95L=multiCoxSum$conf.int[,”lower .95″],<\/p>\n

HR.95H=multiCoxSum$conf.int[,”upper .95″],<\/p>\n

pvalue=multiCoxSum$coefficients[,”Pr(>|z|)”])<\/p>\n

multiTab=cbind(id=row.names(multiTab),multiTab)<\/p>\n

write.table(multiTab,file=multiOutFile,sep=”\\t”,row.names=F,quote=F)<\/p>\n

bioForest(coxFile=multiOutFile, forestFile=multiForest, forestCol=”red”)<\/p>\n

}<\/p>\n

#\u8c03\u7528bioForest\u51fd\u6570\u7ed8\u5236\u591a\u56e0\u7d20\u68ee\u6797\u56fe\uff0c\u5c55\u793a\u591a\u56e0\u7d20\u9884\u540e\u5206\u6790\u7ed3\u679c\u7684\u6548\u5e94\u91cf\u548c\u663e\u8457\u6027\u6c34\u5e73<\/p>\n

#\u8c03\u7528\u51fd\u6570\uff0c\u8fdb\u884c\u72ec\u7acb\u9884\u540e\u5206\u6790\u3002\u8c03\u7528indep\u51fd\u6570\uff0c\u4f20\u5165\u76f8\u5e94\u7684\u53c2\u6570\u8fdb\u884c\u72ec\u7acb\u9884\u540e\u5206\u6790\u3002\u6839\u636e\u8f93\u5165\u7684\u8868\u8fbe\u6570\u636e\u6587\u4ef6\u548c\u4e34\u5e8a\u6570\u636e\u6587\u4ef6\uff0c\u4ee3\u7801\u5c06\u8fdb\u884c\u5355\u56e0\u7d20\u548c\u591a\u56e0\u7d20\u72ec\u7acb\u9884\u540e\u5206\u6790\uff0c\u5e76\u5c06\u7ed3\u679c\u4ee5\u6587\u4ef6\u548c\u68ee\u6797\u56fe\u7684\u5f62\u5f0f\u8f93\u51fa\u3002<\/p>\n

indep(expFile=”expTime.txt”,<\/p>\n

cliFile=”clinical.txt”,<\/p>\n

uniOutFile=”uniCox.txt”,<\/p>\n

multiOutFile=”multiCox.txt”,<\/p>\n

uniForest=”uniForest.pdf”,<\/p>\n

multiForest=”multiForest.pdf”)<\/p>\n

<\/p>\n

<\/p>\n

\u6765\u7ed9\u5927\u5bb6\u5c55\u793a\u5c55\u793a\u7ed3\u679c\u56fe\u5427\uff0c\u4f7f\u7528firefox\u547d\u4ee4\u3002<\/p>\n

<\/p>\n

<\/p>\n

\u72ec\u7acb\u9884\u540e\u5206\u6790\u662f\u4e00\u79cd\u5e38\u7528\u7684\u751f\u5b58\u5206\u6790\u65b9\u6cd5\uff0c\u7528\u4e8e\u8bc4\u4f30\u57fa\u56e0\u8868\u8fbe\u7b49\u56e0\u7d20\u5bf9\u751f\u5b58\u65f6\u95f4\u7684\u5f71\u54cd\u3002\u901a\u8fc7\u8be5\u5206\u6790\uff0c\u53ef\u4ee5\u8bc6\u522b\u4e0e\u751f\u5b58\u98ce\u9669\u76f8\u5173\u7684\u57fa\u56e0\uff0c\u5e76\u4e86\u89e3\u57fa\u56e0\u5bf9\u751f\u5b58\u65f6\u95f4\u7684\u5f71\u54cd\u7a0b\u5ea6\u3002\u68ee\u6797\u56fe\u5219\u63d0\u4f9b\u4e86\u4e00\u79cd\u53ef\u89c6\u5316\u5c55\u793a\u72ec\u7acb\u9884\u540e\u5206\u6790\u7ed3\u679c\u7684\u65b9\u5f0f\uff0c\u65b9\u4fbf\u5bf9\u7ed3\u679c\u8fdb\u884c\u89c2\u5bdf\u548c\u6bd4\u8f83\u3002<\/p>\n

\u4e0b\u671f\u5c06\u4e3a\u4f60\u5e26\u6765\u66f4\u591aR\u8bed\u8a00\u7684\u9a9a\u64cd\u4f5c\u6280\u5de7\uff0c\u4ee5\u4e0b\u63a8\u8350\u7684\u662f\u4e00\u4e2a\u591a\u529f\u80fd\u7684\u751f\u4fe1\u5e73\u53f0\u3002<\/p>\n

\u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/a><\/p>\n

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