{"id":27233,"date":"2024-01-31T16:45:23","date_gmt":"2024-01-31T08:45:23","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27233"},"modified":"2024-01-31T16:45:24","modified_gmt":"2024-01-31T08:45:24","slug":"%e8%b6%85%e8%af%a6%e7%bb%86%ef%bc%8c%e5%b0%8f%e6%9e%9c%e5%b8%a6%e4%bd%a0%e7%94%a8beeswarm%e7%94%bb%e4%bc%98%e9%9b%85%e7%9a%84%e6%95%a3%e7%82%b9%e8%9c%9c%e8%9c%82%e5%9b%be%e5%88%86%e6%9e%90%e5%9f%ba","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27233","title":{"rendered":"\u8d85\u8be6\u7ec6\uff0c\u5c0f\u679c\u5e26\u4f60\u7528beeswarm\u753b\u4f18\u96c5\u7684\u6563\u70b9\u871c\u8702\u56fe\u5206\u6790\u57fa\u56e0\u4e0e\u4e34\u5e8a\u76f8\u5173\u6027"},"content":{"rendered":"

\u5927\u5bb6\u77e5\u9053\u57fa\u56e0\u4e0e\u4e34\u5e8a\u6570\u636e\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u5206\u6790\u5c42\u51fa\u4e0d\u7a77\uff0c\u5e76\u901a\u8fc7\u53ef\u89c6\u5316\u624b\u6bb5\u5c06\u5206\u6790\u7ed3\u679c\u5c55\u793a\u51fa\u6765\uff0c\u4eca\u5929\u5c0f\u679c\u5e26\u7ed9\u5927\u5bb6\u4e00\u4e2abeeswarmR\u5305\uff0c <\/strong>\u7528\u4e8e\u5728R\u4e2d\u7ed8\u5236\u70b9\u56fe\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5c55\u793a\u5c0f\u6837\u672c\u3001\u5206\u7c7b\u6570\u636e\u548c\u591a\u7c7b\u522b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n

beeswarm<\/strong>\u662f\u4e00\u4e2a\u5728R\u8bed\u8a00\u4e2d\u7528\u4e8e\u7ed8\u5236\u70b9\u56fe\uff08swarm plot\uff09<\/strong>\u7684\u5305\u3002\u70b9\u56fe\u662f\u4e00\u79cd\u53ef\u89c6\u5316\u65b9\u6cd5\uff0c\u7528\u4e8e\u5c55\u793a\u6570\u636e\u70b9\u5728\u4e00\u4e2a\u7ef4\u5ea6\u4e0a\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5c0f\u6837\u672c\u548c\u5206\u7c7b\u6570\u636e\u3002\u5728\u70b9\u56fe\u4e2d\uff0c\u6570\u636e\u70b9\u4f1a\u6839\u636e\u53e6\u4e00\u4e2a\u7ef4\u5ea6\u8fdb\u884c\u5206\u7ec4\uff0c\u800c\u4e0d\u4f1a\u4e92\u76f8\u91cd\u53e0\uff0c\u4ee5\u907f\u514d\u6570\u636e\u91cd\u53e0\u548c\u906e\u6321\u3002\u8fd9\u4f7f\u5f97\u70b9\u56fe\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u548c\u5bc6\u5ea6\u3002<\/p>\n

\u672c\u671f\u4e3b\u8981\u7ed9\u5927\u5bb6\u5e26\u6765<\/strong>\u5728\u8fdb\u884c\u57fa\u56e0\u4e0e\u4e34\u5e8a\u6570\u636e\u7684\u76f8\u5173\u6027\u5206\u6790\u65f6\uff0c\u901a\u8fc7\u7ed8\u5236\u7bb1\u7ebf\u56fe\u548c\u70b9\u56fe\u7684\u65b9\u5f0f\uff0c\u5c06\u57fa\u56e0\u8868\u8fbe\u5728\u4e0d\u540c\u4e34\u5e8a\u5206\u7ec4\u4e0b\u7684\u5206\u5e03\u60c5\u51b5\u53ef\u89c6\u5316\u5448\u73b0\u51fa\u6765\u3002\u8fd9\u6709\u52a9\u4e8e\u7814\u7a76\u4eba\u5458\u5feb\u901f\u53d1\u73b0\u57fa\u56e0\u5728\u4e0d\u540c\u4e34\u5e8a\u60c5\u51b5\u4e0b\u7684\u5dee\u5f02\u6027\u3002<\/p>\n

#install.packages(“beeswarm”)<\/p>\n

#\u5b89\u88c5\u540d\u4e3abeeswarm\u7684R\u5305\u3002<\/p>\n

library(beeswarm)<\/p>\n

setwd(“”) #\u4f7f\u7528setwd()\u51fd\u6570\u8bbe\u7f6e\u5de5\u4f5c\u76ee\u5f55\u4e3a\u7279\u5b9a\u8def\u5f84\u4fee\u6539\u5de5\u4f5c\u76ee\u5f55<\/p>\n

expFile=”rocSigExp.txt” <\/strong><\/p>\n

<\/p>\n

#\u8868\u8fbe\u6570\u636e\u6587\u4ef6<\/p>\n

clinicalFile=”clinical.txt” <\/strong><\/p>\n

<\/p>\n

#\u4e34\u5e8a\u6570\u636e\u6587\u4ef6<\/p>\n

exp=read.table(expFile,sep=”\\t”,header=T,check.names=F,row.names=1) <\/strong><\/p>\n

#\u8bfb\u53d6\u8868\u8fbe\u6570\u636e\u6587\u4ef6<\/p>\n

cli=read.table(clinicalFile,sep=”\\t”,header=T,check.names=F,row.names=1) <\/strong><\/p>\n

#\u8bfb\u53d6\u4e34\u5e8a\u6570\u636e\u6587\u4ef6<\/p>\n

exp=exp[,3:ncol(exp)]<\/p>\n

#\u4ece\u8868\u8fbe\u6570\u636e\u4e2d\u9009\u62e9\u7b2c3\u5217\u5230\u6700\u540e\u4e00\u5217\u7684\u6570\u636e<\/p>\n

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

#\u5c06\u4e24\u4e2a\u6570\u636e\u96c6\u7684\u6837\u672cID\u8fdb\u884c\u4ea4\u96c6\u8fd0\u7b97\uff0c\u4ee5\u786e\u4fdd\u8868\u8fbe\u6570\u636e\u548c\u4e34\u5e8a\u6570\u636e\u7684\u6837\u672c\u4e00\u81f4\u6027\u3002<\/p>\n

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

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

pFilter=0.05<\/p>\n

#\u4e34\u5e8a\u76f8\u5173\u6027\u5206\u6790\uff0c\u8f93\u51fa\u8868\u683c<\/p>\n

outTab=c()<\/p>\n

#\u521d\u59cb\u5316\u4e00\u4e2a\u7a7a\u7684\u6570\u636e\u6846 outTab\uff0c\u7528\u4e8e\u5b58\u50a8\u4e34\u5e8a\u76f8\u5173\u6027\u5206\u6790\u7684\u7ed3\u679c\u3002<\/p>\n

outTab=rbind(outTab,c(“id”,colnames(cli),”SigNum”))<\/p>\n

colnames(outTab)=c(“id”,colnames(cli),”SigNum”)<\/p>\n

for(i in colnames(exp)){<\/p>\n

#\u5faa\u73af\u904d\u5386\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u6bcf\u4e00\u5217\u3002<\/p>\n

clinicalPvalVector=c()<\/p>\n

sigSum=0<\/p>\n

#\u5185\u90e8\u5faa\u73af\u4e2d\uff0c\u5bf9\u6bcf\u4e2a\u4e34\u5e8a\u53d8\u91cf\u8fdb\u884c\u904d\u5386\u5206\u6790\uff0c\u68c0\u9a8c\u57fa\u56e0\u8868\u8fbe\u4e0e\u4e34\u5e8a\u53d8\u91cf\u7684\u76f8\u5173\u6027\u3002<\/p>\n

for(clinical in colnames(cli)){<\/p>\n

tab1=table(cli[,clinical])<\/p>\n

labelNum=length(tab1)<\/p>\n

rt1=cbind(expression=exp[,i],clinical=cli[,clinical])<\/p>\n

#\u6839\u636e\u4e34\u5e8a\u53d8\u91cf\u7684\u4e0d\u540c\u6c34\u5e73\uff0c\u9009\u62e9\u9002\u5f53\u7684\u7edf\u8ba1\u68c0\u9a8c\u65b9\u6cd5\uff08Wilcoxon\u79e9\u548c\u68c0\u9a8c\u6216Kruskal-Wallis\u68c0\u9a8c\uff09\u3002<\/p>\n

if(labelNum==2){<\/p>\n

cliTest<-wilcox.test(expression ~ clinical, data=rt1)<\/p>\n

}else{<\/p>\n

cliTest<-kruskal.test(expression ~ clinical, data = rt1)}<\/p>\n

pValue=cliTest$p.value<\/p>\n

clinicalPvalVector=c(clinicalPvalVector,pValue)<\/p>\n

#\u5c06p\u503c\u5b58\u50a8\u5728clinicalPvalVector\u4e2d\uff0c\u5e76\u8ba1\u7b97\u6ee1\u8db3\u663e\u8457\u6027\u6761\u4ef6\uff08\u5c0f\u4e8epFilter\uff09\u7684\u6b21\u6570 sigSum\u3002<\/p>\n

if(pValue<pFilter){<\/p>\n

sigSum=sigSum+1<\/p>\n

#if\u8bed\u53e5\uff0c\u68c0\u67e5labelNum\u7684\u503c\u3002\u6839\u636elabelNum\u7684\u503c\uff0c\u4e0d\u540c\u7684\u989c\u8272\u4f1a\u88ab\u6dfb\u52a0\u5230dotCol\u5411\u91cf\u3002#\u5982\u679clabelNum\u662f3\uff0c\u5c06\u989d\u5916\u7684\u989c\u8272\u6dfb\u52a0\u5230dotCol\u3002\u540c\u6837\uff0c\u5982\u679clabelNum\u662f4\u6216\u5927\u4e8e4\uff0c\u4f1a#\u4f7f\u7528rainbow()\u51fd\u6570\u6dfb\u52a0\u4e0d\u540c\u7684\u989c\u8272\u3002<\/p>\n

xlabel=vector()<\/p>\n

dotCol=c(2,3)<\/p>\n

if(labelNum==3){<\/p>\n

dotCol=c(2,3,4)<\/p>\n

}<\/p>\n

if(labelNum==4){<\/p>\n

dotCol=c(2,3,4,5)<\/p>\n

}<\/p>\n

if(labelNum>4){<\/p>\n

dotCol=rainbow(labelNum)<\/p>\n

}<\/p>\n

for(j in 1:labelNum){<\/p>\n

xlabel=c(xlabel,names(tab1[j]) )<\/p>\n

}<\/p>\n

#\u8bbe\u5b9ap\u503c\u683c\u5f0f<\/p>\n

pval=ifelse(pValue<0.001,”<0.001″,paste0(“=”,sprintf(“%.03f”,pValue)))<\/p>\n

#\u7ed8\u5236\u4e34\u5e8a\u76f8\u5173\u6027\u7bb1\u7ebf\u56fe<\/p>\n

#\u7528boxplot()\u51fd\u6570\u7ed8\u5236\u7bb1\u7ebf\u56fe\uff0c\u663e\u793a\u57fa\u56e0\u8868\u8fbe\u5728\u4e0d\u540c\u4e34\u5e8a\u5206\u7ec4\u95f4\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n

#\u4f7f\u7528beeswarm()\u51fd\u6570\u7ed8\u5236\u70b9\u56fe\uff08beeswarm\u56fe\uff09\uff0c\u5c06\u6570\u636e\u70b9\u5206\u5e03\u5728\u7bb1\u7ebf\u56fe\u9644\u8fd1\uff0c\u4ee5\u66f4\u6e05\u6670\u5730\u663e\u793a\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n

b = boxplot(expression ~ clinical, data = rt1,outline = FALSE, plot=F)<\/p>\n

yMin=min(b$stats)<\/p>\n

yMax = max(b$stats\/5+b$stats)<\/p>\n

#\u8ba1\u7b97\u4e86\u7bb1\u7ebf\u56fe\u4e2d\u7684y\u8f74\u8303\u56f4\u3002b$stats\u662fboxplot\u51fd\u6570\u8ba1\u7b97\u7684\u7edf\u8ba1\u4fe1\u606f\uff0cyMin\u8bbe\u7f6e\u4e3a\u6700\u5c0f\u503c\uff0cyMax\u8bbe\u7f6e\u4e3a\u6700\u5927\u503c\u7684\u4e94\u5206\u4e4b\u4e00\u52a0\u4e0a\u6700\u5927\u503c\u3002<\/p>\n

n = ncol(b$stats)<\/p>\n

outPdf=paste0(“cliCor.”,i,”_”,clinical,”.pdf”)<\/p>\n

width=ifelse(clinical==”diagnosis”,8,7)<\/p>\n

#\u6839\u636eclinical\u7684\u503c\uff0c\u8bbe\u7f6ePDF\u6587\u4ef6\u7684\u5bbd\u5ea6\uff0c\u5982\u679cclinical\u662f”diagnosis”\uff0c\u5bbd\u5ea6\u4e3a8\uff0c\u5426\u5219\u4e3a7\u3002<\/p>\n

pdf(file=outPdf,width = width,height = 5)<\/p>\n

par(mar = c(4.5,6,3,3))<\/p>\n

boxplot(expression ~ clinical, data = rt1,names=xlabel,<\/p>\n

ylab = “Protein expression”,main=paste0(i,” (p”,pval,”)”),xlab=clinical,<\/p>\n

cex.main=1.3, cex.lab=1.2, cex.axis=1.1,ylim=c(yMin,yMax),outline = FALSE)<\/p>\n

beeswarm(expression ~ clinical, data = rt1, col =dotCol, lwd=0.1, pch = 16, add = TRUE, corral=”wrap”)<\/p>\n

#\u7ed8\u5236\u7bb1\u7ebf\u56fe\uff0c\u5176\u4e2dnames\u53c2\u6570\u8bbe\u7f6e\u4e3a\u4e4b\u524d\u5b9a\u4e49\u7684xlabel\u5411\u91cf\uff0cy\u8f74\u6807\u7b7e\u8bbe\u7f6e\u4e3a”Protein expression”\uff0c\u4e3b\u6807\u9898\u7531i\u548cpval\u7ec4\u6210\uff0cx\u8f74\u6807\u7b7e\u8bbe\u7f6e\u4e3aclinical\u7684\u503c\uff0c\u8c03\u6574\u4e86\u6807\u9898\u3001\u6807\u7b7e\u548c\u8f74\u7684\u5b57\u4f53\u5927\u5c0f\u3002<\/p>\n

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

}<\/p>\n

}<\/p>\n

geneClinical=c(i,clinicalPvalVector,sigSum)<\/p>\n

outTab=rbind(outTab,geneClinical)<\/p>\n

#\u5c06\u6bcf\u4e2a\u57fa\u56e0\u4e0e\u5404\u4e34\u5e8a\u53d8\u91cf\u7684\u76f8\u5173\u6027\u5206\u6790\u7ed3\u679c\u5b58\u50a8\u5728\u6570\u636e\u6846 outTab \u4e2d\u3002<\/p>\n

#\u8bbe\u7f6eoutTab \u7684\u5217\u540d\u4e3a\u57fa\u56e0\u540d\u3001\u5404\u4e34\u5e8a\u53d8\u91cf\u540d\u3001\u4ee5\u53ca\u4e00\u4e2a\u989d\u5916\u7684\u5217 “SigNum”\uff0c\u8868\u793a\u663e\u8457\u6027\u76f8\u5173\u7684\u6b21\u6570\u3002<\/p>\n

#\u5c06 outTab \u6570\u636e\u6846\u5199\u5165\u540d\u4e3a “cliCorResult.xls” \u7684\u6587\u4ef6\uff0c\u4fdd\u5b58\u5206\u6790\u7ed3\u679c\u3002<\/p>\n

}<\/p>\n

write.table(outTab,file=”cliCorResult.xls”,sep=”\\t”,col.names=F,row.names=F,quote=F)<\/p>\n

<\/p>\n

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

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

\u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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