\u7ba1\u8154A\u578b\uff08Luminal A\uff09\uff1aER\/PR\u9633\u6027\u4e14PR\u9ad8\u8868\u8fbeHER2\u9634\u6027\uff0cKi-67\u589e\u6b96\u6307\u6570\u4f4e\uff1b<\/p>\n\n\n\n
\u7ba1\u8154B\u578b\uff08Luminal B\uff09HER2\u9634\u6027\uff1aER\/PR\u9633\u6027\uff0cHER2\u9634\u6027\u4e14Ki-67\u589e\u6b96\u6307\u6570\u9ad8\u6216PR\u4f4e\u8868\u8fbe\uff1b<\/p>\n\n\n\n
\u7ba1\u8154B\u578b\uff08Luminal B\uff09HER2\u9633\u6027\uff1aER\/PR\u9633\u6027\uff0cHER2\u9633\u6027\uff08\u86cb\u767d\u8fc7\u8868\u8fbe\u6216\u57fa\u56e0\u6269\u589e\uff09\uff0c\u4efb\u4f55\u72b6\u6001\u7684Ki-67\uff1b<\/p>\n\n\n\n
ERBB2\uff08HER2\u53c8\u79f0ERBB2\uff09\u9633\u6027\u578b\uff1aHER2\u9633\u6027\uff08\u86cb\u767d\u8fc7\u8868\u8fbe\u6216\u57fa\u56e0\u6269\u589e\uff09\uff0cER\u9634\u6027\u548cPR\u9634\u6027\uff1b<\/p>\n\n\n\n
\u57fa\u5e95\u6837\u578b\uff08Basal-like\uff09\uff1a\u4e09\u9634\u6027\uff08\u975e\u7279\u6b8a\u578b\u6d78\u6da6\u6027\u5bfc\u7ba1\u764c\uff09\uff0cER\u9634\u6027\uff0cPR\u9634\u6027\uff0cHER2\u9634\u6027\u3002<\/p>\n\n\n\n
\u4e73\u817a\u764cPAM50\u68c0\u6d4b\u662f\u76ee\u524d\u4e34\u5e8a\u5e94\u7528\u4e2d\u7efc\u5408\u6027\u4ef7\u6bd4\u6700\u9ad8\u7684\u68c0\u6d4b\u9879\u76ee\uff0c\u5b83\u662f\uff08Parker et al., 2009\uff09\u9009\u5b9a\u7684\u6709\u4ee3\u8868\u6027\u768450\u4e2a\u57fa\u56e0\uff0c\u5229\u7528\u5b83\u4eec\u53ef\u4ee5\u5bf9\u75c5\u4eba\u8fdb\u884c\u521d\u6b65\u5206\u7c7b\u3002\u6839\u636e\u57fa\u56e0\u8868\u8fbe\u8c31\uff08Gene expression profile, GEP\uff09\u8fdb\u884c\u5206\u578b\u53ef\u79f0\u4e3a\u5206\u5b50\u5206\u578b\u3002<\/p>\n\n\n\n
genefu\u5305\u4ecb\u7ecd<\/p>\n\n\n\n
genefu\u8f6f\u4ef6\u5305\u5305\u542b\u5b9e\u73b0\u57fa\u56e0\u8868\u8fbe\u5206\u6790\u901a\u5e38\u9700\u8981\u7684\u5404\u79cd\u4efb\u52a1\u7684\u529f\u80fd\uff0c\u5c24\u5176\u662f\u5728\u764c\u75c7\u7814\u7a76\u4e2d\uff1a\u4e0d\u540c\u5fae\u9635\u5217\u5e73\u53f0\u4e4b\u95f4\u7684\u57fa\u56e0\u56fe\u8c31\u3001\u5206\u5b50\u4e9a\u578b\u7684\u9274\u5b9a\u3001\u5df2\u53d1\u8868\u57fa\u56e0\u7b7e\u540d\u7684\u5b9e\u73b0\u3001\u57fa\u56e0\u9009\u62e9\u548c\u751f\u5b58\u5206\u6790\u3002\u8fd9\u4e2a\u5305\u7684\u4f7f\u7528\u7b80\u5355\u76f4\u89c2\uff0c\u4e3a\u751f\u7269\u4fe1\u606f\u5b66\u5bb6\u548c\u751f\u6001\u5b66\u5bb6\u7b49\u9886\u57df\u7684\u7814\u7a76\u4eba\u5458\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u5e2e\u52a9\u4ed6\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5448\u73b0\u7cfb\u7edf\u53d1\u80b2\u6811\u6570\u636e\u3002<\/p>\n\n\n\n
genefu\u5305\u5b89\u88c5<\/p>\n\n\n\n
\u9700\u8981R\u8bed\u8a00\u7248\u672c\u4e3a4.4\u53ca\u4ee5\u4e0a\uff0c\u66f4\u4f4e\u7248\u672c\u65e0\u6cd5\u5b89\u88c5\u9002\u5408\u7248\u672c\u7684BiocManager\uff0c\u4f1a\u5bfc\u81f4\u5b89\u88c5\u5931\u8d25\u3002\u5728\u63a7\u5236\u53f0\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n\n\n\n
if (!require(“BiocManager”, quietly = TRUE))<\/p>\n\n\n\n
install.packages(“BiocManager”)<\/p>\n\n\n\n
# \u5982\u679c\u672a\u5b89\u88c5BiocManager\uff0c\u5219\u5b89\u88c5<\/p>\n\n\n\n
BiocManager::install(“genefu”) # \u5b89\u88c5genefu\u5305<\/p>\n\n\n\n
BiocManager::install(c(“breastCancerMAINZ”,”breastCancerTRANSBIG”, “breastCancerUPP”,”breastCancerUNT”,”breastCancerNKI”))<\/p>\n\n\n\n
# \u5b89\u88c5\u6570\u636e\u96c6<\/p>\n\n\n\n
\u67e5\u770b\u662f\u5426\u5b89\u88c5\u6210\u529f<\/p>\n\n\n\n
packageVersion(“genefu”) # \u67e5\u770bgenefu\u7248\u672c<\/p>\n\n\n\n \u663e\u793a\u4e3a2.36.0\u7248\u672c\u6216\u66f4\u9ad8\uff0c\u5219\u8868\u793a\u5df2\u7ecf\u5b89\u88c5\u4e86genefu\u5305\u3002<\/p>\n\n\n\n \u9664\u6b64\u4e4b\u5916\uff0c\u540e\u7eed\u793a\u4f8b\u8fd8\u9700\u8981\u4f7f\u7528xtable\uff0crmeta\uff0cBiobase\uff0ccaret, survival\u7b49\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u524d\u5b89\u88c5\uff0c\u5b89\u88c5\u547d\u4ee4\u5982\u4e0b\uff1a<\/p>\n\n\n\n install.packages(“xtable”)<\/p>\n\n\n\n install.packages(“rmeta”)<\/p>\n\n\n\n install.packages(“Biobase”)<\/p>\n\n\n\n install.packages(“caret”)<\/p>\n\n\n\n install.packages(“survival”)<\/p>\n\n\n\n \u4f7f\u7528genefu\u5305\u8fdb\u884c\u5206\u5b50\u5206\u578b<\/p>\n\n\n\n \u4e3a\u4e86\u6f14\u793agenefu\u5305\u7684\u7528\u6cd5\uff0c\u672c\u6587\u4e2d\u6211\u4eec\u5c06\u4f7f\u7528\u968f\u673a\u751f\u6210\u7684\u6811\u6587\u4ef6\u548c\u6a21\u62df\u7684\u5173\u8054\u6570\u636e\u96c6\uff0c\u4f7f\u7528\u7684\u6a21\u62df\u6570\u636e\u96c6\u90fd\u5305\u542b\u6811\u7684\u90a3\u4e00\u5217\u5206\u7c7b\u6807\u7b7e\u3002<\/p>\n\n\n\n \u8f7d\u5165\u5305<\/strong>\u4e0e\u6570\u636e<\/strong>\uff1a<\/strong><\/strong><\/p>\n\n\n\n library(genefu)<\/p>\n\n\n\n library(xtable)<\/p>\n\n\n\n library(rmeta)<\/p>\n\n\n\n library(Biobase)<\/p>\n\n\n\n library(caret)<\/p>\n\n\n\n # \u8f7d\u5165\u5305<\/p>\n\n\n\n \u8f7d\u5165\u6570\u636e\uff1a<\/strong><\/strong><\/p>\n\n\n\n library(breastCancerMAINZ)<\/p>\n\n\n\n library(breastCancerTRANSBIG)<\/p>\n\n\n\n library(breastCancerUPP)<\/p>\n\n\n\n library(breastCancerUNT)<\/p>\n\n\n\n library(breastCancerNKI)<\/p>\n\n\n\n # \u8f7d\u5165\u6570\u636e<\/p>\n\n\n\n \u6570\u636e\u6982\u8ff0\u5982\u4e0b\u8868\uff1a<\/p>\n\n\n\n \u7531\u8868\u53ef\u77e5\uff0c\u4e94\u4e2a\u6570\u636e\u96c6\u4e2d\u5404\u81ea\u7684\u75c5\u4eba\u4eba\u6570\u3001ER\u9633\u6027\u4eba\u6570\u3001HER2\u9633\u6027\u4eba\u6570\u3001\u5e74\u9f84\u3001\u7b49\u7ea7\u548c\u6570\u636e\u91c7\u96c6\u6240\u4f7f\u7528\u7684\u5e73\u53f0\u3002\u7531\u4e8e\u6ca1\u6709Ki-67\u589e\u6b96\u6307\u6570\uff0c\u6240\u4ee5\u53ea\u80fd\u6309ER\u4e0eHER2\u8fdb\u884c\u5206\u7c7b\u3002ER\u9633\u6027\uff0cHER2\u9634\u6027\u4e3a\u7ba1\u8154A\u578b\uff08Luminal A\uff09\uff1bER\u9633\u6027\uff0cHER2\u9633\u6027\uff08\u86cb\u767d\u8fc7\u8868\u8fbe\u6216\u57fa\u56e0\u6269\u589e\uff09\u4e3a\u7ba1\u8154B\u578b\uff08Luminal B\uff09\uff1bHER2\u9633\u6027\uff08\u86cb\u767d\u8fc7\u8868\u8fbe\u6216\u57fa\u56e0\u6269\u589e\uff09\uff0cER\u9634\u6027\u4e3aERBB2\uff08HER2\u53c8\u79f0ERBB2\uff09\u9633\u6027\u578b\uff1bER\u9634\u6027\uff0cHER2\u9634\u6027\u57fa\u5e95\u6837\u578b\uff08Basal-like\uff09\u3002<\/p>\n\n\n\n data(mainz)<\/p>\n\n\n\n View(mainz)<\/p>\n\n\n\n # \u8f7d\u5165\u5e76\u67e5\u770bmainz\u6570\u636e\u96c6<\/p>\n\n\n\n \u53ef\u89c1\u6bcf\u4e2a\u6570\u636e\u96c6\u90fd\u5305\u62ec\u8bb8\u591a\u65b9\u9762\u7684\u6570\u636e\uff0c\u6bd4\u5982\u5b9e\u9a8c\u6570\u636e\uff08experimentData\uff09\u3001\u8868\u8fbe\u6570\u636e\uff08assayData\uff09\u3001\u6837\u54c1\u4fe1\u606f\uff08phenoData\uff09\u3001\u63a2\u9488\u6ce8\u91ca\u4fe1\u606f\uff08featureData\uff09\u3001\u65b9\u6cd5\u6570\u636e\uff08protocolData\uff09\u7b49\u3002\u8fd9\u5176\u5b9e\u662fBiobase\u5305\u4e2dExpressionSet\u7c7b\u7684\u683c\u5f0f\uff0c\u5c06\u591a\u79cd\u6765\u6e90\u4fe1\u606f\u7ec4\u5408\u6210\u7684\u5355\u4e2a\u6570\u636e\u7ed3\u6784\uff0c\u5728\u82af\u7247\u6570\u636e\u5206\u6790\u5f88\u5e38\u7528\u3002<\/p>\n\n\n\n data(breastCancerData)<\/p>\n\n\n\n # \u8f7d\u5165\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n\n\n\n cinfo <- colnames(pData(mainz7g))<\/p>\n\n\n\n # \u4f7f\u7528mainz7g\u6837\u54c1\u4fe1\u606f\u7684\u5217\u540d<\/p>\n\n\n\n data.all<-c(“transbig7g”=transbig7g,”unt7g”=unt7g,”upp7g”=upp7g,”mainz7g”=mainz7g, “nki7g”=nki7g)<\/p>\n\n\n\n # \u5408\u5e76\u6240\u6709\u6570\u636e\u96c6<\/p>\n\n\n\n idtoremove.all <- NULL<\/p>\n\n\n\n duplres <- NULL<\/p>\n\n\n\n # \u5df2\u77e5MainZ\u548cNKI\u6570\u636e\u96c6\u6ca1\u6709\u91cd\u590d\uff0c\u5f00\u59cbUNT\u3001UPP\u4e0eTRANSBIG\u7684\u53bb\u91cd<\/p>\n\n\n\n demo.all <- rbind(pData(transbig7g), pData(unt7g), pData(upp7g))<\/p>\n\n\n\n dn2 <- c(“TRANSBIG”, “UNT”, “UPP”)<\/p>\n\n\n\n # \u67e5\u627eVDXKIU, KIU, UPPU\u7cfb\u5217<\/p>\n\n\n\n ds2 <- c(“VDXKIU”, “KIU”, “UPPU”)<\/p>\n\n\n\n demot<-demo.all[complete.cases(demo.all[,c(“series”)])&is.element(demo.all[ , “series”], ds2), ]<\/p>\n\n\n\n # \u67e5\u627e\u4ee5\u4e0a\u7cfb\u5217\u4e2d\u91cd\u590d\u7684\u75c5\u4eba<\/p>\n\n\n\n duplid <- sort(unique(demot[duplicated(demot[ , “id”]), “id”]))<\/p>\n\n\n\n duplrest <- NULL<\/p>\n\n\n\n for(i in 1:length(duplid)) {<\/p>\n\n\n\n tt <- NULL<\/p>\n\n\n\n for(k in 1:length(dn2)) {<\/p>\n\n\n\n myx <- sort(row.names(demot)[complete.cases(demot[ , c(“id”, “dataset”)]) &demot[ , “id”] == duplid[i] & demot[ , “dataset”] == dn2[k]])<\/p>\n\n\n\n if(length(myx) > 0) { tt <- c(tt, myx) }<\/p>\n\n\n\n }<\/p>\n\n\n\n duplrest <- c(duplrest, list(tt))<\/p>\n\n\n\n }<\/p>\n\n\n\n names(duplrest) <- duplid<\/p>\n\n\n\n duplres <- c(duplres, duplrest)<\/p>\n\n\n\n # \u67e5\u627eVVDXOXFU, OXFU\u7cfb\u5217<\/p>\n\n\n\n ds2 <- c(“VDXOXFU”, “OXFU”)<\/p>\n\n\n\n demot <- demo.all[complete.cases(demo.all[ , c(“series”)]) &is.element(demo.all[ , “series”], ds2), ]<\/p>\n\n\n\n # \u67e5\u627e\u4ee5\u4e0a\u7cfb\u5217\u4e2d\u91cd\u590d\u7684\u75c5\u4eba<\/p>\n\n\n\n duplid <- sort(unique(demot[duplicated(demot[ , “id”]), “id”]))<\/p>\n\n\n\n duplrest <- NULL<\/p>\n\n\n\n for(i in 1:length(duplid)) {<\/p>\n\n\n\n tt <- NULL<\/p>\n\n\n\n for(k in 1:length(dn2)) {<\/p>\n\n\n\n myx <- sort(row.names(demot)[complete.cases(demot[ , c(“id”, “dataset”)]) &demot[ , “id”] == duplid[i] &demot[ , “dataset”] == dn2[k]])<\/p>\n\n\n\n if(length(myx) > 0) { tt <- c(tt, myx) }<\/p>\n\n\n\n }<\/p>\n\n\n\n duplrest <- c(duplrest, list(tt))<\/p>\n\n\n\n }<\/p>\n\n\n\n names(duplrest) <- duplid<\/p>\n\n\n\n duplres <- c(duplres, duplrest)<\/p>\n\n\n\n # \u5168\u96c6\u4e2d\u91cd\u590d\u7684\u75c5\u4eba<\/p>\n\n\n\n duPL <- sort(unlist(lapply(duplres, function(x) { return(x[-1]) } )))<\/p>\n\n\n\n # \u52a0\u8f7d\u6570\u636e<\/p>\n\n\n\n data(scmod2.robust)<\/p>\n\n\n\n data(pam50.robust)<\/p>\n\n\n\n data(scmgene.robust)<\/p>\n\n\n\n data(sig.ggi)<\/p>\n\n\n\n data(scmod1.robust)<\/p>\n\n\n\n data(sig.genius)<\/p>\n\n\n\n dn <- c(“transbig”, “unt”, “upp”, “mainz”, “nki”)<\/p>\n\n\n\n dn.platform <- c(“affy”, “affy”, “affy”, “affy”, “agilent”)<\/p>\n\n\n\n res <- ddemo.all <- ddemo.coln <- NULL<\/p>\n\n\n\n for(i in 1:length(dn)) {<\/p>\n\n\n\n # \u52a0\u8f7d\u6570\u636e<\/p>\n\n\n\n dd <- get(data(list=dn[i]))<\/p>\n\n\n\n # \u5220\u9664\u91cd\u590d\u9879<\/p>\n\n\n\n message(“obtained dataset!”)<\/p>\n\n\n\n # \u63d0\u53d6\u6bcf\u4e2a\u6570\u636e\u96c6\u7684pData\u4e0efData<\/p>\n\n\n\n ddata <- t(exprs(dd))<\/p>\n\n\n\n ddemo <- phenoData(dd)@data<\/p>\n\n\n\n if(length(intersect(rownames(ddata),duPL))>0)<\/p>\n\n\n\n {<\/p>\n\n\n\n ddata<-ddata[-which(rownames(ddata) %in% duPL),]<\/p>\n\n\n\n ddemo<-ddemo[-which(rownames(ddemo) %in% duPL),]<\/p>\n\n\n\n }<\/p>\n\n\n\n dannot <- featureData(dd)@data<\/p>\n\n\n\n # \u5206\u5b50\u5206\u578b\u6a21\u578b\u4e3aPAM50\uff0c\u77e9\u9635\u5e94\u6837\u672c\u4e3a\u884c\uff0c\u57fa\u56e0\u4e3a\u5217<\/p>\n\n\n\n PAM50Preds<-molecular.subtyping(sbt.model=”pam50″,data=ddata,annot=dannot,do.mapping=TRUE) <\/p>\n\n\n\n table(PAM50Preds$subtype)<\/p>\n\n\n\n ddemo$PAM50<-PAM50Preds$subtype LumA<-names(PAM50Preds$subtype)[which(PAM50Preds$subtype == “LumA”)] LumB<-names(PAM50Preds$subtype)[which(PAM50Preds$subtype == “LumB”)]<\/p>\n\n\n\n ddemo[LumA,]$PAM50<-“LumA”<\/p>\n\n\n\n ddemo[LumB,]$PAM50<-“LumB”<\/p>\n\n\n\n ddemo.all <- rbind(ddemo, ddemo.all)<\/p>\n\n\n\n }<\/p>\n\n\n\n table(ddemo.all$PAM50)<\/p>\n\n\n\n \u5206\u5b50\u5206\u578b\u5b8c\u6210\u3002<\/p>\n\n\n\n Normals<-rownames(ddemo.all[which(ddemo.all$PAM50 == “Normal”),])<\/p>\n\n\n\n ddemo.all$PAM50<-as.character(ddemo.all$PAM50)<\/p>\n\n\n\n # \u6bd4\u8f83\u4e86\u88abPAM50\u6a21\u578b\u9884\u6d4b\u4e3a\u4e0e\u5206\u5b50\u5206\u578b\u6709\u5173\u7684\u6837\u672c\uff0c\u5ffd\u89c6\u4e86\u2018Normal\u2019\u7684\u6837\u672c<\/p>\n\n\n\n confusionMatrix(factor(ddemo.all[-which(rownames(ddemo.all)%in%Normals),]$PAM50))<\/p>\n\n\n\n library(survival)<\/p>\n\n\n\n ddemo<-ddemo.all<\/p>\n\n\n\n data.for.survival.PAM50<-ddemo[,c(“e.os”,”t.os”,”PAM50″,”age”)] # \u53bb\u6389\u7f3a\u5931\u751f\u5b58\u4fe1\u606f\u7684\u75c5\u4eba<\/p>\n\n\n\n data.for.survival.PAM50<-data.for.survival.PAM50[complete.cases(data.for.survival.PAM50),]<\/p>\n\n\n\n days.per.month <- 30.4368<\/p>\n\n\n\n days.per.year <- 365.242<\/p>\n\n\n\n data.for.survival.PAM50$months_to_death<-data.for.survival.PAM50$t.os \/ days.per.month<\/p>\n\n\n\n data.for.survival.PAM50$vital_status<-data.for.survival.PAM50$e.os == “1”<\/p>\n\n\n\n surv.obj.PAM50<-survfit(Surv(data.for.survival.PAM50$months_to_death,data.for.survival.PAM50$vital_status)~data.for.survival.PAM50$PAM50)<\/p>\n\n\n\n message(“KAPLAN-MEIR CURVE – USING PAM50”)<\/p>\n\n\n\n # KAPLAN-MEIR \u66f2\u7ebf\uff08\u751f\u5b58\u66f2\u7ebf\uff09 – PAM50\u6a21\u578b<\/p>\n\n\n\n plot(main = “Surival Curves PAM50”, surv.obj.PAM50,col =c(“#006d2c”, “#8856a7″,”#a50f15”, “#08519c”, “#000000”),lty = 1,lwd = 3,xlab = “Time (months)”,ylab = “Probability of Survival”)<\/p>\n\n\n\n legend(“topright”,fill=c(“#006d2c”,”#8856a7″,”#a50f15″, “#08519c”,”#000000″),legend=c(“Basal”,”Her2″,”LumA”,”LumB”,”Normal”),bty = “n”)<\/p>\n\n\n\n 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