{"id":25140,"date":"2024-01-18T16:52:19","date_gmt":"2024-01-18T08:52:19","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25140"},"modified":"2024-01-18T16:52:22","modified_gmt":"2024-01-18T08:52:22","slug":"%e5%8f%aa%e9%9c%805%e5%88%86%e9%92%9f%e6%8e%8c%e6%8f%a1%e8%82%bf%e7%98%a4%e7%94%9f%e4%bf%a1%e6%96%87%e7%ab%a0%e5%bf%85%e5%a4%87%e5%88%86%e6%9e%90%e5%86%85%e5%ae%b9%ef%bc%8c%e4%ba%9a%e5%9e%8b%e9%97%b4","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25140","title":{"rendered":"\u53ea\u97005\u5206\u949f\u638c\u63e1\u80bf\u7624\u751f\u4fe1\u6587\u7ae0\u5fc5\u5907\u5206\u6790\u5185\u5bb9\uff0c\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u663e\u8457\u5dee\u5f02\u5206\u6790"},"content":{"rendered":"

\u4eca\u5929\u5c0f\u679c\u5e26\u6765\u7684\u5206\u4eab\u4e3a\u9996\u5148\u5229\u7528ConsensusCluster\u5bf9\u80bf\u7624\u6837\u672c\u8fdb\u884c\u5206\u578b\uff0c\u7136\u540e\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u663e\u8457\u5dee\u5f02\u5206\u6790\uff0c\u8be5\u5206\u6790\u5728\u80bf\u7624\u9884\u540e\u76f8\u5173\u751f\u4fe1\u6587\u7ae0\u4e2d\u51fa\u73b0\u7684\u9891\u7387\u975e\u5e38\u9ad8\uff0c\u503c\u5f97\u5c0f\u4f19\u4f34\u4eec\u5b66\u4e60\u5965\uff0c\u63a5\u4e0b\u6765\u8ddf\u7740\u5c0f\u679c\u5f00\u542f\u4eca\u5929\u7684\u5b66\u4e60\u4e4b\u65c5\u5427\uff01<\/p>\n

    \n
  1. \u5982\u4f55\u8fdb\u884c\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u663e\u8457\u5dee\u5f02\u5206\u6790\uff1f<\/li>\n<\/ol>\n

    \u5728\u8fdb\u884c\u8be5\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u4e3a\u5c0f\u4f19\u4f34\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0b\u9700\u8981\u6ce8\u610f\u7684\u8981\u70b9\uff0c\u7b2c\u4e00\u70b9\u9996\u5148\u5bf9\u80bf\u7624\u6837\u672c\u8fdb\u884c\u5206\u578b\uff0c\u7b2c\u4e8c\u70b9\u51c6\u5907\u597d\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\uff0c\u6700\u540e\u5229\u7528ggpubr\u5305\u8fdb\u884c\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u663e\u8457\u5dee\u5f02\u5206\u6790\uff0c\u7b5b\u9009\u51fa\u5728\u4e0d\u540c\u4e9a\u578b\u95f4\u6709\u663e\u8457\u5dee\u5f02\u7684\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\uff0c\u8fd9\u5c31\u662f\u5927\u6982\u7684\u5206\u6790\u6b65\u9aa4\uff0c\u4e0b\u9762\u548c\u5c0f\u679c\u4e00\u8d77\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u5427\uff01<\/p>\n

    2.\u51c6\u5907\u9700\u8981\u7684R\u5305<\/p>\n

    #\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/p>\n

    install.packages(“tidyverse”)<\/p>\n

    install.packages(“ggplot2”)<\/p>\n

    install.packages(“ggsci”)<\/p>\n

    BiocManager::install(“ConsensusClusterPlus”)<\/p>\n

    #\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/p>\n

    library(reshape2)<\/p>\n

    library(ggpubr)<\/p>\n

    library(ggplot2)<\/p>\n

    library(tidyverse)<\/p>\n

    library(ggsci)<\/p>\n

    library(ConsensusClusterPlus)<\/p>\n

    3.ConsensusCluster\u4e00\u81f4\u6027\u805a\u7c7b\u5206\u6790<\/p>\n

    #\u7b5b\u9009\u51fa\u7684\u6709\u9884\u540e\u7684TEX\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u6587\u4ef6\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\u540d\uff0c\u5217\u4e3a\u6837\u672c\u540d<\/p>\n

    expr1<- read.table(file = “expr_cluster.txt”,#\u5de5\u4f5c\u76ee\u5f55\u4e0b\u7684\u6587\u4ef6\u540d<\/p>\n

    header = T,#\u8bbe\u7f6e\u6709\u5217\u540d<\/p>\n

    sep = “\\t”,#\u5206\u9694\u7b26<\/p>\n

    row.names = 1,#\u8bbe\u7f6e\u884c\u540d<\/p>\n

    check.names=F)#\u4e0d\u68c0\u67e5\u884c\u540d<\/p>\n

    \"Dingtalk_20230710100242\"<\/p>\n

    dir.create(‘ConsensusCluster\/’)#\u6784\u5efa\u6587\u4ef6\u5939<\/p>\n

    #ConsensusCluster\u4e00\u81f4\u6027\u805a\u7c7b<\/p>\n

    results = ConsensusClusterPlus(as.matrix(expr1),<\/p>\n

    maxK=6,<\/p>\n

    reps=100,<\/p>\n

    pItem=0.8,<\/p>\n

    pFeature=1,<\/p>\n

    title=’ConsensusCluster\/’,<\/p>\n

    clusterAlg=”km”,<\/p>\n

    distance=”euclidean”,<\/p>\n

    seed=123456,<\/p>\n

    plot=”pdf”)<\/p>\n

    icl <- calcICL(results,title = ‘ConsensusCluster\/’,plot = ‘pdf’)<\/p>\n

    Kvec = 2:6<\/p>\n

    x1 = 0.1; x2 = 0.6<\/p>\n

    PAC = rep(NA,length(Kvec))<\/p>\n

    names(PAC) = paste(“K=”,Kvec,sep=””)<\/p>\n

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

    M = results[[i]]$consensusMatrix<\/p>\n

    Fn = ecdf(M[lower.tri(M)])<\/p>\n

    PAC[i-1] = Fn(x2) – Fn(x1)<\/p>\n

    }<\/p>\n

    optK = Kvec[which.min(PAC)]<\/p>\n

    optK<\/p>\n

    PAC <- as.data.frame(PAC)<\/p>\n

    PAC$K <- 2:6<\/p>\n

    #\u7ed8\u5236PAC\u9a8c\u8bc1\u66f2\u7ebf<\/p>\n

    ggplot(PAC,aes(factor(K),PAC,group=1))+<\/p>\n

    geom_line()+<\/p>\n

    theme_bw()+theme(panel.grid = element_blank())+<\/p>\n

    geom_point(size=4,shape=21,color=’darkred’,fill=’skyblue’)+<\/p>\n

    ylab(‘Proportion of ambiguous clustering’)+<\/p>\n

    xlab(‘Cluster number K’)<\/p>\n

    ggsave(“PAC.pdf”)<\/p>\n

    #\u6700\u5c0f\u503c\u4e5f\u5c31\u662f\u6700\u4f73K\uff0c\u4ecd\u7136\u4e3a3.<\/p>\n

    #\u63d0\u53d6\u5206\u578b\u4fe1\u606f\uff0ck=3<\/p>\n

    clusterNum=3 #k\u503c\u53d8\u5316\u8c03\u6574<\/p>\n

    cluster=results[[clusterNum]][[“consensusClass”]]<\/p>\n

    sub <- data.frame(Sample=names(cluster),Cluster=cluster)<\/p>\n

    sub$Cluster <- paste0(‘C’,sub$Cluster)<\/p>\n

    table(sub$Cluster)<\/p>\n

    write.table(sub,file = “K3.txt”,sep=”\\t”,quote=F)#\u8f93\u51fa\u7ed3\u679c<\/p>\n

    4.\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u5dee\u5f02\u5206\u6790<\/p>\n

    #EXP.txt\uff0c\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u6587\u4ef6\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\u540d\uff0c\u5217\u540d\u4e3a\u6837\u672c\u4fe1\u606f\u3002<\/p>\n

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

    \"Dingtalk_20230710100348\"<\/p>\n

    #\u63d0\u53d6\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u6587\u4ef6<\/p>\n

    df<-filter(exp,rownames(exp)%in% c(“LAG3″,”CD47″,”TIGIT”,”SIRPA”,”TNFRSF4″,”VTCN1″,”HAVCR2″,”ICOS”,”CTLA4″,”CD274″,”PDCD1″,”BTLA”,”TNFRSF9″))<\/p>\n

    #\u884c\u5217\u8f6c\u7f6e<\/p>\n

    df<-as.data.frame(t(df))<\/p>\n

    #\u5c06\u884c\u540d\u8f6c\u5316\u4e3a\u5217\u540d<\/p>\n

    df<-rownames_to_column(df,var=”Sample”)<\/p>\n

    #\u4fee\u6539\u57fa\u56e0\u540d<\/p>\n

    df<-rename(df,c(PD1=PDCD1,TIM3=HAVCR2,MYD1=SIRPA))<\/p>\n

    ##\u8fde\u63a5\u4e9a\u578b\u5206\u7ec4\u4fe1\u606f\u548c\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u6587\u4ef6<\/p>\n

    immunegene<-left_join(df,sub,by=”Sample”)<\/p>\n

    #\u957f\u5bbd\u6570\u636e\u8f6c\u5316<\/p>\n

    immunegene<-melt(immunegene,id.vars=c(“Sample”,”Cluster”))<\/p>\n

    #\u8bbe\u7f6e\u5217\u540d<\/p>\n

    colnames(immunegene)<-c(“Sample”,”Cluster”,”Gene”,”Expression”)<\/p>\n

    #\u7ed8\u5236\u663e\u8457\u5dee\u5f02\u5c0f\u63d0\u7434+\u7bb1\u7ebf\u56fe<\/p>\n

    boxplot_immunegene<- ggplot(immunegene, aes(x = Gene, y =Expression ))+<\/p>\n

    labs(y=”Expression”,x= “”)+<\/p>\n

    geom_boxplot(aes(fill = Cluster),position=position_dodge(0.5),width=0.5)+<\/p>\n

    scale_fill_npg()+<\/p>\n

    #\u4fee\u6539\u4e3b\u9898<\/p>\n

    theme_classic() +<\/p>\n

    theme(axis.title = element_text(size = 12,color =”black”),<\/p>\n

    axis.text = element_text(size= 12,color = “black”),<\/p>\n

    panel.grid.minor.y = element_blank(),<\/p>\n

    panel.grid.minor.x = element_blank(),<\/p>\n

    axis.text.x = element_text(angle = 45, hjust = 1 ),<\/p>\n

    panel.grid=element_blank(),<\/p>\n

    legend.position = “top”,<\/p>\n

    legend.text = element_text(size= 12),<\/p>\n

    legend.title= element_text(size= 12)<\/p>\n

    ) +<\/p>\n

    stat_compare_means(aes(group = Cluster),<\/p>\n

    label = “p.signif”,#\u6dfb\u52a0\u661f\u53f7\u6807\u7b7e<\/p>\n

    method = “kruskal.test”,#kruskal.test\u591a\u7ec4\u68c0\u9a8c<\/p>\n

    hide.ns = F)#\u663e\u793a\u4e0d\u663e\u8457\u7684<\/p>\n

    #\u4fdd\u5b58\u56fe\u7247<\/p>\n

    ggsave(file=”immunegene.pdf”,boxplot_immunegene,height=10,width=15)<\/p>\n

      \n
    1. \u7ed3\u679c\u6587\u4ef6<\/li>\n
    2. immunegene.pdf<\/li>\n<\/ol>\n

      \u8be5\u7ed3\u679c\u56fe\u7247\u4e3a\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u5dee\u5f02\u5206\u6790\u5c0f\u63d0\u7434+\u7bb1\u7ebf\u56fe\uff0c*\u8868\u793ap\u5c0f\u4e8e0.05\uff0c**\u8868\u793ap\u5c0f\u4e8e0.01\uff0c***\u8868\u793ap\u5c0f\u4e8e0.001,ns\u8868\u793a\u4e0d\u663e\u8457\u3002<\/p>\n

      \"Dingtalk_20230710094807\"<\/p>\n

      2.K3.txt<\/p>\n

      \u8be5\u7ed3\u679c\u6587\u4ef6\u4e3a\u5229\u7528ConsensusCluster\u4e00\u81f4\u6027\u805a\u7c7b\u5206\u6790\uff0cK=3\u65f6\u4e9a\u578b\u5206\u7ec4\u4fe1\u606f\u6587\u4ef6,\u7b2c\u4e00\u5217\u4e3a\u6837\u672c\u540d\uff0c\u7b2c\u4e8c\u5217\u4e3a\u5206\u7ec4\u4fe1\u606f\u3002<\/p>\n

      \"Dingtalk_20230710095556\"<\/p>\n

      \u6700\u7ec8\u5c0f\u679c\u6210\u529f\u7684\u5b8c\u6210\u4e86\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u663e\u8457\u5dee\u5f02\u5206\u6790\uff0c\u770b\u8d77\u6765\u56fe\u7247\u6548\u679c\u975e\u5e38\u4e0d\u9519\uff0c\u6b22\u8fce\u5927\u5bb6\u548c\u5c0f\u679c\u4e00\u8d77\u8ba8\u8bba\u5b66\u4e60\u5440\uff01\u514d\u75ab\u6d78\u6da6\u76f8\u5173\u5206\u6790\u5185\u5bb9\uff0c\u4f8b\u5982\u5355\u6837\u672c\u5bcc\u96c6\u7b97\u6cd5\u5206\u6790\u514d\u75ab\u6d78\u6da6\u4e30\u5ea6(http:\/\/www.biocloudservice.com\/106\/106.php)\uff0c\u8ba1\u7b9764\u79cd\u514d\u75ab\u7ec6\u80de\u76f8\u5bf9\u542b\u91cf(http:\/\/www.biocloudservice.com\/107\/107.php)\u7b49\u90fd\u53ef\u4ee5\u7528\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u96f6\u4ee3\u7801\u4e91\u5e73\u53f0\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\uff0c\u4e00\u952e\u5b8c\u6210\u8be5\u5206\u6790\u5965\uff0c\u611f\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u6b22\u8fce\u6765\u5c1d\u8bd5\u5965\uff0c\u7f51\u5740\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\uff0c\u4e0b\u671f\u5728\u89c1\u5965\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

      \u4eca\u5929\u5c0f\u679c\u5e26\u6765\u7684\u5206\u4eab\u4e3a\u9996\u5148\u5229\u7528ConsensusCluster\u5bf9\u80bf\u7624\u6837\u672c\u8fdb\u884c\u5206\u578b\uff0c\u7136\u540e\u4e9a\u578b\u95f4\u514d\u75ab\u68c0\u67e5\u70b9\u57fa\u56e0\u8868\u8fbe\u77e9 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/25140"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=25140"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/25140\/revisions"}],"predecessor-version":[{"id":25145,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/25140\/revisions\/25145"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=25140"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=25140"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=25140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}