{"id":29022,"date":"2024-02-20T17:35:46","date_gmt":"2024-02-20T09:35:46","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29022"},"modified":"2024-02-20T17:35:48","modified_gmt":"2024-02-20T09:35:48","slug":"%e5%af%b9%e5%a4%9a%e4%b8%aa%e5%88%86%e7%bb%84%e8%bf%9b%e8%a1%8c%e7%94%9f%e5%ad%98%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29022","title":{"rendered":"\u5bf9\u591a\u4e2a\u5206\u7ec4\u8fdb\u884c\u751f\u5b58\u5206\u6790"},"content":{"rendered":"\n

\u4eca\u5929\u5c0f\u679c\u60f3\u5bf9\u5bf93\u7ec4\u53ca\u4ee5\u4e0a\u7684\u4e9a\u7ec4\u8fdb\u884c\u751f\u5b58\u5206\u6790\uff0c\u5e76\u6253\u5370\u914d\u5bf9\u6bd4\u8f83\u7684\u7edf\u8ba1\u8868\u683c\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

    \n
  1. \u5b89\u88c5\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n\n\n\n

    install.packages(\u201cRcolorBrewer\u201d)<\/p>\n\n\n\n

    install.packages(\u201ctibble\u201d)<\/p>\n\n\n\n

    install.packages(\u201cggpp\u201d)<\/p>\n\n\n\n

    install.packages(\u201csurvival\u201d)<\/p>\n\n\n\n

    install.packages(\u201csurvminer\u201d)<\/p>\n\n\n\n

      \n
    1. \u5bfc\u5165\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n\n\n\n

      library(survival)<\/p>\n\n\n\n

      library(survminer)<\/p>\n\n\n\n

      library(RColorBrewer)<\/p>\n\n\n\n

      library(tibble)<\/p>\n\n\n\n

      library(ggpp)<\/p>\n\n\n\n

        \n
      1. \u4ee3\u7801\u5c55\u793a<\/li>\n<\/ol>\n\n\n\n

        #\u5bfc\u5165\u6570\u636e<\/p>\n\n\n\n

        dat<-read.table(\u201csurvival.txt\u201d,sep=”\\t”,row.names=1,check.names=F,stringsAsFactors = F,header = T)<\/p>\n\n\n\n

        \"Dingtalk_20230209105524\"<\/figure>\n\n\n\n

        #\u5c06PAM50\u5206\u578b\u6309\u7167ABCDE\u6807\u8bb0<\/p>\n\n\n\n

        dat$group <- sapply(dat$PAM50,function(x) {<\/p>\n\n\n\n

        switch(x,<\/p>\n\n\n\n

        “Basal” = “A”, # \u5c06Basal\u6807\u8bb0\u4e3aA<\/p>\n\n\n\n

        “Her2” = “B”, # \u5c06Her2\u6807\u8bb0\u4e3aB<\/p>\n\n\n\n

        “LumA” = “C”, # \u5c06LumA\u6807\u8bb0\u4e3aC<\/p>\n\n\n\n

        “LumB” = “D”, # \u5c06LumB\u6807\u8bb0\u4e3aD<\/p>\n\n\n\n

        “Normal” = “E”)}) # \u5c06Normal\u6807\u8bb0\u4e3aE<\/p>\n\n\n\n

        ##\u5c06\u751f\u5b58\u65f6\u95f4\u8f6c\u6362\u4e3a\u6708\u4efd<\/p>\n\n\n\n

        dat$OS.time <- dat$OS.time * 12<\/p>\n\n\n\n

        # \u751f\u5b58\u5206\u6790<\/p>\n\n\n\n

        fitd <- survdiff(Surv(OS.time, OS) ~ group,<\/p>\n\n\n\n

        data = dat,<\/p>\n\n\n\n

        na.action = na.exclude)<\/p>\n\n\n\n

        p.val <- 1 – pchisq(fitd$chisq, length(fitd$n) – 1)<\/p>\n\n\n\n

        fit <- survfit(Surv(OS.time, OS)~ group,<\/p>\n\n\n\n

        data = dat,<\/p>\n\n\n\n

        type = “kaplan-meier”,<\/p>\n\n\n\n

        error = “greenwood”,<\/p>\n\n\n\n

        conf.type = “plain”,<\/p>\n\n\n\n

        na.action = na.exclude)<\/p>\n\n\n\n

        # \u914d\u5bf9\u751f\u5b58\u5206\u6790<\/p>\n\n\n\n

        ps <- pairwise_survdiff(Surv(OS.time, OS)~ group,<\/p>\n\n\n\n

        data = dat,<\/p>\n\n\n\n

        p.adjust.method = “none”) # \u8fd9\u91cc\u4e0d\u4f7f\u7528\u77eb\u6b63\uff0c\u82e5\u9700\u8981\u77eb\u6b63\u53ef\u4ee5\u5c06none\u66ff\u6362\u4e3aBH<\/p>\n\n\n\n

        # \u8bbe\u7f6e\u989c\u8272<\/p>\n\n\n\n

        mycol <- brewer.pal(n = 10, “Paired”)[c(2,4,6,8,10)]<\/p>\n\n\n\n

        # \u7ed8\u5236\u57fa\u7840\u56fe\u5f62<\/p>\n\n\n\n

        ## \u9690\u85cf\u7c7b\u6807\u8bb0<\/p>\n\n\n\n

        names(fit$strata) <- gsub(“group=”, “”, names(fit$strata))<\/p>\n\n\n\n

        ## \u751f\u5b58\u66f2\u7ebf\u56fe<\/p>\n\n\n\n

        p <- ggsurvplot(fit = fit,<\/p>\n\n\n\n

        conf.int = FALSE, # \u4e0d\u7ed8\u5236\u7f6e\u4fe1\u533a\u95f4<\/p>\n\n\n\n

        risk.table = TRUE, # \u751f\u5b58\u98ce\u9669\u8868<\/p>\n\n\n\n

        risk.table.col = “strata”,<\/p>\n\n\n\n

        palette = mycol, # KM\u66f2\u7ebf\u989c\u8272<\/p>\n\n\n\n

        data = dat,<\/p>\n\n\n\n

        xlim = c(0,120), # \u65f6\u95f4\u8f74\uff0c\u4e00\u822c\u8003\u86515\u5e74\uff08\u539f\u6587\uff09\u6216\u800510\u5e74\u957f\u5ea6<\/p>\n\n\n\n

        size = 1,<\/p>\n\n\n\n

        break.time.by = 12, # \u65f6\u95f4\u8f74\u7684\u523b\u5ea6\uff08\u6bcf\u5e74\uff09<\/p>\n\n\n\n

        legend.title = “”,<\/p>\n\n\n\n

        xlab = “Time (months)”,<\/p>\n\n\n\n

        ylab = “Overall survival”,<\/p>\n\n\n\n

        risk.table.y.text = FALSE,<\/p>\n\n\n\n

        tables.height = 0.3) # \u98ce\u9669\u8868\u7684\u9ad8\u5ea6<\/p>\n\n\n\n

        ## \u6dfb\u52a0overall pvalue<\/p>\n\n\n\n

        p.lab <- paste0(“log-rank test P”,<\/p>\n\n\n\n

        ifelse(p.val < 0.001, ” < 0.001″, # \u82e5P\u503c<0.001\u5219\u6807\u8bb0\u4e3a\u201c<0.001\u201d<\/p>\n\n\n\n

        paste0(” = “,round(p.val, 3))))<\/p>\n\n\n\n

        p$plot <- p$plot + annotate(“text”,<\/p>\n\n\n\n

        x = 0, y = 0.55, # \u5728y=0.55\u5904\u6253\u5370overall p\u503c<\/p>\n\n\n\n

        hjust = 0,<\/p>\n\n\n\n

        fontface = 4,<\/p>\n\n\n\n

        label = p.lab)<\/p>\n\n\n\n

        ## \u6dfb\u52a0\u914d\u5bf9\u8868\u683c<\/p>\n\n\n\n

        addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, “<0.001”,<\/p>\n\n\n\n

        round(ps$p.value, 3))))<\/p>\n\n\n\n

        addTab[is.na(addTab)] <- “-“<\/p>\n\n\n\n

        df <- tibble(x = 0, y = 0, tb = list(addTab))<\/p>\n\n\n\n

        p$plot <- p$plot +<\/p>\n\n\n\n

        geom_table(data = df,<\/p>\n\n\n\n

        aes(x = x, y = y, label = tb),<\/p>\n\n\n\n

        table.rownames = TRUE)<\/p>\n\n\n\n

        ##\u751f\u6210\u56fe\u7247<\/p>\n\n\n\n

        pdf(“km_curve_with_pairwise_logrank.pdf”, width = 4.5, height = 6)<\/p>\n\n\n\n

        print(p)<\/p>\n\n\n\n

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

        \"Dingtalk_20230209100412\"<\/figure>\n\n\n\n

        \u6700\u7ec8\u5bf9\u591a\u4e2a\u5206\u7ec4\u7684\u6570\u636e\u8fdb\u884c\u4e86\u5206\u6790\uff0c\u7ed8\u5236\u4e86\u6dfb\u52a0\u914d\u5bf9\u6bd4\u8f83\u7684\u6587\u4ef6\u7684\u56fe\u7247\uff0c\u770b\u8d77\u6765\u6548\u679c\u8fd8\u4e0d\u9519\u3002<\/p>\n\n\n

        \u5bf9\u591a\u4e2a\u5206\u7ec4\u8fdb\u884c\u751f\u5b58\u5206\u6790<\/p>\n

        \u4eca\u5929\u5c0f\u679c\u60f3\u5bf9\u5bf93\u7ec4\u53ca\u4ee5\u4e0a\u7684\u4e9a\u7ec4\u8fdb\u884c\u751f\u5b58\u5206\u6790\uff0c\u5e76\u6253\u5370\u914d\u5bf9\u6bd4\u8f83\u7684\u7edf\u8ba1\u8868\u683c\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n

          \n
        1. \u5b89\u88c5\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n

          install.packages(\u201cRcolorBrewer\u201d)<\/p>\n

          install.packages(\u201ctibble\u201d)<\/p>\n

          install.packages(\u201cggpp\u201d)<\/p>\n

          install.packages(\u201csurvival\u201d)<\/p>\n

          install.packages(\u201csurvminer\u201d)<\/p>\n

            \n
          1. \u5bfc\u5165\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n

            library(survival)<\/p>\n

            library(survminer)<\/p>\n

            library(RColorBrewer)<\/p>\n

            library(tibble)<\/p>\n

            library(ggpp)<\/p>\n

              \n
            1. \u4ee3\u7801\u5c55\u793a<\/li>\n<\/ol>\n

              #\u5bfc\u5165\u6570\u636e<\/p>\n

              dat<-read.table(\u201csurvival.txt\u201d,sep=”\\t”,row.names=1,check.names=F,stringsAsFactors = F,header = T)<\/p>\n

              \"Dingtalk_20230209105524\"<\/p>\n

              #\u5c06PAM50\u5206\u578b\u6309\u7167ABCDE\u6807\u8bb0<\/p>\n

              dat$group <- sapply(dat$PAM50,function(x) {<\/p>\n

              switch(x,<\/p>\n

              “Basal” = “A”, # \u5c06Basal\u6807\u8bb0\u4e3aA<\/p>\n

              “Her2” = “B”, # \u5c06Her2\u6807\u8bb0\u4e3aB<\/p>\n

              “LumA” = “C”, # \u5c06LumA\u6807\u8bb0\u4e3aC<\/p>\n

              “LumB” = “D”, # \u5c06LumB\u6807\u8bb0\u4e3aD<\/p>\n

              “Normal” = “E”)}) # \u5c06Normal\u6807\u8bb0\u4e3aE<\/p>\n

              ##\u5c06\u751f\u5b58\u65f6\u95f4\u8f6c\u6362\u4e3a\u6708\u4efd<\/p>\n

              dat$OS.time <- dat$OS.time * 12<\/p>\n

              # \u751f\u5b58\u5206\u6790<\/p>\n

              fitd <- survdiff(Surv(OS.time, OS) ~ group,<\/p>\n

              data = dat,<\/p>\n

              na.action = na.exclude)<\/p>\n

              p.val <- 1 – pchisq(fitd$chisq, length(fitd$n) – 1)<\/p>\n

              fit <- survfit(Surv(OS.time, OS)~ group,<\/p>\n

              data = dat,<\/p>\n

              type = “kaplan-meier”,<\/p>\n

              error = “greenwood”,<\/p>\n

              conf.type = “plain”,<\/p>\n

              na.action = na.exclude)<\/p>\n

              # \u914d\u5bf9\u751f\u5b58\u5206\u6790<\/p>\n

              ps <- pairwise_survdiff(Surv(OS.time, OS)~ group,<\/p>\n

              data = dat,<\/p>\n

              p.adjust.method = “none”) # \u8fd9\u91cc\u4e0d\u4f7f\u7528\u77eb\u6b63\uff0c\u82e5\u9700\u8981\u77eb\u6b63\u53ef\u4ee5\u5c06none\u66ff\u6362\u4e3aBH<\/p>\n

              # \u8bbe\u7f6e\u989c\u8272<\/p>\n

              mycol <- brewer.pal(n = 10, “Paired”)[c(2,4,6,8,10)]<\/p>\n

              # \u7ed8\u5236\u57fa\u7840\u56fe\u5f62<\/p>\n

              ## \u9690\u85cf\u7c7b\u6807\u8bb0<\/p>\n

              names(fit$strata) <- gsub(“group=”, “”, names(fit$strata))<\/p>\n

              ## \u751f\u5b58\u66f2\u7ebf\u56fe<\/p>\n

              p <- ggsurvplot(fit = fit,<\/p>\n

              conf.int = FALSE, # \u4e0d\u7ed8\u5236\u7f6e\u4fe1\u533a\u95f4<\/p>\n

              risk.table = TRUE, # \u751f\u5b58\u98ce\u9669\u8868<\/p>\n

              risk.table.col = “strata”,<\/p>\n

              palette = mycol, # KM\u66f2\u7ebf\u989c\u8272<\/p>\n

              data = dat,<\/p>\n

              xlim = c(0,120), # \u65f6\u95f4\u8f74\uff0c\u4e00\u822c\u8003\u86515\u5e74\uff08\u539f\u6587\uff09\u6216\u800510\u5e74\u957f\u5ea6<\/p>\n

              size = 1,<\/p>\n

              break.time.by = 12, # \u65f6\u95f4\u8f74\u7684\u523b\u5ea6\uff08\u6bcf\u5e74\uff09<\/p>\n

              legend.title = “”,<\/p>\n

              xlab = “Time (months)”,<\/p>\n

              ylab = “Overall survival”,<\/p>\n

              risk.table.y.text = FALSE,<\/p>\n

              tables.height = 0.3) # \u98ce\u9669\u8868\u7684\u9ad8\u5ea6<\/p>\n

              ## \u6dfb\u52a0overall pvalue<\/p>\n

              p.lab <- paste0(“log-rank test P”,<\/p>\n

              ifelse(p.val < 0.001, ” < 0.001″, # \u82e5P\u503c<0.001\u5219\u6807\u8bb0\u4e3a\u201c<0.001\u201d<\/p>\n

              paste0(” = “,round(p.val, 3))))<\/p>\n

              p$plot <- p$plot + annotate(“text”,<\/p>\n

              x = 0, y = 0.55, # \u5728y=0.55\u5904\u6253\u5370overall p\u503c<\/p>\n

              hjust = 0,<\/p>\n

              fontface = 4,<\/p>\n

              label = p.lab)<\/p>\n

              ## \u6dfb\u52a0\u914d\u5bf9\u8868\u683c<\/p>\n

              addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, “<0.001”,<\/p>\n

              round(ps$p.value, 3))))<\/p>\n

              addTab[is.na(addTab)] <- “-“<\/p>\n

              df <- tibble(x = 0, y = 0, tb = list(addTab))<\/p>\n

              p$plot <- p$plot +<\/p>\n

              geom_table(data = df,<\/p>\n

              aes(x = x, y = y, label = tb),<\/p>\n

              table.rownames = TRUE)<\/p>\n

              ##\u751f\u6210\u56fe\u7247<\/p>\n

              pdf(“km_curve_with_pairwise_logrank.pdf”, width = 4.5, height = 6)<\/p>\n

              print(p)<\/p>\n

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

              \"Dingtalk_20230209100412\"<\/p>\n

              \u6700\u7ec8\u5bf9\u591a\u4e2a\u5206\u7ec4\u7684\u6570\u636e\u8fdb\u884c\u4e86\u5206\u6790\uff0c\u7ed8\u5236\u4e86\u6dfb\u52a0\u914d\u5bf9\u6bd4\u8f83\u7684\u6587\u4ef6\u7684\u56fe\u7247\uff0c\u770b\u8d77\u6765\u6548\u679c\u8fd8\u4e0d\u9519\u3002<\/p>","protected":false},"excerpt":{"rendered":"

              \u4eca\u5929\u5c0f\u679c\u60f3\u5bf9\u5bf93\u7ec4\u53ca\u4ee5\u4e0a\u7684\u4e9a\u7ec4\u8fdb\u884c\u751f\u5b58\u5206\u6790\uff0c\u5e76\u6253\u5370\u914d\u5bf9\u6bd4\u8f83\u7684\u7edf\u8ba1\u8868\u683c\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a install.packag […]<\/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\/29022"}],"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=29022"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29022\/revisions"}],"predecessor-version":[{"id":29025,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29022\/revisions\/29025"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29022"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29022"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29022"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}