{"id":28893,"date":"2024-02-19T18:02:18","date_gmt":"2024-02-19T10:02:18","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28893"},"modified":"2024-02-19T18:02:19","modified_gmt":"2024-02-19T10:02:19","slug":"%e5%88%a9%e7%94%a8ggcor%e5%8c%85%e7%bb%98%e5%88%b6%e7%9b%b8%e5%85%b3%e6%80%a7%e5%9b%be","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28893","title":{"rendered":"\u5229\u7528ggcor\u5305\u7ed8\u5236\u76f8\u5173\u6027\u56fe"},"content":{"rendered":"

\u4eca\u5929\u5c0f\u679c\u5229\u7528ggcor\u5305\u7ed8\u5236\u597d\u770b\u7684\u76f8\u5173\u6027\u56fe\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n

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

    install.packages(\u201cggplot2\u201d)<\/p>\n

    install.packages(\u201cGSVA\u201d)<\/p>\n

    install.packages(\u201cdata.table\u201d)<\/p>\n

    install.packages(\u201cggnewscale\u201d)<\/p>\n

    install.packafes(\u201cade4\u201d)<\/p>\n

    #\u76f4\u63a5\u4e0b\u8f7d\u5b89\u88c5\u5305\u5b89\u88c5<\/p>\n

    install.packages(“ggcor-master.tar.gz”, repos = NULL, type = “source”)<\/p>\n

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

      library(ggplot2)<\/p>\n

      library(ggcor)<\/p>\n

      library(ggplot2)<\/p>\n

      library(data.table)<\/p>\n

      library(GSVA)<\/p>\n

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

        #\u5bfc\u5165\u9700\u8981\u7684\u6570\u636e<\/p>\n

        #\u52a0\u8f7dTCGA-BLCA\u8868\u8fbe\u8c31FPKM<\/p>\n

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

        \"Dingtalk_20230209151017\"<\/p>\n

        tumsam <- colnames(expr)[substr(colnames(expr),11,13) == “01A”] # \u53d6\u51fa\u80bf\u7624\u6837\u672c<\/p>\n

        # \u53d6\u51faSIGLEC15\u8868\u8fbe\u91cf<\/p>\n

        siglec15 <- log2(expr[“SIGLEC15”,tumsam] + 1)<\/p>\n

        write.csv(siglec15, “gene.csv”)<\/p>\n

        # \u52a0\u8f7dimmunotherapy-predicted pathways<\/p>\n

        immPath <- read.table(“immunotherapy predicted pathways.txt”,sep = “\\t”,row.names = 1,check.names = F,stringsAsFactors = F,header = T)<\/p>\n

        \"Dingtalk_20230209151211\"<\/p>\n

        immPath.list <- list()<\/p>\n

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

        tmp <- immPath[i,”Genes”]<\/p>\n

        tmp <- toupper(unlist(strsplit(tmp,”, “,fixed = T)))<\/p>\n

        tmp <- gsub(” “,””,tmp)<\/p>\n

        immPath.list[[i]] <- tmp<\/p>\n

        }<\/p>\n

        # \u8ba1\u7b97immunotherapy-predicted pathways\u7684\u5bcc\u96c6\u5f97\u5206<\/p>\n

        immPath.score <- gsva(expr = as.matrix(log2(expr[,tumsam] + 1)),<\/p>\n

        immPath.list,<\/p>\n

        method = “ssgsea”)<\/p>\n

        write.table(immPath.score, “immPath.txt”,sep = “\\t”,row.names = T,col.names = NA,quote = F)<\/p>\n

        # \u52a0\u8f7d\u5355\u4e2a\u57fa\u56e0\u7684\u8868\u8fbe\u91cf\uff0c\u4e5f\u5c31\u662f\u201c1\u5bf9\u591a\u201d\u7684\u201c1\u201d<\/p>\n

        siglec15 <- read.csv(“gene.csv”, row.names = 1, check.names = F)<\/p>\n

        # \u52a0\u8f7d\u5bcc\u96c6\u5f97\u5206<\/p>\n

        immPath.score <- read.table(“immPath.txt”, check.names = F)<\/p>\n

        # \u8ddf\u76ee\u6807\u57fa\u56e0SIGLEC15\u7684\u8868\u8fbe\u91cf\u5408\u5e76<\/p>\n

        immPath.score <- rbind.data.frame(immPath.score,<\/p>\n

        siglec15)<\/p>\n

        # \u5faa\u73af\u8ba1\u7b97\u76f8\u5173\u6027\u5e76\u7ed8\u5236\u5de6\u4e0b\u89d2<\/p>\n

        immCorSiglec15 <- NULL<\/p>\n

        for (i in rownames(immPath.score)) {<\/p>\n

        cr <- cor.test(as.numeric(immPath.score[i,]),<\/p>\n

        as.numeric(siglec15),<\/p>\n

        method = “pearson”)<\/p>\n

        immCorSiglec15 <- rbind.data.frame(immCorSiglec15,<\/p>\n

        data.frame(gene = “Siglec15″,<\/p>\n

        path = i,<\/p>\n

        r = cr$estimate,<\/p>\n

        p = cr$p.value,<\/p>\n

        stringsAsFactors = F),<\/p>\n

        stringsAsFactors = F)<\/p>\n

        }<\/p>\n

        immCorSiglec15$sign <- ifelse(immCorSiglec15$r > 0,”pos”,”neg”)<\/p>\n

        immCorSiglec15$absR <- abs(immCorSiglec15$r)<\/p>\n

        immCorSiglec15$rSeg <- as.character(cut(immCorSiglec15$absR,c(0,0.25,0.5,0.75,1),labels = c(“0.25″,”0.50″,”0.75″,”1.00”),include.lowest = T))<\/p>\n

        immCorSiglec15$pSeg <- as.character(cut(immCorSiglec15$p,c(0,0.001,0.01,0.05,1),labels = c(“<0.001″,”<0.01″,”<0.05″,”ns”),include.lowest = T))<\/p>\n

        immCorSiglec15[nrow(immCorSiglec15),”pSeg”] <- “Not Applicable”<\/p>\n

        immCorSiglec15$rSeg <- factor(immCorSiglec15$rSeg, levels = c(“0.25″,”0.50″,”0.75″,”1.00”))<\/p>\n

        immCorSiglec15$pSeg <- factor(immCorSiglec15$pSeg, levels = c(“<0.001″,”<0.01″,”<0.05″,”Not Applicable”,”ns”))<\/p>\n

        immCorSiglec15$sign <- factor(immCorSiglec15$sign, levels = c(“pos”,”neg”))<\/p>\n

        p1 <- quickcor(t(immPath.score),<\/p>\n

        type = “lower”,<\/p>\n

        show.diag = TRUE) +<\/p>\n

        geom_colour() +<\/p>\n

        add_link(df = immCorSiglec15,<\/p>\n

        mapping = aes(colour = pSeg, size = rSeg, linetype = sign),<\/p>\n

        spec.key = “gene”,<\/p>\n

        env.key = “path”,<\/p>\n

        diag.label = FALSE) +<\/p>\n

        scale_size_manual(values = c(0.5, 1, 1.5, 2)) +<\/p>\n

        scale_color_manual(values = c(“#19A078″,”#DA6003″,”#7570B4″,”#E8288E”,”#65A818″)) +<\/p>\n

        scale_fill_gradient2(low = “#9483E1”,mid = “white”,high = “#E11953”,midpoint=0) +<\/p>\n

        remove_axis(“x”)<\/p>\n

        ggsave(filename = “ggcor.pdf”, width = 10,height = 8)<\/p>\n

        \"Dingtalk_20230208165553\"<\/p>\n

        #immPath.txt-\u5bcc\u96c6\u5f97\u5206\u6587\u4ef6<\/p>\n

        \"Dingtalk_20230209151416\"<\/p>\n

        \u6700\u7ec8\u5c0f\u679c\u6210\u529f\u7684\u753b\u51fa\u4e86\u76f8\u5173\u6027\u7f51\u7edc\u56fe\uff0c\u770b\u8d77\u6765\u5c0f\u679c\u975e\u5e38\u4e0d\u9519\uff0c\u6709\u9700\u8981\u7684\u53ef\u4ee5\u501f\u9274\u5b66\u4e60\u5965\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        \u4eca\u5929\u5c0f\u679c\u5229\u7528ggcor\u5305\u7ed8\u5236\u597d\u770b\u7684\u76f8\u5173\u6027\u56fe\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a \u5b89\u88c5\u9700\u8981\u7684R\u5305 install.packages(\u201cg […]<\/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\/28893"}],"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=28893"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28893\/revisions"}],"predecessor-version":[{"id":28898,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28893\/revisions\/28898"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28893"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28893"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}