{"id":30366,"date":"2024-02-29T15:32:55","date_gmt":"2024-02-29T07:32:55","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=30366"},"modified":"2024-02-29T15:32:57","modified_gmt":"2024-02-29T07:32:57","slug":"estimate%e5%8c%85%e8%ae%a1%e7%ae%97%e5%a4%9a%e7%a7%8d%e5%be%97%e5%88%86%e5%b9%b6%e7%94%a8ggplot%e5%ae%9e%e7%8e%b0%e5%8f%af%e8%a7%86%e5%8c%96%ef%bc%9a%e4%bb%8e%e5%85%8d%e7%96%ab%e5%9f%ba%e8%b4%a8","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=30366","title":{"rendered":"Estimate\u5305\u8ba1\u7b97\u591a\u79cd\u5f97\u5206\u5e76\u7528ggplot\u5b9e\u73b0\u53ef\u89c6\u5316\uff1a\u4ece\u514d\u75ab\u57fa\u8d28\u5f97\u5206\u5230\u5f69\u8679\u4e4b\u6865\uff01"},"content":{"rendered":"

Estimate\u5305\u8ba1\u7b97\u591a\u79cd\u5f97\u5206\u5e76\u7528ggplot\u5b9e\u73b0\u53ef\u89c6\u5316\uff1a\u4ece\u514d\u75ab\u57fa\u8d28\u5f97\u5206\u5230\u5f69\u8679\u4e4b\u6865\uff01<\/h1>\n

\u563f\u5927\u5bb6\uff01\u597d\u4e45\u4e0d\u89c1\uff0c\u4eca\u5929\u6211\u4eec\u6765\u5b66\u4e60\u4e00\u4e2a\u65b0\u7684R\u5305\uff0c\u5b83\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u514d\u75ab\u5f97\u5206\u7684\u8ba1\u7b97\uff0c\u5e76\u7ed8\u5236\u7bb1\u7ebf\u56fe\u8fdb\u884c\u7ed3\u679c\u5c55\u793a\u3002\u611f\u5174\u8da3\u7684\u8bdd\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u5b66\u8d77\u6765\u5427\uff01<\/p>\n

\u4ec0\u4e48\u662festimate\u5305\uff1f<\/strong><\/p>\n

\u5728\u8fd9\u4e4b\u524d\uff0c\u5c0f\u679c\u5148\u7ed9\u5927\u5bb6\u5bf9estimate\u5305\u505a\u4e00\u4e2a\u7b80\u5355\u7684\u4ecb\u7ecd\u3002<\/p>\n

estimate\u662f\u4e00\u4e2a\u5728\u80bf\u7624\u7ec4\u5b66\u7814\u7a76\u4e2d\u5e38\u7528\u7684R\u5305\uff0c\u5176\u4e3b\u8981\u529f\u80fd\u662f\u7528\u4e8e\u8bc4\u4f30\u80bf\u7624\u7ec4\u7ec7\u4e2d\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u7684\u7a0b\u5ea6\uff0c\u5e76\u5bf9\u514d\u75ab\u76f8\u5173\u7684\u4fe1\u53f7\u901a\u8def\u8fdb\u884c\u5206\u6790\u3002estimate\u57fa\u4e8e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8fdb\u884c\u8ba1\u7b97\uff0c\u4e3b\u8981\u5229\u7528\u4e86\u514d\u75ab\u548c\u80bf\u7624\u7ec6\u80de\u7279\u5f02\u7684\u57fa\u56e0\u8868\u8fbe\u6807\u8bb0\uff0c\u5e76\u6839\u636e\u8fd9\u4e9b\u6807\u8bb0\u7684\u8868\u8fbe\u6c34\u5e73\u4f30\u7b97\u7ec4\u7ec7\u4e2d\u514d\u75ab\u548c\u975e\u514d\u75ab\u7ec6\u80de\u7684\u6570\u91cf\u3002\u6700\u7ec8\uff0cestimate\u4f1a\u8f93\u51fa\u4e09\u4e2a\u6307\u6807\uff1aImmuneScore\u3001StromalScore\u548cESTIMATEScore\uff0c\u7528\u4e8e\u8bc4\u4f30\u514d\u75ab\u548c\u6210\u7ea4\u7ef4\u7ec6\u80de\u6d78\u6da6\u7684\u7a0b\u5ea6\u53ca\u5176\u5f71\u54cd\u3002<\/p>\n

\u5c0f\u679c\u63d0\u9192\uff0c\u4eca\u5929\u6211\u4eec\u8981\u5b66\u7684\u5c31\u662f\u901a\u8fc7estimate\u8ba1\u7b97ImmuneScore\u503c\u5e76\u5c06\u5176\u53ef\u89c6\u5316\uff0c\u540c\u65f6\u5728\u5b66\u4f1a\u8ba1\u7b97ImmuneScore\uff08\u514d\u75ab\u5f97\u5206\uff09\u7684\u57fa\u7840\u4e0a\u5c0f\u679c\u8fd8\u4f1a\u5411\u5927\u5bb6\u4ecb\u7ecd\u5982\u4f55\u8ba1\u7b97StromalScore\uff08\u57fa\u8d28\u5f97\u5206\uff09\uff0c\u90a3\u4e48\u5927\u5bb6\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u6765\u770b\u4e00\u4e0b\u5427\uff01<\/p>\n

\u6570\u636e\u5bfc\u5165\u53ca\u9884\u5904\u7406<\/strong><\/p>\n

\u9996\u5148\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u8f7d\u5165\u4e00\u4e2a\u53eb\u505a\u201cestimate\u201d\u7684\u5305\uff0c\u8fd9\u4e2a\u5305\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97Immune Score\u7684\u5206\u503c\u54e6\u3002<\/p>\n

library(estimate)<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u6765\u5bfc\u5165\u6211\u4eec\u51c6\u5907\u597d\u7684\u6570\u636e\uff1a<\/p>\n

rawdata_file <- “tumor_all_gene_exp.txt”<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u4f7f\u7528\u201cfilterCommonGenes\u201d\u51fd\u6570\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u8fc7\u6ee4\uff0c\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u201cmy.gct\u201d\u7684\u6587\u4ef6\u4e2d\u3002\u5728\u8fd9\u4e2a\u51fd\u6570\u4e2d\uff0c\u6211\u4eec\u8fd8\u8981\u6307\u5b9a\u201cGeneSymbol\u201d\u4f5c\u4e3a\u57fa\u56e0\u7684ID\uff1a<\/p>\n

filterCommonGenes(input.f=rawdata_file,
\noutput.f=”my.gct”,
\nid=”GeneSymbol”)<\/p>\n

\u8ba1\u7b97\u514d\u75ab\u5f97\u5206<\/strong><\/p>\n

\u7136\u540e\u6211\u4eec\u9700\u8981\u4f7f\u7528\u201cestimateScore\u201d\u51fd\u6570\u6765\u8ba1\u7b97\u514d\u75ab\u5f97\u5206\u3002\u5728\u8fd9\u4e2a\u51fd\u6570\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u6307\u5b9a\u8f93\u5165\u6587\u4ef6\u7684\u8def\u5f84\uff0c\u4ee5\u53ca\u8f93\u51fa\u6587\u4ef6\u7684\u8def\u5f84\u3002\u6211\u4eec\u8fd8\u8981\u6307\u5b9a\u6570\u636e\u7684\u5e73\u53f0\uff08\u4f8b\u5982affymetrix\uff09\uff1a<\/p>\n

estimateScore(input.ds = “my.gct”,
\noutput.ds=”estimate_score.gct”,
\nplatform=”affymetrix”)<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u8f93\u51fa\u6587\u4ef6\u201cestimate_score.gct\u201d\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3a\u201craw_data\u201d\u7684\u6570\u636e\u96c6\u4e2d\uff1a<\/p>\n

raw_data = read.table(“estimate_score.gct”,skip = 2,header = F,quote = ”,sep = ‘\\t’,stringsAsFactors=FALSE)
\nrownames(raw_data)=raw_data[,1]
\nraw_data= as.data.frame(t(raw_data[,3:ncol(raw_data)]))
\nrow.names(raw_data)<-c(1:nrow(raw_data))
\ncolnames(raw_data)[1] <- c(“sample”)
\nfor(i in 2:5){
\nraw_data[,i] <- as.numeric(as.character(raw_data[,i]))
\n}<\/p>\n

\u6211\u4eec\u4e00\u8d77\u6765\u770b\u4e0b\u8f93\u51fa\u7684\u6570\u636e\u5427\uff1a<\/p>\n

\u7136\u540e\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u51c6\u5907\u597d\u7684\u201cgroup.txt\u201d\u7684\u6587\u4ef6\uff0c\u8fd9\u4e2a\u6587\u4ef6\u5305\u542b\u4e86\u6bcf\u4e2a\u6837\u672c\u7684\u540d\u79f0\u4ee5\u53ca\u5176\u5bf9\u5e94\u7684\u96c6\u7fa4\uff0c\u6211\u4eec\u9700\u8981\u5c06\u5176\u4e0e\u4e0a\u9762\u7684\u7ed3\u679c\u6587\u4ef6\u5408\u5e76\u624d\u80fd\u65b9\u4fbf\u4e4b\u540e\u7684\u53ef\u89c6\u5316\u5de5\u4f5c\uff1a<\/p>\n

phenotype_file <- read.table(‘group.txt’,header = T,sep = ‘\\t’,quote = ”,stringsAsFactors=FALSE)
\nphenotype_file <- phenotype_file[phenotype_file[,1] %in% raw_data[,”sample”],]
\nmerge_matrix <- left_join(phenotype_file,raw_data,by=”sample”) #\u5408\u5e76\u6570\u636e\u96c6
\nmy_comparisons <- list( c(“cluster1”, “cluster2”),c(“cluster2″,”cluster3”),c(“cluster1”, “cluster3”))<\/p>\n

\u8ba1\u7b97\u7ed3\u679c\u53ef\u89c6\u5316<\/strong><\/p>\n

\u5728\u4e0a\u9762\u7684\u6b65\u9aa4\u4e2d\uff0c\u6211\u4eec\u6210\u529f\u7684\u8ba1\u7b97\u51fa\u4e86Immune Score\u7684\u503c\uff0c\u73b0\u5728\u6211\u4eec\u901a\u8fc7\u6211\u4eec\u719f\u6089\u7684ggplot2\u5305\u6765\u5b9e\u73b0\u8ba1\u7b97\u7ed3\u679c\u7684\u53ef\u89c6\u5316\uff1a<\/p>\n

    \n
  1. \u8bbe\u7f6emy_labely\u6807\u7b7e<\/strong><\/li>\n<\/ol>\n

    max_labely <- max(merge_matrix[,”ImmuneScore”])
    \nmin_labely <- min(merge_matrix[,”ImmuneScore”])
    \nmy_labely <- c(max_labely+1350, max_labely+1000, max_labely+650, max_labely+300)<\/p>\n

      \n
    1. \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/li>\n<\/ol>\n

      ggboxplot(merge_matrix, x = “Group”, y = “ImmuneScore”,
      \ncolor = “black”,alpha = 0.8,size=.3,
      \nadd = “jitter”,
      \nadd.params = list(color = “Group”,alpha = .5,size = 0.8)) +<\/p>\n

      stat_compare_means(comparisons = my_comparisons,method = “t.test”,
      \nlabel.y = my_labely,na.rm=TRUE)+
      \nstat_summary(fun.y = “mean”,
      \ngeom = “point”,
      \ncolor = “#485c70”,
      \nshape = 8,
      \nsize = 3) +<\/p>\n

        \n
      1. \u6dfb\u52a0\u6807\u9898\u548c\u8c03\u6574\u7ed8\u56fe\u7ec6\u8282<\/strong><\/li>\n<\/ol>\n

        ggtitle(“Immune Score”) +theme_bw()+
        \ntheme(legend.position=”none”,
        \nplot.title = element_text(size=15,face=”bold”,hjust = 0.5),
        \naxis.text.x=element_text(colour=”black”),
        \naxis.text.y=element_text(colour=”black”),
        \npanel.grid.major = element_blank(),
        \npanel.grid.minor = element_blank()) +<\/p>\n

          \n
        1. \u8bbe\u7f6ex\u8f74\u548cy\u8f74\u540d\u79f0\u4ee5\u53cay\u8f74\u663e\u793a\u8303\u56f4<\/strong><\/li>\n<\/ol>\n

          ylab(“Immune Score”) +
          \nxlab(“Group”) +
          \nylim(min_labely-500, max_labely+1500)
          \nbox_plot_pdf <- “path\/to\/immune_score_box.pdf”
          \nggsave(box_plot_pdf,width=17.8,height=17.8,units = ‘cm’, dpi = 500)<\/p>\n

          \u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528 ggsave \u51fd\u6570\u5c06\u7ed8\u5236\u597d\u7684\u56fe\u4fdd\u5b58\u4e3a PDF \u683c\u5f0f\u3002<\/p>\n

          \u73b0\u5728\u548c\u6548\u679c\u4e00\u8d77\u6765\u770b\u4e0b\u6700\u540e\u7ed8\u5236\u597d\u7684\u56fe\u957f\u4ec0\u4e48\u6837\u5427\uff01<\/p>\n

          \u600e\u4e48\u6837\uff0c\u4f60\u5b66\u4f1a\u600e\u4e48\u4f7f\u7528estimate\u5305\u4e86\u561b? \u66f4\u591a\u5b66\u4e60\u8d44\u6e90\u8bf7\u5927\u5bb6\u79fb\u6b65\u5c0f\u679c\u4e13\u5c5e\u4e91\u751f\u4fe1\u5e73\u53f0\u641c\u7d22\u66f4\u591a\u8d44\u6e90\u54e6\uff01<\/p>\n

          StromalScore<\/strong><\/p>\n

          \u6709\u4e86\u8ba1\u7b97ImmuneScore\u7684\u57fa\u7840\uff0c\u8ba1\u7b97StromalScore\u4e5f\u662f\u540c\u6837\u7684\u8ba1\u7b97\u65b9\u6cd5\u548c\u53ef\u89c6\u5316\u65b9\u6cd5\uff0c\u53ea\u8981\u63d0\u53d6\u7684\u662f\u7ed3\u679c\u6587\u4ef6\u4e2d\u7684StromalScore\u5217\u5373\u53ef\uff1a<\/p>\n

          \"WPS\u56fe\u7247\u7f16\u8f91\"<\/p>\n

          \u90a3\u4e48\u6211\u4eec\u518d\u6765\u770b\u770b\u6700\u540eStromalScore\u7684\u53ef\u89c6\u5316\u7ed3\u679c\uff1a<\/p>\n

          <\/p>\n

          \u600e\u4e48\u6837\uff0c\u4eca\u5929\u7684\u56fe\u4f60\u5b66\u4f1a\u600e\u4e48\u7ed8\u5236\u4e86\u5417?<\/p>\n

          \u5c0f\u679c\u4e13\u5c5e\u4e91\u751f\u4fe1\u5e73\u53f0\uff1a\u4e91\u751f\u4fe1 \u00a0– \u5b66\u751f\u7269\u4fe1\u606f\u5b66 (biocloudservice.com)<\/a><\/p>\n

          \"\u8868\u683c\n\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\" \u4e91\u751f\u4fe1\u5e73\u53f0\u4e5f\u6709\u514d\u75ab\u4e13\u7248\u7684\u5b66\u4e60\u6a21\u5757\u54e6\uff0c\u5feb\u6765\u627e\u5230\u4f60\u60f3\u5b66\u4e60\u7684\u4e13\u5c5e\u6a21\u5757\u5427\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

          Estimate\u5305\u8ba1\u7b97\u591a\u79cd\u5f97\u5206\u5e76\u7528ggplot\u5b9e\u73b0\u53ef\u89c6\u5316\uff1a\u4ece\u514d\u75ab\u57fa\u8d28\u5f97\u5206\u5230\u5f69\u8679\u4e4b\u6865\uff01 \u563f\u5927\u5bb6\uff01\u597d\u4e45\u4e0d\u89c1\uff0c\u4eca\u5929\u6211 […]<\/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\/30366"}],"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=30366"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/30366\/revisions"}],"predecessor-version":[{"id":30372,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/30366\/revisions\/30372"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=30366"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=30366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}