{"id":16104,"date":"2023-10-20T16:00:13","date_gmt":"2023-10-20T08:00:13","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=16104"},"modified":"2023-10-20T16:00:14","modified_gmt":"2023-10-20T08:00:14","slug":"clusterprofiler-kegg%e5%af%8c%e9%9b%86%e5%88%86%e6%9e%90%ef%bc%9a%e5%b0%8f%e7%99%bd%e5%bf%85%e5%a4%87%ef%bc%8c%e8%bd%bb%e6%9d%be%e6%8e%8c%e6%8f%a1%e7%9a%84%e5%9f%ba%e5%9b%a0%e8%a1%a8%e8%be%be%e8%a7%a3","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=16104","title":{"rendered":"ClusterProfiler KEGG\u5bcc\u96c6\u5206\u6790\uff1a\u5c0f\u767d\u5fc5\u5907\uff0c\u8f7b\u677e\u638c\u63e1\u7684\u57fa\u56e0\u8868\u8fbe\u89e3\u8bfb\u79d8\u7c4d\uff01"},"content":{"rendered":"

\u4e0a\u4e00\u8282\uff0c\u5c0f\u679c\u5411\u5927\u5bb6\u4ecb\u7ecd\u4e86ClusterProfiler\u8fdb\u884cGO\u5bcc\u96c6\u5206\u6790\u7684\u65b9\u6cd5\uff0cClusterProfiler\u5305\u8fd8\u53ef\u4ee5\u8fdb\u884c\u57fa\u56e0\u901a\u8def\u7684\u5bcc\u96c6\u3002KEGG\uff08Kyoto Encyclopedia of Genes and Genomes\uff09\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u57fa\u56e0\u901a\u8def\u6570\u636e\u5e93\uff0c\u63d0\u4f9b\u4e86\u5173\u4e8e\u751f\u7269\u7cfb\u7edf\u529f\u80fd\u548c\u8c03\u63a7\u7684\u4e30\u5bcc\u4fe1\u606f\u3002\u5728\u8fd9\u4e00\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528R\u8bed\u8a00\u4e2d\u7684clusterProfiler\u5305\u8fdb\u884cKEGG\u5bcc\u96c6\u5206\u6790\uff0c\u4ee5\u63ed\u793a\u7ed9\u5b9a\u57fa\u56e0\u96c6\u5408\u4e2d\u5bcc\u96c6\u7684KEGG\u901a\u8def\u3002\u4f7f\u7528clusterProfiler\u8fdb\u884cKEGG\uff08Kyoto Encyclopedia of Genes and Genomes\uff09\u5206\u6790\u7684\u6b65\u9aa4\u4e0eGO\u5206\u6790\u7c7b\u4f3c\uff0c\u4e0b\u9762\u63a5\u7740\u8ddf\u5c0f\u679c\u4e00\u8d77\u6765\u5b66\u4e60\u5427\uff01<\/p>\n

    \n
  1. \u5b89\u88c5\u52a0\u8f7d\u5305<\/li>\n<\/ol>\n

    \u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u786e\u4fdd\u5df2\u5728R\u73af\u5883\u4e2d\u5b89\u88c5\u4e86clusterProfiler\u5305\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u5b89\u88c5\u548c\u52a0\u8f7d\uff1a<\/p>\n

    \u4ee3\u7801\uff1a<\/p>\n

    if (!require(“BiocManager”, quietly = TRUE))<\/p>\n

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

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

    library(clusterProfiler)<\/p>\n

      \n
    1. \u51c6\u5907\u8f93\u5165\u6570\u636e<\/li>\n<\/ol>\n

      \u4e3a\u4e86\u8fdb\u884cKEGG\u5bcc\u96c6\u5206\u6790\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e2a\u57fa\u56e0\u5217\u8868\uff0c\u8be5\u5217\u8868\u5305\u542b\u6211\u4eec\u611f\u5174\u8da3\u7684\u57fa\u56e0\u3002\u57fa\u56e0\u5217\u8868\u53ef\u4ee5\u662f\u57fa\u56e0\u7b26\u53f7\u3001Ensembl ID\u6216\u5176\u4ed6\u5e38\u89c1\u7684\u57fa\u56e0\u8bc6\u522b\u7b26\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u51c6\u5907\u4e86\u4e00\u4e2a\u540d\u4e3a”gene.txt”\u7684\u6587\u672c\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542bEnsembl\u7b26\u53f7\u5217\u8868\uff0c\u6bcf\u884c\u4e00\u4e2a\u57fa\u56e0\u3002<\/p>\n

      <\/p>\n

      \u56fe1 \u57fa\u56e0\u5217\u8868\u6587\u4ef6\u5185\u5bb9\u793a\u4f8b<\/p>\n

      \u4ee3\u7801\uff1a<\/p>\n

      genes <- read.delim(‘gene.txt’, header = TRUE, stringsAsFactors = FALSE)[[1]]<\/p>\n

        \n
      1. \u8fdb\u884c\u57fa\u56e0ID\u8f6c\u6362<\/li>\n<\/ol>\n

        \u5728\u8fdb\u884cKEGG\u5bcc\u96c6\u5206\u6790\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u57fa\u56e0\u5217\u8868\u8fdb\u884c\u57fa\u56e0\u6ce8\u91ca\uff0c\u5c06\u57fa\u56e0\u8bc6\u522b\u7b26\u6620\u5c04\u5230ENTREZID\u4fe1\u606f\u3002\u5728clusterProfiler\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528bitr\u51fd\u6570\u8fdb\u884c\u57fa\u56e0\u6ce8\u91ca\u3002<\/p>\n

        \u4ee3\u7801\uff1a<\/p>\n

        # \u5c06ENSEMBL ID\u6620\u5c04\u5230ENTREZID\uff08\u4ee5\u4eba\u7c7b\u4e3a\u4f8b\uff09<\/p>\n

        gene2kegg <- bitr(genes, fromType = “ENSEMBL”, toType = “ENTREZID”, OrgDb = org.Hs.eg.db)<\/p>\n

          \n
        1. \u8fdb\u884cKEGG\u5bcc\u96c6\u5206\u6790<\/li>\n<\/ol>\n

          \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528enrichKEGG\u51fd\u6570\u5bf9\u6ce8\u91ca\u540e\u7684\u57fa\u56e0\u8fdb\u884cKEGG\u5bcc\u96c6\u5206\u6790\u3002\u8be5\u51fd\u6570\u6839\u636e\u57fa\u56e0\u5728\u7279\u5b9aKEGG\u901a\u8def\u4e2d\u7684\u5bcc\u96c6\u7a0b\u5ea6\u8ba1\u7b97\u5bcc\u96c6\u5206\u6570\u548c\u8c03\u6574\u7684p\u503c\u3002\u60a8\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u7edf\u8ba1\u65b9\u6cd5\u548c\u53c2\u6570\u8bbe\u7f6e\u3002\u76f8\u6bd4\u4e0a\u8ff0GO\u5bcc\u96c6\uff0cclusterProfiler\u7684KEGG\u5bcc\u96c6\u5206\u6790\u53ef\u4ee5\u76f4\u63a5\u94fe\u63a5\u5230KEGG\u7684\u5728\u7ebf\u6570\u636e\u5e93\u83b7\u53d6\u5df2\u6709\u7269\u79cd\u7684\u6ce8\u91ca\u3002<\/p>\n

          \u4ee3\u7801\uff1a<\/p>\n

          getOption(“clusterProfiler.download.method”)<\/p>\n

          R.utils::setOption(“clusterProfiler.download.method”,”auto”)<\/p>\n

          #KEGG \u5bcc\u96c6\u5206\u6790<\/p>\n

          #\u6bcf\u6b21\u6253\u5f00 R \u8ba1\u7b97\u65f6\uff0c\u5b83\u4f1a\u81ea\u52a8\u8fde\u63a5 KEGG \u5b98\u7f51\u83b7\u5f97\u6700\u8fd1\u7684\u7269\u79cd\u6ce8\u91ca\u4fe1\u606f<\/p>\n

          kegg <- enrichKEGG(<\/p>\n

          gene = gene2kegg$ENTREZID, #\u57fa\u56e0\u5217\u8868\u6587\u4ef6\u4e2d\u7684\u57fa\u56e0\u540d\u79f0<\/p>\n

          keyType = ‘kegg’, #KEGG \u5bcc\u96c6<\/p>\n

          organism = “hsa”, #\u4f8b\u5982\uff0chsa \u4ee3\u8868\u4eba\u7c7b\uff0c\u5176\u5b83\u7269\u79cd\u66f4\u6539\u8fd9\u884c\u5373\u53ef<\/p>\n

          pAdjustMethod = “BH”,<\/p>\n

          pvalueCutoff = 0.1, #\u6307\u5b9a p \u503c\u9608\u503c\uff08\u53ef\u6307\u5b9a 1 \u4ee5\u8f93\u51fa\u5168\u90e8\uff09<\/p>\n

          qvalueCutoff = 0.2) #\u6307\u5b9a q \u503c\u9608\u503c\uff08\u53ef\u6307\u5b9a 1 \u4ee5\u8f93\u51fa\u5168\u90e8\uff09<\/p>\n

          #\u8f93\u51fa\u7ed3\u679c<\/p>\n

          write.table(kegg, ‘kegg.txt’, sep = ‘\\t’, quote = FALSE, row.names = FALSE)<\/p>\n

          \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u6307\u5b9a\u4e86\u6ce8\u91ca\u540e\u7684\u57fa\u56e0\uff08gene = gene2kegg$ENTREZID\uff09\uff0c\u9009\u62e9\u4e86\u4eba\u7c7b\uff08organism = “hsa”\uff09\u4f5c\u4e3a\u7814\u7a76\u5bf9\u8c61\uff0c\u5e76\u8bbe\u7f6e\u4e86p\u503c\u548cq\u503c\u7684\u9608\u503c\uff08pvalueCutoff = 0.05, qvalueCutoff = 0.2\uff09\u3002\u53ef\u4ee5\u901a\u8fc7KEGG\u5b98\u7f51\uff08KEGG Organisms: Complete Genomes<\/a>\uff09\u67e5\u770bKEGG\u6570\u636e\u5e93\u4e2d\u63d0\u4f9b\u7684\u7269\u79cd\u6ce8\u91ca\u5217\u8868\u3002<\/p>\n

            \n
          1. \u7ed3\u679c\u89e3\u91ca\u548c\u53ef\u89c6\u5316<\/li>\n<\/ol>\n

            \u7ecf\u8fc7KEGG\u5bcc\u96c6\u5206\u6790\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u7ed3\u679c\u8fdb\u884c\u89e3\u91ca\u548c\u53ef\u89c6\u5316\uff0c\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u57fa\u56e0\u96c6\u5408\u4e2d\u5bcc\u96c6\u7684KEGG\u901a\u8def\u3002\u53ef\u4ee5\u4f7f\u7528summary\u51fd\u6570\u548cbarplot\u3001dotplot\u51fd\u6570\u5bf9\u7ed3\u679c\u8fdb\u884c\u6458\u8981\u548c\u53ef\u89c6\u5316\u3002<\/p>\n

            summary\u51fd\u6570\uff1asummary(kegg)<\/p>\n

            <\/p>\n

            \u56fe2 \u67e5\u770b\u5bcc\u96c6\u5206\u6790\u7ed3\u679c\u6458\u8981<\/p>\n

            \u901a\u8fc7summary\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u67e5\u770bKEGG\u5bcc\u96c6\u5206\u6790\u7684\u7ed3\u679c\u6458\u8981\u4fe1\u606f\uff0c\u5305\u62ec\u5bcc\u96c6\u7684KEGG\u901a\u8def\u3001\u8c03\u6574\u7684p\u503c\u3001\u57fa\u56e0\u6570\u7b49\u3002\u8fd9\u4e9b\u4fe1\u606f\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u57fa\u56e0\u96c6\u5408\u5728\u4e0d\u540c\u901a\u8def\u4e2d\u7684\u5bcc\u96c6\u60c5\u51b5\u3002<\/p>\n

            barplot\u51fd\u6570\uff1abarplot(kegg)<\/p>\n

            <\/p>\n

            \u56fe3 \u5bcc\u96c6\u67f1\u5f62\u56fe<\/p>\n

            dotplot\u51fd\u6570\uff1adotplot(kegg)<\/p>\n

            <\/p>\n

            \u56fe4 \u5bcc\u96c6\u6c14\u6ce1\u56fe<\/p>\n

            \u4f7f\u7528barplot\u51fd\u6570\u548cdotplot\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u5bcc\u96c6\u7ed3\u679c\u7684\u53ef\u89c6\u5316\u56fe\u8868\uff0c\u4ee5\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u57fa\u56e0\u96c6\u5408\u7684KEGG\u5bcc\u96c6\u60c5\u51b5\u3002\u8be5\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u67f1\u72b6\u56fe\u6216\u6c14\u6ce1\u56fe\uff0c\u5176\u4e2d\u6bcf\u4e2a\u67f1\u5b50\u6216\u6c14\u6ce1\u4ee3\u8868\u4e00\u4e2a\u5bcc\u96c6\u7684KEGG\u901a\u8def\uff0c\u5176\u9ad8\u5ea6\u6216\u5927\u5c0f\u8868\u793a\u5bcc\u96c6\u7a0b\u5ea6\u7684\u6307\u6807\uff08\u5982p\u503c\u3001\u57fa\u56e0\u6570\u7b49\uff09\u3002<\/p>\n

            pathview\u5305\u53ef\u4ee5\u53ef\u89c6\u5316KEGG \u901a\u8def\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u5b83\u67e5\u770b\u7279\u5b9a\u901a\u8def\u56fe\u3002<\/p>\n

            \u4ee3\u7801\uff1a<\/p>\n

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

            library(pathview)<\/p>\n

            hsa04750 <- pathview(gene.data = geneList,<\/p>\n

            pathway.id = “hsa04750”, #\u4e0a\u8ff0\u7ed3\u679c\u4e2d\u7684hsa04750\u901a\u8def<\/p>\n

            species = “hsa”,<\/p>\n

            limit = list(gene=max(abs(geneList)), cpd=1))<\/p>\n

            <\/p>\n

            \u56fe5 \u7279\u5b9a\u901a\u8def\uff08hsa04750\uff09\u56fe<\/p>\n

            \u9664\u4e86\u4e0a\u8ff0\u57fa\u672c\u7684KEGG\u5bcc\u96c6\u5206\u6790\u5916\uff0cclusterProfiler\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u529f\u80fd\u548c\u5de5\u5177\uff0c\u4ee5\u8fdb\u4e00\u6b65\u6df1\u5165\u5206\u6790\u548c\u89e3\u91ca\u57fa\u56e0\u96c6\u5408\u7684\u751f\u7269\u5b66\u610f\u4e49\u3002\u4f8b\u5982\uff1a<\/p>\n