{"id":25040,"date":"2024-01-18T16:34:37","date_gmt":"2024-01-18T08:34:37","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25040"},"modified":"2024-01-18T16:34:39","modified_gmt":"2024-01-18T08:34:39","slug":"ggplot2-%e4%b9%8b%e4%bd%9c%ef%bc%9a%e5%a4%9a%e5%bd%a9%e5%92%8c%e8%b0%90%e7%9a%84%e9%80%9a%e8%b7%af%e5%af%8c%e9%9b%86%e5%8f%af%e8%a7%86%e5%8c%96%e4%b9%8b%e6%97%85-%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25040","title":{"rendered":"ggplot2 \u4e4b\u4f5c\uff1a\u591a\u5f69\u548c\u8c10\u7684\u901a\u8def\u5bcc\u96c6\u53ef\u89c6\u5316\u4e4b\u65c5 \uff01"},"content":{"rendered":"

hello\uff0c\u4eca\u5929\u5c0f\u679c\u6765\u6559\u5927\u5bb6\u5982\u4f55\u5bf9KEGG\u5bcc\u96c6\u5206\u6790\u7684\u7ed3\u679c\u901a\u8fc7ggplot2\u5b9e\u73b0\u53ef\u89c6\u5316\u5e76\u7ed8\u5236\u5bf9\u5e94\u7684KEGG\u901a\u8def\u7f51\u7edc\u56fe \uff0c\u8ba9\u4f60\u7684\u5206\u6790\u7ed3\u679c\u66f4\u52a0\u7f8e\u89c2\uff01\u611f\u5174\u8da3\u7684\u8bdd\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u6765\u770b\u4e00\u4e0b\u5427\uff01<\/p>\n

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

\u5148\u6765\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0b\u6211\u4eec\u4eca\u5929\u7528\u5230\u7684R\u5305\uff0c\u5206\u522b\u662fDOSE\u3001clusterProfiler\u3001org.Hs.eg.db\u3001ggplot2\u3001tidyverse\u4ee5\u53caenrichplot\u5305\u3002\u540c\u5b66\u4eec\u8981\u63d0\u524d\u4e0b\u8f7d\u597d\u54e6\uff01\u5728\u8fd9\u91cc\u5c0f\u679c\u4e3a\u5927\u5bb6\u7b80\u5355\u7684\u4ecb\u7ecd\u4e00\u4e0b\u5176\u4e2d\u51e0\u4e2aR\u5305\uff1a<\/p>\n

    \n
  1. DOSE\uff1a<\/strong>DOSE\u5305\u4e3b\u8981\u7528\u4e8e\u57fa\u56e0\u529f\u80fd\u5bcc\u96c6\u5206\u6790\uff0c\u5b83\u80fd\u591f\u901a\u8fc7\u6bd4\u8f83\u8f93\u5165\u7684\u57fa\u56e0\u5217\u8868\u4e0e\u5df2\u77e5\u7684\u57fa\u56e0\u529f\u80fd\u6ce8\u91ca\u4fe1\u606f\uff0c\u627e\u51fa\u5728\u7279\u5b9a\u529f\u80fd\u7c7b\u522b\u4e2d\u5bcc\u96c6\u7684\u57fa\u56e0\u3002<\/li>\n
  2. clusterProfiler\uff1a<\/strong>clusterProfiler\u662f\u4e00\u4e2a\u7528\u4e8e\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u7684R\u5305\uff0c\u4e3b\u8981\u7528\u4e8e\u529f\u80fd\u6ce8\u91ca\u548c\u5bcc\u96c6\u5206\u6790\u3002\u5b83\u63d0\u4f9b\u4e86\u9488\u5bf9\u591a\u79cd\u751f\u7269\u6570\u636e\u7c7b\u578b\u7684\u529f\u80fd\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5\uff0c\u5305\u62ecGO\u3001KEGG\u3001Reactome\u7b49\u3002\u8be5\u5305\u8fd8\u63d0\u4f9b\u4e86\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u548c\u89e3\u91ca\u529f\u80fd\u5bcc\u96c6\u7ed3\u679c\u3002<\/li>\n
  3. org.Hs.eg.db\uff1a<\/strong>org.Hs.eg.db\u662f\u4e00\u4e2aR\u8bed\u8a00\u4e2d\u7528\u4e8e\u4eba\u7c7b\u57fa\u56e0\u6ce8\u91ca\u7684\u6570\u636e\u5e93\u5305\u3002\u5b83\u63d0\u4f9b\u4e86\u5bf9\u4eba\u7c7b\u57fa\u56e0\u7ec4\u7684\u6ce8\u91ca\u4fe1\u606f\uff0c\u5305\u62ec\u57fa\u56e0ID\u3001\u57fa\u56e0\u540d\u79f0\u3001\u57fa\u56e0\u7b26\u53f7\u3001\u901a\u8def\u4fe1\u606f\u7b49\u3002\u4f7f\u7528org.Hs.eg.db\u5305\uff0c\u7528\u6237\u53ef\u4ee5\u5c06\u57fa\u56e0ID\u8f6c\u6362\u4e3a\u6613\u8bfb\u7684\u57fa\u56e0\u7b26\u53f7\uff0c\u4ece\u800c\u66f4\u5bb9\u6613\u7406\u89e3\u548c\u89e3\u91ca\u529f\u80fd\u5bcc\u96c6\u7ed3\u679c\u3002<\/li>\n
  4. tidyverse\uff1a<\/strong>tidyverse\u662f\u4e00\u4e2a\u7531\u591a\u4e2aR\u5305\u7ec4\u6210\u7684\u96c6\u5408\uff0c\u65e8\u5728\u63d0\u4f9b\u4e00\u5957\u4e00\u81f4\u4e14\u6613\u4e8e\u4f7f\u7528\u7684\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\u5de5\u5177\u3002\u8fd9\u4e9b\u5305\u5305\u62ecggplot2\u3001dplyr\u3001tidyr\u3001readr\u7b49\u3002\u5176\u4e2d\uff0cggplot2\u662f\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u521b\u5efa\u9ad8\u8d28\u91cf\u7684\u56fe\u8868\uff0c\u7528\u4e8e\u6570\u636e\u63a2\u7d22\u548c\u5c55\u793a\u3002<\/li>\n
  5. enrichplot\uff1a<\/strong>enrichplot\u662f\u4e00\u4e2a\u7528\u4e8e\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u7684R\u5305\uff0c\u4e3b\u8981\u7528\u4e8e\u529f\u80fd\u5bcc\u96c6\u7ed3\u679c\u7684\u53ef\u89c6\u5316\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u7ed8\u56fe\u51fd\u6570\uff0c\u53ef\u4ee5\u7ed8\u5236\u57fa\u56e0\u96c6\u7684\u5bcc\u96c6\u5206\u6790\u7ed3\u679c\uff0c\u5982GO\u6761\u5f62\u56fe\u3001KEGG\u901a\u8def\u7f51\u7edc\u56fe\u7b49\u3002enrichplot\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u53c2\u6570\u8bbe\u7f6e\uff0c\u4ee5\u6ee1\u8db3\u7528\u6237\u5bf9\u7ed3\u679c\u53ef\u89c6\u5316\u7684\u4e2a\u6027\u5316\u9700\u6c42\u3002<\/li>\n<\/ol>\n

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

    enrichKEGG\u8fdb\u884c\u5bcc\u96c6\u5206\u6790<\/strong><\/p>\n

    \u9996\u5148\uff0c\u52a0\u8f7dDOSE\u5305\u5e76\u4f7f\u7528\u8be5\u5305\u4e2d\u7684\u57fa\u56e0\u96c6\u4f5c\u4e3a\u6211\u4eec\u5206\u6790\u7684\u5bf9\u8c61\uff1a<\/p>\n

    library(“DOSE”)
    \ndata(geneList, package = “DOSE”)
    \ng_list <- names(geneList)[1:100]
    \nhead(g_list)<\/p>\n

    <\/p>\n

    \u5229\u7528enrichKEGG\u51fd\u6570\u8fdb\u884c\u5bcc\u96c6\u5206\u6790\uff0c\u5bf9\u6307\u5b9a\u7684\u57fa\u56e0\u5217\u8868\u8fdb\u884cKEGG\u901a\u8def\u5bcc\u96c6\u5206\u6790\uff0c\u5e76\u8bbe\u7f6ep-value\u548cq-value\u7684\u9608\u503c\u3002<\/p>\n

    library(“clusterProfiler”)
    \nlibrary(“org.Hs.eg.db”)
    \n\u200b
    \nek <- enrichKEGG(gene = g_list,
    \norganism = “hsa”,
    \npvalueCutoff = 0.05,
    \nqvalueCutoff = 0.05)
    \nwrite.table(ek, file = “ek.txt”, sep = “\\t”, quote = FALSE, row.names = FALSE)<\/p>\n

    \u6ce8\u610f\uff1a<\/strong><\/p>\n

      \n
    • pvalueCutoff = 0.05\uff1a\u8bbe\u7f6ep-value\u7684\u9608\u503c\uff0c\u7528\u4e8e\u7b5b\u9009\u5bcc\u96c6\u5206\u6790\u7ed3\u679c\u4e2d\u7edf\u8ba1\u663e\u8457\u7684\u901a\u8def\u3002\u5728\u8fd9\u91cc\uff0c\u8bbe\u5b9a\u4e3a0.05\uff0c\u5373p-value\u5c0f\u4e8e0.05\u7684\u901a\u8def\u88ab\u8ba4\u4e3a\u662f\u663e\u8457\u5bcc\u96c6\u7684\u901a\u8def\u3002<\/li>\n
    • qvalueCutoff = 0.05\uff1a\u8bbe\u7f6eq-value\u7684\u9608\u503c\uff0cq-value\u662f\u7ecf\u8fc7FDR\u6821\u6b63\u7684p-value\uff0c\u7528\u4e8e\u63a7\u5236\u591a\u91cd\u5047\u8bbe\u68c0\u9a8c\u7684\u9519\u8bef\u53d1\u73b0\u7387\u3002\u540c\u6837\uff0c\u8bbe\u5b9a\u4e3a0.05\uff0c\u5373q-value\u5c0f\u4e8e0.05\u7684\u901a\u8def\u88ab\u8ba4\u4e3a\u662f\u663e\u8457\u5bcc\u96c6\u7684\u901a\u8def\u3002<\/li>\n<\/ul>\n

      \u81f3\u6b64\u6211\u4eec\u5df2\u7ecf\u5b8c\u6210\u4e86\u5bcc\u96c6\u5206\u6790\u7684\u6d41\u7a0b\uff0c\u8ba9\u6211\u4eec\u4e00\u8d77\u6765\u770b\u4e00\u4e0b\u5206\u6790\u7684\u7ed3\u679c\u5427\uff01<\/p>\n

      <\/p>\n

      \u9700\u8981\u6ce8\u610f\u7684\u662f\u7ed3\u679c\u4e2dgeneID\u4e3aEntrezID\u3002<\/p>\n

      head(ek@result$geneID)<\/p>\n

      \"\u6587\u672c\n\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

      \u5c06EntrezID\u8f6c\u6362\u4e3aSymbol<\/strong><\/p>\n

      \u4f7f\u7528setReadable\u51fd\u6570\u5c06EntrezID\u8f6c\u6362\u4e3aSymbol\uff1a<\/p>\n

      ek2 <- setReadable(ek,
      \nOrgDb = “org.Hs.eg.db”,
      \nkeyType = “ENTREZID”)<\/p>\n

      \"\u6587\u672c\n\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

      ggplot2\u53ef\u89c6\u5316\u7ed3\u679c<\/strong><\/p>\n

      \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u901a\u8fc7\u5b89\u88c5tidyverse\u5305\u5e76\u8bfb\u53d6\u4fdd\u5b58\u7684\u5bcc\u96c6\u5206\u6790\u7ed3\u679c\u6587\u672c\u6587\u4ef6\uff0c\u5bf9\u8868\u683c\u8fdb\u884c\u5904\u7406\u548c\u8ba1\u7b97Enrichment Factor\uff0c\u5728\u8fd9\u91cc\u5c0f\u679c\u7528tidyverse\u5305\u7684separate\u51fd\u6570\u5c06GeneRatio\u548cBgRatio\u7684\u5206\u5b50\u5206\u6bcd\u5148\u5206\u5f00\uff0c\u518d\u8fdb\u884c\u8ba1\u7b97\uff1a<\/p>\n

      library(tidyverse)
      \nek.rt <- read.table(“ek.txt”, header = TRUE, sep = “\\t”, quote = “”)
      \nek.rt <- separate(data = ek.rt, col = GeneRatio, into = c(“GR1”, “GR2”), sep = “\/”)
      \nek.rt <- separate(data = ek.rt, col = BgRatio, into = c(“BR1”, “BR2”), sep = “\/”)
      \nek.rt <- mutate(ek.rt, enrichment_factor = (as.numeric(GR1) \/ as.numeric(GR2)) \/ (as.numeric(BR1) \/ as.numeric(BR2)))<\/p>\n

      \u7b5b\u9009\u51fa\u524d10\u4e2a\u901a\u8def\u8fdb\u884c\u53ef\u89c6\u5316\uff1a<\/p>\n

      ek.rt10 <- ek.rt %>% filter(row_number() >= 1, row_number() <= 10)<\/p>\n

      ggplot2\u7ed8\u5236dotplot<\/strong><\/p>\n

      \u4f7f\u7528ggplot2\u5305\u7ed8\u5236Enrichment Factor\u4e0eKEGG term\u4e4b\u95f4\u7684\u5173\u7cfb\u56fe\uff1a<\/p>\n

      library(“ggplot2”)
      \n\u200b
      \np <- ggplot(ek.rt10, aes(enrichment_factor, fct_reorder(factor(Description), enrichment_factor))) +
      \ngeom_point(aes(size = Count, color = -1 * log10(pvalue))) +
      \nscale_color_gradient(low = “green”, high = “red”) +
      \nlabs(color = expression(-log[10](p_value)), size = “Count”,
      \nx = “Enrichment Factor”, y = “KEGG term”, title = “KEGG enrichment”) +
      \ntheme_bw()
      \nggsave(“er.rt10_KEGG.png”, width = 6, height = 4)<\/p>\n

      \u6211\u4eec\u4e00\u8d77\u6765\u770b\u770b\u6548\u679c\u56fe\u5427\uff01<\/p>\n

      \"\u65e5\u7a0b\u8868\n\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

      \u7ed8\u5236KEGG\u901a\u8def\u7f51\u7edc\u56fe<\/strong><\/p>\n

      \u4f7f\u7528enrichplot\u5305\u7684cnetplot\u51fd\u6570\u7ed8\u5236KEGG\u901a\u8def\u7f51\u7edc\u56fe\uff0c\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u4e3aPDF\u6587\u4ef6\u3002<\/p>\n

      library(“enrichplot”)
      \n\u200b
      \npdf(file = “net-pathway.pdf”, width = 8, height = 10)
      \ncnetplot(ek2)
      \ndev.off()<\/p>\n

      \u6548\u679c\u56fe\u5982\u4e0b\uff1a<\/p>\n

      \"\u96f7\u8fbe\u56fe\n\n\u4e2d\u5ea6\u53ef\u4fe1\u5ea6\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

      \u4ee5\u4e0a\u6559\u7a0b\u5c0f\u679c\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528R\u8bed\u8a00\u4e2d\u7684DOSE\u548cclusterProfiler\u5305\u8fdb\u884c\u57fa\u56e0\u96c6\u7684KEGG\u901a\u8def\u5bcc\u96c6\u5206\u6790\uff0c\u5e76\u901a\u8fc7ggplot2\u548cenrichplot\u5b9e\u73b0\u53ef\u89c6\u5316\u5316\u3002\u901a\u8fc7\u8fd9\u4e9b\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5c06\u81ea\u5df1\u7684\u57fa\u56e0\u5217\u8868\u8fdb\u884c\u5bcc\u96c6\u5206\u6790\uff0c\u5e76\u8fdb\u4e00\u6b65\u5c06\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u4ee5\u5e2e\u52a9\u89e3\u91ca\u548c\u7406\u89e3\u6570\u636e\u54e6\uff01\u600e\u4e48\u6837\uff0c\u4f60\u5b66\u4f1a\u4e86\u561b\uff1f<\/p>\n","protected":false},"excerpt":{"rendered":"

      hello\uff0c\u4eca\u5929\u5c0f\u679c\u6765\u6559\u5927\u5bb6\u5982\u4f55\u5bf9KEGG\u5bcc\u96c6\u5206\u6790\u7684\u7ed3\u679c\u901a\u8fc7ggplot2\u5b9e\u73b0\u53ef\u89c6\u5316\u5e76\u7ed8\u5236\u5bf9\u5e94\u7684KEGG\u901a\u8def\u7f51 […]<\/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\/25040"}],"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=25040"}],"version-history":[{"count":2,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/25040\/revisions"}],"predecessor-version":[{"id":25050,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/25040\/revisions\/25050"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=25040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=25040"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=25040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}