{"id":24327,"date":"2024-01-11T17:10:55","date_gmt":"2024-01-11T09:10:55","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24327"},"modified":"2024-01-11T17:10:56","modified_gmt":"2024-01-11T09:10:56","slug":"%e5%9f%ba%e5%9b%a0%e6%8e%a2%e7%a7%98%ef%bc%9a%e7%94%a8edger%e5%8c%85%e6%8f%ad%e7%a4%ba%e5%9f%ba%e5%9b%a0%e5%b7%ae%e5%bc%82%e4%b9%8b%e8%b0%9c-%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24327","title":{"rendered":"\u57fa\u56e0\u63a2\u79d8\uff1a\u7528edgeR\u5305\u63ed\u793a\u57fa\u56e0\u5dee\u5f02\u4e4b\u8c1c\u00a0\uff01"},"content":{"rendered":"

hello\uff0c\u4f60\u77e5\u9053\u5982\u4f55\u4f7f\u7528edgR\u5305\u6765\u5bf9RNA-seq\u6570\u636e\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u5dee\u5f02\u5206\u6790\u561b\uff1f\u4eca\u5929\u5c0f\u679c\u5c31\u6765\u53eb\u4f60\u5982\u4f55\u64cd\u4f5c\uff0c\u611f\u5174\u8da3\u7684\u8bdd\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u770b\u4e0b\u53bb\u5427\uff01<\/p>\n

\u4ec0\u4e48\u662fedgR\uff1f<\/strong><\/p>\n

edgeR\u5305\u4e3b\u8981\u7528\u4e8e\u5904\u7406RNA-seq\u6216\u5176\u4ed6\u9ad8\u901a\u91cf\u6d4b\u5e8f\u6280\u672f\u751f\u6210\u7684\u6570\u5b57\u5316\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u5957\u7edf\u8ba1\u5b66\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u7528\u4e8e\u9274\u5b9a\u548c\u5206\u6790\u4e0d\u540c\u6761\u4ef6\u4e0b\u57fa\u56e0\u8868\u8fbe\u7684\u5dee\u5f02\u3002edgeR\u5305\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u751f\u7269\u533b\u5b66\u7814\u7a76\u548c\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df~<\/p>\n

\u4ee5\u4e0b\u662fedgeR\u5305\u7684\u4e00\u4e9b\u4e3b\u8981\u529f\u80fd\u548c\u7279\u70b9\uff1a<\/p>\n

    \n
  1. \u9ad8\u5ea6\u7cbe\u786e\u7684\u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff1aedgeR\u4f7f\u7528\u8d1f\u4e8c\u9879\u5206\u5e03\u6a21\u578b\u548c\u6cca\u677e\u5206\u5e03\u6a21\u578b\u6765\u51c6\u786e\u5730\u4f30\u8ba1\u57fa\u56e0\u8868\u8fbe\u7684\u79bb\u6563\u5ea6\uff0c\u5e76\u8ba1\u7b97\u5dee\u5f02\u8868\u8fbe\u7684\u663e\u8457\u6027\u7edf\u8ba1\u91cf\u3002<\/li>\n
  2. \u4f18\u5316\u7684\u7edf\u8ba1\u65b9\u6cd5\uff1aedgeR\u63d0\u4f9b\u4e86\u9488\u5bf9\u4e0d\u540c\u5b9e\u9a8c\u8bbe\u8ba1\u548c\u6570\u636e\u7c7b\u578b\u7684\u4f18\u5316\u7edf\u8ba1\u65b9\u6cd5\u3002<\/li>\n
  3. \u6570\u636e\u9884\u5904\u7406\u548c\u6807\u51c6\u5316\uff1aedgeR\u5305\u63d0\u4f9b\u4e86\u5e38\u7528\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u6807\u51c6\u5316\u65b9\u6cd5\uff0c\u5982\u53bb\u9664\u4f4e\u8868\u8fbe\u57fa\u56e0\u3001\u6d88\u9664\u5e93\u5927\u5c0f\u5dee\u5f02\u548c\u6807\u51c6\u5316\u6837\u672c\u95f4\u6280\u672f\u53d8\u5f02\u7b49\u3002<\/li>\n
  4. \u7075\u6d3b\u7684\u7ed3\u679c\u89e3\u8bfb\u548c\u53ef\u89c6\u5316\uff1aedgeR\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u548c\u56fe\u5f62\u5de5\u5177\uff0c\u7528\u4e8e\u89e3\u8bfb\u548c\u53ef\u89c6\u5316\u5dee\u5f02\u8868\u8fbe\u7ed3\u679c\u3002<\/li>\n
  5. \u9ad8\u5ea6\u53ef\u6269\u5c55\u6027\uff1aedgeR\u53ef\u4ee5\u5904\u7406\u5305\u542b\u6570\u767e\u4e07\u4e2a\u57fa\u56e0\u548c\u6570\u5341\u4e2a\u6837\u672c\u7684\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/li>\n<\/ol>\n

    edgR\u5206\u6790\u57fa\u56e0\u8868\u8fbe\u6570\u636e<\/strong><\/p>\n

      \n
    • \u8bfb\u53d6\u6570\u636e<\/strong><\/li>\n<\/ul>\n

      \u8fd9\u91cc\u6211\u4eec\u63d0\u4f9b\u7684\u6570\u636e\u4e3a”GSE168466_series_matrix.txt”\uff0c\u5c0f\u679c\u540e\u9762\u4e5f\u4f1a\u9644\u4e0a\u8be5\u8868\u8fbe\u77e9\u9635\u7684\u5177\u4f53\u6587\u4ef6\uff0c\u5f53\u7136\u4e5f\u53ef\u4ee5\u5bfc\u5165\u4f60\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6570\u636e\u54e6~<\/p>\n

      library(edgeR)
      \nx <- read.table(“GSE168466_series_matrix.txt”, header = TRUE, sep = “\\t”, check.names = FALSE)<\/p>\n

        \n
      • \u63d0\u53d6\u8868\u8fbe\u77e9\u9635<\/strong><\/li>\n<\/ul>\n

        \u4ece\u8bfb\u53d6\u7684\u6570\u636e\u4e2d\u63d0\u53d6\u8868\u8fbe\u77e9\u9635\u3002\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u8868\u8fbe\u77e9\u9635\u662f\u4ee5\u57fa\u56e0\u4e3a\u884c\uff0c\u6837\u672c\u4e3a\u5217\u7684\u5f62\u5f0f\uff0c\u76f8\u4fe1\u8fd9\u4e00\u70b9\u5927\u5bb6\u90fd\u5df2\u7ecf\u719f\u6089~<\/p>\n

        rawdata <- as.matrix(x[, -1])<\/p>\n

          \n
        • \u68c0\u67e5\u548c\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ul>\n

          \u5bf9\u4e8e\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u9009\u62e9\u79fb\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002<\/p>\n

          # \u68c0\u67e5\u662f\u5426\u5b58\u5728\u7f3a\u5931\u503c
          \nanyNA(rawdata)
          \n# \u79fb\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217
          \nrawdata <- na.omit(rawdata)<\/p>\n

          \u6211\u4eec\u6765\u4e00\u8d77\u770b\u4e0b\u6700\u540e\u5904\u7406\u540e\u7684\u8868\u8fbe\u77e9\u9635\u5427\uff01<\/p>\n

          <\/p>\n

            \n
          • \u8bbe\u8ba1\u5206\u7ec4\u4fe1\u606f<\/strong><\/li>\n<\/ul>\n

            \u6839\u636e\u5b9e\u9a8c\u8bbe\u8ba1\uff0c\u4e3a\u6bcf\u4e2a\u6837\u672c\u5206\u914d\u4e00\u4e2a\u7ec4\u522b\u6807\u7b7e\u3002\u8fd9\u4e9b\u6807\u7b7e\u5c06\u7528\u4e8e\u540e\u7eed\u7684\u5dee\u5f02\u5206\u6790\u3002\u4f8b\u5982\uff0c\u5728\u4f60\u7684\u793a\u4f8b\u4e2d\uff0c\u5c06”CK”\u548c”Treat”\u4e24\u4e2a\u5206\u7ec4\u5206\u522b\u5206\u914d\u7ed949\u4e2a\u6837\u672c\u3002<\/p>\n

            group <- factor(c(rep(“CK”, 49), rep(“Treat”, 49)))<\/p>\n

            \u6ce8\u610f\uff1a\u4e00\u5b9a\u8981\u786e\u4fdd\u5206\u7ec4\u603b\u6570\u548c\u8868\u8fbe\u77e9\u9635\u603b\u5217\u6570\u4e00\u81f4\u54e6\uff01\u5c0f\u679c\u7684\u6570\u636e\u96c6\u4e2d\u5171\u670998\u4e2a\u6837\u672c\uff0c\u6240\u4ee5\u5206\u522b\u8bbe\u7f6e\u4e24\u4e2a49\u4e2a\u6837\u672c\u7684\u5206\u7ec4\uff1a<\/strong><\/p>\n

            \"\u56fe\u5f62\u7528\u6237\u754c\u9762\n\n\u4f4e\u53ef\u4fe1\u5ea6\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

              \n
            • \u521b\u5efaDGEList\u5bf9\u8c61<\/strong><\/li>\n<\/ul>\n

              \u7528 DGEList() \u51fd\u6570\u521b\u5efa\u4e00\u4e2a DGEList \u5bf9\u8c61 y\uff0c\u5176\u4e2d\u5305\u62ec\u539f\u59cb\u8ba1\u6570\u6570\u636e rawdata\u3001\u57fa\u56e0\u540d\u79f0\uff08\u4ece\u884c\u540d\u4e2d\u63d0\u53d6\uff09\u548c\u5206\u7ec4\u4fe1\u606f group\u3002<\/p>\n

              y <- DGEList(counts = rawdata, genes = rownames(rawdata), group = group)<\/p>\n

                \n
              • \u8fc7\u6ee4\u8868\u8fbe\u77e9\u9635<\/strong><\/li>\n<\/ul>\n

                \u5982\u679c\u6709\u9700\u8981\uff0c\u53ef\u4ee5\u6839\u636e\u4e00\u5b9a\u7684\u8fc7\u6ee4\u6761\u4ef6\u6765\u9009\u53d6\u611f\u5174\u8da3\u7684\u57fa\u56e0\u54e6\uff1a<\/p>\n

                # \u4f7f\u7528cpm\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u7684\u8bfb\u6570\uff0c\u7136\u540e\u9009\u62e9\u5927\u4e8e\u7b49\u4e8e1\u7684\u57fa\u56e0
                \nkeep <- rowSums(cpm(y) > 1) >= 1
                \n\u200b
                \n# \u6839\u636e\u8fc7\u6ee4\u6761\u4ef6\u5bf9\u8868\u8fbe\u77e9\u9635\u8fdb\u884c\u5b50\u96c6\u64cd\u4f5c
                \ny <- y[keep, , keep.lib.sizes = FALSE]<\/p>\n

                  \n
                • \u6570\u636e\u6807\u51c6\u5316<\/strong><\/li>\n<\/ul>\n

                  \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u8868\u8fbe\u77e9\u9635\u8fdb\u884c\u6807\u51c6\u5316\u4ee5\u6d88\u9664\u6837\u672c\u95f4\u7684\u6280\u672f\u5dee\u5f02\u3002edgeR\u63d0\u4f9b\u4e86TMM\uff08trimmed mean of M-values\uff09\u65b9\u6cd5\u8fdb\u884c\u6807\u51c6\u5316\u3002<\/p>\n

                  y <- calcNormFactors(y)
                  \ny$samples<\/p>\n

                  <\/p>\n

                    \n
                  • \u5dee\u5f02\u5206\u6790<\/strong><\/li>\n<\/ul>\n

                    \u6211\u4eec\u5728\u8fd9\u91cc\u4f7f\u7528exactTest()\u51fd\u6570\u8fdb\u884c\u5dee\u5f02\u5206\u6790\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u63a8\u6d4b\u7684\u79bb\u6563\u5ea6\uff08bcv\uff09\u6765\u63a7\u5236\u5bf9\u5dee\u5f02\u7684\u654f\u611f\u6027\u3002\u8f83\u9ad8\u7684bcv\u503c\u9002\u7528\u4e8e\u66f4\u4fdd\u5b88\u7684\u5dee\u5f02\u68c0\u6d4b\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002<\/p>\n

                    ###\u63a8\u6d4b\u79bb\u6563\u5ea6\uff0c\u82e5\u6837\u672c\u662f\u4eba\uff0c\u8bbe\u7f6ebcv = 0.4\uff0c\u6a21\u5f0f\u751f\u7269\u8bbe\u7f6e0.1(\u6b64\u5904\u662f\u6839\u636e\u6709\u76f8\u5173\u6559\u7a0b\u8fdb\u884c\u8bbe\u7f6e\uff0c\u4e5f\u53ef\u4ee5\u4f60\u6839\u636e\u4f60\u7684\u7ed3\u679c\u81ea\u884c\u8bbe\u7f6e\uff0c\u6700\u7ec8\u5f97\u5230\u4f60\u7684\u7406\u60f3\u503c)
                    \nbcv <- 0.2
                    \net <- exactTest(y, dispersion = bcv^2)<\/p>\n

                      \n
                    • \u7ed3\u679c\u89e3\u8bfb<\/strong><\/li>\n<\/ul>\n

                      \u4f7f\u7528topTags()\u548csummary()\u51fd\u6570\u6765\u67e5\u770b\u5dee\u5f02\u57fa\u56e0\u7684\u7ed3\u679c\u7edf\u8ba1\u6458\u8981\uff1a<\/p>\n

                      results <- topTags(et)
                      \nprint(results)
                      \nsummary(de <- decideTestsDGE(et))<\/p>\n

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

                        \n
                      • \u7ed3\u679c\u53ef\u89c6\u5316<\/strong><\/li>\n<\/ul>\n

                        \u4f7f\u7528plotSmear()\u51fd\u6570\u7ed8\u5236MA\u56fe\uff0c\u7528\u4e8e\u53ef\u89c6\u5316\u5dee\u5f02\u8868\u8fbe\u7ed3\u679c\u3002\u8be5\u56fe\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u4e86\u89e3\u4e0d\u540c\u57fa\u56e0\u5728\u6837\u672c\u4e4b\u95f4\u7684\u5dee\u5f02\u54e6<\/p>\n

                        detags <- rownames(y)[as.logical(de)]
                        \npng(‘0h_vs_2h_MAplot.png’)
                        \nplotSmear(et, de.tags = detags)
                        \nabline(h = c(-4, 4), col = “blue”)
                        \ndev.off()<\/p>\n

                        \u63a5\u4e0b\u6765\u8ba9\u6211\u4eec\u4e00\u8d77\u6765\u770b\u770b\u53ef\u89c6\u5316\u540e\u7684\u7ed3\u679c\u5427\uff01<\/p>\n

                        \"\u56fe\u8868,<\/p>\n

                        \u4ee5\u4e0a\u6559\u7a0b\u5c31\u662f\u4f7f\u7528edgeR\u5305\u8fdb\u884c\u57fa\u56e0\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u7684\u7b80\u5355\u6b65\u9aa4\u3002\u6839\u636e\u4f60\u81ea\u5df1\u7684\u7814\u7a76\u95ee\u9898\u548c\u6570\u636e\u96c6\uff0c\u5927\u5bb6\u53ef\u80fd\u9700\u8981\u8fdb\u4e00\u6b65\u4e86\u89e3\u548c\u8c03\u6574\u65b9\u6cd5\u54e6\u3002\u5e0c\u671b\u80fd\u5bf9\u4f60\u7684\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u63d0\u4f9b\u4e00\u4e9b\u5e2e\u52a9\uff01\u600e\u4e48\u6837\uff0c\u4f60\u5b66\u4f1a\u4e86\u561b\uff01\uff1f<\/p>\n","protected":false},"excerpt":{"rendered":"

                        hello\uff0c\u4f60\u77e5\u9053\u5982\u4f55\u4f7f\u7528edgR\u5305\u6765\u5bf9RNA-seq\u6570\u636e\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u5dee\u5f02\u5206\u6790\u561b\uff1f\u4eca\u5929\u5c0f\u679c\u5c31\u6765\u53eb\u4f60\u5982\u4f55\u64cd\u4f5c\uff0c\u611f […]<\/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\/24327"}],"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=24327"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/24327\/revisions"}],"predecessor-version":[{"id":24335,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/24327\/revisions\/24335"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24327"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24327"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}