{"id":61619,"date":"2024-11-19T15:32:56","date_gmt":"2024-11-19T07:32:56","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61619"},"modified":"2024-11-19T15:32:56","modified_gmt":"2024-11-19T07:32:56","slug":"%e4%bb%8e%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e5%88%b0%e5%8f%af%e8%a7%86%e5%8c%96%ef%bc%9a%e4%ba%94%e5%a4%a7r%e5%8c%85%e5%b8%a6%e4%bd%a0%e8%bd%bb%e6%9d%be%e9%a9%be%e9%a9%ad%e5%9f%ba%e5%9b%a0%e8%a1%a8","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61619","title":{"rendered":"\u4ece\u6570\u636e\u5904\u7406\u5230\u53ef\u89c6\u5316\uff1a\u4e94\u5927R\u5305\u5e26\u4f60\u8f7b\u677e\u9a7e\u9a6d\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5206\u6790\u7684\u6bcf\u4e00\u6b65\uff01"},"content":{"rendered":"\n

\u5927\u5bb6\u597d~\u6211\u662f\u5c0f\u679c\uff01\u5728\u8fd9\u4e2a\u4fe1\u606f\u7206\u70b8\u7684\u65f6\u4ee3\uff0c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u6210\u4e3a\u4e86\u89e3\u9501\u751f\u547d\u79d1\u5b66\u79d8\u5bc6\u7684\u5173\u952e\u3002\u4f5c\u4e3a\u751f\u4fe1\u9886\u57df\u7684\u63a2\u7d22\u8005\uff0c\u6211\u4eec\u6df1\u77e5\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5b83\u4e0d\u4ec5\u5173\u7cfb\u5230\u75be\u75c5\u7684\u8bca\u65ad\u4e0e\u6cbb\u7597\uff0c\u8fd8\u6d89\u53ca\u5230\u751f\u7269\u8fdb\u5316\u548c\u4e2a\u4f53\u5dee\u5f02\u7684\u6df1\u5c42\u7406\u89e3\u3002\u4eca\u5929\uff0c\u5c0f\u679c\u5c06\u5e26\u9886\u5927\u5bb6\u4e00\u8d77\u63a2\u7d22\u5982\u4f55\u5c06\u8fd9\u4e9b\u590d\u6742\u7684\u6570\u636e\u8f6c\u5316\u4e3a\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002\u4ece\u6570\u636e\u5f00\u59cb\u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u9884\u5904\u7406\u6b65\u9aa4\uff0c\u7b5b\u9009\u51fa\u5173\u952e\u4fe1\u606f\uff0c\u53bb\u9664\u90a3\u4e9b\u53ef\u80fd\u5e72\u6270\u7684\u566a\u58f0\u3002\u518d\u8fd0\u7528\u7edf\u8ba1\u5b66\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u6316\u6398\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u5173\u8054\u3002\u6700\u540e\uff0c\u901a\u8fc7\u76f4\u89c2\u7684\u53ef\u89c6\u5316\uff0c\u5c06\u8fd9\u4e9b\u590d\u6742\u7684\u6570\u636e\u8f6c\u5316\u4e3a\u6613\u4e8e\u7406\u89e3\u7684\u56fe\u5f62\u3002\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\uff0c\u5c0f\u679c\u5c06\u4ecb\u7ecd\u4e00\u7cfb\u5217\u5b9e\u7528\u7684R\u8f6f\u4ef6\u5305\uff0c\u5b83\u4eec\u5c06\u662f\u6211\u4eec\u63a2\u7d22\u4e4b\u65c5\u4e2d\u7684\u5f97\u529b\u52a9\u624b\u3002Limma\u5305\u3001stats\u5305\u3001ggplot2\u5305\u3001ggrepel\u5305\u4ee5\u53capheatmap\u5305\uff0c\u6bcf\u4e00\u4e2a\u90fd\u6709\u5176\u72ec\u7279\u7684\u529f\u80fd\u548c\u4f18\u52bf\uff0c\u5b83\u4eec\u5c06\u5e2e\u52a9\u6211\u4eec\u4ece\u4e0d\u540c\u89d2\u5ea6\u5206\u6790\u548c\u5c55\u793a\u6570\u636e\u3002\u5c0f\u679c\u4f1a\u901a\u8fc7\u5b9e\u9645\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u8ba9\u5927\u5bb6\u770b\u5230\u6bcf\u4e00\u6b65\u662f\u5982\u4f55\u64cd\u4f5c\u7684\u3002\u73b0\u5728\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u63ed\u5f00\u57fa\u56e0\u8868\u8fbe\u6570\u636e<\/a>\u7684\u795e\u79d8\u9762\u7eb1\u5427\uff01\u540c\u65f6\u5462\uff0c\u5341\u5e74\u7684\u751f\u4fe1\u4e4b\u8def\uff0c\u5c0f\u679c\u5df2\u7ecf\u7ec3\u5c31\u4e86\u4e00\u8eab\u624e\u5b9e\u7684\u672c\u9886\uff0c\u73b0\u5728\u51c6\u5907\u7528\u8fd9\u8eab\u672c\u4e8b\u4e3a\u5c0f\u4f19\u4f34\u4eec\u670d\u52a1\u5566\uff01\u5982\u679c\u4f60\u5728\u751f\u4fe1\u5206\u6790\u4e0a\u9047\u5230\u4e86\u96be\u9898\uff0c\u90a3\u5c31\u6765\u627e\u5c0f\u679c\u5427\uff01\u5c0f\u679c\u4f1a\u7528\u81ea\u5df1\u7684\u4e13\u4e1a\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u4e3a\u4f60\u89e3\u51b3\u56f0\u6270\uff0c\u52a9\u4f60\u4e00\u81c2\u4e4b\u529b\u3002\u671f\u5f85\u4f60\u7684\u8054\u7cfb\u54e6~<\/strong><\/p>\n\n\n\n

\u4e00\u3001 <\/strong>\u7528\u5230\u7684\u5305\u7b80\u8981\u4ecb\u7ecd<\/strong><\/strong><\/h3>\n\n\n\n
    \n
  • Limma<\/em><\/strong><\/em><\/strong>\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

    Limma\u5305\u662f\u4e13\u4e3a\u5fae\u9635\u5217\u6570\u636e\u548cRNA-seq\u6570\u636e\u5206\u6790\u800c\u8bbe\u8ba1\u7684R\u8f6f\u4ef6\u5305\uff0c\u4e0d\u4ec5\u652f\u6301cDNA\u82af\u7247\u7684RAW\u6570\u636e\u8f93\u5165\u548c\u9884\u5904\u7406\uff08\u5305\u62ec\u5f52\u4e00\u5316\u7b49\uff09\u529f\u80fd<\/strong>\uff0c\u8fd8\u62e5\u6709\u72ec\u7279\u7684\u201c\u7ebf\u6027\u201d\u7b97\u6cd5\uff0c\u7279\u522b\u9002\u7528\u4e8e\u591a\u56e0\u7d20\u5b9e\u9a8c<\/strong>\u3002<\/p>\n\n\n\n

    Limma\u5305\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u80fd\u591f\u5f88\u597d\u5730\u63a7\u5236\u5047\u9633\u6027\u7387<\/strong>\u3002\u5176\u5206\u6790\u8fc7\u7a0b\u5305\u62ecANOVA\u5206\u6790\u548c\u7ebf\u6027\u56de\u5f52\u7b49\uff0c\u5bf9\u4e8e\u5355\u901a\u9053\u548c\u53cc\u901a\u9053\u6570\u636e\u90fd\u80fd\u8fdb\u884c\u5206\u6790\uff0c\u751a\u81f3\u5305\u62ec\u5b9a\u91cfPCR\u548cRNA-seq\u6570\u636e\u3002<\/p>\n\n\n\n

      \n
    • \u5c0f\u679c\u63d0\u793a\uff1a<\/em><\/strong><\/em><\/strong>\u5728\u4f7f\u7528Limma\u5305\u65f6\uff0c\u901a\u5e38\u8981\u6c42\u8f93\u5165\u7684\u8868\u8fbe\u77e9\u9635\u662f\u7ecf\u8fc7log2\u5904\u7406\u4e4b\u540e\u7684\u3002<\/li>\n\n\n\n
    • stats<\/em><\/strong><\/em><\/strong>\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

      stats\u662fR\u8bed\u8a00\u4e2d\u4e00\u4e2a\u975e\u5e38\u57fa\u7840\u7684\u5305\uff0c stats\u5305\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u5206\u6790\u51fd\u6570<\/strong>\uff0c\u5305\u62ec\u63cf\u8ff0\u6027\u7edf\u8ba1\uff08\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u7b49\uff09\u3001\u6982\u7387\u8ba1\u7b97\u3001\u5047\u8bbe\u68c0\u9a8c\u3001\u7ebf\u6027\u56de\u5f52\u7b49\u3002\u9664\u4e86\u7edf\u8ba1\u5206\u6790\u5916\uff0cstats\u5305\u8fd8\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u56fe\u5f62\u7684\u7ed8\u5236\u529f\u80fd<\/strong>\uff0c\u5982\u76f4\u65b9\u56fe\u3001\u6563\u70b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u7b49\u3002<\/p>\n\n\n\n

        \n
      • ggplot2<\/em><\/strong><\/em><\/strong>\u5305\u548c<\/em><\/strong><\/em><\/strong>ggrepel<\/em><\/strong><\/em><\/strong>\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

        ggrepel\u5305\u4e3b\u8981\u7528\u4e8e\u89e3\u51b3\u5728\u6570\u636e\u53ef\u89c6\u5316\u8fc7\u7a0b\u4e2d\u6807\u7b7e\u91cd\u53e0<\/strong>\u7684\u95ee\u9898\u3002ggrepel\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u548c\u5de5\u5177\uff0c\u7528\u4e8e\u81ea\u52a8\u8c03\u6574\u6807\u7b7e\u7684\u4f4d\u7f6e<\/strong>\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u5438\u5f15\u529b\u3002ggrepel\u5305\u662f\u57fa\u4e8eggplot2\u7684\u63d2\u4ef6<\/strong>\uff0c\u56e0\u6b64\u4e0eggplot2\u5177\u6709\u826f\u597d\u7684\u517c\u5bb9\u6027\u3002<\/p>\n\n\n\n

          \n
        • pheatmap<\/em><\/strong><\/em><\/strong>\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

          pheatmap\u5305\u662fR\u8bed\u8a00\u4e2d\u7684\u4e00\u4e2a\u7528\u4e8e\u7ed8\u5236\u805a\u7c7b\u70ed\u56fe<\/strong>\u7684\u5f3a\u5927\u5de5\u5177\u3002\u8fd9\u4e2a\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u76f4\u89c2\u7684\u51fd\u6570\u548c\u53c2\u6570\uff0c\u4f7f\u5f97\u6211\u4eec\u80fd\u591f\u8f7b\u677e\u5730\u81ea\u5b9a\u4e49\u70ed\u56fe\u7684\u5916\u89c2\u548c\u5e03\u5c40\u3002<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\n

          \u4e8c\u3001 <\/strong>\u4ee3\u7801\u5b9e\u64cd<\/strong><\/strong><\/h3>\n\n\n\n

          \u5e9f\u8bdd\u4e0d\u591a\u8bf4\uff0c\u5c0f\u679c\u76f4\u63a5\u5e26\u5c0f\u4f19\u4f34\u4eec\u6765\u5b9e\u64cd\u4ee3\u7801\uff0c\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5904\u7406\u7684\u5168\u9762\u793a\u4f8b\uff01\u672c\u6b21\u4ecb\u7ecd\u7684R\u5305\u64cd\u4f5c\u4f1a\u5360\u7528\u5185\u5b58\u8f83\u5927\uff0c\u5c0f\u679c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668\uff0c\u6b22\u8fce\u5c0f\u4f19\u4f34\u4eec\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\u6027\u4ef7\u6bd4\u5c45\u9ad8\u7684\u670d\u52a1\u5668\uff01<\/strong><\/strong><\/p>\n\n\n\n

            \n
          • \u6570\u636e\u751f\u6210\uff1a<\/strong>\u5c0f\u679c\u9996\u5148\u4f7f\u7528set.seed\u786e\u4fdd\u7ed3\u679c\u7684\u53ef\u91cd\u590d\u6027\u3002\u7136\u540e\uff0c\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846data\uff0c\u5305\u542b100\u4e2a\u57fa\u56e0\uff08Gene\uff09\uff0c\u6bcf\u4e2a\u57fa\u56e0\u7684Fold Change\u548cPvalue\u662f\u968f\u673a\u751f\u6210\u7684\uff0c\u6761\u4ef6\uff08condition\uff09\u5206\u4e3a\u63a7\u5236\u7ec4\uff08control\uff09\u548c\u5904\u7406\u7ec4\uff08treatment\uff09\uff0c\u6bcf\u7ec450\u4e2a\u57fa\u56e0\u3002<\/strong><\/li>\n<\/ul>\n\n\n\n

            set.seed(123)<\/strong><\/strong><\/p>\n\n\n\n

            data <- data.frame(<\/strong><\/strong><\/p>\n\n\n\n

              Gene = paste(“Gene”, 1:100), <\/strong><\/strong><\/p>\n\n\n\n

              FoldChange = runif(100, min = -3, max = 3),  <\/strong><\/strong><\/p>\n\n\n\n

              Pvalue = runif(100, min = 0, max = 1),<\/strong><\/strong><\/p>\n\n\n\n

              condition = factor(rep(c(“control”, “treatment”), each = 50))  <\/strong><\/strong><\/p>\n\n\n\n

            )<\/strong><\/strong><\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n
              \n
            • \u6570\u636e\u9884\u5904\u7406\uff1a<\/strong>\u5c0f\u679c\u63a5\u7740\u4f7f\u7528\u4e86limma\u5305\u4e2d\u7684\u51fd\u6570\u5c06\u975e\u6570\u503c\u578b\u6570\u636e\u8f6c\u6362\u4e3a\u6570\u503c\u578b\u3002\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\uff0c\u9996\u5148\u51cf\u53bb\u6bcf\u884c\u7684\u5747\u503c\uff0c\u7136\u540e\u9664\u4ee5\u6bcf\u5217\u7684\u6807\u51c6\u5dee\u3002<\/strong><\/li>\n<\/ul>\n\n\n\n

              library(limma)<\/strong><\/strong><\/p>\n\n\n\n

              data[, -1] <- sapply(data[, -1], as.numeric)<\/strong><\/strong><\/p>\n\n\n\n

              normalized_data <- sweep(data[, -1], 1, rowMeans(data[, -1], na.rm = TRUE), “-“)<\/strong><\/strong><\/p>\n\n\n\n

              normalized_data <- sweep(normalized_data, 2, apply(data[, -1], 2, sd, na.rm = TRUE), “\/”)<\/strong><\/strong><\/p>\n\n\n\n

              head(normalized_data)<\/strong><\/strong><\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n
              \"\"<\/figure>\n\n\n\n
                \n
              • \u6570\u636e\u5206\u79bb\uff1a<\/strong>\u5c0f\u679c\u6839\u636e\u76f8\u5173\u6761\u4ef6\u5c06\u6570\u636e\u5206\u4e3a\u4e86\u63a7\u5236\u7ec4\u548c\u5904\u7406\u7ec4\u3002<\/li>\n<\/ul>\n\n\n\n

                control_data <- data[data$condition ==1, ]<\/strong><\/strong><\/p>\n\n\n\n

                treatment_data <- data[data$condition == 2, ]<\/strong><\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n
                \"\"<\/figure>\n\n\n\n
                  \n
                • \u8ba1\u7b97\u5bf9\u6570\u53d8\u5316\u500d\u6570\uff08logFC\uff09\uff1a<\/strong>\u5bf9\u63a7\u5236\u7ec4\u548c\u5904\u7406\u7ec4\u7684Fold Change\u8fdb\u884c\u5bf9\u6570\u53d8\u5316\u500d\u6570\u7684\u8ba1\u7b97\uff0c\u5e76\u4e14\u53bb\u9664NA\u503c\u3002<\/li>\n<\/ul>\n\n\n\n

                  logFC <- data.frame(Gene=1:50,<\/strong><\/strong><\/p>\n\n\n\n

                                      FoldChange = log2(treatment_data$FoldChange \/ control_data$FoldChange))<\/strong><\/strong><\/p>\n\n\n\n

                  logFC <- na.omit(logFC)<\/strong><\/strong><\/p>\n\n\n\n

                  head(logFC)<\/strong><\/strong><\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n
                    \n
                  • \u53ef\u89c6\u5316<\/strong>\u7ed8\u56fe\uff1a<\/strong>\u4f7f\u7528ggplot2\u7ed8\u5236\u7684\u6563\u70b9\u56fe\u5c55\u793a\u4e86\u57fa\u56e0\uff08Gene\uff09\u4e0e\u5bf9\u6570\u53d8\u5316\u500d\u6570\uff08Fold Change\uff09\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u57fa\u56e0\u540d\u79f0\u5728x\u8f74\u4e0a\uff0c\u5bf9\u6570\u53d8\u5316\u500d\u6570\u5728y\u8f74\u4e0a\u3002\u70b9\u7684\u5927\u5c0f\u8bbe\u7f6e\u4e3a4\uff0c\u989c\u8272\u6839\u636eFold Change\u7684\u503c\u53d8\u5316\uff0c\u4f7f\u7528\u5f69\u8679\u8272\u6e10\u53d8\uff0c\u8fd9\u6837\u53ef\u4ee5\u76f4\u89c2\u5730\u770b\u51fa\u54ea\u4e9b\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u8f83\u5927\u3002\u7136\u540e\u7ed8\u5236\u57fa\u56e0\u548cFold Change\u7684\u6761\u5f62\u56fe\u3002<\/li>\n<\/ul>\n\n\n\n

                    ggplot(logFC, aes(x = Gene, y = FoldChange, color = FoldChange)) +<\/strong><\/strong><\/p>\n\n\n\n

                      geom_point(size = 4) +  <\/strong><\/strong><\/p>\n\n\n\n

                      scale_color_gradientn(colors = rainbow(100), limit = range(logFC$FoldChange), na.value = “grey50”) +  <\/strong><\/strong><\/p>\n\n\n\n

                      theme_minimal() +  <\/strong><\/strong><\/p>\n\n\n\n

                      labs(title = “Scatter Plot of Genes and Fold Changes with Rainbow Colors”,<\/strong><\/strong><\/p>\n\n\n\n

                           x = “Gene”,<\/strong><\/strong><\/p>\n\n\n\n

                           y = “Fold Change”,<\/strong><\/strong><\/p>\n\n\n\n

                           color = “Fold Change”) +  <\/strong><\/strong><\/p>\n\n\n\n

                      theme(axis.text.x = element_text(angle = 90, hjust = 1), plot.title = element_text(hjust = 0.5))  <\/strong><\/strong><\/p>\n\n\n\n

                    \"\"<\/figure>\n\n\n\n

                    ggplot(logFC, aes(x = reorder(Gene, FoldChange), y = FoldChange)) +<\/strong><\/strong><\/p>\n\n\n\n

                      geom_bar(stat = “identity”, fill = “skyblue”, color = “black”, width = 0.5) +<\/strong><\/strong><\/p>\n\n\n\n

                      theme_minimal() +<\/strong><\/strong><\/p>\n\n\n\n

                      labs(title = “Bar Plot of Genes and Fold Changes”,<\/strong><\/strong><\/p>\n\n\n\n

                           x = “Gene”,<\/strong><\/strong><\/p>\n\n\n\n

                           y = “Fold Change”) +<\/strong><\/strong><\/p>\n\n\n\n

                      theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8),<\/strong><\/strong><\/p>\n\n\n\n

                            axis.title.x = element_blank(),<\/strong><\/strong><\/p>\n\n\n\n

                            axis.title.y = element_text(size = 12),<\/strong><\/strong><\/p>\n\n\n\n

                            plot.title = element_text(hjust = 0.5)) +<\/strong><\/strong><\/p>\n\n\n\n

                      coord_flip()<\/strong><\/strong><\/p>\n\n\n\n

                    \"\"<\/figure>\n\n\n\n
                      \n
                    • \u7ebf\u6027\u6a21\u578b\u5206\u6790\uff1a<\/strong>\u4f7f\u7528limma\u5305\u4e2d\u7684\u51fd\u6570\u521b\u5efa\u8bbe\u8ba1\u77e9\u9635\u3002\u62df\u5408\u7ebf\u6027\u6a21\u578b\uff0c\u5206\u6790\u4e0d\u540c\u6761\u4ef6\u5bf9\u57fa\u56e0\u8868\u8fbe\u7684\u5f71\u54cd\u3002<\/li>\n<\/ul>\n\n\n\n

                      data$condition <- factor(data$condition, levels = c(“control”, “treatment”))<\/strong><\/strong><\/p>\n\n\n\n

                      design <- model.matrix(~condition, data = data)<\/strong><\/strong><\/p>\n\n\n\n

                      print(design)<\/strong><\/strong><\/p>\n\n\n\n

                      model <- lm(FoldChange ~ condition, data = data)<\/strong><\/strong><\/p>\n\n\n\n

                      summary(model)<\/strong><\/strong><\/p>\n\n\n\n

                      \"\"<\/figure>\n\n\n\n
                      \"\"<\/figure>\n\n\n\n
                        \n
                      • \u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\uff1a<\/strong>\u5bf9\u6807\u51c6\u5316\u540e\u7684\u6570\u636e\u8fdb\u884cPCA\u5206\u6790\uff0c\u5e76\u7ed8\u5236\u53cc\u4e3b\u6210\u5206\u5f97\u5206\u56fe\u3002PCA\u56fe\u5c55\u793a\u4e86\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5728\u4e24\u4e2a\u4e3b\u6210\u5206\u65b9\u5411\u4e0a\u7684\u5206\u5e03\u60c5\u51b5\u3002\u7b2c\u4e00\u4e3b\u6210\u5206\uff08PC1\uff09\u548c\u7b2c\u4e8c\u4e3b\u6210\u5206\uff08PC2\uff09\u5206\u522b\u89e3\u91ca\u4e86\u6570\u636e\u53d8\u5316\u7684\u767e\u5206\u6bd4\uff0c\u8fd9\u4e9b\u6210\u5206\u53ef\u4ee5\u7528\u4e8e\u533a\u5206\u4e0d\u540c\u7684\u6761\u4ef6\u6216\u7fa4\u4f53\u3002<\/li>\n<\/ul>\n\n\n\n

                        library(stats)<\/strong><\/strong><\/p>\n\n\n\n

                        pca <- prcomp(normalized_data, scale. = TRUE)<\/strong><\/strong><\/p>\n\n\n\n

                        summary(pca)<\/strong><\/strong><\/p>\n\n\n\n

                        \"\"<\/figure>\n\n\n\n

                        biplot(pca$x[, 1:2], pca$rotation[, 1:2], <\/strong><\/strong><\/p>\n\n\n\n

                               main = “PCA of Gene Expression Data”,<\/strong><\/strong><\/p>\n\n\n\n

                               xlab = “PC1: 54.81%”, <\/strong><\/strong><\/p>\n\n\n\n

                               ylab = “PC2: 45.19%”,<\/strong><\/strong><\/p>\n\n\n\n

                               col = ifelse(pca$x[, 1] > 0, “green”, “yellow”))<\/strong><\/strong><\/p>\n\n\n\n

                        \"\"<\/figure>\n\n\n\n
                          \n
                        • \u706b\u5c71<\/strong>\u56fe\uff1a<\/strong>\u7ed8\u5236\u706b\u5c71\u56fe\u7528\u4e8e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5206\u6790\uff0c\u4ee5Fold Change\u4e3ax\u8f74\uff0c\u8d1f\u5bf9\u6570P\u503c\uff08-Log10 P-value\uff09\u4e3ay\u8f74\u3002\u663e\u8457\u6027\u9608\u503c\uff08\u5982P < 0.05\uff09\u901a\u5e38\u7528\u6765\u533a\u5206\u663e\u8457\u548c\u975e\u663e\u8457\u7684\u57fa\u56e0\u3002\u989c\u8272\u533a\u5206\u663e\u8457\u6027\uff0c\u4f8b\u5982\u7ea2\u8272\u53ef\u80fd\u8868\u793aP\u503c\u5c0f\u4e8e0.05\uff0c\u800c\u84dd\u8272\u8868\u793aP\u503c\u5927\u4e8e\u6216\u7b49\u4e8e0.05\u3002\u706b\u5c71\u56fe\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u8868\u8fbe\u5dee\u5f02\u663e\u8457\u7684\u57fa\u56e0\u3002<\/li>\n<\/ul>\n\n\n\n

                          library(ggplot2)<\/strong><\/strong><\/p>\n\n\n\n

                          library(ggrepel) <\/strong><\/strong><\/p>\n\n\n\n

                          data$NegLogPvalue <- -log10(data$Pvalue)<\/strong><\/strong><\/p>\n\n\n\n

                          data$Color <- ifelse(data$Pvalue < 0.05, “red”, “blue”)<\/strong><\/strong><\/p>\n\n\n\n

                          ggplot(data, aes(x = FoldChange, y = NegLogPvalue, color = Color)) +<\/strong><\/strong><\/p>\n\n\n\n

                            geom_point(alpha = 0.8,size=5) + <\/strong><\/strong><\/p>\n\n\n\n

                            scale_color_manual(values = c(“red”=”red” ,”blue”=”blue”)) + <\/strong><\/strong><\/p>\n\n\n\n

                            theme_minimal() + <\/strong><\/strong><\/p>\n\n\n\n

                            labs(title = “Volcano Plot”, x = “Fold Change”, y = “-Log10 P-value”) + <\/strong><\/strong><\/p>\n\n\n\n

                            theme(legend.title = element_blank()) + <\/strong><\/strong><\/p>\n\n\n\n

                            geom_text(data = data[data$Pvalue < 0.05, ], <\/strong><\/strong><\/p>\n\n\n\n

                                      aes(label = Gene),<\/strong><\/strong><\/p>\n\n\n\n

                                      vjust = -1, hjust = 1, size = 3, color = “black”)<\/strong><\/strong><\/p>\n\n\n\n

                          \"\"<\/figure>\n\n\n\n
                            \n
                          • \u70ed\u56fe\uff1a<\/strong>\u4f7f\u7528pheatmap\u5305\u7ed8\u5236\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u70ed\u56fe\uff0c\u6839\u636e\u6761\u4ef6\u8fdb\u884c\u989c\u8272\u6ce8\u91ca\u3002\u70ed\u56fe\u5c55\u793a\u4e86\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u77e9\u9635\u89c6\u56fe\uff0c\u884c\u4ee3\u8868\u57fa\u56e0\uff0c\u5217\u4ee3\u8868\u6837\u672c\u3002<\/li>\n<\/ul>\n\n\n\n

                            \u989c\u8272\u7684\u6df1\u6d45\u8868\u793a\u8868\u8fbe\u6c34\u5e73\u7684\u9ad8\u4f4e\uff0c\u901a\u5e38\u7528\u4e8e\u89c2\u5bdf\u57fa\u56e0\u8868\u8fbe\u6a21\u5f0f\u548c\u6837\u672c\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u3002<\/p>\n\n\n\n

                            if (!requireNamespace(“pheatmap”, quietly = TRUE)) {<\/strong><\/strong><\/p>\n\n\n\n

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

                            }<\/strong><\/strong><\/p>\n\n\n\n

                            library(pheatmap)<\/strong><\/strong><\/p>\n\n\n\n

                            pheatmap(normalized_data,<\/strong><\/strong><\/p>\n\n\n\n

                                               cluster_rows = FALSE,<\/strong><\/strong><\/p>\n\n\n\n

                                               show_rownames = TRUE,<\/strong><\/strong><\/p>\n\n\n\n

                                               annotation_col = data$Condition)<\/strong><\/strong><\/p>\n\n\n\n

                            \"\"<\/figure>\n\n\n\n
                              \n
                            • \u7bb1\u7ebf\u56fe\uff1a<\/strong>\u7ed8\u5236\u7bb1\u7ebf\u56fe\uff0c\u5c55\u793a\u4e0d\u540c\u6761\u4ef6\u4e0b\u57fa\u56e0\u8868\u8fbe\u7684\u5206\u5e03\u60c5\u51b5\u3002\u7bb1\u7ebf\u56fe\u5c55\u793a\u4e86\u4e0d\u540c\u6761\u4ef6\u4e0b\u57fa\u56e0\u8868\u8fbe\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5305\u62ec\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u4ee5\u53ca\u5f02\u5e38\u503c\u3002\u989c\u8272\u533a\u5206\u4e0d\u540c\u7684\u6761\u4ef6\uff0c\u4f8b\u5982\u201ccontrol\u201d\u548c\u201ctreatment\u201d\uff0c\u6709\u52a9\u4e8e\u6bd4\u8f83\u4e24\u7ec4\u4e4b\u95f4\u7684\u8868\u8fbe\u5dee\u5f02\u3002<\/li>\n<\/ul>\n\n\n\n

                              ggplot(data, aes(x = condition, y = FoldChange, fill = condition)) +<\/strong><\/strong><\/p>\n\n\n\n

                                geom_boxplot() +  <\/strong><\/strong><\/p>\n\n\n\n

                                scale_fill_manual(values = c(“control” = “pink”, “treatment” = “lightblue”)) +  <\/strong><\/strong><\/p>\n\n\n\n

                                theme_minimal() +  <\/strong><\/strong><\/p>\n\n\n\n

                                labs(title = “Boxplot of Gene Expression by Condition”,<\/strong><\/strong><\/p>\n\n\n\n

                                     x = “Condition”,<\/strong><\/strong><\/p>\n\n\n\n

                                     y = “Fold Change”) +<\/strong><\/strong><\/p>\n\n\n\n

                                theme(axis.text.x = element_text(angle = 45, hjust = 1)) <\/strong><\/strong><\/p>\n\n\n\n

                              \"\"<\/figure>\n\n\n\n

                              \u4e09\u3001 <\/strong>\u6587\u7ae0\u5c0f\u7ed3<\/strong><\/strong><\/h3>\n\n\n\n

                              \u672c\u671f\u5c0f\u679c\u548c\u5c0f\u4f19\u4f34\u4eec\u4e00\u8d77\u5b66\u4e60\u4e86\u4e00\u4e2a\u5b8c\u6574\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5206\u6790\u6d41\u7a0b\uff0c\u5305\u62ec\u6570\u636e\u751f\u6210\u3001\u9884\u5904\u7406\u3001\u7edf\u8ba1\u5efa\u6a21\u3001\u4ee5\u53ca\u591a\u79cd\u53ef\u89c6\u5316\u6280\u672f\u3002\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u6a21\u62df\u6570\u636e\u96c6\uff0c\u968f\u540e\u901a\u8fc7\u6807\u51c6\u5316\u5904\u7406\u4f7f\u6570\u636e\u6ee1\u8db3\u5206\u6790\u7684\u524d\u63d0\u6761\u4ef6\u3002\u63a5\u7740\uff0c\u4ee3\u7801\u5206\u79bb\u51fa\u63a7\u5236\u7ec4\u548c\u5904\u7406\u7ec4\u7684\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u4e86\u5bf9\u6570\u53d8\u5316\u500d\u6570\uff08logFC\uff09\u3002\u5728\u53ef\u89c6\u5316\u90e8\u5206\uff0c\u4f7f\u7528\u4e86ggplot2\u5305\u6765\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u706b\u5c71\u56fe\uff0c\u4ee5\u53capheatmap\u5305\u6765\u751f\u6210\u70ed\u56fe\uff0c\u8fd9\u4e9b\u56fe\u8868\u76f4\u89c2\u5730\u5c55\u793a\u4e86\u57fa\u56e0\u8868\u8fbe\u7684\u5dee\u5f02\u548c\u7edf\u8ba1\u663e\u8457\u6027\u3002\u6b64\u5916\uff0c\u8fd8\u8fdb\u884c\u4e86\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u4ee5\u63a2\u7d22\u6570\u636e\u7684\u5185\u5728\u7ed3\u6784\uff0c\u5e76\u901a\u8fc7\u7bb1\u7ebf\u56fe\u6bd4\u8f83\u4e86\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u57fa\u56e0\u8868\u8fbe\u5206\u5e03\u3002\u8ba9\u6211\u4eec\u7ee7\u7eed\u63a2\u7d22\u751f\u4fe1\u7684\u65e0\u9650\u53ef\u80fd\uff0c\u5171\u540c\u5f00\u542f\u66f4\u591a\u79d1\u7814\u65b0\u7bc7\u7ae0\uff01\u6700\u540e\u5982\u679c\u5404\u4f4d\u5c0f\u4f19\u4f34\u4eec\u89c9\u5f97\u81ea\u5df1\u8fd0\u884c\u4ee3\u7801\u592a\u9ebb\u70e6\uff0c\u6b22\u8fce\u7528\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\u5c31\u53ef\u4ee5\u76f4\u63a5\u51fa\u60f3\u8981\u7684\u56fe\u54e6\uff0c\u9644\u4e91\u751f\u4fe1\u94fe\u63a5\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u3002<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

                              \u5927\u5bb6\u597d~\u6211\u662f\u5c0f\u679c\uff01\u5728\u8fd9\u4e2a\u4fe1\u606f\u7206\u70b8\u7684\u65f6\u4ee3\uff0c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u6210\u4e3a\u4e86\u89e3\u9501\u751f\u547d\u79d1\u5b66\u79d8\u5bc6\u7684\u5173\u952e\u3002\u4f5c\u4e3a\u751f\u4fe1\u9886\u57df\u7684\u63a2\u7d22\u8005\uff0c\u6211\u4eec\u6df1 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","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\/61619"}],"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=61619"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61619\/revisions"}],"predecessor-version":[{"id":61639,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61619\/revisions\/61639"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61619"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61619"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}