{"id":61640,"date":"2024-11-19T15:32:45","date_gmt":"2024-11-19T07:32:45","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61640"},"modified":"2024-11-19T15:32:45","modified_gmt":"2024-11-19T07:32:45","slug":"%e5%b0%8f%e7%99%bd%e4%b9%9f%e8%83%bd%e7%a7%92%e5%8f%98%e9%ab%98%e6%89%8b%ef%bc%81genomeinfodb-r%e5%8c%85%e5%ae%9e%e6%88%98%e7%a7%98%e7%b1%8d%ef%bc%8c%e8%bd%bb%e6%9d%be%e9%a9%be%e9%a9%ad%e5%9f%ba","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61640","title":{"rendered":"\u5c0f\u767d\u4e5f\u80fd\u79d2\u53d8\u9ad8\u624b\uff01GenomeInfoDb R\u5305\u5b9e\u6218\u79d8\u7c4d\uff0c\u8f7b\u677e\u9a7e\u9a6d\u57fa\u56e0\u7ec4\u4fe1\u606f\u5206\u6790\uff01"},"content":{"rendered":"\n

<\/h1>\n\n\n\n

\u5c0f\u4f19\u4f34\u4eec\uff0c\u662f\u5426\u66fe\u4e3a\u590d\u6742\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u800c\u5934\u75bc\u4e0d\u5df2\uff1f\u522b\u62c5\u5fc3\uff0c\u5c0f\u679c\u4eca\u5929\u7ed9\u5927\u5bb6\u5e26\u6765\u4e86\u4e00\u4f4d\u751f\u4fe1\u754c\u7684\u6551\u661f\u2014\u2014GenomeInfoDb R\u5305\uff01\u8fd9\u4e2a\u5f3a\u5927\u7684R\u8bed\u8a00\u5de5\u5177\u5305\u53ef\u662f\u4e13\u95e8\u7528\u4e8e\u5b58\u50a8\u548c\u67e5\u8be2\u57fa\u56e0\u7ec4\u6ce8\u91ca\u4fe1\u606f\u7684\uff0c\u5b83\u80fd\u52a9\u4f60\u4e00\u81c2\u4e4b\u529b\uff0c\u8ba9\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u53d8\u5f97\u8f7b\u677e\u53c8\u9ad8\u6548\uff01\u65e0\u8bba\u662f\u521d\u5b66\u8005\u8fd8\u662f\u8d44\u6df1\u73a9\u5bb6\uff0cGenomeInfoDb R\u5305\u90fd\u80fd\u4e3a\u4f60\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\u3002\u5b83\u80fd\u5e2e\u52a9\u4f60\u7ba1\u7406\u548c\u6574\u5408\u5404\u79cd\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\uff0c\u8ba9\u4f60\u7684\u5206\u6790\u5de5\u4f5c\u66f4\u52a0\u5f97\u5fc3\u5e94\u624b\u3002\u800c\u4e14\u5462\uff0c\u5c0f\u679c\u5728\u8fd9\u91cc\u8981\u544a\u8bc9\u5927\u5bb6\u4e00\u4e2a\u597d\u6d88\u606f\uff01\u5982\u679c\u4f60\u6709\u751f\u4fe1\u5206\u6790\u65b9\u9762\u7684\u96be\u9898\uff0c\u4e0d\u59a8\u8054\u7cfb\u5c0f\u679c\u54e6\uff01\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u7684\u5c0f\u679c\u53ef\u662f\u6709\u7740\u4e30\u5bcc\u7684\u7ecf\u9a8c\u548c\u5b9e\u529b\uff0c\u5b9a\u80fd\u4e3a\u4f60\u63d0\u4f9b\u4e13\u4e1a\u7684\u5e2e\u52a9\u548c\u89e3\u7b54\u3002<\/strong>\u5e0c\u671b\u672c\u671f\u5185\u5bb9\u80fd\u591f\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u8ba9\u751f\u4fe1\u5206\u6790\u4e0d\u518d\u662f\u4f60\u7684\u7eca\u811a\u77f3\uff01\u4e00\u8d77\u52a0\u6cb9\uff0c\u5f00\u542f\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u7684\u65b0\u7bc7\u7ae0\u5427\uff01<\/p>\n\n\n\n

(\u4e00) <\/strong>GenomeInfoDb R\u5305\u57fa\u672c\u4ecb\u7ecd<\/strong><\/strong><\/h2>\n\n\n\n

GenomeInfoDb\u5305<\/strong>\u662f\u4e00\u4e2a\u7528\u4e8e\u5b58\u50a8\u548c\u67e5\u8be2\u57fa\u56e0\u7ec4\u6ce8\u91ca\u4fe1\u606f\u7684R\u8bed\u8a00\u5de5\u5177\u5305\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u7ba1\u7406\u548c\u6574\u5408\u5404\u79cd\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\uff0c\u5305\u62ec\u57fa\u56e0\u540d\u79f0\u3001\u67d3\u8272\u4f53\u4f4d\u7f6e\u3001\u8f6c\u5f55\u672c\u4fe1\u606f\u7b49\u3002\u901a\u8fc7GenomeInfoDb\u5305\uff0c\u5c0f\u4f19\u4f34\u4eec\u53ef\u4ee5\u521b\u5efa\u81ea\u5b9a\u4e49\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u5e93\uff0c\u4ee5\u4fbf\u5728\u540e\u7eed\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u4e2d\u4f7f\u7528\u3002<\/p>\n\n\n\n

GenomeInfoDb\u5305\u4e0d\u4ec5\u529f\u80fd\u5f3a\u5927\uff0c\u800c\u4e14\u975e\u5e38\u7075\u6d3b\u3002\u5b83\u80fd\u591f\u652f\u6301\u591a\u79cd\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u7684\u5bfc\u5165\uff0c\u65e0\u8bba\u662f\u57fa\u56e0\u540d\u79f0\u3001\u67d3\u8272\u4f53\u4f4d\u7f6e\u8fd8\u662f\u8f6c\u5f55\u672c\u4fe1\u606f\uff0c\u90fd\u80fd\u8f7b\u677e\u5e94\u5bf9\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0c\u5b83\u5141\u8bb8\u5c0f\u4f19\u4f34\u4eec\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u8fdb\u884c\u5b9a\u5236\uff0c\u521b\u5efa\u5c5e\u4e8e\u81ea\u5df1\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u5e93\u3002\u8fd9\u6837\uff0c\u5728\u540e\u7eed\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u8f7b\u677e\u5730\u4f7f\u7528\u8fd9\u4e9b\u6ce8\u91ca\u6570\u636e\uff0c\u63d0\u9ad8\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n\n\n\n

\u90a3\u4e48\uff0cGenomeInfoDb\u5305\u5177\u4f53\u5728\u54ea\u4e9b\u9886\u57df\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u5462\uff1f<\/p>\n\n\n\n

    \n
  1. \u5728\u57fa\u56e0\u8868\u8fbe\u5206\u6790<\/em><\/strong><\/em><\/strong>\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528GenomeInfoDb\u5305\u6765\u67e5\u8be2\u548c\u6574\u5408\u4e0d\u540c\u57fa\u56e0\u7684\u8868\u8fbe\u6570\u636e\uff0c\u4ece\u800c\u66f4\u6df1\u5165\u5730\u4e86\u89e3\u57fa\u56e0\u7684\u529f\u80fd\u548c\u8c03\u63a7\u673a\u5236\u3002<\/li>\n\n\n\n
  2. \u5728\u57fa\u56e0\u53d8\u5f02\u6ce8\u91ca<\/em><\/strong><\/em><\/strong>\u65b9\u9762\uff0cGenomeInfoDb\u5305\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u5b9a\u4f4d\u548c\u5206\u6790\u57fa\u56e0\u7ec4\u4e2d\u7684\u53d8\u5f02\u4f4d\u70b9\uff0c\u63ed\u793a\u53d8\u5f02\u5bf9\u57fa\u56e0\u529f\u80fd\u7684\u5f71\u54cd\u3002<\/li>\n\n\n\n
  3. \u5728\u4ee3\u8c22\u901a\u8def\u5206\u6790<\/em><\/strong><\/em><\/strong>\u4e2d\uff0c\u901a\u8fc7\u5229\u7528GenomeInfoDb\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u4ee3\u8c22\u9014\u5f84\u4e2d\u5404\u4e2a\u57fa\u56e0\u7684\u4f5c\u7528\u548c\u76f8\u4e92\u5173\u7cfb\u3002<\/li>\n<\/ol>\n\n\n\n

    (\u4e8c) <\/strong>GenomeInfoDb R\u5305\u4ee3\u7801\u5b9e\u64cd<\/strong><\/strong><\/h2>\n\n\n\n

    \u90a3\u4e48\u5e9f\u8bdd\u4e0d\u591a\u8bf4\uff0c\u5c0f\u679c\u76f4\u63a5\u5e26\u5c0f\u4f19\u4f34\u4eec\u4e0a\u4ee3\u7801\u5b9e\u64cd\uff01<\/p>\n\n\n\n

    \u5c0f\u679c\u8fd9\u91cc\u5c06\u4ee5GenomeInfoDb R\u5305\u5728\u4e0d\u540c\u65b9\u9762\u7684\u5e94\u7528\u8fdb\u884c\u4e3e\u4f8b~\u5c0f\u4f19\u4f34\u4eec\u8981\u662f\u89c9\u5f97\u81ea\u5df1\u4e0b\u8f7dR\u5305\u64cd\u4f5c\u592a\u9ebb\u70e6\uff0c\u53ef\u4ee5\u4f7f\u7528\u76f8\u5173\u670d\u52a1\u5668\u54e6~\u6b22\u8fce\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\u6027\u4ef7\u6bd4\u5c45\u9ad8\u7684\u670d\u52a1\u5668\uff01\u5c0f\u679c\u968f\u65f6\u606d\u5019\uff01<\/strong><\/strong><\/p>\n\n\n\n

      \n
    1. \u57fa\u56e0\u8868\u8fbe\u5206\u6790<\/li>\n\n\n\n
    2. \u5b89\u88c5\u548c\u52a0\u8f7d\u6240\u9700\u7684R\u5305<\/li>\n<\/ol>\n\n\n\n

      library(GenomeInfoDb)<\/p>\n\n\n\n

      library(GenomicFeatures)<\/p>\n\n\n\n

      library(DESeq2)<\/p>\n\n\n\n

        \n
      • \u4eceGenomeInfoDb\u5305\u4e2d\u83b7\u53d6\u57fa\u56e0\u4fe1\u606f\uff1a\u4f7f\u7528makeTxDbFromUCSC\u51fd\u6570\u4eceUCSC\u6570\u636e\u5e93\u4e2d\u83b7\u53d6\u4eba\u7c7b\u57fa\u56e0\u7ec4\u6ce8\u91ca\u4fe1\u606f\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5728txdb\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

        txdb <- makeTxDbFromUCSC(“hg19”)<\/p>\n\n\n\n

          \n
        • \u4eceGenomicFeatures\u5305\u4e2d\u83b7\u53d6\u57fa\u56e0\u7ec4\u6ce8\u91ca\u4fe1\u606f<\/li>\n\n\n\n
        • \u4f7f\u7528genes\u51fd\u6570\u4ecetxdb\u4e2d\u63d0\u53d6\u57fa\u56e0\u4fe1\u606f\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5728genes\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

          genes <- genes(txdb)<\/p>\n\n\n\n

          exons <- exonsBy(txdb, by=”gene”)<\/p>\n\n\n\n

            \n
          • \u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u8fd9\u91cc\u4f7f\u7528DESeq2\u5305\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u5206\u6790<\/li>\n<\/ul>\n\n\n\n

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

            countData <- matrix(data = round(runif(15, min = 10, max = 100)), nrow = 5, ncol = 3)<\/p>\n\n\n\n

            colnames(countData) <- c(“Sample1”, “Sample2”, “Sample3”)<\/p>\n\n\n\n

            rownames(countData) <- c(“Gene1”, “Gene2”, “Gene3”, “Gene4”, “Gene5”)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n
              \n
            • \u521b\u5efacolData\uff1a\u521b\u5efa\u5b9e\u9a8c\u4fe1\u606fcolData\uff0c\u5305\u62ec\u6837\u672c\u540d\u79f0\u548c\u6761\u4ef6\u4fe1\u606f\uff08\u8fd9\u91cc\u5047\u8bbe\u6709\u4e24\u79cd\u6761\u4ef6\uff1aControl\u548cTreatment\uff09<\/li>\n<\/ul>\n\n\n\n

              sample_names <- c(“Sample1”, “Sample2”, “Sample3”)<\/p>\n\n\n\n

              conditions <- c(“Control”, “Treatment”, “Control”)<\/p>\n\n\n\n

              colData<-data.frame(sampleName=sample_names,condition = factor(conditions))<\/p>\n\n\n\n

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

              countData\u662f\u57fa\u56e0\u8868\u8fbe\u8ba1\u6570\u6570\u636e\uff0ccolData\u662f\u5b9e\u9a8c\u4fe1\u606f\uff08\u5c0f\u4f19\u4f34\u4eec\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u5b9e\u9645\u9700\u6c42\u4ee3\u6362\u6570\u636e\u96c6\uff01\uff09<\/p>\n\n\n\n

                \n
              • \u4f7f\u7528DESeqDataSetFromMatrix\u51fd\u6570\u521b\u5efaDESeq2\u6570\u636e\u96c6\u5bf9\u8c61dds\uff0c\u5e76\u6307\u5b9a\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u7684\u8bbe\u8ba1\u77e9\u9635<\/li>\n<\/ul>\n\n\n\n

                dds <- DESeqDataSetFromMatrix(countData, colData, design=~condition)<\/p>\n\n\n\n

                  \n
                • \u4f7f\u7528DESeq\u51fd\u6570\u5bf9dds\u8fdb\u884c\u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff1bresults\u51fd\u6570\u63d0\u53d6\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u7684\u7ed3\u679c\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728res\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

                  dds <- DESeq(dds)<\/p>\n\n\n\n

                  res <- results(dds)<\/p>\n\n\n\n

                    \n
                  • \u8f93\u51fa\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff1a\u63d0\u53d6\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u4e2d\u7684\u524d10\u4e2a\u57fa\u56e0\u540dtopGenes\uff0c\u5e76\u6839\u636e\u57fa\u56e0\u540d\u5728genes\u5bf9\u8c61\u4e2d\u63d0\u53d6\u76f8\u5e94\u7684\u57fa\u56e0\u4fe1\u606ftopGenesInfo<\/li>\n<\/ul>\n\n\n\n

                    topGenes <- head(rownames(res), 10)<\/p>\n\n\n\n

                    topGenesInfo <- genes[rowRanges(dds)$gene_id %in% topGenes,]<\/p>\n\n\n\n

                    print(topGenesInfo)<\/p>\n\n\n\n

                    \"\"<\/figure>\n\n\n\n
                      \n
                    • \u57fa\u56e0\u53d8\u5f02\u6ce8\u91ca<\/li>\n\n\n\n
                    • \u76f8\u5173R\u5305\u7684\u5b89\u88c5<\/li>\n<\/ul>\n\n\n\n

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

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

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

                      library(GenomeInfoDb)<\/p>\n\n\n\n

                      library(GenomicRanges)<\/p>\n\n\n\n

                      library(GenomicFeatures)<\/p>\n\n\n\n

                      library(DESeq2)<\/p>\n\n\n\n

                        \n
                      • \u67e5\u8be2\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\uff08\u5c0f\u679c\u8fd9\u91cc\u5c31\u4ee5TP53\u4e3e\u4f8b\u4e86\uff0c\u5c0f\u4f19\u4f34\u4eec\u53ef\u6839\u636e\u81ea\u5df1\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u4fee\u6539\uff09\uff1a\u67e5\u8be2\u7279\u5b9a\u57fa\u56e0\uff08TP53\uff09\u7684\u6ce8\u91ca\u4fe1\u606f\u3001\u8f6c\u5f55\u672c\u4fe1\u606f\u548c\u5916\u663e\u5b50\u4fe1\u606f\uff0c\u5e76\u5b58\u50a8\u5728query_gene\u3001query_transcript\u548cquery_exon\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

                        transcripts <- transcripts(txdb)<\/p>\n\n\n\n

                        exons <- exons(txdb)<\/p>\n\n\n\n

                        query_gene <- genes[genes$gene_name == “TP53”, ]<\/p>\n\n\n\n

                        query_transcript <- transcripts[transcripts$transcript_name == “NM_000546.5”, ]<\/p>\n\n\n\n

                        query_exon <- exons[exons$exon_id == “ENSE00003554982”, ]<\/p>\n\n\n\n

                          \n
                        • \u83b7\u53d6\u57fa\u56e0\u7ec4\u5750\u6807\u4fe1\u606f\uff1a\u4f7f\u7528mapToGenome\u51fd\u6570\u83b7\u53d6\u7279\u5b9a\u57fa\u56e0\uff08TP53\uff09\u7684\u57fa\u56e0\u7ec4\u5750\u6807\u4fe1\u606f\uff0c\u5e76\u5b58\u50a8\u5728gene_coords\u3001transcript_coords\u548cexon_coords\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

                          gene_coords <- mapToGenome(“TP53”, txdb)<\/p>\n\n\n\n

                          transcript_coords <- mapToGenome(“NM_000546.5”, txdb)<\/p>\n\n\n\n

                          exon_coords <- mapToGenome(“ENSE00003554982”, txdb)<\/p>\n\n\n\n

                            \n
                          • \u83b7\u53d6\u57fa\u56e0\u7ec4\u5e8f\u5217\u4fe1\u606f\uff1a\u4f7f\u7528getSeq\u51fd\u6570\u83b7\u53d6\u7279\u5b9a\u57fa\u56e0\uff08TP53\uff09\u7684\u57fa\u56e0\u7ec4\u5e8f\u5217\u4fe1\u606f\uff0c\u5e76\u5b58\u50a8\u5728gene_seq\u3001transcript_seq\u548cexon_seq\u5bf9\u8c61\u4e2d<\/li>\n<\/ul>\n\n\n\n

                            gene_seq <- getSeq(txdb, “TP53”)<\/p>\n\n\n\n

                            transcript_seq <- getSeq(txdb, “NM_000546.5”)<\/p>\n\n\n\n

                            exon_seq <- getSeq(txdb, “ENSE00003554982”)<\/p>\n\n\n\n

                            \"\"<\/figure>\n\n\n\n
                              \n
                            • \u8f93\u51fa\u76f8\u5173\u7ed3\u679c\uff1a\u5305\u62ec\u57fa\u56e0\u7ec4\u6ce8\u91ca\u4fe1\u606f\u3001\u67e5\u8be2\u7ed3\u679c\u3001\u57fa\u56e0\u7ec4\u5750\u6807\u548c\u57fa\u56e0\u7ec4\u5e8f\u5217\u4fe1\u606f<\/li>\n<\/ul>\n\n\n\n

                              print(“Genome annotation information:”)<\/p>\n\n\n\n

                              print(head(genes))<\/p>\n\n\n\n

                              print(head(transcripts))<\/p>\n\n\n\n

                              print(head(exons))<\/p>\n\n\n\n

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

                              print(“Query results:”)<\/p>\n\n\n\n

                              print(query_gene)<\/p>\n\n\n\n

                              print(query_transcript)<\/p>\n\n\n\n

                              print(query_exon)<\/p>\n\n\n\n

                              \"\"<\/figure>\n\n\n\n
                                \n
                              • \u76f8\u5173\u53ef\u89c6\u5316<\/li>\n\n\n\n
                              • \u5c06 res \u5bf9\u8c61\u8f6c\u6362\u4e3a\u6570\u636e\u6846 res_df\uff0c\u5e76\u6dfb\u52a0\u4e24\u4e2a\u65b0\u5217 pvalue \u548c Log10_pvalue\uff0c\u7ed8\u5236\u4e86\u4e94\u4e2a\u56fe\u5f62\u8fdb\u884c\u53ef\u89c6\u5316\uff1a\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u7684\u7bb1\u7ebf\u56fe\u3001\u5e73\u5747\u8868\u8fbe\u6c34\u5e73\u4e0e\u5bf9\u6570\u500d\u6570\u53d8\u5316\u7684\u6563\u70b9\u56fe\uff08MA\u56fe\uff09\u3001\u7bb1\u7ebf\u56fe\u7b49\u3002\u5176\u4e2d\uff0cscale_color_manual \u548c scale_fill_manual \u7528\u4e8e\u81ea\u5b9a\u4e49\u989c\u8272\uff0clabs \u7528\u4e8e\u8bbe\u7f6e\u56fe\u5f62\u7684\u6807\u9898\u548c\u8f74\u6807\u7b7e\u3002<\/li>\n<\/ul>\n\n\n\n

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

                                res$pvalue <- 10^-abs(res$pvalue)<\/p>\n\n\n\n

                                res$Log10_pvalue <- -log10(res$pvalue)<\/p>\n\n\n\n

                                res_df <- as.data.frame(res)<\/p>\n\n\n\n

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

                                ggplot(res_df, aes(x = log2FoldChange, y = padj, size = baseMean, color = stat)) +<\/p>\n\n\n\n

                                  geom_point(alpha = 0.7) +   <\/p>\n\n\n\n

                                  scale_size_continuous(range = c(3, 10)) +<\/p>\n\n\n\n

                                  scale_color_gradient(low = “blue”, high = “red”) +  <\/p>\n\n\n\n

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

                                  labs(title = “Visualization of log2 Fold Change and Adjusted P-values”,<\/p>\n\n\n\n

                                       x = “Log2 Fold Change”,<\/p>\n\n\n\n

                                       y = “Adjusted P-value (padj)”,<\/p>\n\n\n\n

                                       size = “Base Mean”,<\/p>\n\n\n\n

                                       color = “Statistic Value”)<\/p>\n\n\n\n

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

                                res_df$significant <- ifelse(res_df$padj < 1.0, “Significant”, “Not Significant”)<\/p>\n\n\n\n

                                ggplot(res_df, aes(x = “”, y = baseMean, fill = significant)) +<\/p>\n\n\n\n

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

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

                                  scale_fill_manual(values = c(“pink”)) +<\/p>\n\n\n\n

                                  labs(title = “Boxplot of Gene Expression”, x = “”, y = “Base Mean”, fill = “Significance”)<\/p>\n\n\n\n

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

                                ggplot(res_df, aes(x = “”, y = log2FoldChange, fill = factor(padj < 1.0))) +<\/p>\n\n\n\n

                                  geom_violin(trim = FALSE, scale = “count”) +<\/p>\n\n\n\n

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

                                  labs(title = “Density Plot of Log2 Fold Change”, fill = “Significant”)<\/p>\n\n\n\n

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

                                ggplot(res_df, aes(x = baseMean, y = log2FoldChange)) +<\/p>\n\n\n\n

                                  geom_point(aes(color = padj < 1.0), alpha = 0.8) +<\/p>\n\n\n\n

                                  scale_color_manual(values = c(“green”)) +<\/p>\n\n\n\n

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

                                  labs(title = “MA Plot”, x = “Average Expression Level (baseMean)”, y = “Log2 Fold Change”)<\/p>\n\n\n\n

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

                                ggplot(res_df, aes(x = factor(padj < 1.0), y = log2FoldChange, fill = “factor(padj <1.0)”)) +<\/p>\n\n\n\n

                                  geom_bar(stat = “summary”, fun = “mean”, position = position_dodge()) +<\/p>\n\n\n\n

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

                                  labs(title = “Mean Log2 Fold Change by Significance”)<\/p>\n\n\n\n

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

                                (\u4e09) <\/strong>\u6587\u7ae0\u5c0f\u7ed3<\/strong><\/strong><\/h2>\n\n\n\n

                                \u672c\u671f\uff0c\u5c0f\u679c\u4e3a\u5c0f\u4f19\u4f34\u4eec\u5206\u4eab\u4e86GenomeInfoDb \u5305\u7684\u5e94\u7528\u5b9e\u4f8b\uff0c\u5b83\u5728\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df\u4e2d\u53d1\u6325\u7740\u4e3e\u8db3\u8f7b\u91cd\u7684\u4f5c\u7528\u3002\u4f5c\u4e3a\u4e00\u5957\u9ad8\u6548\u4e14\u7075\u6d3b\u7684\u5de5\u5177\u96c6\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u7ba1\u7406\u548c\u6574\u5408\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u7684\u4fbf\u6377\u9014\u5f84\uff0c\u663e\u8457\u63d0\u5347\u4e86\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u51fd\u6570\uff0c\u4f7f\u6211\u4eec\u80fd\u591f\u8f7b\u677e\u5730\u5904\u7406\u590d\u6742\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u3002\u5b83\u4e0d\u4ec5\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u8f93\u5165\uff0c\u800c\u4e14\u80fd\u591f\u5c06\u8fd9\u4e9b\u6570\u636e\u6574\u5408\u6210\u4e00\u4e2a\u7edf\u4e00\u7684\u7ed3\u6784\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u5206\u6790\u64cd\u4f5c\u3002\u8fd9\u5bf9\u4e8e\u6211\u4eec\u6765\u8bf4\uff0c\u80fd\u591f\u66f4\u52a0\u4e13\u6ce8\u4e8e\u751f\u7269\u5b66\u95ee\u9898\u7684\u7814\u7a76\uff0c\u800c\u65e0\u9700\u82b1\u8d39\u5927\u91cf\u65f6\u95f4\u5728\u6570\u636e\u683c\u5f0f\u7684\u8f6c\u6362\u548c\u6574\u5408\u4e0a\u3002<\/p>\n\n\n\n

                                \u5176\u6b21\uff0cGenomeInfoDb \u5305\u8ba9\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u67e5\u8be2\u57fa\u56e0\u7684\u4f4d\u7f6e\u3001\u67d3\u8272\u4f53\u4fe1\u606f\u3001\u8f6c\u5f55\u672c\u7ed3\u6784\u7b49\u5173\u952e\u4fe1\u606f\uff0c\u4ece\u800c\u66f4\u52a0\u5168\u9762\u5730\u4e86\u89e3\u57fa\u56e0\u7ec4\u7684\u7ed3\u6784\u548c\u529f\u80fd\u3002\u540c\u65f6\u8be5\u5305\u8fd8\u652f\u6301\u4e0e\u5176\u4ed6\u751f\u7269\u4fe1\u606f\u5b66\u5de5\u5177\u7684\u96c6\u6210\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u5c06\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u4e0e\u5176\u4ed6\u7c7b\u578b\u7684\u6570\u636e\u8fdb\u884c\u8054\u5408\u5206\u6790\uff0c\u8fdb\u4e00\u6b65\u6316\u6398\u751f\u7269\u5b66\u4fe1\u606f\u3002\u5e76\u4e14\u5b83\u63d0\u4f9b\u4e86\u4e25\u683c\u7684\u6570\u636e\u6821\u9a8c\u548c\u8d28\u91cf\u63a7\u5236\u673a\u5236\uff0c\u786e\u4fdd\u6240\u4f7f\u7528\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002\u8fd9\u5bf9\u4e8e\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u6765\u8bf4\u81f3\u5173\u91cd\u8981\uff0c\u56e0\u4e3a\u4e0d\u51c6\u786e\u7684\u6570\u636e\u53ef\u80fd\u5bfc\u81f4\u8bef\u5bfc\u6027\u7684\u5206\u6790\u7ed3\u679c\u3002<\/p>\n\n\n\n

                                \u603b\u4e4b\uff0cGenomeInfoDb\u5305\u662f\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df\u7684\u4e00\u6b3e\u795e\u5668\uff0c\u5b83\u5e2e\u52a9\u6211\u4eec\u8f7b\u677e\u7ba1\u7406\u548c\u6574\u5408\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\uff0c\u63d0\u9ad8\u4e86\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002\u5982\u679c\u4f60\u8fd8\u5728\u4e3a\u590d\u6742\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u800c\u5934\u75bc\u4e0d\u5df2\uff0c\u4e0d\u59a8\u8bd5\u8bd5GenomeInfoDb\u5305\u5427\uff01\u76f8\u4fe1\u5b83\u4e00\u5b9a\u4f1a\u6210\u4e3a\u4f60\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u4e2d\u7684\u5f97\u529b\u52a9\u624b\u3002\u5982\u679c\u5404\u4f4d\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\u5462\uff0c\u9644\u4e91\u751f\u4fe1\u94fe\u63a5\uff08http:\/\/www.biocloudservice.com\/home.html\uff09<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

                                \u5c0f\u4f19\u4f34\u4eec\uff0c\u662f\u5426\u66fe\u4e3a\u590d\u6742\u7684\u57fa\u56e0\u7ec4\u6ce8\u91ca\u6570\u636e\u800c\u5934\u75bc\u4e0d\u5df2\uff1f\u522b\u62c5\u5fc3\uff0c\u5c0f\u679c\u4eca\u5929\u7ed9\u5927\u5bb6\u5e26\u6765\u4e86\u4e00\u4f4d\u751f\u4fe1\u754c\u7684\u6551\u661f\u2014\u2014Genome […]<\/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\/61640"}],"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=61640"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61640\/revisions"}],"predecessor-version":[{"id":61654,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61640\/revisions\/61654"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61640"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}