{"id":61320,"date":"2024-10-28T16:47:40","date_gmt":"2024-10-28T08:47:40","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61320"},"modified":"2024-10-28T16:47:40","modified_gmt":"2024-10-28T08:47:40","slug":"%e4%b8%80%e9%94%ae%e5%90%af%e5%8a%a8rna-seq%e5%88%86%e6%9e%90%ef%bc%81edaseq%e5%8c%85%ef%bc%8c%e8%ae%a9%e5%9f%ba%e5%9b%a0%e6%95%b0%e6%8d%ae%e8%87%aa%e5%b7%b1%e8%af%b4%e8%af%9d%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61320","title":{"rendered":"\u4e00\u952e\u542f\u52a8RNA-Seq\u5206\u6790\uff01EDASeq\u5305\uff0c\u8ba9\u57fa\u56e0\u6570\u636e\u81ea\u5df1\u8bf4\u8bdd\uff01"},"content":{"rendered":"\n

\u9762\u5bf9\u590d\u6742\u7684RNA-Seq\u6570\u636e\uff0c\u4f60\u662f\u4e0d\u662f\u4e5f\u66fe\u611f\u5230\u65e0\u4ece\u4e0b\u624b\u5462\uff1f\u522b\u62c5\u5fc3\uff0c\u4eca\u5929\uff0c\u5c0f\u5e08\u59b9\u8981\u7ed9\u4f60\u4eec\u5c55\u793a\u4e00\u4e2a\u8d85\u7ed9\u529b\u7684R\u5305\u2014\u2014EDASeq\u3002\u6709\u4e86\u5b83\uff0c\u8fd9\u4e00\u5207\u90fd\u5c06\u53d8\u5f97\u7b80\u5355\u8d77\u6765\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f1a\u4e00\u8d77\u5b66\u4e60\u5982\u4f55\u5bf9RNA\u6d4b\u5e8f\u5b9e\u9a8c\u6570\u636e\u8fdb\u884c\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\u548c\u6807\u51c6\u5316\u5904\u7406\u3002<\/p>\n\n\n\n

\u4f60\u77e5\u9053\u5417\uff1f\u5728RNA\u6d4b\u5e8f\u7684\u4e16\u754c\u91cc\uff0c\u6211\u4eec\u901a\u5e38\u628aEDA\u770b\u4f5c\u662f\u4e24\u6b65\u8d70\u7684\u6218\u7565\uff1a\u5148\u662f\u8bfb\u6bb5\u7ea7\u522b\u7684EDA\uff0c\u5b83\u80fd\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u6d4b\u5e8f\u6df1\u5ea6\u4f4e\u3001\u8d28\u91cf\u6709\u95ee\u9898\u7684\u901a\u9053\uff0c\u4ee5\u53ca\u90a3\u4e9b\u4e0d\u5bfb\u5e38\u7684\u6838\u82f7\u9178\u9891\u7387\u3002\u7136\u540e\u662f\u57fa\u56e0\u7ea7\u522b\u7684EDA\uff0c\u5b83\u80fd\u8ba9\u6211\u4eec\u6355\u6349\u5230\u9519\u8bef\u6807\u8bb0\u7684\u901a\u9053\u3001\u5206\u5e03\u5047\u8bbe\u95ee\u9898\uff08\u6bd4\u5982\u8fc7\u5ea6\u79bb\u6563\uff09\u548cGC\u542b\u91cf\u504f\u5dee\u3002<\/p>\n\n\n\n

EDASeq\u5305\u4e0d\u4ec5\u63d0\u4f9b\u4e86\u201c\u5355\u901a\u9053\u201d\u548c\u201c\u591a\u901a\u9053\u201d\u7684\u6807\u51c6\u5316\u7a0b\u5e8f\uff0c\u6765\u5206\u522b\u5904\u7406\u901a\u9053\u5185\u57fa\u56e0\u7279\u5f02\u6027\uff08\u53ef\u80fd\u8fd8\u6709\u901a\u9053\u7279\u5f02\u6027\uff09\u7684\u8bfb\u6570\u5f71\u54cd\uff0c\u6bd4\u5982\u4e0e\u57fa\u56e0\u957f\u5ea6\u6216GC\u542b\u91cf\u6709\u5173\u7684\u56e0\u7d20\uff0c\u8fd8\u6709\u5904\u7406\u901a\u9053\u95f4\u8bfb\u6570\u5206\u5e03\u5dee\u5f02\u7684\u5f71\u54cd\uff0c\u6bd4\u5982\u6d4b\u5e8f\u6df1\u5ea6\u3002<\/p>\n\n\n\n

\u8981\u6ce8\u610f\u7684\u662f\uff0cEDASeq\u5728\u5904\u7406\u5927\u91cf\u6570\u636e\u65f6\u53ef\u80fd\u4f1a\u9700\u8981\u6bd4\u8f83\u5927\u7684\u5185\u5b58\uff0c\u6240\u4ee5<\/strong>\u5c0f\u5e08\u59b9<\/strong>\u5efa\u8bae\u4f60\u4eec\u7528\u670d\u52a1\u5668\u6765\u8dd1\u8fd9\u4e9b\u5206\u6790\u3002\u5982\u679c\u4f60\u4eec\u9700\u8981\u79df\u7528\u670d\u52a1\u5668\uff0c\u8bb0\u5f97\u627e<\/strong>\u5c0f\u5e08\u59b9<\/strong>\uff0c\u7ed9\u4f60\u4eec\u5b89\u6392\u5f97\u660e\u660e\u767d\u767d\u3002<\/strong><\/strong><\/p>\n\n\n\n

\u597d\u4e86\uff0c\u73b0\u5728\u4f60\u4eec\u51c6\u5907\u597d\u4e86\u5417\uff1f\u63a5\u4e0b\u6765\uff0c\u5c31\u8ba9\u6211\u4eec\u4ece\u5b89\u88c5\u5305\u5f00\u59cb\uff0c\u4e00\u6b65\u6b65\u8d70\u8fdbEDASeq\u7684\u4e16\u754c\u3002\u9047\u5230\u4efb\u4f55\u95ee\u9898\uff0c<\/strong>\u5c0f\u5e08\u59b9<\/strong>\u90fd\u4f1a\u5728\u8fd9\u91cc\uff0c\u624b\u628a\u624b\u6559\u4f60\u4eec\u600e\u4e48\u7528\u3002<\/strong>\u4e00\u8d77\u52a0\u6cb9\uff0c\u63ed\u5f00RNA-Seq\u6570\u636e\u7684\u6240\u6709\u79d8\u5bc6\u5427\uff01<\/p>\n\n\n\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5f00\u59cb\u5b89\u88c5EDASeq\u5305\u548c\u53e6\u5916\u4e24\u4e2a\u9700\u8981\u7528\u5230\u7684\u5305\uff1a<\/p>\n\n\n\n

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

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

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

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

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

\u5b89\u88c5\u597d\u4e86\u4e4b\u540e\uff0c\u8bb0\u5f97\u52a0\u8f7d\u8fd9\u4e9b\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\n

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

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

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

\u73b0\u5728\uff0c\u6211\u4eec\u5df2\u7ecf\u5168\u526f\u6b66\u88c5\uff0c\u51c6\u5907\u5f00\u59cb\u8fd9\u573a\u6fc0\u52a8\u4eba\u5fc3\u7684\u6570\u636e\u5206\u6790\u4e4b\u65c5\u4e86\uff01<\/p>\n\n\n\n

\u4e00\u3001 \u8bfb\u53d6\u672a\u6bd4\u5bf9\u548c\u5df2\u6bd4\u5bf9\u7684\u6d4b\u5e8f\u6570\u636e<\/p>\n\n\n\n

    \n
  1.  \u672a\u6bd4\u5bf9\u7684\u6d4b\u5e8f\u6570\u636e<\/li>\n<\/ol>\n\n\n\n

    \u5b58\u50a8\u5728FASTQ\u683c\u5f0f\u4e2d\u7684\u672a\u6bd4\u5bf9\uff08\u672a\u6620\u5c04\uff09\u6d4b\u5e8f\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4eceShortRead\u5305\u4e2d\u5bfc\u5165\u7684FastqFileList\u7c7b\u6765\u7ba1\u7406\u3002\u53ef\u4ee5\u5728FastqFileList\u5bf9\u8c61\u7684elementMetadata\u69fd\u4e2d\u5b58\u50a8\u4e0e\u6bcf\u4e2a\u901a\u9053\u4e2d\u5e8f\u5217\u5316\u7684\u5e93\u6709\u5173\u7684\u4fe1\u606f\u3002<\/p>\n\n\n\n

    files <- list.files(file.path(system.file(package = “yeastRNASeq”), “reads”), pattern = “fastq”, full.names = TRUE)<\/p>\n\n\n\n

    names(files) <- gsub(“ll.fastq.*”, “”, basename(files))<\/p>\n\n\n\n

    met <- DataFrame(conditions = c(rep(“mut”, 2), rep(“wt”, 2)), row.names = names(files))<\/p>\n\n\n\n

    fastq <- FastqFileList(files)<\/p>\n\n\n\n

    elementMetadata(fastq) <- met<\/p>\n\n\n\n

    \u9664\u4e86\u8fd9\u79cd\u65b9\u6cd5\uff0c\u4f60\u4e5f\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u5c0f\u5e08\u59b9\u51c6\u5907\u597d\u7684\u6570\u636e\u6587\u4ef6\u54e6~<\/p>\n\n\n\n

    fastq <- readRDS(file = “fastq_data.rds”)<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n
      \n
    • \u5df2\u6bd4\u5bf9\u7684\u6d4b\u5e8f\u6570\u636e<\/li>\n<\/ul>\n\n\n\n

      \u8be5\u5305\u53ef\u4ee5\u5904\u7406BAM\u683c\u5f0f\u7684\u5df2\u6bd4\u5bf9\uff08\u5df2\u6620\u5c04\uff09\u6d4b\u5e8f\u6570\u636e\uff0c\u4f7f\u7528\u7684\u662fRsamtools\u5305\u4e2d\u7684BamFileList\u7c7b\u3002\u540c\u6837\uff0celementMetadata\u69fd\u53ef\u4ee5\u7528\u6765\u5b58\u50a8\u901a\u9053\u7ea7\u522b\u7684\u6837\u672c\u4fe1\u606f\u3002<\/p>\n\n\n\n

      files <- list.files(file.path(system.file(package = “leeBamViews”), pattern = “bams”, full.names = TRUE))<\/p>\n\n\n\n

      names(files) <- gsub(“.bam”, “”, basename(files))<\/p>\n\n\n\n

      gt <- gsub(“*\/”, “-“, files)<\/p>\n\n\n\n

      gt <- gsub(“_.*”, “”, gt)<\/p>\n\n\n\n

      lane <- gsub(“.*(.)s”, “ll”, gt)<\/p>\n\n\n\n

      geno <- gsub(“.$”, “”, gt)<\/p>\n\n\n\n

      pd <- DataFrame(geno = geno, lane = lane, row.names = paste(geno, lane, sep = “.”))<\/p>\n\n\n\n

      bfs <- BamFileList(files)<\/p>\n\n\n\n

      elementMetadata(bfs) <- pd<\/p>\n\n\n\n

      \u9664\u4e86\u8fd9\u79cd\u65b9\u6cd5\uff0c\u4f60\u4e5f\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u5c0f\u5e08\u59b9\u51c6\u5907\u597d\u7684\u6570\u636e\u6587\u4ef6\u54e6~<\/p>\n\n\n\n

      bfs <- readRDS(file = “bfs_data.rds”)<\/p>\n\n\n\n

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

      \u4e8c\u3001\u8bfb\u6bb5\u7ea7\u522b\u7684EDA<\/p>\n\n\n\n

        \n
      1.  \u672a\u6bd4\u5bf9\u548c\u5df2\u6bd4\u5bf9\u8bfb\u6bb5\u7684\u6570\u91cf<\/li>\n<\/ol>\n\n\n\n

        \u8fdb\u884c\u8d28\u91cf\u63a7\u5236\u7684\u4e00\u4e2a\u91cd\u8981\u68c0\u67e5\u662f\u67e5\u770b\u6bcf\u4e2a\u901a\u9053\u4ea7\u751f\u7684\u603b\u8bfb\u6bb5\u6570\uff0c\u4ee5\u53ca\u5df2\u6620\u5c04\u5230\u53c2\u8003\u57fa\u56e0\u7ec4\u7684\u8bfb\u6bb5\u6570\u548c\u767e\u5206\u6bd4\u3002\u603b\u8bfb\u6bb5\u6570\u4f4e\u53ef\u80fd\u8868\u793a\u8f93\u5165RNA\u7684\u8d28\u91cf\u5dee\uff0c\u800c\u6620\u5c04\u767e\u5206\u6bd4\u4f4e\u53ef\u80fd\u8868\u660e\u8bfb\u6bb5\u8d28\u91cf\u5dee\uff08\u590d\u6742\u6027\u4f4e\uff09\u3001\u53c2\u8003\u57fa\u56e0\u7ec4\u7684\u95ee\u9898\u6216\u901a\u9053\u88ab\u9519\u8bef\u6807\u8bb0\u3002<\/p>\n\n\n\n

        colors <- c(rep(rgb(1, 0, 0, alpha = 0.7), 2), rep(rgb(0, 0, 1, alpha = 0.7), 2), rep(rgb(0, 1, 0, alpha = 0.7), 2), rep(rgb(1, 1, 0, alpha = 0.7), 2))<\/p>\n\n\n\n

        barplot(bfs, las = 2, col = colors)<\/p>\n\n\n\n

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

        \u4f7f\u7528BamFileList\u7c7b\u7684barplot\u65b9\u6cd5\u751f\u6210\u7684\u56fe\u663e\u793a\u4e86\u5305\u542b\u5728\u5305`leeBamViews`\u4e2d\u7684\u9175\u6bcd\u6570\u636e\u96c6\u7684\u5b50\u96c6\u7684\u5df2\u6620\u5c04\u8bfb\u6bb5\u6570\u3002\u9057\u61be\u7684\u662f\uff0c`leeBamViews`\u6ca1\u6709\u63d0\u4f9b\u672a\u6bd4\u5bf9\u7684\u8bfb\u6bb5\uff0c\u4f46\u662f\u53ef\u4ee5\u4f7f\u7528FastqFileList\u7c7b\u7684barplot\u65b9\u6cd5\u83b7\u5f97\u603b\u8bfb\u6bb5\u6570\u7684\u6761\u5f62\u56fe\u3002\u7c7b\u4f3c\u5730\uff0c\u53ef\u4ee5\u4f7f\u7528\u5e26\u6709\u7b7e\u540d`c(x = “BamFileList”, y = “FastqFileList”)`\u7684plot\u65b9\u6cd5\u7ed8\u5236\u5df2\u6620\u5c04\u8bfb\u6bb5\u7684\u767e\u5206\u6bd4\u3002<\/p>\n\n\n\n

          \n
        •  \u8bfb\u6bb5\u8d28\u91cf\u5f97\u5206<\/li>\n<\/ul>\n\n\n\n

          \u4f5c\u4e3a\u989d\u5916\u7684\u8d28\u91cf\u68c0\u67e5\uff0c\u53ef\u4ee5\u7ed8\u5236\u6bcf\u4e2a\u901a\u9053\u4e2d\u672a\u6620\u5c04\u6216\u5df2\u6620\u5c04\u8bfb\u6bb5\u7684\u5e73\u5747\u6bcf\u4e2a\u78b1\u57fa\uff08\u5373\u6bcf\u4e2a\u5468\u671f\uff09\u7684\u8d28\u91cf\u5f97\u5206\u3002<\/p>\n\n\n\n

          plotQuality(bfs, col = colors, lty = 1)<\/p>\n\n\n\n

          legend(“topright”,unique(elementMetadata(bfs)[,1]), fill=unique(colors))<\/p>\n\n\n\n

          \"\"<\/figure>\n\n\n\n
            \n
          •  \u5355\u72ec\u901a\u9053\u7684\u603b\u7ed3<\/li>\n<\/ul>\n\n\n\n

            \u5982\u679c\u5bf9\u67d0\u4e2a\u901a\u9053\u66f4\u611f\u5174\u8da3\uff0c\u53ef\u4ee5\u66f4\u8be6\u7ec6\u5730\u67e5\u770b\u6bcf\u4e2a\u901a\u9053\u7684\u6bcf\u4e2a\u78b1\u57fa\u7684\u8d28\u91cf\u5f97\u5206\u5206\u5e03\uff0c\u4ee5\u53ca\u6309\u67d3\u8272\u4f53\u6216\u94fe\u5206\u9694\u7684\u5df2\u6620\u5c04\u8bfb\u6bb5\u6570\u3002\u6b63\u5982\u9884\u671f\u7684\u90a3\u6837\uff0c\u6240\u6709\u8bfb\u6bb5\u90fd\u6620\u5c04\u5230\u7b2cXII\u67d3\u8272\u4f53\u3002<\/p>\n\n\n\n

            plotQuality(bfs[[1]], cex.axis = 0.8)<\/p>\n\n\n\n

            barplot(bfs[[1]], las = 2)<\/p>\n\n\n\n

            \"\"<\/figure>\n\n\n\n
            \"\"<\/figure>\n\n\n\n
              \n
            • \u8bfb\u6bb5\u6838\u82f7\u9178\u5206\u5e03<\/li>\n<\/ul>\n\n\n\n

              \u4e0e\u5e8f\u5217\u7ec4\u6210\u76f8\u5173\u7684\u6f5c\u5728\u504f\u5dee\u6e90\u3002`plotNtFrequency`\u51fd\u6570\u7ed8\u5236\u7ed9\u5b9a\u901a\u9053\u4e2d\u6240\u6709\u8bfb\u6bb5\u7684\u6bcf\u4e2a\u78b1\u57fa\u7684\u6838\u82f7\u9178\u9891\u7387\u3002<\/p>\n\n\n\n

              plotNtFrequency(bfs[[1]])<\/p>\n\n\n\n

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

              \u4e0a\u8ff0\u5185\u5bb9\u5305\u62ec\u4e86\u5982\u4f55\u8fdb\u884c\u8bfb\u6bb5\u7ea7\u522b\u7684\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528R\u4ee3\u7801\u6765\u68c0\u67e5\u8bfb\u6bb5\u7684\u8d28\u91cf\u3002<\/p>\n\n\n\n

              \u4e09\u3001<\/strong> \u57fa\u56e0\u7ea7\u522b\u7684EDA<\/strong><\/strong><\/p>\n\n\n\n

                \n
              1.  \u57fa\u56e0\u7ea7\u522b\u8ba1\u6570\u7684\u7c7b\u522b\u548c\u65b9\u6cd5<\/strong><\/strong><\/li>\n<\/ol>\n\n\n\n

                \u57fa\u56e0\u7ea7\u522b\u7684EDA\u540c\u6837\u91cd\u8981\uff0c\u56e0\u4e3a\u6709\u65f6\u4e00\u4e9b\u504f\u5dee\u5728\u8f83\u5c0f\u7684\u5c3a\u5ea6\u4e0a\u96be\u4ee5\u68c0\u6d4b\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528`EDASeq`\u5305\u6765\u5904\u7406\u57fa\u56e0\u7ea7\u522b\u7684\u8ba1\u6570\u6570\u636e\u3002<\/p>\n\n\n\n

                \u9996\u5148\uff0c\u6211\u4eec\u52a0\u8f7d\u57fa\u56e0\u7ea7\u522b\u7684\u8ba1\u6570\u6570\u636e\uff1a<\/p>\n\n\n\n

                data(geneLevelData)<\/p>\n\n\n\n

                head(geneLevelData)<\/p>\n\n\n\n

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

                \u7136\u540e\uff0c\u6211\u4eec\u52a0\u8f7d\u57fa\u56e0\u957f\u5ea6\u548cGC\u542b\u91cf\u4fe1\u606f\uff1a<\/p>\n\n\n\n

                data(yeastGC)<\/p>\n\n\n\n

                head(yeastGC)<\/p>\n\n\n\n

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

                data(yeastLength)<\/p>\n\n\n\n

                head(yeastLength)<\/p>\n\n\n\n

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

                \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8fc7\u6ee4\u6389\u975e\u8868\u8fbe\u7684\u57fa\u56e0\uff0c\u5373\u53ea\u8003\u8651\u90a3\u4e9b\u5728\u56db\u4e2a\u901a\u9053\u4e2d\u5e73\u5747\u8bfb\u6570\u8ba1\u6570\u8d85\u8fc710\u7684\u57fa\u56e0\uff0c\u5e76\u4e14\u6211\u4eec\u6709\u5b83\u4eec\u7684\u957f\u5ea6\u548cGC\u542b\u91cf\u4fe1\u606f\u3002\u8fd9\u7ed9\u6211\u4eec\u7559\u4e0b\u4e864994\u4e2a\u57fa\u56e0\u3002<\/p>\n\n\n\n

                filter <- apply(geneLevelData,1,function(x) mean(x)>10)<\/p>\n\n\n\n

                table(filter)<\/p>\n\n\n\n

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

                common <- intersect(names(yeastGC),rownames(geneLevelData[filter,]))<\/p>\n\n\n\n

                length(common)<\/p>\n\n\n\n

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

                EDASeq\u5305\u63d0\u4f9b\u4e86SeqExpressionSet\u7c7b\u6765\u5b58\u50a8\u57fa\u56e0\u8ba1\u6570\u3001\u901a\u9053\u7ea7\u522b\u7684\u4fe1\u606f\u4ee5\u53ca\u57fa\u56e0\u7ea7\u522b\u7684\u7279\u5f81\u4fe1\u606f\u3002\u6211\u4eec\u4f7f\u7528\u4e4b\u524d\u521b\u5efa\u7684\u6570\u636e\u6846met\u4f5c\u4e3a\u901a\u9053\u7ea7\u522b\u7684\u6570\u636e\u3002\u81f3\u4e8e\u7279\u5f81\u6570\u636e\uff0c\u6211\u4eec\u4f7f\u7528\u57fa\u56e0\u957f\u5ea6\u548cGC\u542b\u91cf\u3002<\/p>\n\n\n\n

                feature <- data.frame(gc=yeastGC,length=yeastLength)<\/p>\n\n\n\n

                data <- newSeqExpressionSet(counts=as.matrix(geneLevelData[common,]),<\/p>\n\n\n\n

                                            featureData=feature[common,],<\/p>\n\n\n\n

                                            phenoData=data.frame(<\/p>\n\n\n\n

                                              conditions=factor(c(rep(“mut”,2),rep(“wt”,2))),<\/p>\n\n\n\n

                                              row.names=colnames(geneLevelData)))<\/p>\n\n\n\n

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

                \u8bf7\u6ce8\u610f\uff0c\u8ba1\u6570\u548c\u7279\u5f81\u6570\u636e\u7684\u884c\u540d\u5fc5\u987b\u76f8\u540c\uff1b\u540c\u6837\uff0cphenoData\u7684\u884c\u540d\u548c\u8ba1\u6570\u7684\u5217\u540d\u4e5f\u5fc5\u987b\u76f8\u540c\u3002\u8868\u8fbe\u503c\u53ef\u4ee5\u901a\u8fc7counts\u8bbf\u95ee\uff0c\u901a\u9053\u4fe1\u606f\u53ef\u4ee5\u901a\u8fc7pData\u8bbf\u95ee\uff0c\u7279\u5f81\u4fe1\u606f\u53ef\u4ee5\u901a\u8fc7fData\u8bbf\u95ee\u3002<\/p>\n\n\n\n

                head(counts(data))<\/p>\n\n\n\n

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

                pData(data)<\/p>\n\n\n\n

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

                head(fData(data))<\/p>\n\n\n\n

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

                SeqExpressionSet\u7c7b\u6709\u4e24\u4e2a\u989d\u5916\u7684\u69fd\uff1anormalizedCounts\u548coffset\uff08\u4e0e\u8ba1\u6570\u77e9\u9635\u76f8\u540c\u7ef4\u5ea6\uff09\uff0c\u8fd9\u4e9b\u53ef\u4ee5\u7528\u6765\u5b58\u50a8\u540e\u7eed\u5206\u6790\u4e2d\u4f7f\u7528\u7684\u6807\u51c6\u5316\u8ba1\u6570\u77e9\u9635\u548c\u6807\u51c6\u5316\u504f\u79fb\u91cf\u3002\u5982\u679c\u6ca1\u6709\u6307\u5b9a\uff0c\u504f\u79fb\u91cf\u5c06\u521d\u59cb\u5316\u4e3a\u96f6\u77e9\u9635\u3002<\/p>\n\n\n\n

                head(offset(data))<\/p>\n\n\n\n

                \"\"<\/figure>\n\n\n\n
                  \n
                • \u57fa\u56e0\u7ea7\u522b\u8ba1\u6570\u7684\u901a\u9053\u95f4\u5206\u5e03<\/strong><\/strong><\/li>\n<\/ul>\n\n\n\n

                  \u5904\u7406\u57fa\u56e0\u7ea7\u522b\u8ba1\u6570\u6570\u636e\u65f6\uff0c\u4e00\u4e2a\u4e3b\u8981\u8003\u8651\u56e0\u7d20\u662f\u901a\u9053\u95f4\u8ba1\u6570\u5206\u5e03\u7684\u5dee\u5f02\u3002`boxplot`\u65b9\u6cd5\u63d0\u4f9b\u4e86\u4e00\u79cd\u7b80\u5355\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u751f\u6210\u6bcf\u4e2a\u901a\u9053\u4e2d\u57fa\u56e0\u8ba1\u6570\u5bf9\u6570\u7684\u7bb1\u7ebf\u56fe\u3002<\/p>\n\n\n\n

                  boxplot(data, col = colors[1:4])<\/p>\n\n\n\n

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

                  MDPlot\u65b9\u6cd5\u53ef\u4ee5\u751f\u6210\u4e24\u4e2a\u901a\u9053\u7684\u8ba1\u6570\u5e73\u5747\u5dee\u5f02\u56fe\uff08MD-plot\uff09\u3002\u4e2d\u5fc3\u7ebf\u5468\u56f4\u7684\u70b9\u8868\u793a\u57fa\u56e0\u8868\u8fbe\u76f8\u4f3c\u3002\u8fdc\u79bb\u4e2d\u5fc3\u7ebf\u7684\u70b9\u53ef\u80fd\u8868\u793a\u91cd\u8981\u7684\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u3002<\/p>\n\n\n\n

                  MDPlot(data,c(1,3))<\/p>\n\n\n\n

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