{"id":25058,"date":"2024-01-18T16:37:33","date_gmt":"2024-01-18T08:37:33","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25058"},"modified":"2024-01-18T16:37:34","modified_gmt":"2024-01-18T08:37:34","slug":"%e5%b7%ae%e5%bc%82%e5%88%86%e6%9e%90%e9%ad%94%e6%b3%95%e5%b8%88%ef%bc%9adeseq2%e4%b8%8e%e5%9f%ba%e5%9b%a0%e7%9a%84%e9%ad%94%e5%b9%bb%e5%86%92%e9%99%a9%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25058","title":{"rendered":"\u5dee\u5f02\u5206\u6790\u9b54\u6cd5\u5e08\uff1aDESeq2\u4e0e\u57fa\u56e0\u7684\u9b54\u5e7b\u5192\u9669\uff01"},"content":{"rendered":"

hello\uff0c\u4eca\u5929\u5c0f\u679c\u6559\u5927\u5bb6\u5982\u4f55\u4f7f\u7528DESeq2\u5305\u5bf9counts\u8868\u8fbe\u77e9\u9635\u8fdb\u884c\u5dee\u5f02\u57fa\u56e0\u5206\u6790\uff0c\u5e76\u5bf9\u5206\u6790\u7ed3\u679c\u7ed8\u5236\u706b\u5c71\u56fe\u5b9e\u73b0\u5dee\u5f02\u57fa\u56e0\u5206\u6790\u7684\u53ef\u89c6\u5316\uff0c\u5982\u679c\u4f60\u611f\u5174\u8da3\u7684\u8bdd\u5c31\u548c\u5c0f\u679c\u4e00\u8d77\u770b\u4e0b\u53bb\u5427\uff01<\/p>\n

\u9996\u5148\u5c0f\u679c\u5148\u6765\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u4e2a\u5305\uff1a<\/p>\n

\u4e86\u89e3DESeq2<\/strong><\/p>\n

DESeq2\u662f\u4e00\u4e2aR\u8bed\u8a00\u4e2d\u5e38\u7528\u7684\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u5305\uff0c\u7528\u4e8e\u5206\u6790\u9ad8\u901a\u91cf\u6d4b\u5e8f\u6570\u636e\u4e2d\u7684\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u3002\u5b83\u662fBioconductor\u9879\u76ee\u7684\u4e00\u90e8\u5206\uff0c\u63d0\u4f9b\u4e86\u4e00\u5957\u7edf\u8ba1\u65b9\u6cd5\u548c\u529f\u80fd\uff0c\u7528\u4e8e\u68c0\u6d4b\u57fa\u56e0\u5728\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u8868\u8fbe\u5dee\u5f02\uff0c\u5e76\u627e\u51fa\u5177\u6709\u751f\u7269\u5b66\u610f\u4e49\u7684\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u3002DESeq2\u5305\u7684\u8bbe\u8ba1\u76ee\u6807\u662f\u9488\u5bf9RNA-seq\u6570\u636e\u8fdb\u884c\u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff0c\u5e76\u4e14\u80fd\u591f\u5904\u7406\u5177\u6709\u4f4e\u590d\u5236\u6570\u3001\u9ad8\u53d8\u5f02\u6027\u548c\u6709\u504f\u5dee\u7684\u6570\u636e\u3002\u5b83\u91c7\u7528\u4e86\u8d1f\u4e8c\u9879\u5206\u5e03\u6a21\u578b\u548c\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff08GLM\uff09\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u8003\u8651\u6837\u672c\u4e4b\u95f4\u7684\u6280\u672f\u548c\u751f\u7269\u5b66\u53d8\u5f02\uff0c\u8fdb\u884c\u51c6\u786e\u7684\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u3002<\/p>\n

DESeq2\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u529f\u80fd\uff0c\u5305\u62ec\uff1a<\/p>\n

    \n
  1. \u6570\u636e\u5bfc\u5165\uff1a<\/strong>\u652f\u6301\u4ece\u4e0d\u540c\u7684\u6570\u636e\u6e90\u5bfc\u5165RNA-seq\u6570\u636e\uff0c\u5305\u62ec\u539f\u59cb\u8ba1\u6570\u6570\u636e\u548c\u5df2\u7ecf\u8fdb\u884c\u4e86\u57fa\u56e0\u6ce8\u91ca\u7684\u6570\u636e\u3002<\/li>\n
  2. \u6570\u636e\u9884\u5904\u7406\uff1a<\/strong>\u5305\u62ec\u6570\u636e\u89c4\u8303\u5316\u3001\u53bb\u9664\u4f4e\u8868\u8fbe\u57fa\u56e0\u3001\u53bb\u9664\u6279\u6b21\u6548\u5e94\u7b49\u3002<\/li>\n
  3. \u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff1a<\/strong>\u4f7f\u7528\u8d1f\u4e8c\u9879\u5206\u5e03\u6a21\u578b\u548cGLM\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u8ba1\u7b97\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u663e\u8457\u6027\u3002<\/li>\n
  4. \u591a\u7ec4\u5b9e\u9a8c\u8bbe\u8ba1\uff1a<\/strong>\u652f\u6301\u591a\u7ec4\u6761\u4ef6\u4e0b\u7684\u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff0c\u5305\u62ec\u4e24\u7ec4\u6bd4\u8f83\u3001\u591a\u7ec4\u6bd4\u8f83\u548c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002<\/li>\n
  5. \u57fa\u56e0\u6ce8\u91ca\u548c\u529f\u80fd\u5bcc\u96c6\u5206\u6790\uff1a<\/strong>\u652f\u6301\u5c06\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u8fdb\u884c\u6ce8\u91ca\uff0c\u5e76\u8fdb\u884c\u529f\u80fd\u5bcc\u96c6\u5206\u6790\uff0c\u4ee5\u4e86\u89e3\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u751f\u7269\u5b66\u529f\u80fd\u3002<\/li>\n
  6. \u53ef\u89c6\u5316\uff1a<\/strong>DESeq2\u5305\u96c6\u6210\u4e86ggplot2\u548c\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u56fe\u8868\uff0c\u5982\u706b\u5c71\u56fe\u3001MA\u56fe\u7b49\u3002<\/li>\n<\/ol>\n

    \u5dee\u5f02\u57fa\u56e0\u5206\u6790\u6d41\u7a0b<\/strong><\/p>\n

    R\u5305\u4e0e\u6570\u636e\u52a0\u8f7d<\/strong><\/p>\n

    \u9996\u5148\uff0c\u8bbe\u7f6e\u5de5\u4f5c\u76ee\u5f55\u4e3a\u6570\u636e\u6587\u4ef6\u6240\u5728\u7684\u8def\u5f84\uff0c\u5e76\u52a0\u8f7dDESeq2\u548c\u5176\u4ed6\u6240\u9700\u7684R\u5305\uff0c\u5e76\u5bfc\u5165\u6211\u4eec\u51c6\u5907\u597d\u7684counts\u8868\u8fbe\u77e9\u9635\uff1a<\/p>\n

    setwd(“\/media\/desk16\/iyun007\/kegg”)
    \n#BiocManager::install(“DESeq2”)
    \nlibrary(DESeq2)
    \ndata1 <- read.csv(“Gene Symbol_matrix.csv”,header = T, row.names = 1)
    \ndim(data1) #59427 \u00a0 5 \u00a0 \uff08COUNT\u77e9\u9635\u670959427 \u4e2a\u57fa\u56e0\u30015\u4e2a\u6837\u672c\uff09<\/p>\n

    \u6ce8\u610f\uff0c\u6211\u4eec\u5bfc\u5165\u7684\u6570\u636e\u57fa\u672c\u683c\u5f0f\u5982\u4e0b\uff0c\u540c\u5b66\u4eec\u8981\u63d0\u4f9b\u7b26\u5408\u8981\u6c42\u7684\u6570\u636e\u96c6\u54e6\uff01<\/p>\n

    <\/p>\n

    \u6570\u636e\u9884\u5904\u7406<\/strong><\/p>\n

    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8981\u6784\u5efa\u5206\u7ec4\u77e9\u9635\uff0c\u5373\u6839\u636e\u6570\u636e\u6846\u4e2d\u6837\u672c\u7684\u547d\u540d\u89c4\u5219\uff0c\u63d0\u53d6\u51fa\u764c\u75c7\u6837\u672c\u548c\u6b63\u5e38\u6837\u672c\u7684\u5217\u540d\uff0c\u5e76\u5c06\u5b83\u4eec\u5206\u522b\u5b58\u50a8\u5728tumor\u548cnormal\u5411\u91cf\u4e2d\uff1a<\/p>\n

    #\u6784\u5efa\u5206\u7ec4\u77e9\u9635
    \ntumor <- colnames(data1)[as.integer(substr(colnames(data1),14,15)) < 10] #data1\u8868\u8fbe\u77e9\u9635\u4e2d\u7684\u6240\u6709\u764c\u75c7\u6837\u672c\uff088\u4e2a\uff09
    \ntumor
    \nnormal <- colnames(data1)[as.integer(substr(colnames(data1),14,15)) >= 10] #data1\u8868\u8fbe\u77e9\u9635\u4e2d\u7684\u6240\u6709\u6b63\u5e38\u6837\u672c\uff082\u4e2a\uff09
    \nnormal
    \n\u200b
    \ntumor_sample <- data1[,tumor] #\u764c\u75c7\u6837\u672c\u7684\u8868\u8fbe\u77e9\u9635
    \nView(tumor_sample)
    \ntumor_sample
    \nncol(tumor_sample)
    \n\u200b
    \nnormal_sample <- data1[,normal] \u00a0 #\u6b63\u5e38\u6837\u672c\u7684\u8868\u8fbe\u77e9\u9635
    \nView(normal_sample)
    \nncol(normal_sample)
    \ndata1 <- cbind(tumor_sample,normal_sample) #\u5408\u5e76\u764c\u75c7\u548c\u6b63\u5e38\u6837\u672c\u77e9\u9635
    \ncol_index <- which(colnames(matrix) == “normal_sample”)
    \ncolnames(matrix)[col_index] <- normal
    \n#colnames(data1)[5] <- “TCGA.AA.3525.11A.01R.A32Z.07”
    \nView(data1)
    \ngroup_list <- c(rep(‘tumor’,ncol(tumor_sample)),rep(‘normal’,1)) #\u6839\u636e\u81ea\u5df1\u7684\u6570\u636e\u96c6\u8bbe\u7f6etumor\u548cnormal\u6570\u636e\u7684\u5206\u7ec4\u6570
    \n\u200b
    \ncondition = factor(group_list)
    \ncoldata <- data.frame(row.names = colnames(data1), condition) \u00a0 #coldata\u662f\u5206\u7ec4\u77e9\u9635
    \nView(coldata)<\/p>\n

    \u6700\u540e\u5408\u5e76\u597d\u7684\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n

    \"\u7535\u8111\u5c4f\u5e55\u7684\u7167\u7247\n\n\u4e2d\u5ea6\u53ef\u4fe1\u5ea6\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

    \u5206\u7ec4\u4fe1\u606f\u5982\u4e0b\uff1a<\/p>\n

    <\/p>\n

    \u5dee\u5f02\u5206\u6790<\/strong><\/p>\n

    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\uff1a<\/p>\n

      \n
    1. \u521b\u5efaDESeq2\u6570\u636e\u5bf9\u8c61\uff1a\u4f7f\u7528DESeqDataSetFromMatrix()\u51fd\u6570\u521b\u5efaDESeq2\u6240\u9700\u7684\u6570\u636e\u5bf9\u8c61\uff0c\u5c06\u5408\u5e76\u540e\u7684\u6570\u636e\u6846\u548c\u5206\u7ec4\u77e9\u9635\u4f5c\u4e3a\u53c2\u6570\u4f20\u9012\u7ed9\u8be5\u51fd\u6570\u3002<\/li>\n
    2. \u8fdb\u884c\u5dee\u5f02\u5206\u6790\uff1a\u4f7f\u7528DESeq()\u51fd\u6570\u5bf9DESeq2\u6570\u636e\u5bf9\u8c61\u8fdb\u884c\u5dee\u5f02\u5206\u6790\u3002<\/li>\n
    3. \u4fdd\u5b58\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7ed3\u679c\uff1a\u5c06\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7ed3\u679c\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\u3002<\/li>\n<\/ol>\n

      #\u6784\u5efadds\u5bf9\u8c61\uff0c\u6784\u5efa\u5dee\u5f02\u57fa\u56e0\u5206\u6790\u6240\u9700\u7684\u6570\u636e\u683c\u5f0f
      \ndds <- DESeqDataSetFromMatrix(countData = data1, colData = coldata, design = ~ condition)
      \n#\u5dee\u5f02\u5206\u6790
      \ndds <- DESeq(dds)
      \n#\u63d0\u53d6\u7ed3\u679c
      \nresult <- as.data.frame(results(dds)) \u00a0 \u00a0 \u00a0 # results()\u4eceDESeq\u5206\u6790\u4e2d\u63d0\u53d6\u51fa\u7ed3\u679c\u8868
      \n\u200b
      \nDGE <-subset(result,padj < 0.05 & (log2FoldChange > 2 | log2FoldChange < -2)) \u00a0 #\u8fd9\u91cc\u6211\u4eec\u63d0\u53d6\u51fa2\u500d\u5dee\u5f02\u8868\u8fbe\u3001\u6821\u6b63p\u503c<0.05\u7684\u663e\u8457\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0
      \nDGE <- DGE[order(DGE$log2FoldChange),]
      \n\u200b
      \nwrite.csv(DGE,’DGE.csv’,row.names = TRUE)<\/p>\n

      \u6211\u4eec\u4e00\u8d77\u6765\u770b\u4e0b\u5206\u6790\u540e\u7684\u7ed3\u679c\u628a\uff01<\/p>\n

      <\/p>\n

      \u7ed8\u5236\u706b\u5c71\u56fe<\/strong><\/p>\n

      \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u706b\u5c71\u56fe\u53ef\u89c6\u5316\u6211\u4eec\u7684\u5206\u6790\u7ed3\u679c\uff0c\u5177\u4f53\u64cd\u4f5c\u5982\u4e0b\uff1a<\/p>\n

      \u8fd9\u91cc\u5c0f\u679c\u4f7f\u7528ggplot()\u51fd\u6570\u521b\u5efa\u7ed8\u56fe\u5bf9\u8c61\uff0c\u5e76\u8bbe\u7f6e\u6570\u636e\u6e90\u4e3aresult\u6570\u636e\u6846\u3002\u7136\u540e\u4f7f\u7528geom_point()\u51fd\u6570\u7ed8\u5236\u6563\u70b9\u56fe\uff0c\u5176\u4e2dx\u8f74\u4e3alog2FoldChange\uff0cy\u8f74\u4e3a-log10(padj)\uff0c\u5e76\u6839\u636e\u5dee\u5f02\u8868\u8fbe\u7684\u65b9\u5411\u548c\u663e\u8457\u6027\u8fdb\u884c\u989c\u8272\u5206\u7c7b\u3002\u901a\u8fc7\u8c03\u6574\u7ed8\u56fe\u7684\u5404\u79cd\u53c2\u6570\uff0c\u5982\u6807\u9898\u3001\u5750\u6807\u8f74\u6807\u7b7e\u548c\u56fe\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u706b\u5c71\u56fe\u66f4\u5177\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\u54e6\u3002<\/p>\n

      ## 5.\u63d0\u53d6\u7ed3\u679c
      \nresult <- as.data.frame(results(dds)) # results()\u4eceDESeq\u5206\u6790\u4e2d\u63d0\u53d6\u51fa\u7ed3\u679c\u8868
      \n\u200b
      \nresult <- na.omit(result)
      \nresult1 <- result[c(2,6)]
      \n\u200b
      \n## 6.\u706b\u5c71\u56fe
      \nresult1$change = ifelse(result1$padj < 0.05 & abs(result1$log2FoldChange) >= 2, ifelse(result1$log2FoldChange> 2 ,’Up’,’Down’),’Stable’)
      \nlibrary(‘ggplot2′)
      \nggplot(result1, aes(x = log2FoldChange, y = -log10(padj),colour=change)) +
      \ngeom_point(alpha=0.3, size=3.5) +
      \nscale_color_manual(values=c(“#546de5″,”#d2dae2″,”#ff4757″))+
      \nlabs(title=’Volcano plot’,x=”log2(fold change)”,y=”-log10 (padj)”)+ # \u5750\u6807\u8f74# \u5750\u6807\u8f74\u548c\u56fe\u6807\u9898title=”Volcano plot”,
      \ntheme_bw()+ #\u53bb\u9664\u80cc\u666f\u8272
      \n#xlim(-20, 20)+ #\u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4
      \n#ylim(0,40)+
      \n# \u8f85\u52a9\u7ebf
      \ngeom_vline(xintercept=c(-2,2),lty=3,col=”black”,lwd=0.8) +
      \ngeom_hline(yintercept = -log10(0.05),lty=3,col=”black”,lwd=0.8) +
      \ntheme(panel.grid = element_blank())+ #\u53bb\u9664\u7f51\u683c\u7ebf
      \ntheme(plot.title = element_text(hjust = 0.5,size=24),
      \nlegend.position=”bottom”,
      \nlegend.title = element_blank(),
      \nlegend.text=element_text(size=18),
      \nlegend.key.size = unit(1, ‘cm’),
      \nlegend.background = element_rect(fill=”gray90″, linetype=”solid”,colour =”gray”),
      \naxis.title.x =element_text(size=18),
      \naxis.title.y=element_text(size=18),
      \naxis.text=element_text(size=14,face = “bold”)
      \n)
      \nggsave(“De_ana.png”,width=8,height=6)# \u4fdd\u5b58\u4e3apng\u683c\u5f0f<\/p>\n

      \u6211\u4eec\u4e00\u8d77\u6765\u770b\u4e0b\u6700\u540e\u7684\u7ed3\u679c\u56fe\u5427\uff01<\/p>\n

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

      \u600e\u4e48\u6837\uff0c\u4f60\u5b66\u4f1a\u600e\u4e48\u4f7f\u7528DESeq2\u5305\u6765\u8fdb\u884c\u5dee\u5f02\u5206\u6790\u4e86\u561b\uff1f\u662f\u4e0d\u662f\u5f88\u7b80\u5355\uff01\u66f4\u591a\u5b66\u4e60\u5e72\u6d3b\u8981\u7ee7\u7eed\u5173\u6ce8\u5c0f\u679c\u54e6\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

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