{"id":24082,"date":"2024-01-10T17:17:53","date_gmt":"2024-01-10T09:17:53","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24082"},"modified":"2024-01-10T17:17:55","modified_gmt":"2024-01-10T09:17:55","slug":"geo%e8%8a%af%e7%89%87%e6%b3%a8%e9%87%8a%e7%bc%ba%e5%b0%91%e5%9f%ba%e5%9b%a0%e6%b3%a8%e9%87%8a%e4%bf%a1%e6%81%af%e5%92%8b%e5%8a%9e%ef%bc%9f%e4%b8%8d%e6%85%8c%ef%bc%81%e5%b0%8f%e6%9e%9c%e6%95%99","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=24082","title":{"rendered":"GEO\u82af\u7247\u6ce8\u91ca\u7f3a\u5c11\u57fa\u56e0\u6ce8\u91ca\u4fe1\u606f\u548b\u529e\uff1f\u4e0d\u614c\uff01\u5c0f\u679c\u6559\u4f60\u91cd\u6ce8\u91ca"},"content":{"rendered":"

\u4eca\u5929\u5c0f\u679c\u5728\u4e0b\u8f7d\u4e00\u4e2aGEO\u6570\u636e\u96c6\u7684\u65f6\u5019\u53d1\u73b0\u8be5\u82af\u7247\u5e73\u53f0\u6ce8\u91ca\u4fe1\u606f\u4e0d\u5168\uff0c\u6ca1\u6709\u76f8\u5173\u7684\u57fa\u56e0\u6ce8\u91ca\u4fe1\u606f\u53ea\u6709\u63a2\u9488\u4fe1\u606f\uff0c\u6ca1\u6709\u57fa\u56e0\u6ce8\u91ca\u4fe1\u606f\u5c31\u65e0\u6cd5\u63d0\u53d6\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\uff0c\u5c0f\u679c\u5c06\u901a\u8fc7\u91cd\u65b0\u6ce8\u91ca\u7684\u65b9\u6cd5\u6765\u83b7\u5f97\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\uff0c\u6709\u9700\u8981\u7684\u5c0f\u4f19\u4f34\uff0c\u53ef\u4ee5\u8ddf\u7740\u5c0f\u679c\u4e00\u8d77\u5f00\u59cb\u4eca\u5929\u7684\u5b66\u4e60\u3002<\/p>\n

    \n
  1. \u5982\u4f55\u8fdb\u884c\u82af\u7247\u6570\u636e\u91cd\u6ce8\u91ca\uff1f<\/li>\n<\/ol>\n

    \u5c0f\u679c\u5728\u8fdb\u884cGSE92681\u6570\u636e\u96c6\u4e0b\u8f7d\u7684\u65f6\u5019\u53d1\u73b0\u8be5\u6570\u636e\u96c6\u6ca1\u6709\u57fa\u56e0\u6ce8\u91ca\u4fe1\u606f\uff0c\u53ea\u5305\u542b\u63a2\u9488ID\uff0cSEQUENCE\u548ctranscript\u7c7b\u578b\uff0c\u5982\u4e0b\u56fe\uff1a<\/p>\n

    \"Dingtalk_20230615171402\"<\/p>\n

    \u9047\u5230\u8fd9\u79cd\u60c5\u51b5\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u662f\u4e0d\u662f\u6ca1\u6709\u529e\u6cd5\u5566\uff01\u4e0d\u7528\u7740\u6025\uff0c\u5c0f\u679c\u6765\u4e3a\u5c0f\u4f19\u4f34\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u4e00\u822c\u901a\u8fc7\u82af\u7247\u91cd\u65b0\u6ce8\u91ca\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u4e00\u822c\u901a\u8fc7\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u6765\u8fdb\u884c\u91cd\u65b0\u6ce8\u91ca\uff0c\u7b2c\u4e00\u6b65\uff0c\u9700\u8981\u51c6\u5907\u82af\u7247\u4e0a\u7684\u63a2\u9488\u5e8f\u5217\u548c\u8f6c\u5f55\u672cDNA\u5e8f\u5217\uff0c\u63a2\u9488\u5e8f\u5217\u5df2\u7ecf\u5b58\u5728\uff0c\u9700\u8981\u4e0b\u8f7d\u8f6c\u5f55\u672cDNA\u5e8f\u5217\uff0c\u4e0b\u8f7d\u4e8eGENCODE(https:\/\/www.gencodegenes.org\/human\/)\uff0c<\/p>\n

    \"Dingtalk_20230616082310\"<\/p>\n

    \u5c0f\u679c\u4e0b\u8f7d\u6700\u65b0\u7248\u672c\u7684\u8f6c\u5f55\u672cDNA\u5e8f\u5217(gencode.v43.transcripts.fa),\u8f6c\u5f55\u672cid\u5305\u62ec\u4e86Gene symbol\u548cbiotype\u3002<\/p>\n

    \"Dingtalk_20230616083550\"<\/p>\n

    \u7b2c\u4e8c\u6b65\uff0c\u51c6\u5907\u597d\u4e86\u63a2\u9488\u5e8f\u5217\u548c\u8f6c\u5f55\u672cDNA\u5e8f\u5217\u540e\uff0c\u53ef\u4ee5\u9009\u62e9blast\u6216\u8005seqman\u8f6f\u4ef6\u8fdb\u884c\u5e8f\u5217\u6bd4\u5bf9\uff0c\u672c\u63a8\u6587\u5c0f\u679c\u5229\u7528seqman\u8f6f\u4ef6\u8fdb\u884c\u6bd4\u5bf9\uff0cseqman\u76f8\u6bd4blast\u8f6f\u4ef6\uff0c\u901f\u5ea6\u8981\u5f88\u5feb\uff0c\u4f7f\u7528\u53c2\u6570\u6bd4\u8f83\u7b80\u5355\uff0c\u6700\u540e\u6839\u636e\u5e8f\u5217\u6bd4\u5bf9\u7ed3\u679c\u63d0\u53d6\u63a2\u9488id,Gene symbol\u548cbiotype\u5bf9\u5e94\u5173\u7cfb\u6587\u4ef6\u3002<\/p>\n

    #\u5b89\u88c5seqman,\u7f51\u5740\uff1ahttp:\/\/www-personal.umich.edu\/~jianghui\/seqmap\/<\/p>\n

    \"Dingtalk_20230616103048\"<\/p>\n

    #\u4e0b\u8f7dseqman\u5b89\u88c5\u5305<\/p>\n

    wget https:\/\/jhui2014.github.io\/seqmap\/download\/seqmap-1.0.13-src.zip<\/p>\n

    #\u89e3\u538bseqmap\u538b\u7f29\u5305<\/p>\n

    unzip seqmap-1.0.13-src.zip<\/p>\n

    #\u8fdb\u884c\u7f16\u8bd1<\/p>\n

    cd seqmap-1.0.13-src<\/p>\n

    make<\/p>\n

    #\u6dfb\u52a0\u73af\u5883\u53d8\u91cf<\/p>\n

    export PATH=\/media\/desk16\/wangd\/seqmap-1.0.13-src:$PATH<\/p>\n

    source .bashrc<\/p>\n

    \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\u5c31\u53ef\u4ee5\u6210\u529f\u7684\u5b89\u88c5seqman\u8f6f\u4ef6\u5566\uff01<\/p>\n

    \u7b2c\u4e09\u6b65\uff0c\u5408\u5e76\u8868\u8fbe\u77e9\u9635\u548c\u63a2\u9488id,Gene symbol\u548cbiotype\u6587\u4ef6\uff0c\u83b7\u5f97\u6700\u7ec8\u7684\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u6587\u4ef6\u3002<\/p>\n

    \u8fd9\u5c31\u662f\u5c0f\u679c\u4e3a\u5927\u5bb6\u5c55\u793a\u7684\u82af\u7247\u91cd\u6ce8\u91ca\u6b65\u9aa4\uff0c\u901a\u8fc7\u4e09\u6b65\u5c31\u53ef\u4ee5\u8be5\u5206\u6790\uff0c\u63a5\u4e0b\u6765\u5c0f\u679c\u5c06\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u3002<\/p>\n

      \n
    1. \u51c6\u5907\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n

      #\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/p>\n

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

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

      #\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/p>\n

      library(GEOquery)<\/p>\n

      library(tidyverse)<\/p>\n

      3.GSE92681\u6570\u636e\u96c6\u8868\u8fbe\u77e9\u9635\u548c\u6ce8\u91ca\u4fe1\u606f\u4e0b\u8f7d<\/p>\n

      #GSE92681\u6570\u636e\u96c6\u4e0b\u8f7d<\/p>\n

      GSEdata <- getGEO(“GSE92681”, GSEMatrix=T, AnnotGPL=FALSE)<\/p>\n

      eset <- GSEdata[[1]]<\/p>\n

      #\u63d0\u53d6\u8868\u8fbe\u77e9\u9635,\u901a\u8fc7exprs\u51fd\u6570\u63d0\u53d6\uff0c\u884c\u540d\u4e3a\u63a2\u9488ID\uff0c\u5217\u540d\u4e3a\u6837\u672c\u540d<\/p>\n

      exprSet <- as.data.frame(exprs(eset))<\/p>\n

      \"Dingtalk_20230615162209\"<\/p>\n

      # \u63d0\u53d6GSM ID\u4f5c\u4e3a\u6837\u54c1\u540d\uff0cpData\u51fd\u6570\u6765\u83b7\u53d6\u6837\u672c\u4fe1\u606f<\/p>\n

      GSMID <- pData(eset)[,c(1,2,8,10,12)]<\/p>\n

      #\u5229\u7528fData\u51fd\u6570\u63d0\u53d6\u82af\u7247\u6ce8\u91ca\u4fe1\u606f\uff0c\u53d1\u73b0\u6ca1\u6709\u57fa\u56e0\u6ce8\u91ca\u4fe1\u606f<\/p>\n

      annotation<-fData(eset)<\/p>\n

      \"Dingtalk_20230615161839\"<\/p>\n

      #\u53ea\u4fdd\u7559\u63a2\u9488ID\u548c\u5e8f\u5217<\/p>\n

      gpl <- annotation[,c(1,2,4)]<\/p>\n

      gp <- dplyr::filter(gpl,!is.na(SEQUENCE))<\/p>\n

      #\u83b7\u5f97\u63a2\u9488\u5bf9\u5e94\u7684\u5e8f\u5217<\/p>\n

      gpl <- paste0(‘>’,gpl$ID,’\\n’, gpl$SEQUENCE)<\/p>\n

      #\u4fdd\u5b58\u5168\u90e8\u5e8f\u5217\uff0c\u8fd0\u884c\u4e0b\u9762\u8fd9\u884c\uff1a<\/p>\n

      write.table(gpl,’GPL.fasta’, quote = F, row.names = F, col.names = F)<\/p>\n

      4.GSE92681\u6570\u636e\u96c6\u91cd\u6ce8\u91ca<\/p>\n

      #\u5229\u7528seqmap\u8f6f\u4ef6\u5c06\u8f6c\u5f55\u672c\u7684DNA\u5e8f\u5217\u6587\u4ef6\u548c\u82af\u7247\u4e0a\u63a2\u9488\u7684\u5e8f\u5217\u8fdb\u884c\u5e8f\u5217\u6bd4\u5bf9<\/p>\n

      seqmap\u8f6f\u4ef6\u53c2\u6570\u8bf4\u660e\uff1a<\/p>\n

      0\u4ee3\u8868\u5339\u914d\u63a2\u9488\u9519\u8bef\u4e2a\u65700<\/p>\n

      GPL.fasta \u9700\u8981\u6bd4\u5bf9\u7684\u63a2\u9488\u5e8f\u5217<\/p>\n

      gencode.v43.transcripts.fa \u8f6c\u5f55\u672c\u7684fasta\u6587\u4ef6<\/p>\n

      seqmap_gene.txt \u8f93\u51fa\u6587\u4ef6\uff0c\u6bd4\u5bf9\u7ed3\u679c\u6587\u4ef6<\/p>\n

      system(‘seqmap 0 GPL.fasta gencode.v43.transcripts.fa seqmap_gene.tmp \/output_all_matches’)<\/p>\n

      # seqmap_gene.tmp\uff0c\u751f\u6210\u7684\u6bd4\u5bf9\u7ed3\u679c\u6587\u4ef6\uff0c\u9700\u8981\u5173\u6ce8\u7b2c\u4e00\u5217\u8f6c\u5f55\u672c\u5e8f\u5217id\u548c\u7b2c\u56db\u5217\u63a2\u9488id<\/p>\n

      \"Dingtalk_20230615163055\"<\/p>\n

      gplTOgene <- read_delim(‘seqmap_gene.tmp’,delim = ‘\\t’)<\/p>\n

      #\u63d0\u53d6\u7b2c1\u5217\u548c\u7b2c4\u5217\u6570\u636e<\/p>\n

      gplTOgene <- gplTOgene[,c(1,4)]<\/p>\n

      #\u5c06\u7b2c\u4e00\u5217\u6570\u636e\u5206\u5272\u83b7\u5f97gene gsym<\/p>\n

      gplTOgene$gsym<-unlist(lapply(gplTOgene$trans_id,function(x) strsplit(as.character(x),”\\\\|”)[[1]][6]))<\/p>\n

      #\u5c06\u7b2c\u4e00\u5217\u6570\u636e\u5206\u5272\u83b7\u5f97biotype<\/p>\n

      gplTOgene$biotype<-unlist(lapply(gplTOgene$trans_id,function(x) strsplit(as.character(x),”\\\\|”)[[1]][8]))<\/p>\n

      gplTOgene$gsym_biotype <- paste(gplTOgene$gsym,gplTOgene$biotype,sep = ‘|’)<\/p>\n

      #\u8f93\u51fa\u4e09\u5217\uff0c\u63a2\u9488ID\u3001gene symbol\u3001biotype<\/p>\n

      gplTOgene <- gplTOgene[,c(2:4)]<\/p>\n

      #\u53bb\u9664\u91cd\u590d<\/p>\n

      gplTOgene <- distinct(gplTOgene)<\/p>\n

      write_csv(gplTOgene,’gplTOgene.csv’)<\/p>\n

      #\u63d0\u53d6mRNA\u82af\u7247\u6ce8\u91ca\u4fe1\u606f\uff0c\u4e5f\u53ef\u4ee5\u9009\u62e9\u63d0\u53d6LcRNA\u6ce8\u91ca\u4fe1\u606f<\/p>\n

      mRNA.probe1 <- filter(gplTOgene,biotype ==”protein_coding”)<\/p>\n

      #\u83b7\u5f97\u552f\u4e00\u7684\u63a2\u9488id<\/p>\n

      uni <- names(table(mRNA.probe1$probe_id)[table(mRNA.probe1$probe_id) == 1])<\/p>\n

      mRNA.probe2 <- mRNA.probe1[match(uni,mRNA.probe1$probe_id),]<\/p>\n

      #\u83b7\u5f97\u4e00\u4e2agene\u5bf9\u5e94\u4e24\u4e2a\u63a2\u9488\u7684Gene symbol<\/p>\n

      nProbes <- names(table(mRNA.probe2$gsym)[table(mRNA.probe2$gsym) >= 2])<\/p>\n

      mRNA.probe3 <- mRNA.probe2[mRNA.probe2$gsym %in% nProbes,]<\/p>\n

      mRNA.probe4 <- mRNA.probe3[order(mRNA.probe3$gsym),]<\/p>\n

      #\u4fdd\u5b58\u82af\u7247id,Gene symbol\u548cbiotype\u4e09\u8005\u5173\u7cfb\u6587\u4ef6<\/p>\n

      write.table(mRNA.probe4,”mRNA.probes.txt”, quote = F,row.names = F, col.names = T)<\/p>\n

      mRNA.probe4 <- read.table(“mRNA.probes.txt”, header = T)<\/p>\n

      #\u5c06\u884c\u540d\u8f6c\u5316\u4e3a\u5217\u540dprobe_id<\/p>\n

      exprSet<-rownames_to_column(exprSet,var=”probe_id”)<\/p>\n

      #\u5408\u5e76\u6ce8\u91ca\u4fe1\u606f\u548c\u8868\u8fbe\u77e9\u9635\u6587\u4ef6<\/p>\n

      mRNA.probe.expr <- merge(mRNA.probe4[,1:2],exprSet , by = “probe_id”)<\/p>\n

      #\u5c06\u5217\u540d\u8f6c\u5316\u4e3a\u884c\u540d<\/p>\n

      row.names(mRNA.probe.expr) <- mRNA.probe.expr$probe_id<\/p>\n

      #\u53bb\u9664probe_id\u5217<\/p>\n

      mRNA.probe.expr$probe_id <- NULL<\/p>\n

      # \u8ba1\u7b97\u591a\u4e2a\u63a2\u9488\u5bf9\u5e94\u540c\u4e00\u4e2a\u57fa\u56e0\u8868\u8fbe\u91cf\u7684\u4e2d\u4f4d\u6570\u503c<\/p>\n

      mRNA.expr_uniq <- aggregate(.~gsym,mRNA.probe.expr, median)<\/p>\n

      # \u5c06 gene symbol \u5217\u8f6c\u5316\u4e3a\u884c\u540d<\/p>\n

      rownames(mRNA.expr_uniq) <- mRNA.expr_uniq$gsym<\/p>\n

      #\u53bb\u9664gene symbol \u5217<\/p>\n

      mRNA.expr_uniq$gsym <- NULL<\/p>\n

      #\u83b7\u5f97\u91cd\u65b0\u6ce8\u91ca\u7684mRNA(protein_coding)\u8868\u8fbe\u77e9\u9635\u6587\u4ef6<\/p>\n

      GSE92681<-mRNA.expr_uniq<\/p>\n

      #\u4fdd\u5b58\u7ed3\u679c\u6587\u4ef6<\/p>\n

      write.table(GSE92681,file=”GSE92681_gene_expr.txt”,col.names=T,row.names=T,quote=F,sep=”\\t”)<\/p>\n

        \n
      1. \u7ed3\u679c\u6587\u4ef6<\/li>\n<\/ol>\n

        GSE92681_gene_expr.txt<\/p>\n

        \u8be5\u7ed3\u679c\u6587\u4ef6\u8fdb\u884c\u91cd\u6ce8\u91ca\u540e\u83b7\u5f97\u7684\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\uff0c\u884c\u540d\u4e3aGene symbol,\u5217\u540d\u4e3a\u6837\u672c\u540d\u3002<\/p>\n

        \"Dingtalk_20230616092314\"<\/p>\n

        \u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u5566\uff01\u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u5e73\u53f0\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u975e\u5e38\u65b9\u4fbf\u5965\uff0c\u4e91\u5e73\u53f0\u7f51\u5740\u4e3a\uff1ahttp:\/\/www.biocloudservice.com\/home.html,\u5305\u62ec\u4e86GEO\u6570\u636e\u4e0b\u8f7d(http:\/\/www.biocloudservice.com\/371\/371.php)\u7b49\u5c0f\u5de5\u5177\uff0c\u6b22\u8fce\u5927\u5bb6\u548c\u5c0f\u679c\u4e00\u8d77\u8ba8\u8bba\u5b66\u4e60\u54c8\uff01\uff01\uff01\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

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