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[富集分析] 2、clusterprofile富集分析--上游分

[富集分析] 2、clusterprofile富集分析--上游分

作者: 小贝学生信 | 来源:发表于2021-08-15 21:26 被阅读0次

    1、富集分析的背景知识 - 简书 (jianshu.com)
    2、clusterprofile富集分析--上游分析 - 简书 (jianshu.com)
    3、clusterprofile富集分析--下游可视化 - 简书 (jianshu.com)
    4、clusterprofile富集分析--多组设计的富集及可视化 - 简书 (jianshu.com)

    附:[R]基因ID转换:两个R包 - 简书 (jianshu.com)

    clusterprofile包目前已经更新到版本4,主要提供基于ORA/GSEA算法的富集分析函数。关于富集的背景基因集,除了常见的GO、KEGG、WikiPathways富集分析,还可以是自定义的基因集。除此以外,系列包ReactomePADOSEmeshes可以方便得进行对Reactome、Disease ontology、Medical Subject Headings三类基因集进行ORA/GSEA富集分析

    clusterprofile 4

    0、示例差异基因数据

    • 差异基因列表:元素为差异倍数、name为ENTREZID基因ID、降序排序
    data(geneList, package="DOSE")
    str(geneList)
    # Named num [1:12495] 4.57 4.51 4.42 4.14 3.88 ...
    # - attr(*, "names")= chr [1:12495] "4312" "8318" "10874" "55143" ...
    
    • 显著差异基因:ENTREZID基因ID为元素的字符串
    gene <- names(geneList)[abs(geneList) > 2]
    str(gene)
    # chr [1:207] "4312" "8318" "10874" "55143" "55388" "991" "6280" "2305" "9493" "1062" "3868" ...
    

    1、指定基因集富集分析

    1.1 GO

    ORA:enrichGO {clusterProfiler}

    相关重要参数有
    ont : One of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
    keyType:差异基因的基因ID类型,默认为ENTREZID。因此如下示例没有单独设置;但是只有这里的GO富集分析的差异基因可以是其它类基因ID,下面其它的富集函数都只能是ENTREZID
    readable:返回的富集分析结果里,基因ID是否转换为SYMBOL格式

    library(clusterProfiler)
    library(org.Hs.eg.db)
    ego <- enrichGO(gene          = gene,
                    universe      = names(geneList),
                    OrgDb         = org.Hs.eg.db,
                    ont           = "CC",
                    pAdjustMethod = "BH",
                    pvalueCutoff  = 0.01,
                    qvalueCutoff  = 0.05,
                    readable      = TRUE)
    t(ego@result[1,])  #查看结果第一行的示例
    #             GO:0005819                                                                                                                                                 
    # ID          "GO:0005819"                                                                                                                                               
    # Description "spindle"                                                                                                                                                  
    # GeneRatio   "26/201"                                                                                                                                                   
    # BgRatio     "299/11840"                                                                                                                                                
    # pvalue      "6.490593e-12"                                                                                                                                             
    # p.adjust    "1.908234e-09"                                                                                                                                             
    # qvalue      "1.73538e-09"                                                                                                                                              
    # geneID      "CDCA8/CDC20/KIF23/CENPE/ASPM/DLGAP5/SKA1/NUSAP1/TPX2/TACC3/NEK2/CDK1/MAD2L1/KIF18A/BIRC5/KIF11/TRAT1/TTK/AURKB/PRC1/KIFC1/KIF18B/KIF20A/AURKA/CCNB1/KIF4A"
    # Count       "26" 
    
    GSEA:gseGO {clusterProfiler}

    值得注意是此函数没有readable参数。如果差异基因是ENTREZID类型,可使用setReadable()函数再进行转换

    ego2 <- gseGO(geneList     = geneList,
                  OrgDb        = org.Hs.eg.db,
                  ont          = "CC",
                  minGSSize    = 100,
                  maxGSSize    = 500,
                  pvalueCutoff = 0.05,
                  verbose      = FALSE)
    t(ego2@result[1,])
    #                   GO:0000228                                                                                                                                                                                                                                                                                                                                 
    # ID              "GO:0000228"                                                                                                                                                                                                                                                                                                                               
    # Description     "nuclear chromosome"                                                                                                                                                                                                                                                                                                                       
    # setSize         "176"                                                                                                                                                                                                                                                                                                                                      
    # enrichmentScore "0.5697288"                                                                                                                                                                                                                                                                                                                                
    # NES             "2.495232"                                                                                                                                                                                                                                                                                                                                 
    # pvalue          "1e-10"                                                                                                                                                                                                                                                                                                                                    
    # p.adjust        "1.52e-09"                                                                                                                                                                                                                                                                                                                                 
    # qvalues         "1.010526e-09"  
    # rank            "2215"                                                                                                                                                                                                                                                                                                                                     
    # leading_edge    "tags=36%, list=18%, signal=30%"                                                                                                                                                                                                                                                                                                           
    # core_enrichment "55388/10403/7153/4751/9837/332/51659/1111/891/4174/4171/5347/701/5888/23594/4998/4175/4173/54962/9918/1058/84296/699/81611/5111/64785/55506/641/8357/5427/23649/4176/58516/79980/5557/3066/11169/8607/1104/5558/4172/5424/5885/7283/10592/8914/51377/86/5393/6839/23212/3014/3619/5425/54107/11177/7273/6119/672/55320/675/5119/988/79172"
    
    ego3 = setReadable(ego2, OrgDb = org.Hs.eg.db)
    t(ego3@result[1,])
    #                 GO:0000228                                                                                                                                                                                                                                                                                                                                                                                      
    # ID              "GO:0000228"                                                                                                                                                                                                                                                                                                                                                                                    
    # Description     "nuclear chromosome"                                                                                                                                                                                                                                                                                                                                                                            
    # setSize         "176"                                                                                                                                                                                                                                                                                                                                                                                           
    # enrichmentScore "0.5697288"                                                                                                                                                                                                                                                                                                                                                                                     
    # NES             "2.495232"                                                                                                                                                                                                                                                                                                                                                                                      
    # pvalue          "1e-10"                                                                                                                                                                                                                                                                                                                                                                                         
    # p.adjust        "1.52e-09" 
    # qvalues         "1.010526e-09"                                                                                                                                                                                                                                                                                                                                                                                  
    # rank            "2215"                                                                                                                                                                                                                                                                                                                                                                                          
    # leading_edge    "tags=36%, list=18%, signal=30%"                                                                                                                                                                                                                                                                                                                                                                
    # core_enrichment "MCM10/NDC80/TOP2A/NEK2/GINS1/BIRC5/GINS2/CHEK1/CCNB1/MCM5/MCM2/PLK1/BUB1B/RAD51/ORC6/ORC1/MCM6/MCM4/TIPIN/NCAPD2/CENPA/GINS4/BUB1/ANP32E/PCNA/GINS3/MACROH2A2/BLM/H3C10/POLE2/POLA2/MCM7/SINHCAF/DSN1/PRIM1/HDAC2/WDHD1/RUVBL1/RCC1/PRIM2/MCM3/POLD1/RAD21/TUBG1/SMC2/TIMELESS/UCHL5/ACTL6A/EXOSC9/SUV39H1/RRS1/H2AX/INCENP/POLD2/POLE3/BAZ1A/TTN/RPA3/BRCA1/MIS18BP1/BRCA2/CHMP1A/CDC5L/CENPO"
    

    关于富集分析结果的可视化会在第三点记录,这里展示了一张专门使用于GO term的富集分析结果可视化。goplot(ego)

    1.2 KEGG

    ORA:enrichKEGG {clusterProfiler}
    • 这里的差异基因id类型必须要是entrez gene才可以
    kk <- enrichKEGG(gene         = gene,
                     organism     = 'hsa',
                     pvalueCutoff = 0.05)
    kk <- setReadable(kk, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(kk@result[1,])
    #             hsa04110                                                        
    # ID          "hsa04110"                                                      
    # Description "Cell cycle"                                                    
    # GeneRatio   "11/94"                                                         
    # BgRatio     "124/8096"                                                      
    # pvalue      "1.641616e-07"                                                  
    # p.adjust    "3.398145e-05"                                                  
    # qvalue      "3.335072e-05"                                                  
    # geneID      "CDC45/CDC20/CCNB2/CCNA2/CDK1/MAD2L1/TTK/CHEK1/CCNB1/MCM5/PTTG1"
    # Count       "11" 
    
    GSEA:gseKEGG {clusterProfiler}
    kk2 <- gseKEGG(geneList     = geneList,
                   organism     = 'hsa',
                   minGSSize    = 120,
                   pvalueCutoff = 0.05,
                   verbose      = FALSE)
    kk2 <- setReadable(kk2, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(kk2@result[1,])
    #                   hsa05169                                                                                                                                                                                                                                                                                                                                                                                                                                                         
    # ID              "hsa05169"                                                                                                                                                                                                                                                                                                                                                                                                                                                       
    # Description     "Epstein-Barr virus infection"                                                                                                                                                                                                                                                                                                                                                                                                                                   
    # setSize         "193"                                                                                                                                                                                                                                                                                                                                                                                                                                                            
    # enrichmentScore "0.433501"                                                                                                                                                                                                                                                                                                                                                                                                                                                       
    # NES             "1.937523"                                                                                                                                                                                                                                                                                                                                                                                                                                                       
    # pvalue          "2.243845e-07"                                                                                                                                                                                                                                                                                                                                                                                                                                                   
    # p.adjust        "1.705322e-05"      
    # qvalues         "1.157352e-05"                                                                                                                                                                                                                                                                                                                                                                                                                                                   
    # rank            "2820"                                                                                                                                                                                                                                                                                                                                                                                                                                                           
    # leading_edge    "tags=39%, list=23%, signal=31%"                                                                                                                                                                                                                                                                                                                                                                                                                                 
    # core_enrichment "CXCL10/CCNA2/TAP1/ISG15/CCNE1/CCNE2/SKP2/STAT1/HLA-DRB4/HLA-DOB/MYC/CD3G/PSMD3/E2F1/IRAK1/CD247/CD3D/LYN/OAS1/RUNX3/OAS3/PSMD7/PLCG2/ADRM1/HDAC2/CYCS/E2F3/BAK1/CDK4/BID/CD3E/ICAM1/OAS2/PSMD14/DDX58/NFKBIB/MAPK13/SEM1/TNFAIP3/TAP2/CD19/PSMD8/IFNA21/SYK/PSMC3/NFKBIE/TNF/IL6/TLR2/PSMD2/FCER2/FADD/HLA-DQB1/PSMC4/TRAF2/RELB/HLA-G/CR2/CD40/EIF2AK2/NFKBIA/BCL2L11/SAP30/HLA-F/IRF9/IKBKE/CHUK/PSMD12/MAPK12/HLA-DMB/CALR/MAP2K3/PDIA3/HLA-DMA/PSMD1/MAPK14"
    
    kegg通路可视化
    • 单纯看一下被富集到的kegg通路
    browseKEGG(kk, 'hsa04110')
    
    • 结合差异倍数,看下被富集到的kegg通路
    library("pathview")
    pathview(gene.data  = geneList,
             pathway.id = "hsa04110",
             species    = "hsa",
             limit      = list(gene=max(abs(geneList)), cpd=1))
    

    1.3 WikiPathways

    ORA:enrichWP {clusterProfiler}

    同样只接受entrez gene id

    wp = enrichWP(gene, organism = "Homo sapiens") 
    wp <- setReadable(wp, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(wp@result[1,])
    #             WP2446                                                        
    # ID          "WP2446"                                                      
    # Description "Retinoblastoma gene in cancer"                               
    # GeneRatio   "11/108"                                                      
    # BgRatio     "88/7847"                                                     
    # pvalue      "2.650788e-08"                                                
    # p.adjust    "6.388399e-06"                                                
    # qvalue      "5.748024e-06"                                                
    # geneID      "CDC45/CCNB2/TOP2A/RRM2/CCNA2/CDK1/CDT1/TTK/CHEK1/CCNB1/KIF4A"
    # Count       "11"
    
    GSEA:gseWP {clusterProfiler}
    wp2 = gseWP(geneList, organism = "Homo sapiens") 
    wp2 <- setReadable(wp2, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(wp2@result[1,])
    #                 WP179                                                                                                                                                                                                                                                
    # ID              "WP179"                                                                                                                                                                                                                                              
    # Description     "Cell cycle"                                                                                                                                                                                                                                         
    # setSize         "111"                                                                                                                                                                                                                                                
    # enrichmentScore "0.6632572"                                                                                                                                                                                                                                          
    # NES             "2.764158"                                                                                                                                                                                                                                           
    # pvalue          "1e-10"                                                                                                                                                                                                                                              
    # p.adjust        "1.846667e-08"                                                                                                                                                                                                                                       
    # qvalues         "1.536842e-08"                                                                                                                                                                                                                                       
    # rank            "1234"                                                                                                                                                                                                                                               
    # leading_edge    "tags=40%, list=10%, signal=36%"                                                                                                                                                                                                                     
    # core_enrichment "CDC45/CDC20/CCNB2/CCNA2/CDK1/TTK/CHEK1/CCNB1/MCM5/PTTG1/MCM2/CDC25A/CDC6/PLK1/ESPL1/CCNE1/ORC6/ORC1/CCNE2/MCM6/MCM4/DBF4/SKP2/CDC25B/BUB1/MYC/PCNA/E2F1/CDKN2A/CDC7/MCM7/SFN/HDAC2/E2F3/CDKN2C/PKMYT1/CDC25C/CDK4/MCM3/RAD21/CHEK2/TFDP1/E2F5/YWHAZ"
    

    1.4 ReactomePA

    ORA:enrichPathway {ReactomePA}

    可以设置readable参数

    library(ReactomePA)
    rp <- enrichPathway(gene, pvalueCutoff = 0.05, readable=TRUE)
    t(rp@result[1,])
    #               R-HSA-69620                                                                                                                          
    # ID          "R-HSA-69620"                                                                                                                        
    # Description "Cell Cycle Checkpoints"                                                                                                             
    # GeneRatio   "22/142"                                                                                                                             
    # BgRatio     "294/10856"                                                                                                                          
    # pvalue      "2.904514e-11"                                                                                                                       
    # p.adjust    "1.289604e-08"                                                                                                                       
    # qvalue      "1.054797e-08"                                                                                                                       
    # geneID      "CDC45/CDCA8/MCM10/CDC20/CENPE/CCNB2/NDC80/UBE2C/SKA1/CENPM/CENPN/UBE2S/CCNA2/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/CHEK1/CCNB1/MCM5"
    # Count       "22"
    
    GSEA:gsePathway {ReactomePA}
    rp2 <- gsePathway(geneList, 
                    pvalueCutoff = 0.2,
                    pAdjustMethod = "BH")
    rp2 <- setReadable(rp2, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(rp2[1,])
    #                 R-HSA-141424                                                                                                                                           
    # ID              "R-HSA-141424"                                                                                                                                         
    # Description     "Amplification of signal from the kinetochores"                                                                                                        
    # setSize         "81"                                                                                                                                                   
    # enrichmentScore "0.7111667"                                                                                                                                            
    # NES             "2.825675"                                                                                                                                             
    # pvalue          "1e-10"                                                                                                                                                
    # p.adjust        "3.889189e-09"                                                                                                                                         
    # qvalues         "2.8734e-09"                                                                                                                                           
    # rank            "759"                                                                                                                                                  
    # leading_edge    "tags=31%, list=6%, signal=29%"                                                                                                                        
    # core_enrichment "CDCA8/CDC20/CENPE/NDC80/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/CENPA/BUB1/CENPF/ZWILCH/DSN1/KNTC1"
    
    ReactomePA富集通路可视化
    viewPathway("Cell Cycle Checkpoints", 
                readable = TRUE, 
                foldChange = geneList)
    

    1.5 Disease

    DO:disease ontology
    NCG:Network of Cancer Gene
    DisGeNET:disease gene network

    ORA
    • enrichDO {DOSE}
    • enrichNCG {DOSE}
    • enrichDGN {DOSE}
      以disease ontology为例
    library(DOSE)
    edo <- enrichDO(gene          = gene,
                  ont           = "DO",
                  pvalueCutoff  = 0.05,
                  pAdjustMethod = "BH",
                  universe      = names(geneList),
                  minGSSize     = 5,
                  maxGSSize     = 500,
                  qvalueCutoff  = 0.05,
                  readable      = TRUE)
    t(edo[1,])
    #             DOID:1612                                                                                                                                                 
    # ID          "DOID:1612"                                                                                                                                               
    # Description "breast cancer"                                                                                                                                           
    # GeneRatio   "26/147"                                                                                                                                                  
    # BgRatio     "480/6268"                                                                                                                                                
    # pvalue      "4.099574e-05"                                                                                                                                            
    # p.adjust    "0.01182727"                                                                                                                                              
    # qvalue      "0.01106885"                                                                                                                                              
    # geneID      "MMP1/S100A9/FOXM1/S100A8/TOP2A/S100A7/NEK2/CCNA2/MAD2L1/BIRC5/S100P/EZH2/AURKA/CCNB1/PTTG1/CA12/SCGB2A2/CCN5/ERBB4/FOXA1/SCGB1D2/PIP/GATA3/NAT1/PGR/AGR2"
    # Count       "26" 
    
    由于疾病与基因突变有密不可分的关系,因此也可以对snp突变位点做疾病类型基因集的富集分析
    GSEA
    • gseDO{DOSE}
    • gseNCG{DOSE}
    • gseDGN{DOSE}
      以disease ontology为例
    edo2 <- gseDO(geneList,
               minGSSize     = 120,
               pvalueCutoff  = 0.2,
               pAdjustMethod = "BH",
               verbose       = FALSE)
    edo2 <- setReadable(edo2, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(edo2[1,])
    #                 DOID:0050338                                                                                                                                                                                                                                                                                                                                                                                                                 
    # ID              "DOID:0050338"                                                                                                                                                                                                                                                                                                                                                                                                               
    # Description     "primary bacterial infectious disease"                                                                                                                                                                                                                                                                                                                                                                                       
    # setSize         "214"                                                                                                                                                                                                                                                                                                                                                                                                                        
    # enrichmentScore "0.4569856"                                                                                                                                                                                                                                                                                                                                                                                                                  
    # NES             "2.083916"                                                                                                                                                                                                                                                                                                                                                                                                                   
    # pvalue          "1.194332e-09"                                                                                                                                                                                                                                                                                                                                                                                                               
    # p.adjust        "1.875102e-07"                                                                                                                                                                                                                                                                                                                                                                                                               
    # qvalues         "7.165995e-08"        
    # rank            "1850"                                                                                                                                                                                                                                                                                                                                                                                                                       
    # leading_edge    "tags=35%, list=15%, signal=30%"                                                                                                                                                                                                                                                                                                                                                                                             
    # core_enrichment "MMP1/BCL2A1/CXCL10/CXCL11/CAMP/IDO1/CCL20/ICOS/MMP9/CXCL8/PHGDH/TAP1/CD38/CTLA4/CCL5/LCN2/TREM1/LCK/PRF1/IL2RA/CCL2/SELL/PRDM1/MUC16/HLA-DRB4/GZMA/CCL4/CCR7/PSMB9/LDHC/CD247/IFNG/CD40LG/TXNRD1/DERL1/KIR2DL3/MC3R/CD86/HSPD1/IL32/CCR5/TLR1/ICAM1/SH2D1A/CCL22/PTX3/ADA/IRF1/MRC1/CD27/TAP2/MEFV/BPI/VEGFA/CD14/PTPN22/SLAMF1/B3GAT1/MIF/TNF/P2RX7/PLAUR/IL6/LTA/TLR2/BTNL2/CXCR4/CR1/PDCD1/PTGS2/APOE/CHIT1/HLA-DQB1/VCP"
    

    1.6 meshes

    ms <- enrichMeSH(gene, MeSHDb = "MeSH.Hsa.eg.db", database='gendoo', category = 'C')
    ms <- setReadable(ms, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(ms[1,])
    #             D043171                                                                                                       
    # ID          "D043171"                                                                                                     
    # Description "Chromosomal Instability"                                                                                     
    # GeneRatio   "19/196"                                                                                                      
    # BgRatio     "198/16528"                                                                                                   
    # pvalue      "2.431961e-12"                                                                                                
    # p.adjust    "3.051352e-09"                                                                                                
    # qvalue      "2.413276e-09"                                                                                                
    # geneID      "MMP1/CDC20/FOXM1/CENPE/MYBL2/NDC80/TOP2A/HJURP/NEK2/MAD2L1/CDT1/BIRC5/TTK/AURKB/CHEK1/AURKA/CCNB1/PTTG1/MAPT"
    # Count       "19"  
    ms2 <- gseMeSH(geneList, MeSHDb = "MeSH.Hsa.eg.db", database = 'gene2pubmed', category = "G")
    ms2 <- setReadable(ms2, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(ms[2,])
    #             D000782                                                                                                                              
    # ID          "D000782"                                                                                                                            
    # Description "Aneuploidy"                                                                                                                         
    # GeneRatio   "23/196"                                                                                                                             
    # BgRatio     "320/16528"                                                                                                                          
    # pvalue      "4.68358e-12"                                                                                                                        
    # p.adjust    "3.051352e-09"                                                                                                                       
    # qvalue      "2.413276e-09"                                                                                                                       
    # geneID      "MMP1/CDCA8/CDC20/CENPE/TOP2A/NEK2/CENPM/CENPN/CCNA2/CDK1/MAD2L1/BIRC5/TTK/AURKB/CHAF1B/CHEK1/AURKA/KIF4A/PTTG1/SCGB2A2/PIP/NAT1/PGR"
    # Count       "23"
    

    2、自定义基因集富集分析

    • 关键是设置TERM2NAME参数,提供自定义的基因集。
    • 格式为一个两列的dataframe:term列为基因集名(重复),gene列为基因名。例如下图
    • 来自MsigDB的基因集数据
    msigdb = clusterProfiler::read.gmt("h.all.v7.4.entrez.gmt")
    head(msigdb)
    #                               term gene
    # 1 HALLMARK_TNFA_SIGNALING_VIA_NFKB 3726
    # 2 HALLMARK_TNFA_SIGNALING_VIA_NFKB 2920
    # 3 HALLMARK_TNFA_SIGNALING_VIA_NFKB  467
    # 4 HALLMARK_TNFA_SIGNALING_VIA_NFKB 4792
    # 5 HALLMARK_TNFA_SIGNALING_VIA_NFKB 7128
    # 6 HALLMARK_TNFA_SIGNALING_VIA_NFKB 5743
    

    2.1 ORA:enricher {clusterProfiler}

    x <- enricher(gene, TERM2GENE = msigdb)
    x <- setReadable(x, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(x[1,])
    # HALLMARK_G2M_CHECKPOINT                                                                                                                                                             
    # ID          "HALLMARK_G2M_CHECKPOINT"                                                                                                                                                           
    # Description "HALLMARK_G2M_CHECKPOINT"                                                                                                                                                           
    # GeneRatio   "31/108"                                                                                                                                                                            
    # BgRatio     "200/4383"                                                                                                                                                                          
    # pvalue      "1.484553e-17"                                                                                                                                                                      
    # p.adjust    "5.938212e-16"                                                                                                                                                                      
    # qvalue      "5.313137e-16"                                                                                                                                                                      
    # geneID      "CDC45/CDC20/KIF23/CENPE/MYBL2/CCNB2/NDC80/TOP2A/UBE2C/PBK/NUSAP1/TPX2/TACC3/NEK2/SLC7A5/UBE2S/CCNA2/CDK1/MAD2L1/BIRC5/KIF11/EZH2/TTK/AURKB/GINS2/CHEK1/PRC1/AURKA/KIF4A/MCM5/PTTG1"
    # Count       "31"
    

    2.2 GSEA:GSEA {clusterProfiler}

    y <- GSEA(geneList, TERM2GENE = msigdb)
    y <- setReadable(y, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
    t(y[1,])
    #                 HALLMARK_ALLOGRAFT_REJECTION                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     
    # ID              "HALLMARK_ALLOGRAFT_REJECTION"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   
    # Description     "HALLMARK_ALLOGRAFT_REJECTION"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   
    # setSize         "199"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
    # enrichmentScore "0.5555282"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
    # NES             "2.456838"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       
    # pvalue          "1e-10"         
    # p.adjust        "5e-10"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          
    # qvalues         "2e-10"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          
    # rank            "2657"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           
    # leading_edge    "tags=53%, list=21%, signal=42%"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 
    # core_enrichment "CXCL13/CXCL9/GZMB/TRAT1/MMP9/TAP1/CCL5/WARS1/LCK/PRF1/IL2RA/CCR1/STAT1/CCL2/CD2/HLA-DOB/GZMA/RIPK2/IL2RG/FYB1/CCL4/CD3G/CD7/CDKN2A/NME1/LTB/IL7/CD247/CD3D/LYN/CCL7/MAP4K1/SIT1/IFNG/CTSS/CD40LG/KLRD1/ITK/CD8A/BCAT1/CD86/ELANE/CD96/ITGB2/PTPRC/CCR5/CD3E/TLR1/ICAM1/IL27RA/LCP2/CCL22/IRF8/AARS1/SRGN/IL15/IL18RAP/CD28/GBP2/NCF4/TAP2/CSK/CCL13/GPR65/ABCE1/PSMB10/NCK1/IGSF6/TNF/MRPL3/CD1D/IL6/CRTAM/TLR2/WAS/HIF1A/EGFR/TRAF2/FCGR2B/HLA-G/GCNT1/BRCA1/IL13/ELF4/CXCR3/FASLG/CD40/HCLS1/CCL11/HDAC9/NCR1/UBE2D1/IL9/DEGS1/IL12B/PRKCG/SOCS1/UBE2N/IL2/CCR2/IL12A/IL2RB/IL11/TLR6/HLA-DMB"
    

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        本文标题:[富集分析] 2、clusterprofile富集分析--上游分

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