2020-02-27 clusterProfiler富集分析之疾病富集

參考學習資料：https://yulab-smu.github.io/clusterProfiler-book/index.html
網(wǎng)紅教授的一本書第四章

Chapter 4 Disease analysis

DOSE(Yu et al. 2015) 支持 Disease Ontology (DO) 富集分析. 函數(shù) enrichDO 可以identifying disease association of interesting genes, 同時函數(shù)gseDO被設計用來對 DO進行GSEA.

此外 DOSE 還支持分析 Network of Cancer Gene (NCG)(A. et al. 2016)和 Disease Gene Network(Janet et al. 2015),更多更詳細信息請查看 DOSE vignettes文檔.

4.1 enrichDO function

下面是一個例子,選擇fold change大于1.5的基因作為差異基因進行DO分析顶瞳。

library(DOSE)
data(geneList)
gene <- names(geneList)[abs(geneList) > 1.5]
head(gene)

## [1] "4312"  "8318"  "10874" "55143" "55388" "991"

x <- enrichDO(gene          = gene,
              ont           = "DO",
              pvalueCutoff  = 0.05,
              pAdjustMethod = "BH",
              universe      = names(geneList),
              minGSSize     = 5,
              maxGSSize     = 500,
              qvalueCutoff  = 0.05,
              readable      = FALSE)
head(x)

##                    ID                    Description
## DOID:170     DOID:170         endocrine gland cancer
## DOID:10283 DOID:10283                prostate cancer
## DOID:3459   DOID:3459               breast carcinoma
## DOID:3856   DOID:3856 male reproductive organ cancer
## DOID:824     DOID:824                  periodontitis
## DOID:3905   DOID:3905                 lung carcinoma
##            GeneRatio  BgRatio       pvalue    p.adjust
## DOID:170      48/331 472/6268 5.662129e-06 0.004784499
## DOID:10283    40/331 394/6268 3.859157e-05 0.013921739
## DOID:3459     37/331 357/6268 4.942629e-05 0.013921739
## DOID:3856     40/331 404/6268 6.821467e-05 0.014410349
## DOID:824      16/331 109/6268 1.699304e-04 0.018859464
## DOID:3905     43/331 465/6268 1.749754e-04 0.018859464
##                 qvalue
## DOID:170   0.003826407
## DOID:10283 0.011133923
## DOID:3459  0.011133923
## DOID:3856  0.011524689
## DOID:824   0.015082872
## DOID:3905  0.015082872
##                                                                                                                                                                                                                                                   geneID
## DOID:170   10874/7153/1381/6241/11065/10232/332/6286/2146/10112/891/9232/4171/993/5347/4318/3576/1515/4821/8836/3159/7980/5888/333/898/9768/4288/3551/2152/9590/185/7043/3357/2952/5327/3667/1634/1287/4582/7122/3479/4680/6424/80310/652/8839/9547/1524
## DOID:10283                                          4312/6280/6279/597/3627/332/6286/2146/4321/4521/891/5347/4102/4318/701/3576/79852/10321/6352/4288/3551/2152/247/2952/3487/367/3667/4128/4582/563/3679/4117/7031/3479/6424/10451/80310/652/4036/10551
## DOID:3459                                                          4312/6280/6279/7153/4751/890/4085/332/6286/6790/891/9232/10855/4171/5347/4318/701/2633/3576/9636/898/8792/4288/2952/4982/4128/4582/7031/3479/771/4250/2066/3169/10647/5304/5241/10551
## DOID:3856                                           4312/6280/6279/597/3627/332/6286/2146/4321/4521/891/5347/4102/4318/701/3576/79852/10321/6352/4288/3551/2152/247/2952/3487/367/3667/4128/4582/563/3679/4117/7031/3479/6424/10451/80310/652/4036/10551
## DOID:824                                                                                                                                                                   4312/6279/820/7850/4321/3595/4318/4069/3576/1493/6352/8842/185/2952/5327/4982
## DOID:3905                          4312/6280/2305/9133/6279/7153/6278/6241/55165/11065/8140/10232/332/6286/3002/9212/4521/891/4171/9928/8061/4318/3576/1978/1894/7980/7083/898/6352/8842/4288/2152/2697/2952/3572/4582/7049/563/3479/1846/3117/2532/2922
##            Count
## DOID:170      48
## DOID:10283    40
## DOID:3459     37
## DOID:3856     40
## DOID:824      16
## DOID:3905     43

函數(shù)enrichDO需要entrezgene ID向量作為input數(shù)據(jù), 通常是differential gene list of gene expression profile studies.如果使用者需要進行g(shù)ene ID轉(zhuǎn)換,推薦使用clusterProfiler包的bitr函數(shù).

以上函數(shù)中ont 參數(shù)可以是“DO”或“DOLite”, DOLite(Du et al. 2009)也是基于DO構(gòu)造的. 由于DOLite數(shù)據(jù)不支持更新, 更推薦使用ont="DO". pvalueCutoff 設置cutoff value of p value and p value adjust; pAdjustMethod setting the p value 矯正方法 包括 Bonferroni correction (“bonferroni”), Holm (“holm”), Hochberg (“hochberg”), Hommel (“hommel”), Benjamini & Hochberg (“BH”) and Benjamini & Yekutieli (“BY”)而 qvalueCutoff 用相較于對照的q-values.

universe 設置背景基因用于檢驗。如果不特意設置該參數(shù)， enrichDO 將會設置所有涉及人類的DO注釋所用的基因。

minGSSize (and maxGSSize)指定的是那些DO terms大于minGSSize (小于maxGSSize) 注釋的的基因被檢測.

readable是一個邏輯值參數(shù), 用來指示entrezgene IDs是否同時展示匹配的gene symbols。

還可以用setReadable 函數(shù)幫助用戶進行entrezgene IDs 到 gene symbols的轉(zhuǎn)換眶痰。

x <- setReadable(x, 'org.Hs.eg.db')
head(x)

##                    ID                    Description
## DOID:170     DOID:170         endocrine gland cancer
## DOID:10283 DOID:10283                prostate cancer
## DOID:3459   DOID:3459               breast carcinoma
## DOID:3856   DOID:3856 male reproductive organ cancer
## DOID:824     DOID:824                  periodontitis
## DOID:3905   DOID:3905                 lung carcinoma
##            GeneRatio  BgRatio       pvalue    p.adjust
## DOID:170      48/331 472/6268 5.662129e-06 0.004784499
## DOID:10283    40/331 394/6268 3.859157e-05 0.013921739
## DOID:3459     37/331 357/6268 4.942629e-05 0.013921739
## DOID:3856     40/331 404/6268 6.821467e-05 0.014410349
## DOID:824      16/331 109/6268 1.699304e-04 0.018859464
## DOID:3905     43/331 465/6268 1.749754e-04 0.018859464
##                 qvalue
## DOID:170   0.003826407
## DOID:10283 0.011133923
## DOID:3459  0.011133923
## DOID:3856  0.011524689
## DOID:824   0.015082872
## DOID:3905  0.015082872
##                                                                                                                                                                                                                                                                                         geneID
## DOID:170   NMU/TOP2A/CRABP1/RRM2/UBE2C/MSLN/BIRC5/S100P/EZH2/KIF20A/CCNB1/PTTG1/MCM2/CDC25A/PLK1/MMP9/CXCL8/CTSV/NKX2-2/GGH/HMGA1/TFPI2/RAD51/APLP1/CCNE1/PCLAF/MKI67/IKBKB/F3/AKAP12/AGTR1/TGFB3/HTR2B/GSTT1/PLAT/IRS1/DCN/COL4A5/MUC1/CLDN5/IGF1/CEACAM6/SFRP4/PDGFD/BMP4/CCN5/CXCL14/CX3CR1
## DOID:10283                                                  MMP1/S100A9/S100A8/BCL2A1/CXCL10/BIRC5/S100P/EZH2/MMP12/NUDT1/CCNB1/PLK1/MAGEA3/MMP9/BUB1B/CXCL8/EPHX3/CRISP3/CCL5/MKI67/IKBKB/F3/ALOX15B/GSTT1/IGFBP4/AR/IRS1/MAOA/MUC1/AZGP1/ITGA7/MAK/TFF1/IGF1/SFRP4/VAV3/PDGFD/BMP4/LRP2/AGR2
## DOID:3459                                                          MMP1/S100A9/S100A8/TOP2A/NEK2/CCNA2/MAD2L1/BIRC5/S100P/AURKA/CCNB1/PTTG1/HPSE/MCM2/PLK1/MMP9/BUB1B/GBP1/CXCL8/ISG15/CCNE1/TNFRSF11A/MKI67/GSTT1/TNFRSF11B/MAOA/MUC1/TFF1/IGF1/CA12/SCGB2A2/ERBB4/FOXA1/SCGB1D2/PIP/PGR/AGR2
## DOID:3856                                                   MMP1/S100A9/S100A8/BCL2A1/CXCL10/BIRC5/S100P/EZH2/MMP12/NUDT1/CCNB1/PLK1/MAGEA3/MMP9/BUB1B/CXCL8/EPHX3/CRISP3/CCL5/MKI67/IKBKB/F3/ALOX15B/GSTT1/IGFBP4/AR/IRS1/MAOA/MUC1/AZGP1/ITGA7/MAK/TFF1/IGF1/SFRP4/VAV3/PDGFD/BMP4/LRP2/AGR2
## DOID:824                                                                                                                                                                                       MMP1/S100A8/CAMP/IL1R2/MMP12/IL12RB2/MMP9/LYZ/CXCL8/CTLA4/CCL5/PROM1/AGTR1/GSTT1/PLAT/TNFRSF11B
## DOID:3905                           MMP1/S100A9/FOXM1/CCNB2/S100A8/TOP2A/S100A7/RRM2/CEP55/UBE2C/SLC7A5/MSLN/BIRC5/S100P/GZMB/AURKB/NUDT1/CCNB1/MCM2/KIF14/FOSL1/MMP9/CXCL8/EIF4EBP1/ECT2/TFPI2/TK1/CCNE1/CCL5/PROM1/MKI67/F3/GJA1/GSTT1/IL6ST/MUC1/TGFBR3/AZGP1/IGF1/DUSP4/HLA-DQA1/ACKR1/GRP
##            Count
## DOID:170      48
## DOID:10283    40
## DOID:3459     37
## DOID:3856     40
## DOID:824      16
## DOID:3905     43

4.2 enrichNCG function

Network of Cancer Gene (NCG)(A. et al. 2016) 是一個人工管理的癌癥基因庫。 NCG 5.0版本 (Aug. 2015) 共收集了1,571 cancer genes來自175篇已發(fā)表的文獻。 DOSE 支持分析gene list 同時determine whether they are enriched in genes known to be mutated in a given cancer type.

gene2 <- names(geneList)[abs(geneList) < 3]
ncg <- enrichNCG(gene2)
head(ncg)

##                                        ID
## soft_tissue_sarcomas soft_tissue_sarcomas
## bladder                           bladder
## glioma                             glioma
##                               Description GeneRatio BgRatio
## soft_tissue_sarcomas soft_tissue_sarcomas   28/1172 28/1571
## bladder                           bladder   61/1172 67/1571
## glioma                             glioma   68/1172 76/1571
##                            pvalue    p.adjust      qvalue
## soft_tissue_sarcomas 0.0002517511 0.008056037 0.006360029
## bladder              0.0005108168 0.008173069 0.006452423
## glioma               0.0008511747 0.009079196 0.007167787
##                                                                                                                                                                                                                                                                                                                                                                  geneID
## soft_tissue_sarcomas                                                                                                                                                                                                       1029/999/6850/4914/4342/2185/55294/2041/4851/23512/2044/4058/5290/8726/4486/5297/5728/3815/2324/7403/5925/4763/1499/7157/5159/2045/3667/2066
## bladder                                            9700/2175/9603/1029/8997/688/1026/896/677/6256/55294/8085/4851/3265/1999/3845/8243/10605/8295/4854/5290/2033/4780/23224/23217/2064/23385/55252/10735/4853/387/288/30849/9794/7403/287/463/472/4297/2065/2262/8289/9611/5925/2068/4763/7157/2186/1387/3910/2261/7248/23037/23345/7832/79633/10628/22906/388/4036/3169
## glioma               4603/4609/1029/3418/8877/1019/7027/4613/1030/1956/1106/2264/3417/6597/4914/55359/896/894/2321/3954/5335/5781/8439/673/9444/4851/8087/2050/8493/3845/3482/667/56999/5290/2033/4233/577/5894/5156/80036/9407/3020/1021/5598/5728/8621/1828/63035/23592/8880/2260/54880/4916/2263/1639/90/546/8289/4763/7157/23152/5295/4602/595/2261/6938/4915/26137
##                      Count
## soft_tissue_sarcomas    28
## bladder                 61
## glioma                  68

4.3 enrichDGN and enrichDGNv functions

DisGeNET(Janet et al. 2015) 是一個綜合和全面的基因-疾病關(guān)聯(lián)資源描馅，它來自幾個公共數(shù)據(jù)源和文獻。它包含基因-疾病關(guān)聯(lián)和SNP-基因-疾病關(guān)聯(lián)而线。

疾病-基因關(guān)聯(lián)的富集分析得到了enrichDGN函數(shù)的支持铭污，SNP-基因-疾病關(guān)聯(lián)的分析得到了enrichDGNv 函數(shù)的支持恋日。

dgn <- enrichDGN(gene)
head(dgn)

##                          ID
## umls:C1134719 umls:C1134719
## umls:C0032460 umls:C0032460
## umls:C0206698 umls:C0206698
## umls:C0007138 umls:C0007138
## umls:C0031099 umls:C0031099
## umls:C0005695 umls:C0005695
##                                    Description GeneRatio
## umls:C1134719 Invasive Ductal Breast Carcinoma    28/476
## umls:C0032460        Polycystic Ovary Syndrome    38/476
## umls:C0206698               Cholangiocarcinoma    36/476
## umls:C0007138     Carcinoma, Transitional Cell    35/476
## umls:C0031099                    Periodontitis    28/476
## umls:C0005695                 Bladder Neoplasm    36/476
##                 BgRatio       pvalue     p.adjust
## umls:C1134719 231/17381 4.312190e-11 1.225524e-07
## umls:C0032460 434/17381 2.819624e-10 3.521620e-07
## umls:C0206698 399/17381 3.717403e-10 3.521620e-07
## umls:C0007138 389/17381 7.093837e-10 5.040171e-07
## umls:C0031099 270/17381 1.634417e-09 9.290027e-07
## umls:C0005695 442/17381 5.871618e-09 2.781190e-06
##                     qvalue
## umls:C1134719 9.164539e-08
## umls:C0032460 2.633487e-07
## umls:C0206698 2.633487e-07
## umls:C0007138 3.769068e-07
## umls:C0031099 6.947133e-07
## umls:C0005695 2.079789e-06
##                                                                                                                                                                                                         geneID
## umls:C1134719                                                 9133/7153/6241/55165/11065/51203/22974/4751/5080/332/2568/3902/6790/891/24137/9232/10855/79801/4318/55635/5888/1493/9768/3070/4288/367/4582/5241
## umls:C0032460 4312/6280/6279/7153/259266/6241/55165/55872/4085/6286/7272/366/891/4171/7941/1164/3161/4603/990/29127/4318/53335/3294/3070/2952/5327/367/3667/4582/563/27324/3479/114899/9370/2167/652/5346/5241
## umls:C0206698             4312/2305/55872/4751/8140/10635/10232/5918/332/6286/2146/4521/891/10855/2921/7941/1164/4318/3576/1978/79852/8842/4485/214/65982/6863/1036/6935/4128/3572/4582/7031/7166/4680/80310/9
## umls:C0007138                       4312/991/6280/6241/55165/10460/6373/8140/890/10232/4085/332/6286/2146/4171/1033/6364/5347/4318/3576/8836/9700/898/4288/2952/367/8382/2947/3479/9338/23158/2167/2066/2625/9
## umls:C0031099                                                       4312/6279/3669/820/7850/332/4321/6364/3595/4318/3576/3898/8792/1493/4485/10472/185/6863/2205/2952/5327/4982/23261/2200/3572/2006/1308/2625
## umls:C0005695                   4312/10874/6280/3868/6279/597/7153/6241/9582/10460/4085/5080/332/2146/6790/10855/4171/5347/4318/3576/8836/9636/9700/898/4288/214/2952/367/2947/4582/3479/6424/9338/2066/1580/9
##               Count
## umls:C1134719    28
## umls:C0032460    38
## umls:C0206698    36
## umls:C0007138    35
## umls:C0031099    28
## umls:C0005695    36

snp <- c("rs1401296", "rs9315050", "rs5498", "rs1524668", "rs147377392",
         "rs841", "rs909253", "rs7193343", "rs3918232", "rs3760396",
         "rs2231137", "rs10947803", "rs17222919", "rs386602276", "rs11053646",
         "rs1805192", "rs139564723", "rs2230806", "rs20417", "rs966221")
dgnv <- enrichDGNv(snp)
head(dgnv)

##                          ID
## umls:C3272363 umls:C3272363
## umls:C0948008 umls:C0948008
## umls:C0038454 umls:C0038454
## umls:C0027051 umls:C0027051
## umls:C0010054 umls:C0010054
## umls:C0010068 umls:C0010068
##                                     Description GeneRatio
## umls:C3272363 Ischemic Cerebrovascular Accident     20/20
## umls:C0948008                   Ischemic stroke     20/20
## umls:C0038454          Cerebrovascular accident      7/20
## umls:C0027051             Myocardial Infarction      6/20
## umls:C0010054         Coronary Arteriosclerosis      6/20
## umls:C0010068            Coronary heart disease      6/20
##                 BgRatio       pvalue     p.adjust
## umls:C3272363 141/46589 1.014503e-51 1.379725e-49
## umls:C0948008 148/46589 2.867870e-51 1.950151e-49
## umls:C0038454 243/46589 7.045680e-12 3.194042e-10
## umls:C0027051 163/46589 6.222154e-11 1.889883e-09
## umls:C0010054 166/46589 6.948100e-11 1.889883e-09
## umls:C0010068 314/46589 3.198889e-09 7.250815e-08
##                     qvalue
## umls:C3272363 1.922217e-50
## umls:C0948008 2.716929e-50
## umls:C0038454 4.449903e-11
## umls:C0027051 2.632964e-10
## umls:C0010054 2.632964e-10
## umls:C0010068 1.010175e-08
##                                                                                                                                                                                                              geneID
## umls:C3272363 rs1401296/rs9315050/rs5498/rs1524668/rs147377392/rs841/rs909253/rs7193343/rs3918232/rs3760396/rs2231137/rs10947803/rs17222919/rs386602276/rs11053646/rs1805192/rs139564723/rs2230806/rs20417/rs966221
## umls:C0948008 rs1401296/rs9315050/rs5498/rs1524668/rs147377392/rs841/rs909253/rs7193343/rs3918232/rs3760396/rs2231137/rs10947803/rs17222919/rs386602276/rs11053646/rs1805192/rs139564723/rs2230806/rs20417/rs966221
## umls:C0038454                                                                                                                              rs1524668/rs147377392/rs2231137/rs10947803/rs386602276/rs2230806/rs20417
## umls:C0027051                                                                                                                                              rs5498/rs147377392/rs909253/rs11053646/rs1805192/rs20417
## umls:C0010054                                                                                                                                             rs5498/rs147377392/rs11053646/rs1805192/rs2230806/rs20417
## umls:C0010068                                                                                                                                             rs5498/rs147377392/rs11053646/rs1805192/rs2230806/rs20417
##               Count
## umls:C3272363    20
## umls:C0948008    20
## umls:C0038454     7
## umls:C0027051     6
## umls:C0010054     6
## umls:C0010068     6

4.4 gseDO fuction

在下面的示例中，為了加快本文的編寫速度嘹狞，只測試了大小超過120的基因集岂膳，并且只執(zhí)行了100個排列。

library(DOSE)
data(geneList)
y <- gseDO(geneList,
           nPerm         = 100,
           minGSSize     = 120,
           pvalueCutoff  = 0.2,
           pAdjustMethod = "BH",
           verbose       = FALSE)
head(y, 3)

##                  ID            Description setSize
## DOID:114   DOID:114          heart disease     462
## DOID:1492 DOID:1492 eye and adnexa disease     459
## DOID:5614 DOID:5614            eye disease     450
##           enrichmentScore       NES     pvalue  p.adjust
## DOID:114       -0.2978223 -1.347617 0.01234568 0.1121429
## DOID:1492      -0.3105160 -1.403120 0.01234568 0.1121429
## DOID:5614      -0.3125247 -1.401403 0.01265823 0.1121429
##              qvalues rank                   leading_edge
## DOID:114  0.06992481 1904 tags=22%, list=15%, signal=19%
## DOID:1492 0.06992481 1793 tags=22%, list=14%, signal=19%
## DOID:5614 0.06992481 1768 tags=22%, list=14%, signal=19%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      core_enrichment
## DOID:114  4057/6649/10268/3567/4882/3910/3371/6548/3082/4153/29119/3791/182/3554/5813/1129/5624/3240/8743/7450/947/78987/1843/4179/7168/948/4314/10272/4881/2628/5021/4018/4256/187/6403/4322/2308/3752/1907/1511/283/3953/7078/2247/2281/10398/5468/10411/10203/1281/4023/83700/11167/7056/3952/126/6310/4313/5502/2944/6444/3075/2273/2099/3480/1471/7079/775/1909/2690/1363/4306/23414/5167/213/5350/5744/11188/2152/2697/185/2952/367/4982/7349/2200/4056/3572/2053/7122/1489/3479/2006/10266/9370/10699/4629/2167/652/1524/7021
## DOID:1492            8106/3371/3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/1195/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/1524
## DOID:5614                      3371/3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/1524

4.5 gseNCG fuction

ncg <- gseNCG(geneList,
              nPerm         = 100,
              minGSSize     = 120,
              pvalueCutoff  = 0.2,
              pAdjustMethod = "BH",
              verbose       = FALSE)
ncg <- setReadable(ncg, 'org.Hs.eg.db')
head(ncg, 3)

##                ID Description setSize enrichmentScore
## breast     breast      breast     133      -0.4869070
## lung         lung        lung     173      -0.3880662
## lymphoma lymphoma    lymphoma     188       0.2999589
##                NES     pvalue   p.adjust    qvalues rank
## breast   -1.904542 0.01492537 0.06666667 0.03508772 2930
## lung     -1.592997 0.02816901 0.06666667 0.03508772 2775
## lymphoma  1.346949 0.03333333 0.06666667 0.03508772 2087
##                            leading_edge
## breast   tags=33%, list=23%, signal=26%
## lung     tags=31%, list=22%, signal=25%
## lymphoma tags=21%, list=17%, signal=18%
##                                                                                                                                                                                                                 core_enrichment
## breast                                                                                     PTPRD/KMT2A/ERBB3/SETD2/ARID1A/GPS2/NCOR1/RB1/MAP2K4/NF1/TP53/PIK3R1/STK11/CDKN1B/PTGFR/APC/CCND1/TRAF5/MAP3K1/ESR1/TBX3/FOXA1/GATA3
## lung     PIK3C2B/SETD2/ATXN3L/LRP1B/BRD3/ARID1A/INHBA/RB1/ADCY1/LYRM9/NF1/CTNNB1/TP53/SATB2/STK11/CTIF/CTNNA3/KDR/COL11A1/FLT3/APC/ADGRL3/FGFR3/NCAM2/DIP2C/APLNR/SLIT2/EPHA3/RUNX1T1/ZMYND10/ZFHX4/GLI3/TNN/PLSCR4/DACH1/ERBB4
## lymphoma                                                                DUSP2/EZH2/PRDM1/MYC/ZWILCH/IKZF3/PLCG2/IDH2/H1-2/MAGEC3/CD79B/ETV6/H1-4/H1-5/IRF8/CD28/SLC29A2/DUSP9/TNFAIP3/DNMT3A/SYK/TNF/BCR/H1-3/DSC3/UBE2A/PABPC1

4.6 gseDGN fuction

dgn <- gseDGN(geneList,
              nPerm         = 100,
              minGSSize     = 120,
              pvalueCutoff  = 0.2,
              pAdjustMethod = "BH",
              verbose       = FALSE)
dgn <- setReadable(dgn, 'org.Hs.eg.db')
head(dgn, 3)

##                          ID       Description setSize
## umls:C0029456 umls:C0029456      Osteoporosis     375
## umls:C0011570 umls:C0011570 Mental Depression     483
## umls:C0042133 umls:C0042133  Uterine Fibroids     289
##               enrichmentScore       NES     pvalue
## umls:C0029456      -0.3439046 -1.519917 0.01190476
## umls:C0011570      -0.2874181 -1.281686 0.01265823
## umls:C0042133      -0.3210059 -1.374001 0.01265823
##                p.adjust    qvalues rank
## umls:C0029456 0.1123876 0.06861559 1766
## umls:C0011570 0.1123876 0.06861559 2587
## umls:C0042133 0.1123876 0.06861559 2105
##                                 leading_edge
## umls:C0029456 tags=23%, list=14%, signal=20%
## umls:C0011570 tags=25%, list=21%, signal=20%
## umls:C0042133 tags=25%, list=17%, signal=21%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  core_enrichment
## umls:C0029456                                                                                                                                                                                            RXRG/HGF/PTH1R/CYP1A1/JAG1/ROR2/FLT3/CUL9/EEF1A2/THSD4/BCL2/ITGAV/WIF1/GREM2/COL15A1/HPGDS/VGLL3/SLIT3/NRIP1/TMEM135/MGP/PLCL1/OSBPL1A/PIBF1/SELP/SPRY1/MMP13/ID4/SPP2/COL1A2/AOX1/ARHGEF3/GSN/TSC22D3/ATP1B1/NR5A2/ANKH/COL1A1/LEPR/THSD7A/GC/FGF2/PPARG/NOX4/ZNF266/GHRH/BHLHE40/SLC19A2/THBD/FLNB/KL/LEP/HSD17B4/CTSK/FTO/MMP2/ESR1/IGF1R/PTN/IRAK3/HSPA1L/CST3/GHR/SPARC/KDM4B/LRP1/INPP4B/BMPR1B/PTHLH/DPT/FRZB/GSTT1/AR/TNFRSF11B/IRS1/WLS/GSTM3/TGFBR3/TPH1/IGF1/SFRP4/CORIN/BMP4/CHAD/FOXA1/PGR
## umls:C0011570 SMPD1/ETS2/RGN/GRIA1/PTGS1/NLGN1/PDE4A/ADAMTS2/EHD3/NR5A1/SORCS3/A2M/KCNQ1/CRY1/ADRB2/FZD1/MYOM2/ADCY1/POU6F1/MAPK3/BICC1/SLC6A4/AHI1/TP53/DBP/SLC12A2/BDNF/NR3C1/SRSF5/PCLO/GABRA6/WWC1/IL5/GLUL/ELK3/GAD1/RARA/GRM5/ASAH1/IMPACT/CHRM2/WFS1/TSPAN31/ARGLU1/HP/PVALB/HTR1A/GPM6A/CYP2A6/DUSP1/NLGN4Y/F2R/CD36/DBH/BECN1/CCND1/PER3/OXTR/SGCE/CFB/CLASP2/LPAR1/NRP1/AVPR1B/ARSD/GC/FAAH/BHLHE41/FGF2/CD1C/ABCB1/PPARG/SRPX/RAPGEF3/CRHBP/CDH13/HSPA2/BHLHE40/PDE1A/LEP/FTO/PER2/ALPK1/GSTM1/DIXDC1/XBP1/TCF4/ESR1/IGF1R/NTF3/CACNA1C/NR3C2/SLC18A2/NTRK2/RAPGEF4/F3/AGTR1/TAC1/GSTT1/AR/UCN/FBN1/MAOA/CARTPT/TAT/ADRA2A/MUC1/TGFBR3/TPH1/IGF1/MAOB/ADIPOQ/TBC1D9/ADH1B/EMX2/MAPT/CRY2/GATA3/TFAP2B
## umls:C0042133                                                                                                                                                                                                                                                                                  PBX1/CTNNB1/TP53/FZD2/CYP2A13/SMAD3/ADAM12/COL4A6/HSD17B7/KAT6B/CYP1A1/BCL6/SST/EGR1/SALL1/NAALADL1/IGFBP7/BCL2/CD34/CCN2/HPGDS/MMP3/AHR/CCND1/HOXA5/OXTR/FERMT2/NR4A2/LAMB1/ADGRV1/FOXO1/FNDC3A/FOS/MME/FGF2/PPARG/TAGLN/CCNG1/ALDH1A1/IGFBP2/WNT5B/LEP/MMP2/GSTM1/GAS6/ESR1/IGF1R/CAV1/VCAN/EDNRA/GHR/LTBP2/SLC7A8/PTHLH/NTS/DPT/MST1/ZKSCAN7/F3/GJA1/ANO1/TGFB3/AR/FBN1/COL4A5/XIST/IGF1/MYH11/CCN5/CXCL14/PGR

References

A., Omer, Giovanni M. D., Thanos P. M., and Francesca D. C. 2016. “NCG 5.0: Updates of a Manually Curated Repository of Cancer Genes and Associated Properties from Cancer Mutational Screenings.” Nucleic Acids Research 44 (D1): D992–D999. https://doi.org/10.1093/nar/gkv1123.

Du, Pan, Gang Feng, Jared Flatow, Jie Song, Michelle Holko, Warren A. Kibbe, and Simon M. Lin. 2009. “From Disease Ontology to Disease-Ontology Lite: Statistical Methods to Adapt a General-Purpose Ontology for the Test of Gene-Ontology Associations.” Bioinformatics 25 (12): i63–i68. https://doi.org/10.1093/bioinformatics/btp193.

Janet, P., Núria Q. R., àlex B., Jordi D. P., Anna B. M., Martin B., Ferran S., and Laura I. F. 2015. “DisGeNET: A Discovery Platform for the Dynamical Exploration of Human Diseases and Their Genes.” Database 2015 (March): bav028. https://doi.org/10.1093/database/bav028.

Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” Bioinformatics 31 (4): 608–9. https://doi.org/10.1093/bioinformatics/btu684.

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