参考学习资料：https://yulab-smu.github.io/clusterProfiler-book/index.html
网红教授的一本书第四章
Chapter 4 Disease analysis
DOSE(Yu et al. 2015) 支持 Disease Ontology (DO) 富集分析. 函数 enrichDO 可以identifying disease association of interesting genes, 同时函数gseDO被设计用来对 DO进行GSEA.
此外 DOSE 还支持分析 Network of Cancer Gene (NCG)(A. et al. 2016)和 Disease Gene Network(Janet et al. 2015),更多更详细信息请查看 DOSE vignettes文档.
4.1 enrichDO function
下面是一个例子,选择fold change大于1.5的基因作为差异基因进行DO分析。
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
函数enrichDO需要entrezgene ID向量作为input数据, 通常是differential gene list of gene expression profile studies.如果使用者需要进行gene ID转换,推荐使用clusterProfiler包的bitr函数.
以上函数中ont 参数可以是“DO”或“DOLite”, DOLite(Du et al. 2009)也是基于DO构造的. 由于DOLite数据不支持更新, 更推荐使用ont="DO". pvalueCutoff 设置cutoff value of p value and p value adjust; pAdjustMethod setting the p value 矫正方法 包括 Bonferroni correction (“bonferroni”), Holm (“holm”), Hochberg (“hochberg”), Hommel (“hommel”), Benjamini & Hochberg (“BH”) and Benjamini & Yekutieli (“BY”)而 qvalueCutoff 用相较于对照的q-values.
universe 设置背景基因用于检验。如果不特意设置该参数， enrichDO 将会设置所有涉及人类的DO注释所用的基因。
minGSSize (and maxGSSize)指定的是那些DO terms大于minGSSize (小于maxGSSize) 注释的的基因被检测.
readable是一个逻辑值参数, 用来指示entrezgene IDs是否同时展示匹配的gene symbols。
还可以用setReadable 函数帮助用户进行entrezgene IDs 到 gene symbols的转换。
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) 是一个人工管理的癌症基因库。 NCG 5.0版本 (Aug. 2015) 共收集了1,571 cancer genes来自175篇已发表的文献。 DOSE 支持分析gene list 同时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) 是一个综合和全面的基因-疾病关联资源，它来自几个公共数据源和文献。它包含基因-疾病关联和SNP-基因-疾病关联。
疾病-基因关联的富集分析得到了enrichDGN函数的支持，SNP-基因-疾病关联的分析得到了enrichDGNv 函数的支持。
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，为了加快本文的编写速度，只测试了大小超过120的基因集，并且只执行了100个排列。
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
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