geneList datasetDOSE provides an example dataset geneList which was derived from R package breastCancerMAINZ that contained 200 samples, including 29 samples in grade I, 136 samples in grade II and 35 samples in grade III. We computed the ratios of geometric means of grade III samples versus geometric means of grade I samples. Logarithm of these ratios (base 2) were stored in geneList dataset.
Over-representation test1 is a widely used approach to identify biological themes. DOSE implements hypergeometric model to assess whether the number of selected genes associated with disease is larger than expected.
To determine whether any terms annotate a specified list of genes at frequency greater than that would be expected by chance, DOSE calculates a p-value using the hypergeometric distribution:
\(p = 1 - \displaystyle\sum_{i = 0}^{k-1}\frac{{M \choose i}{{N-M} \choose {n-i}}} {{N \choose n}}\)
In this equation, N is the total number of genes in the background distribution,
M is the number of genes within that distribution that are annotated (either directly or indirectly) to the
node of interest,
n is the size of the list of genes of interest and k is the number of genes within that list which
are annotated to the node. The background distribution by default is
all the genes that have annotation. User can set the background via universe parameter.
P-values were adjusted for multiple comparison, and q-values were also calculated for FDR control.
enrichDO functionIn the following example, we selected fold change above 1 as the differential genes and analyzing their disease association.
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 GeneRatio BgRatio
## DOID:170 DOID:170 endocrine gland cancer 48/331 472/6268
## DOID:10283 DOID:10283 prostate cancer 40/331 394/6268
## DOID:3459 DOID:3459 breast carcinoma 37/331 357/6268
## DOID:3856 DOID:3856 male reproductive organ cancer 40/331 404/6268
## DOID:824 DOID:824 periodontitis 16/331 109/6268
## DOID:3905 DOID:3905 lung carcinoma 43/331 465/6268
## pvalue p.adjust qvalue
## DOID:170 5.662129e-06 0.004784499 0.003826407
## DOID:10283 3.859157e-05 0.013921739 0.011133923
## DOID:3459 4.942629e-05 0.013921739 0.011133923
## DOID:3856 6.821467e-05 0.014410349 0.011524689
## DOID:824 1.699304e-04 0.018859464 0.015082872
## DOID:3905 1.749754e-04 0.018859464 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 43The enrichDO function requires an entrezgene ID vector as input, mostly is the differential gene list of gene expression profile studies. If user needs to convert other gene ID type to entrezgene ID, we recommend using bitr function provided by clusterProfiler.
The ont parameter can be “DO” or “DOLite”, DOLite2 was constructed to aggregate the redundant DO terms. The DOLite data is not updated, we recommend user use ont="DO". pvalueCutoff setting the cutoff value of p value and p value adjust; pAdjustMethod setting the p value correction methods, include the Bonferroni correction (“bonferroni”), Holm (“holm”), Hochberg (“hochberg”), Hommel (“hommel”), Benjamini & Hochberg (“BH”) and Benjamini & Yekutieli (“BY”) while qvalueCutoff is used to control q-values.
The universe setting the background gene universe for testing. If user do not explicitly setting this parameter, enrichDO will set the universe to all human genes that have DO annotation.
The minGSSize (and maxGSSize) indicates that only those DO terms that have more than minGSSize (and less than maxGSSize) genes annotated will be tested.
The readable is a logical parameter, indicates whether the entrezgene IDs will mapping to gene symbols or not.
We also implement setReadable function that helps the user to convert entrezgene IDs to gene symbols.
x <- setReadable(x, 'org.Hs.eg.db')
head(x)## ID Description GeneRatio BgRatio
## DOID:170 DOID:170 endocrine gland cancer 48/331 472/6268
## DOID:10283 DOID:10283 prostate cancer 40/331 394/6268
## DOID:3459 DOID:3459 breast carcinoma 37/331 357/6268
## DOID:3856 DOID:3856 male reproductive organ cancer 40/331 404/6268
## DOID:824 DOID:824 periodontitis 16/331 109/6268
## DOID:3905 DOID:3905 lung carcinoma 43/331 465/6268
## pvalue p.adjust qvalue
## DOID:170 5.662129e-06 0.004784499 0.003826407
## DOID:10283 3.859157e-05 0.013921739 0.011133923
## DOID:3459 4.942629e-05 0.013921739 0.011133923
## DOID:3856 6.821467e-05 0.014410349 0.011524689
## DOID:824 1.699304e-04 0.018859464 0.015082872
## DOID:3905 1.749754e-04 0.018859464 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/WISP2/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 43enrichNCG functionNetwork of Cancer Gene (NCG)3 is a manually curated repository of cancer genes. NCG release 5.0 (Aug. 2015) collects 1,571 cancer genes from 175 published studies. DOSE supports analyzing gene list and 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 Description GeneRatio
## soft_tissue_sarcomas soft_tissue_sarcomas soft_tissue_sarcomas 28/1172
## bladder bladder bladder 61/1172
## glioma glioma glioma 68/1172
## BgRatio pvalue p.adjust qvalue
## soft_tissue_sarcomas 28/1571 0.0002517511 0.008056037 0.006360029
## bladder 67/1571 0.0005108168 0.008173069 0.006452423
## glioma 76/1571 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 68enrichDGN and enrichDGNv functionsDisGeNET4 is an integrative and comprehensive resources of gene-disease associations from several public data sources and the literature. It contains gene-disease associations and snp-gene-disease associations.
The enrichment analysis of disease-gene associations is supported by the enrichDGN function and analysis of snp-gene-disease associations is supported by the enrichDGNv function.
dgn <- enrichDGN(gene)
head(dgn)## ID Description GeneRatio
## umls:C1134719 umls:C1134719 Invasive Ductal Breast Carcinoma 28/476
## umls:C0032460 umls:C0032460 Polycystic Ovary Syndrome 38/476
## umls:C0206698 umls:C0206698 Cholangiocarcinoma 36/476
## umls:C0007138 umls:C0007138 Carcinoma, Transitional Cell 35/476
## umls:C0031099 umls:C0031099 Periodontitis 28/476
## umls:C0005695 umls:C0005695 Bladder Neoplasm 36/476
## BgRatio pvalue p.adjust qvalue
## umls:C1134719 231/17381 4.312190e-11 1.225524e-07 9.164539e-08
## umls:C0032460 434/17381 2.819624e-10 3.521620e-07 2.633487e-07
## umls:C0206698 399/17381 3.717403e-10 3.521620e-07 2.633487e-07
## umls:C0007138 389/17381 7.093837e-10 5.040171e-07 3.769068e-07
## umls:C0031099 270/17381 1.634417e-09 9.290027e-07 6.947133e-07
## umls:C0005695 442/17381 5.871618e-09 2.781190e-06 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 36snp <- 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 Description GeneRatio
## umls:C3272363 umls:C3272363 Ischemic Cerebrovascular Accident 20/20
## umls:C0948008 umls:C0948008 Ischemic stroke 20/20
## umls:C0038454 umls:C0038454 Cerebrovascular accident 7/20
## umls:C0027051 umls:C0027051 Myocardial Infarction 6/20
## umls:C0010054 umls:C0010054 Coronary Arteriosclerosis 6/20
## umls:C0010068 umls:C0010068 Coronary heart disease 6/20
## BgRatio pvalue p.adjust qvalue
## umls:C3272363 141/46589 1.014503e-51 1.379725e-49 1.922217e-50
## umls:C0948008 148/46589 2.867870e-51 1.950151e-49 2.716929e-50
## umls:C0038454 243/46589 7.045680e-12 3.194042e-10 4.449903e-11
## umls:C0027051 163/46589 6.222154e-11 1.889883e-09 2.632964e-10
## umls:C0010054 166/46589 6.948100e-11 1.889883e-09 2.632964e-10
## umls:C0010068 314/46589 3.198889e-09 7.250815e-08 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 6Disease analysis using NGS data (eg, RNA-Seq and ChIP-Seq) can be performed by linking coding and non-coding regions to coding genes via ChIPseeker package, which can annotates genomic regions to their nearest genes, host genes, and flanking genes respectivly. In addtion, it provides a function, seq2gene, that simultaneously considering host genes, promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function maps genomic regions to genes in a many-to-many manner and facilitate functional analysis. For more details, please refer to ChIPseeker5.
We have developed an R package clusterProfiler6 for comparing biological themes among gene clusters.
DOSE works fine with clusterProfiler and can compare biological themes at disease perspective.
library(clusterProfiler)
data(gcSample)
cdo <- compareCluster(gcSample, fun="enrichDO")
plot(cdo)We provide enrichment analysis in r Biocpkg("clusterProfiler")6 for GO, KEGG,
DAVID, Molecular Signatures Database and others (user’s annotation),
meshes for MeSH enrichment analysis and Reactome pathway enrichment analysis in ReactomePA7 package. Both hypergeometric test and GSEA are supported.
1. Boyle, E. I. et al. GO::TermFinder–open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics (Oxford, England) 20, 3710–3715 (2004).
2. Du, P. et al. From disease ontology to disease-ontology lite: Statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics 25, i63–i68 (2009).
3. A., O., D., G. M., M., T. P. & C., F. D. NCG 5.0: Updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings. Nucleic Acids Research 44, D992–D999 (2016).
4. Janet, P. et al. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).
5. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: An r/bioconductor package for chip peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
6. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an r package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 16, 284–287 (2012).
7. Yu, G. & He, Q.-Y. ReactomePA: An r/bioconductor package for reactome pathway analysis and visualization. Mol. BioSyst. 12, 477–479 (2016).