We developed DOSE(Yu et al. 2015) package to promote the investigation of diseases. DOSE provides five methods for measuring semantic similarities among DO terms and gene products, hypergeometric model and gene set enrichment analysis (GSEA) for associating disease with gene list and extracting disease association insight from genome wide expression profiles.
The enrichDO() function requires an entrezgene ID vector as input, which is mostly the differential gene list from gene expression profile studies. Please refer to Section 19.1 if you need to convert other gene ID types to entrezgene ID.
The ont parameter can be “HDO” (Human Disease Ontology), “HPO” (Human Phenotype Ontology) or “MPO” (Mouse Phenotype Ontology). pvalueCutoff setting the cutoff value of p value and adjusted p value; 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 sets the background gene universe for testing. If users do not explicitly set 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) annotated genes will be tested.
The readable is a logical parameter that indicates whether the entrezgene IDs will be mapped to gene symbols or not, see also Section 19.2.
9.1.2 Over-representation analysis for the network of cancer gene
Network of Cancer Gene (NCG) (A. et al. 2016) 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.
enrichDGN(), enrichDGNv() and gseDGN() no longer exist. DOSE used to provide over-representation and GSEA against DisGeNET(Janet et al. 2015), an integrative resource of gene-disease associations. DisGeNET changed its licence from CC BY-NC-SA to a proprietary one, so the data can no longer be redistributed with the package, and the functions and their bundled datasets were removed in DOSE 4.7.0 (June 2026).
If you have code calling them, use the Disease Ontology route instead:
## over-representationx <-enrichDO(gene, ont ="HDO")## GSEAy <-gseDO(geneList, ont ="HDO")
ont = "HDO" is the human Disease Ontology and is the default. This is a genuine change of data source: DO annotates genes to disease terms, whereas DisGeNET aggregated gene-disease associations from several sources, so the results are not interchangeable. DisGeNET itself is still available for direct download from its own website under its current terms.
9.2 Disease gene set enrichment analysis
9.2.1gseDO function
In the following example, in order to speed up the compilation of this document, only gene sets with size above 120 were tested and only 100 permutations were performed.
ID
breast_fibroepithelial_tumours breast_fibroepithelial_tumours
pan-gynecological and breast pan-gynecological and breast
pan-gastric pan-gastric
Description setSize
breast_fibroepithelial_tumours breast_fibroepithelial_tumours 17
pan-gynecological and breast pan-gynecological and breast 43
pan-gastric pan-gastric 49
enrichmentScore NES pvalue p.adjust
breast_fibroepithelial_tumours -0.6421578 -1.706444 0.011340497 0.2296451
pan-gynecological and breast -0.5263429 -1.696113 0.005250418 0.1809026
pan-gastric -0.4993799 -1.654372 0.006700095 0.1809026
qvalue rank leading_edge
breast_fibroepithelial_tumours 0.12292893 2700 tags=53%, list=22%, signal=42%
pan-gynecological and breast 0.09683708 2464 tags=40%, list=20%, signal=32%
pan-gastric 0.09683708 3280 tags=49%, list=26%, signal=36%
core_enrichment
breast_fibroepithelial_tumours SETD2/RB1/PCNX4/NF1/TP53/RARA/SYNE1/MAP3K1/ERBB4
pan-gynecological and breast NIPBL/SPOP/ARID1A/RASA1/RB1/RNF43/MAP2K4/NF1/CTNNB1/TP53/PIK3R1/CDKN1B/CCND1/ARID5B/MAP3K1/TBX3/GATA3
pan-gastric BCOR/SOX9/TCF7L2/ATM/CALD1/SEMG2/HTR7/ARID1A/RASA1/RB1/TTBK2/RNF43/CTNNB1/TP53/BCL9/SMAD3/APC/ZFP36L2/TGFBR2/MUC6/MAP3K1/CACNA1C/ATP8B1/CYP4B1
log2err
breast_fibroepithelial_tumours 0.3807304
pan-gynecological and breast 0.4070179
pan-gastric 0.4070179
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): D992–99. https://doi.org/10.1093/nar/gkv1123.
Janet, P., Núria Q. R., Àlex B., et al. 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.
Schriml, L. M., C. Arze, S. Nadendla, et al. 2011. “Disease Ontology: A Backbone for Disease Semantic Integration.”Nucleic Acids Research 40 (D1): D940–46. https://doi.org/10.1093/nar/gkr972.
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.