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.
13.1 Chapter overview
Aspect
Disease and phenotype enrichment
Questions
Are selected genes associated with disease/phenotype terms, or are disease gene sets enriched across a ranked profile?
Input
Entrez IDs for ORA or a named ranked vector for GSEA; choose HDO, HPO, or MPO as appropriate.
Methods
Disease Ontology and phenotype ORA/GSEA, plus Network of Cancer Gene enrichment.
Main functions
enrichDO(), gseDO(), enrichNCG(), gseNCG().
Output
enrichResult / gseaResult objects for filtering, comparison, and visualization.
Main limitations
Interpretation depends on ontology and annotation coverage; NCG is a separately curated resource. Keep organism, ID type, universe, and ontology explicit.
13.2 Disease over-representation analysis
DOSE supports enrichment analysis of Disease Ontology (DO) (Schriml et al. 2011) and Network of Cancer Gene(An et al. 2016). NCG has not been updated since release 5.0, so enrichment results against it should be read as a historical resource. In addition, several visualization methods were provided by enrichplot to help interpreting semantic and enrichment results.
13.2.1 Over-representation analysis for disease ontology
In the following example, we selected genes with fold change above 1.5 as the differential genes and analyzed their disease association.
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 18.1 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) indicate that only those DO terms whose overlap with the provided universe has size in [minGSSize, maxGSSize] are tested. When no universe is set, enrichDO() uses all annotated genes as the background, so the bound applies to the raw annotation size instead. Pass a character universe to use the overlap rule; this matches the GO enrichment behavior described in Tuning minGSSize and maxGSSize; to disable the upper cap, set maxGSSize = Inf.
The readable is a logical parameter that indicates whether the entrezgene IDs will be mapped to gene symbols or not, see also Section 18.2.
13.2.2 Over-representation analysis for the network of cancer gene
Network of Cancer Gene (NCG) (An 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. The database has not been updated since that release, so the genes it contributes should be read as a historical snapshot rather than a current cancer gene census. DOSE supports analyzing gene list and determine whether they are enriched in genes known to be mutated in a given cancer type.
NCG is a remote service served by the KCL group and can fail transiently, so the calls in this chapter are wrapped in the retry() helper defined in _common.R; readers who prefer a single attempt can simply drop the wrapper in their own analysis.
enrichDGN(), enrichDGNv() and gseDGN() no longer exist. DOSE used to provide over-representation and GSEA against DisGeNET(Piñero 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.
13.3 Disease gene set enrichment analysis
13.3.1gseDO 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
pan-gynecological and breast pan-gynecological and breast
breast_fibroepithelial_tumours breast_fibroepithelial_tumours
pan-gastric pan-gastric
Description setSize
pan-gynecological and breast pan-gynecological and breast 43
breast_fibroepithelial_tumours breast_fibroepithelial_tumours 17
pan-gastric pan-gastric 49
enrichmentScore NES pvalue p.adjust
pan-gynecological and breast -0.5263429 -1.678271 0.003781255 0.1640769
breast_fibroepithelial_tumours -0.6421578 -1.662979 0.010655047 0.2157647
pan-gastric -0.4993799 -1.649388 0.004051281 0.1640769
qvalue rank leading_edge
pan-gynecological and breast 0.09753237 2464 tags=40%, list=20%, signal=32%
breast_fibroepithelial_tumours 0.12825722 2700 tags=53%, list=22%, signal=42%
pan-gastric 0.09753237 3280 tags=49%, list=26%, signal=36%
core_enrichment
pan-gynecological and breast NIPBL/SPOP/ARID1A/RASA1/RB1/RNF43/MAP2K4/NF1/CTNNB1/TP53/PIK3R1/CDKN1B/CCND1/ARID5B/MAP3K1/TBX3/GATA3
breast_fibroepithelial_tumours SETD2/RB1/PCNX4/NF1/TP53/RARA/SYNE1/MAP3K1/ERBB4
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
pan-gynecological and breast 0.4317077
breast_fibroepithelial_tumours 0.3807304
pan-gastric 0.4070179
An, Omer, Giovanni M. Dall’Olio, Thanos P. Mourikis, and Francesca D. Ciccarelli. 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.
Piñero, Janet, Núria Queralt-Rosinach, Àlex Bravo, 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.