ArticleBioinformatics (Oxford, England)2010
GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks.Biophysical reviews · 2019Review
- The Gpr1-regulated Sur7 family protein Sfp2 is required for hyphal growth and cell wall stability in the mycoparasite Trichoderma atroviride.Scientific reports · 2018Article
- A decision analysis model for KEGG pathway analysis.BMC bioinformatics · 2016Article
- Human germline and pan-cancer variomes and their distinct functional profiles.Nucleic acids research · 2014Article
- A Bayesian extension of the hypergeometric test for functional enrichment analysis.Biometrics · 2014Article
- Enrichment analysis applied to disease prognosis.Journal of biomedical semantics · 2013Article
- Enlarging cells initiating apomixis in Hieracium praealtum transition to an embryo sac program prior to entering mitosis.Plant physiology · 2013Article
- Bayesian assignment of gene ontology terms to gene expression experiments.Bioinformatics (Oxford, England) · 2012Article
- Pathway analysis of genomic data: concepts, methods, and prospects for future development.Trends in genetics : TIG · 2012Review
- nEASE: a method for gene ontology subclassification of high-throughput gene expression data.Bioinformatics (Oxford, England) · 2012Article
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- SegMine workflows for semantic microarray data analysis in Orange4WS.BMC bioinformatics · 2011Article
- Glucan Biosynthesis Protein G Is a Suitable Reference Gene in Escherichia coli K-12.ISRN microbiology · 2011Article
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4 authors.
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Abstract
motivationA typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy.
resultsWe have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.
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