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bayesian functional enrichment analysis for the Reactome database

Statistical Theory and Related Fields 2017年第2期1卷 185-193页

作者： Jing Cao Department of Statistical Science Southern Methodist UniversityDallasTXU.S.A

The first step in the analysis of high-throughput experiment results is often to identify genes orproteins with certain characteristics, such as genes being differentially expressed (DE). To gainmore insights into the underlying biology, functional enrichment analysis is then conductedto provide functional interpretation for the identified genes or proteins. The hypergeometricP value has been widely used to investigate whether genes from predefined functional terms,e.g., Reactome, are enriched in the DE genes. The hypergeometric P value has several limitations: (1) computed independently for each term, thus neglecting biological dependence;(2) subject to a size constraint that leads to the tendency of selecting less-specific terms. In this paper,a bayesian approach is proposed to overcome these limitations by incorporating the interconnected dependence structure of biological functions in the Reactome database through a CARprior in a bayesian hierarchical logistic model. The inference on functional enrichment is thenbased on posterior probabilities that are immune to the size constraint. This method can detectmoderate but consistent enrichment signals and identify sets of closely related and biologicallymeaningful functional terms rather than isolated terms. The performance of the bayesian methodis demonstrated via a simulation study and a real data application.

关键词： Functional enrichment analysis Reactome hypergeometric P value bayesian hierarchical logistic model conditional autoregressive prior

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