Bayesian functional enrichment analysis for the Reactome database  

作　　者：Jing Cao 

机构地区：[1]Department of Statistical Science,Southern Methodist University,Dallas,TX,U.S.A

出　　处：《Statistical Theory and Related Fields》2017年第2期185-193,共9页统计理论及其应用（英文）

基　　金：This work has been supported in part by National Institutes of Health(NIH)[grant number 1R15HG006365-01];National Science Foundation(NSF)[grant number IIS-1302564].

摘　　要：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 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]