NEAT: an efficient network enrichment analysis test

BMC Bioinformatics

Methodology Article

Veronica Vinciotti¹  Ernst C. Wit²  Mirko Signorelli³ 

[1] Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, London, UK;Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, Netherlands;Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, Netherlands;Department of Statistical Sciences, University of Padova, Via C. Battisti 241, 35121, Padova, Italy;

关键词: Network;    Enrichment analysis;    Gene expression;    Hypergeometric;   

DOI  :  10.1186/s12859-016-1203-6

 received in 2016-01-07, accepted in 2016-08-24,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundNetwork enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions.ResultsWe propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves.ConclusionsNEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN (https://cran.r-project.org/package=neat).

【 授权许可】

CC BY   
© The Author(s) 2016

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