NEAT: an efficient network enrichment analysis test.
BACKGROUND
Network 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.


RESULTS
We 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.


CONCLUSIONS
NEAT 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 ).
Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands.|Department of Statistical Sciences, University of Padova, Via C. Battisti 241, Padova, 35121, Italy.
Mirko
M
Signorelli
Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, London, UK.
Veronica
V
Vinciotti
Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands. e.c.wit@rug.nl.
Ernst C
http://orcid.org/0000-0002-3671-9610
EC
Wit
D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D012984:Software; D013312:Stress, Physiological / Q000235:genetics
Enrichment analysis; Gene expression; Hypergeometric; Network