NEAT: an efficient network enrichment analysis test

TL;DR

NEAT is a fast, flexible network enrichment analysis test that extends to directed networks and outperforms existing methods in speed and detection capacity, with applications in gene network analysis.

Contribution

NEAT introduces a hypergeometric distribution-based test for network enrichment analysis applicable to directed and partially directed networks, improving speed and flexibility.

Findings

NEAT is significantly faster than resampling-based methods.

NEAT's detection capacity is comparable or superior to existing tests.

Applications demonstrate NEAT's effectiveness in gene network analysis.

Abstract

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. 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 testing for associations between GO Slim categories themselves. 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 (http://cran.r-project.org/package=neat).