Abstract
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).
Highlights
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
One of the advantages of these sets of genes, whose function is already known, is that they can be used to interpret the results of new experiments: this has led to the implementation of a large number of methods for gene enrichment analysis [3]
Network enrichment analysis incorporates an evaluation of the level of association between genes in the target set and genes in the functional gene set into the test
Summary
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. One of the advantages of these sets of genes, whose function is already known, is that they can be used to interpret the results of new experiments: this has led to the implementation of a large number of methods for gene enrichment analysis [3]. Their aim is to compare gene expression levels under two different conditions (experimental vs control), and to detect which sets of genes are differentially expressed (enriched) in the experimental condition. A limitation that is common to both single and gene set enrichment analysis, is that these methods base computations on the level of overlap between sets of genes only, without considering associations and interactions between genes
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