Network enrichment analysis (NEA) is a powerful method, that integrates gene enrichment analysis with information on dependences between genes . Existing tests for NEA rely on normality assumptions, they can deal only with undirected networks and are computationally slow . We propose PNEA, an alternative test based on the hypergeometric distribution . PNEA can be applied also to directed and mixed networks , and our simulations show that it is faster and more powerful than existing NEA tests. The method is implemented in the R package pnea, that can be freely downloaded from CRAN repositories. Application to genetic data shows that PNEA detects most of the enrichments that are found with traditional GEA tests, and unveils some further enrichments that would be overlooked, if dependences between genes were ignored.
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 URL
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information on dependences between genes
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NEA rely on normality assumptions, they can
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and are computationally slow
, they can be computationally slow and are based on normality assumptions
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PNEA, an alternative test
NEAT, a test for network enrichment analysis. The test is
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also to directed and mixed networks , and our simulations show that it is faster and more powerful than existing NEA
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
R
pnea, that
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repositories. Application to genetic data shows that PNEA detects most of the enrichments that are found with traditional GEA tests, and unveils some further enrichments that would be overlooked, if dependences between genes were ignored.
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