  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).
