NEAT：一种高效的网络富集分析测试
NEAT: an efficient network enrichment analysis test
摘要：
网络富集分析是一种强大的方法，它允许将基因富集分析与基因网络提供的基因之间关系的信息相结合。现有的网络富集分析测试仅处理无向网络，它们可能计算缓慢并且基于正态性假设。我们提出了NEAT，网络富集分析的测试。该测试是基于超几何分布，这自然会出现在这种情况下的零分布。NEAT不仅适用于无向网络，也适用于有向网络和部分有向网络。我们的模拟表明，NEAT比其他基于重采样的方法快得多，并且其检测富集的能力至少与替代测试一样好。我们讨论了应用NEAT网络分析在酵母中的环境应激反应的目标基因集与GO苗条和KEGG功能基因集的富集测试，也通过测试GO苗条类别之间的关联。NEAT是一种灵活有效的网络富集分析测试，旨在克服现有基于重采样的测试的一些局限性。该方法在R包neat中实现，可以从CRAN（http：//cran.r-project.org/package=neat）免费下载。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
