School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.
BMC Bioinformatics. 2012 Sep 11;13:226. doi: 10.1186/1471-2105-13-226.
Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.
We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.
The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.
基因集富集分析（GEA 或 GSEA）常用于对实验基因集进行生物学特征描述。这是通过找到已知的功能类别（如途径或基因本体论术语）来完成的，这些功能类别在实验集中过度表达；评估基于重叠统计量。现在广泛提供了关于基因相互作用网络的丰富生物学信息，但 GEA 并未利用这种拓扑信息，因此需要在高通量数据分析中利用这种类型信息的方法。
我们开发了一种网络富集分析（NEA）方法，该方法将 GEA 中的重叠统计量扩展到实验集中基因与功能类别中基因之间的网络链接。对于统计推断的关键步骤，我们开发了一种快速网络随机化算法，以便在实验基因集与功能类别之间不存在关联的零假设下获得任何网络统计量的分布。我们使用来自肺癌研究的基因和蛋白质表达数据说明了 NEA 方法。
结果表明，NEA 方法比传统的 GEA 更有效，主要是因为基因集之间的关系通过网络连接而不是简单的重叠得到了更强烈的捕捉。
