NetGen: Network-based probabilistic generative model for gene set functional enrichment analysis

  • Last updated: October 26, 2016


High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression genes derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than other existing methods on the simulation studies. The effectiveness of NetGen was explored further on four real datasets applications. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. We believed that NetGen is an efficient computational tool for enrichment analysis and can help to explore the underlying pathogenesis of complex diseases.


  • Duanchen Sun, Yinliang Liu, Xiang-Sun Zhang, Ling-Yun Wu. NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis. In submission, 2016.


R package#

The NetGen method has been implemented as function netgen in R package CopTea, which can be found at:

Additional Materials#

Category: Supplementary Software

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