NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis

doi:10.1186/s12918-017-0456-7

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	NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis
	Sun, Duanchen 1,2,3; Liu, Yinliang 1,2,3; Zhang, Xiang-Sun1 ; Wu, Ling-Yun1,2,3
	2017-09-21
发表期刊	BMC SYSTEMS BIOLOGY
ISSN	1752-0509
卷号	11 页码:14
摘要	Background: 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 gene sets 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. Results: 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 the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. 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 (http://github.com/wulingyun/CopTea/). Conclusion: Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.
关键词	Gene ontology Enrichment analysis Network-based probabilistic generative model Integer programming Complex diseases
DOI	10.1186/s12918-017-0456-7
语种	英语
资助项目	Chinese Academy of Sciences[XDB13040600] ; National Natural Science Foundation of China[11131009] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[91330114]
WOS研究方向	Mathematical & Computational Biology
WOS类目	Mathematical & Computational Biology
WOS记录号	WOS:000411365600007
出版者	BIOMED CENTRAL LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/26718
专题	应用数学研究所
通讯作者	Wu, Ling-Yun
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Sun, Duanchen,Liu, Yinliang,Zhang, Xiang-Sun,et al. NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis[J]. BMC SYSTEMS BIOLOGY,2017,11:14.
APA	Sun, Duanchen,Liu, Yinliang,Zhang, Xiang-Sun,&Wu, Ling-Yun.(2017).NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.BMC SYSTEMS BIOLOGY,11,14.
MLA	Sun, Duanchen,et al."NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis".BMC SYSTEMS BIOLOGY 11(2017):14.