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NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis
Sun, Duanchen1,2,3; Liu, Yinliang1,2,3; Zhang, Xiang-Sun1; Wu, Ling-Yun1,2,3
2017-09-21
Source PublicationBMC SYSTEMS BIOLOGY
ISSN1752-0509
Volume11Pages:14
AbstractBackground: 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.
KeywordGene ontology Enrichment analysis Network-based probabilistic generative model Integer programming Complex diseases
DOI10.1186/s12918-017-0456-7
Language英语
Funding ProjectChinese 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 Research AreaMathematical & Computational Biology
WOS SubjectMathematical & Computational Biology
WOS IDWOS:000411365600007
PublisherBIOMED CENTRAL LTD
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26718
Collection应用数学研究所
Corresponding AuthorWu, Ling-Yun
Affiliation1.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
Recommended Citation
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.
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