Enhanced construction of gene regulatory networks using hub gene information

BMC Bioinformatics

Methodology Article

Faming Liang¹  Guanghua Xiao²  Xinlei Wang³  Donghyeon Yu⁴  Johan Lim⁵ 

[1] Department of Biostatistics, University of Florida, 2004 Mowry Road, FL 32611, Gainesville, USA;Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., TX 75390, Dallas, USA;Department of Statistical Science, Southern Methodist University, 6425 Boaz Lane, TX 75205, Dallas, USA;Department of Statistics, Inha University, Incheon, Korea;Department of Statistics, Seoul National University, Seoul, Korea;

关键词: Gene regulatory network;    Hub gene;    Partial correlation;    Sparse partial correlation estimation;    Escherichia coli;    Lung cancer;   

DOI  :  10.1186/s12859-017-1576-1

 received in 2016-12-21, accepted in 2017-03-03,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method.ResultsIn simulation studies, we show that the proposed strategy reduces errors in estimating network structures compared to the existing methods. When applied to Escherichia coli, the regulation network constructed by our proposed ESPACE method is more consistent with current biological knowledge than the SPACE method. Furthermore, application of the proposed method in lung cancer has identified hub genes whose mRNA expression predicts cancer progress and patient response to treatment.ConclusionsWe have demonstrated that incorporating hub gene information in estimating network structures can improve the performance of the existing methods.

【 授权许可】

CC BY   
© The Author(s) 2017

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