Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes

BMC Systems Biology

Jin Wang¹  Junfeng Zhang¹  Kunming Cao¹  Jialin Liu¹  Zuguang Gu¹ 

[1] The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, School of Life Science, Nanjing University, Nanjing, 210093, China

关键词: Gene expression data;    Centrality;    Biological network;    Pathway enrichment;   

Others  :  1144346
DOI  :  10.1186/1752-0509-6-56

 received in 2012-01-31, accepted in 2012-05-24,  发布年份 2012

【 摘 要 】

Background

Biological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability.

Results

A systematic and extensible pathway enrichment method in which nodes are weighted by network centrality was proposed. We demonstrate how choice of pathway structure and centrality measurement, as well as the presence of key genes, affects pathway significance. We emphasize two improvements of our method over current methods. First, allowing for the diversity of genes’ characters and the difficulty of covering gene importance from all aspects, we set centrality as an optional parameter in the model. Second, nodes rather than genes form the basic unit of pathways, such that one node can be composed of several genes and one gene may reside in different nodes. By comparing our methodology to the original enrichment method using both simulation data and real-world data, we demonstrate the efficacy of our method in finding new pathways from biological perspective.

Conclusions

Our method can benefit the systematic analysis of biological pathways and help to extract more meaningful information from gene expression data. The algorithm has been implemented as an R package CePa, and also a web-based version of CePa is provided.

【 授权许可】

   
2012 Gu et al.; licensee BioMed Central Ltd.

【 图 表 】

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