Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

BMC Bioinformatics

Methodology Article

Yudi Pawitan¹  Woojoo Lee²  Vladimir Lazar³  Philippe Dessen³  Justin Guegan³  Andrey Alexeyenko⁴  Janne Lehtiö⁵  Maria Pernemalm⁵ 

[1] Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;Department of Statistics, Inha University, Incheon, South Korea;Functional Genomics, Institut Gustave Roussy, Villejuif, France;School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden;Science for Life Laboratory, Stockholm, Sweden;Science for Life Laboratory, Stockholm, Sweden;Clinical Proteomics Mass Spectrometry, Karolinska Institutet, Stockholm, Sweden;

关键词: Gene Ontology;    Degree Distribution;    Differentially Express;    Driver Gene;    Gene Interaction Network;   

DOI  :  10.1186/1471-2105-13-226

 received in 2011-11-17, accepted in 2012-08-09,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundGene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.ResultsWe developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.ConclusionsThe results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

【 授权许可】

Unknown   
© Alexeyenko et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.