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Open Access Highly Accessed Methodology article

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

Andrey Alexeyenko12, Woojoo Lee3, Maria Pernemalm24, Justin Guegan5, Philippe Dessen5, Vladimir Lazar5, Janne Lehtiö24 and Yudi Pawitan6*

Author affiliations

1 School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden

2 , Science for Life Laboratory, Stockholm, Sweden

3 Department of Statistics, Inha University, Incheon, South Korea

4 Clinical Proteomics Mass Spectrometry, Karolinska Institutet, Stockholm, Sweden

5 Functional Genomics, Institut Gustave Roussy, Villejuif, France

6 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

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Citation and License

BMC Bioinformatics 2012, 13:226  doi:10.1186/1471-2105-13-226

Published: 11 September 2012

Abstract

Background

Gene-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.

Results

We 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.

Conclusions

The 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.