Publication Date:
2012-09-12
Description:
Background: Gene-set enrichment analyses (GEA or GSEA) are commonly used for biologicalcharacterization of an experimental gene-set. This is done by finding known functionalcategories, such as pathways or Gene Ontology terms, that are over-represented in theexperimental set; the assessment is based on an overlap statistic. Rich biologicalinformation in terms of gene interaction network is now widely available, but thistopological information is not used by GEA, so there is a need for methods that exploit thistype of information in high-throughput data analysis. Results: We developed a method of network enrichment analysis (NEA) that extends the overlapstatistic in GEA to network links between genes in the experimental set and those in thefunctional categories. For the crucial step in statistical inference, we developed a fastnetwork randomization algorithm in order to obtain the distribution of any network statisticunder the null hypothesis of no association between an experimental gene-set and afunctional category. We illustrate the NEA method using gene and protein expression datafrom 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 bynetwork connectivity rather than by simple overlaps.
Electronic ISSN:
1471-2105
Topics:
Biology
,
Computer Science
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