PLoS ONE · 2010 · 2.4K citations · 45 references
Life SciencesBioinformaticsVisualizationNetwork BiologyNetwork VisualizationScientific VisualizationFunctional Enrichment AnalysisGene Set Enrichment AnalysisNetwork ExplorationPathway VisualizationEnrichmentEnrichment AnalysisGene Network AnalysisNetwork-based Enrichment Analysis
BACKGROUND: Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. PRINCIPAL FINDINGS: To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. CONCLUSIONS: Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).
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Gene Ontology: tool for the unification of biology
Michael Ashburner, Catherine A. Ball, Judith A. Blake et al. · Nature Genetics · 2000 · 44.6K citations · Full text
KEGG: Kyoto Encyclopedia of Genes and Genomes
Hiroyuki Ogata, Susumu Goto, Kazushige Sato et al. · Nucleic Acids Research · 1999 · 32.4K citations · Full text