Abstract
BackgroundGene-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 FindingsTo 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.ConclusionsEnrichment 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/).
Highlights
High-throughput genomic experiments often lead to the identification of large gene lists [1,2,3]
ClueGO and Molecular Concept Maps (MCM) create similar networks, Enrichment Map uses a visual style that we find more intuitive and offers improved functionality: two different enrichment experiments can be comparatively analyzed by displaying them in the same map; new query gene-sets can be compared to existing gene-sets postanalysis; a heat-map can be used to explore the data underlying the enrichment results for any geneset; Enrichment Map is modular, enabling use with any type of enrichment test or gene-set source
To simplify the navigation and interpretation of enrichment results, we have developed Enrichment Map, a network-based gene-set enrichment result visualization method
Summary
High-throughput genomic experiments often lead to the identification of large gene lists [1,2,3]. Gene expression values are ranked to identify the top-most list of expressed genes, based on an arbitrary expression threshold, or a set of gene expression experiments are clustered, each cluster defining a potentially large gene list These methods for finding interesting genes often do not help the interpretation of the resulting gene lists and the formulation of consistent biological hypotheses from these results still poses a major challenge for experimentalists. 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. The increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal
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