Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

PLoS One. 2010 Nov 15;5(11):e13984. doi: 10.1371/journal.pone.0013984.

Abstract

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/).

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Breast Neoplasms / genetics
  • Cluster Analysis
  • Colonic Neoplasms / genetics
  • Computational Biology / methods*
  • Estrogens / pharmacology
  • Female
  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic / drug effects
  • Gene Regulatory Networks*
  • Humans
  • Internet
  • Reproducibility of Results
  • Software*

Substances

  • Estrogens