Cited by 2,235Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

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

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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/).

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Crossref · 10.1371/journal.pone.0013984

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  1. S Draghici, 2003, Global functional profiling of gene expression., Genomics, 81, 98

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Metadata

Authors
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D.
Affiliations
University of Toronto (CA)
Corresponding author
  • Merico, Daniele
  • Bader, Gary D.
Venue
PLoS ONE
Published
2010-11-15
Volume
5
Issue
11
Pages
e13984
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2097948046
Cited by
2,235
References
58
Citations by year
2026: 89 · 2025: 145 · 2024: 176 · 2023: 216 · 2022: 238 · 2021: 233 · 2020: 237 · 2019: 164 · 2018: 158 · 2017: 145 · 2016: 127 · 2015: 101 · 2014: 79 · 2013: 69 · 2012: 43
FWCI
16.30
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • Heart and Stroke Foundation of Canada
  • Genome Canada
  • Ontario Genomics
  • Ontario Genomics Institute
  • Hospital for Sick Children
  • National Institutes of Health
  • National Human Genome Research Institute (P41 P41HG04118, P41HG04118, P41HG004118)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
gene set enrichment analysis, network visualization, gene set overlap, network clustering, pathway over-representation, gene expression data interpretation, Cytoscape plugin
MeSH terms
Algorithms, Breast Neoplasms, Colonic Neoplasms, Estrogens, Female, Humans, Software, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Cluster Analysis, Computational Biology, Internet, Gene Expression Profiling, Gene Regulatory Networks

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