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Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
Open accessAbstract
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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“Furthermore, gene set enrichment analysis (GSEA) revealed that the hypoxia pathway is impaired in miR-140G/G, but not in miR-140−/− chondrocytes”
From Gain-of-function mutation of microRNA-140 in human skeletal dysplasia (2019) · cites this paper as [26]
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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
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-11-15
- Volume
- 5
- Issue
- 11
- Pages
- e13984
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2981572
- 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
- gold
- doi.org
- 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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