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

By Daniele Merico, Ruth Isserlin, Oliver Stueker, Andrew Emili, Gary D. Bader. PLoS ONE, 2010

2,139 citations recorded by the source corpus.

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/).

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48a4e4bc15679533e4f8d97674fbcef29c3a4606

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Citations in the corpus

A highly conserved program of neuronal microexons is misregulated in autistic brains (2014, methodology): “Gene Ontology analyses The Gene Ontology (GO) network for the human genes harboring neural-regulated AS events predicted to generate alternative protein isoforms (Figure 1C) was built using Enrichment Map (Merico et al., 2010) in Cytoscape 2.”
KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis (2021, background): “Therefore, several tools have provided downstream visualization of these enriched terms, including WebGestalt (29), Metascape (30), Enrichment map (31), WEGO (32) and GOPlot (33), in selecting and understanding the enriched results.”
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs (2016, background): “, 2011) and visualized as Enrichment Map (Merico et al., 2010).”
NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis (2019, methodology): “To improve the interpretability of the results from enrichment analysis, we have implemented an interactive enrichment network viewer based on a similar concept introduced by ClueGO and EnrichmentMap (32,33).”
Using clusterProfiler to characterize multiomics data (2024, positive): “For example, it introduces visualization methods such as enrichment maps, which effectively depict the relationships between enriched analysis pathways 26 .”
Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder (2017, positive): “The network was visualized using Cytoscape. r e s o u r c e potential interventions that could be used in addressing the biological deficits created by loss of function in these genes.”
GeneMANIA update 2018 (2018, methodology): “Also within Cytoscape, GeneMANIA services are being integrated with the Enrichment Map pathway analysis workflow (13).”