EnrichmentMap Cytoscape App 3.3¶
Enrichment analysis (also known as functional enrichment) is an helpful technique for high-throughput data interpretation. Given a list of genes resulting from an experiment, enrichment analysis enables to identify functional categories that are over-represented. Such functional categories are typically derived from functional annotations (such as the Gene Ontology), or from pathway databases (such as KEGG), or other resources (such as the collection of disease signatures in MSig DB, or protein complexes in MIPS).
However, enrichment results are often characterized by lots of redundancy and inter-dependencies between gene-sets representing functional categories. For instance, Response to radiation, DNA Integrity Checkpoint and p53 Pathway have several genes in common. Since the typical enrichment analysis can output up to 300 hundred different gene-sets, some form of organization is required to navigate results.
To address this, we organize gene-sets into a network, called enrichment map. Two gene-sets are connected in the enrichment map network if they have a high overlap, i.e. if they share many genes. Applying automatic layout techniques, groups of inter-related gene-sets tend to cluster together, providing for a much easier and intuitive visualization.
Feature Requests and Reporting Bugs¶
The EnrichmentMap GitHub issue tracker can be used to report a bug or request a feature.
To Report a bug:
- Go to https://github.com/BaderLab/EnrichmentMapApp/issues
- Click on New Issue
- Write a short description of the issue. It is very helpful to provide a series of steps that can be taken to reproduce the issue.
- If possible attach a session file (.cys) or example input files.
- Enter App version, Cytoscape version and operating system.
- Click on Submit new issue
Examples of Use¶
- Functional impact of global rare copy number variation in autism spectrum disorders.Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, et al.
Papers Citing Enrichment Map¶
- Pathway analysis of expression data: deciphering functional building blocks of complex diseases.Emmert-Streib F, Glazko GV.PLoS Comput Biol. 2011 May;7(5):e1002053.
- Inflammasome is a central player in the induction of obesity and insulin resistance.Stienstra R, van Diepen JA, Tack CJ, Zaki MH, van de Veerdonk FL, Perera D, Neale GA, Hooiveld GJ, Hijmans A, Vroegrijk I, van den Berg S, Romijn J, Rensen PC, Joosten LA, Netea MG, Kanneganti TD.Proc Natl Acad Sci U S A. 2011 Aug 29.
- Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa EpendymomaWitt H, Mack SC, Ryzhova M, Bender S, Sill M, Isserlin R, Benner A, Hielscher T, Milde T, Remke M, Jones DTW, Northcott PA, Garzia L, Bertrand KC, Wittmann A, Yao Y, Roberts SS, Massimi L, Van Meter T, Weiss WA, Gupta N, Grajkowska W, Lach B, Cho YJ, von Deimling A, Kulozik AE, Witt O, Bader GD, Hawkins CE, Tabori U, Guha A, Rutka JT, Lichter P, Korshunov A, Taylor MD, Pfister SM
- What’s New
- Quick Tutorial
- Creating a Network
- Network Visualization
- Main Panel
- Heat Map Panel
- Legend Dialog
- Post Analysis (Add Signature Gene Sets)
- Creating Networks in Other Apps
- Creating Networks from Other Apps
- GSEA Integration
- File Formats
- Tips on Parameter Choice
- EnrichmentMap Protocol
- Download Gene Set Files
- Automating EnrichmentMap