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CluMa-GO: Bring Gene Ontologies and Hierarchical Clusterings Together
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (ISOVIS)ORCID iD: 0000-0002-0519-2537
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (ISOVIS)ORCID iD: 0000-0001-6745-4398
2011 (English)Other (Refereed)
Abstract [en]

Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.

Place, publisher, year, pages
2011.
Keyword [en]
Information Visualization, Gene Ontology, Clustering, Networks, Graph Drawing
National Category
Computer Science Biological Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Natural Science, Biochemistry
Identifiers
URN: urn:nbn:se:lnu:diva-14009OAI: oai:DiVA.org:lnu-14009DiVA: diva2:438734
Note
Extended Abstract, IEEE BioVis 11, Providence, RI, USAAvailable from: 2011-09-05 Created: 2011-09-05 Last updated: 2015-02-17Bibliographically approved

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