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Exploring Biological Data: Mappings between Ontology- and Cluster-based Representations
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0001-6745-4398
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
Univ Halle Wittenberg, Inst Comp Sci, D-06108 Halle, Germany.
2013 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 3-4, 291-307 p.Article in journal (Refereed) Published
Abstract [en]

Ontologies and hierarchical clustering are both important tools in biology and medicineto study high-throughput data such as transcriptomics and metabolomics data. Enrichmentof ontology terms in the data is used to identify statistically overrepresented ontology terms,giving insight into relevant biological processes or functional modules. Hierarchical clusteringis a standard method to analyze and visualize data to find relatively homogeneousclusters of experimental data points. Both methods support the analysis of the same dataset, 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 clusteringof the data. This article proposes new visualization methods for this task. They allow forinteractive selection and navigation to explore the data under consideration as well as visualanalysis of mappings between ontology- and cluster-based space-filling representations. Inthis context, we discuss our approach together with specific properties of the biological inputdata and identify features that make our approach easily usable for domain experts.

Place, publisher, year, edition, pages
Sage Publications, 2013. Vol. 12, no 3-4, 291-307 p.
Keyword [en]
Gene Ontology, ontology, hierarchical clustering, visualization, mappings
National Category
Computer and Information Science
Research subject
Computer and Information Sciences Computer Science; Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-21951DOI: 10.1177/1473871612468880ISI: 000329572700006Scopus ID: 2-s2.0-84889071205OAI: oai:DiVA.org:lnu-21951DiVA: diva2:559307
Available from: 2012-10-08 Created: 2012-10-08 Last updated: 2017-12-07Bibliographically approved

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Jusufi, IlirKerren, Andreas

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CiteExportLink to record
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  • apa
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  • de-DE
  • en-GB
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  • Other locale
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Output format
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