Overview

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.

With our tool called CluMa-GO, we present a new solution to this problem of visualizing heterogeneous network structures in biology. It allows for interactive selection and navigation to explore the data under consideration as well as visual analysis of mappings between ontology- and cluster-based space-filling representations.

Publications

Please, cite the following publication if you use CluMa-GO:

Exploring Biological Data: Mappings between Ontology- and Cluster-based Representations (to appear)

I. Jusufi, A. Kerren, and F. Schreiber

In Information Visualization, 2013. SAGE Publications. (to appear)

Visualization of Mappings between the Gene Ontology and Cluster Trees

(Post-print)

I. Jusufi, A. Kerren, V. Aleksakhin, and F. Schreiber

In Proceedings of the SPIE 2012 Conference on Visualization and Data Analysis (VDA '12), SPIE 8294, 8294-0N, Best Paper Award, Burlingame, CA, USA, 2012. IS&T/SPIE.

CluMa-GO: Bring Gene Ontologies and Hierarchical Clusterings Together

A. Kerren, I. Jusufi, V. Aleksakhin, and F. Schreiber

Extended Abstract, BioVis 11, Providence, RI, USA, 2011.