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Bioinformatics 2006 22(14):e99-e107; doi:10.1093/bioinformatics/btl205
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an ‘information landscape’

Timothy M. D. Ebbels 1,3, Bernard F. Buxton 2 and David T. Jones 1,*

1 Bioinformatics Unit, Department of Computer Science, University College London Gower Street, London, WC1E 6BT
2 Department of Computer Science, University College London Gower Street, London, WC1E 6BT
3 Current address: Biological Chemistry Section, Biomedical Sciences Division, Imperial College London Sir Alexander Fleming Building, South Kensington, London SW1 2AZ

*To whom correspondence should be addressed.

The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on ‘spring embedding’ and an ‘information landscape’ allowing several related data sources to be dynamically combined while highlighting one particular feature.

Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The ‘information landscape’ highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.

Contact: d.jones{at}cs.ucl.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.


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