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Proceedings Paper

Visualizing large high-throughput datasets based on the cognitive representation of biological pathways
Author(s): Axel Nagel; Marc Lohse; Anthony Bolger; Mark Stitt; Björn Usadel
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Paper Abstract

The data explosion in the biological sciences has led to many novel challenges for the individual researcher. One of these is to interpret the sheer mass of data at hand. Typical high-throughput data sets from transcriptomic data can easily comprise hundred thousand data points. It is thus necessary to provide tools to interactively visualize these data sets in a way that aids in their interpretation. Thus we have developed the MAPMAN application. This application renders individual data points from different domains as different glyphs that are color coded to reflect underlying changes in the magnitude/abundance of the underlying data. In order to augment the human comprehensibility of the biologist domain experts these data are organized on meaningful pathway diagrams that the biologist has encountered numerous times. Using these representations together with a high level organization thus helps to quickly realize the main outcome of such a high throughput study and to further decide on additional tasks that should be performed to explore the data.

Paper Details

Date Published: 8 March 2011
PDF: 7 pages
Proc. SPIE 7865, Human Vision and Electronic Imaging XVI, 78650H (8 March 2011);
Show Author Affiliations
Axel Nagel, Max-Planck-Institut für Molekulare Pflanzenphysiologie (Germany)
Marc Lohse, Max-Planck-Institut für Molekulare Pflanzenphysiologie (Germany)
Anthony Bolger, Max-Planck-Institut für Molekulare Pflanzenphysiologie (Germany)
Mark Stitt, Max-Planck-Institut für Molekulare Pflanzenphysiologie (Germany)
Björn Usadel, Max-Planck-Institut für Molekulare Pflanzenphysiologie (Germany)

Published in SPIE Proceedings Vol. 7865:
Human Vision and Electronic Imaging XVI
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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