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RCytoscape: tools for exploratory network analysis

Paul T Shannon13*, Mark Grimes2, Burak Kutlu3, Jan J Bot4 and David J Galas5

Author Affiliations

1 Fred Hutchison Cancer Research Institute, Seattle Washington, and the Institute for Systems Biology, 401 Terry Ave. N, Seattle, WA, USA

2 Division of Biological Sciences, Center for Structural and Functional Neuroscience, University of Montana, Missoula, MT, USA

3 Institute for Systems Biology, 401 Terry Ave. N, Seattle, WA, USA

4 Delft University of Technology, Delft Bioinformatics Lab, Delft, The Netherlands

5 Pacific Northwest Diabetes Research Institute, 720 Broadway, Seattle, WA 98120, USA

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BMC Bioinformatics 2013, 14:217  doi:10.1186/1471-2105-14-217

Published: 9 July 2013

Abstract

Background

Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.

Results

RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.

Conclusions

Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.

Keywords:
Biological networks; Visualization; Exploratory data analysis; Statistical programming; Bioinformatics