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Open Access Highly Accessed Software

SpectralNET – an application for spectral graph analysis and visualization

Joshua J Forman1, Paul A Clemons1, Stuart L Schreiber12 and Stephen J Haggarty1*

Author Affiliations

1 The Broad Institute of MIT & Harvard University, 320 Bent Street, Cambridge, MA 02141, USA

2 Howard Hughes Medical Institute, Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA 02138, USA

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BMC Bioinformatics 2005, 6:260  doi:10.1186/1471-2105-6-260

Published: 19 October 2005

Abstract

Background

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks.

Results

Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors).

Conclusion

SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/ webcite. Source code is available upon request.