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Bioinformatics Advance Access published online on May 31, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm290
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

VISDA: An open-source caBIGTM analytical tool for data clustering and beyond

Jiajing Wang 1, Huai Li 2, Yitan Zhu 1, Malik Yousef 3, Michael Nebozhyn 3, Michael Showe 3, Louise Showe 3, Jianhua Xuan 1, Robert Clarke 4 and Yue Wang 1,*

1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, 2Bioinformatics Unit, RRB, National Institute on Aging, NIH, Baltimore, MD 21224, USA, 3Systems Biology Division, The Wistar Institute, Philadelphia, PA 19104, USA, 4Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC 20057, USA

*To whom correspondence should be addressed. Prof. Joseph (Yue) Wang, E-mail: yuewang{at}vt.edu


  Abstract

Summary: VISDA (VIsual Statistical Data Analyzer) is a caBIGTM analytical tool for cluster modeling, visualization, and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically-principled and visually-interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical data sets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data.

Availability: http://gforge.nci.nih.gov/projects/visda/

Associate Editor: Prof. John Quackenbush

Received on November 8, 2006; revised on May 22, 2007; accepted on May 22, 2007

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