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Gene Regulation and Systems Biology

Gene Module Identification from Microarray Data Using Nonnegative Independent Component Analysis

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Gene Regulation and Systems Biology 2007:1 349-363

Published on 15 Jan 2008


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Ting Gong1, Jianhua Xuan1, Chen Wang1, Huai Li2, Eric Hoffman3, Robert Clarke4 and Yue Wang1

1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, U.S.A. 2Bioinformatics Unit, Research Resources Branch, National Institute on Aging, NIH, Baltimore, MD 21224, U.S.A. 3Research Center for Genetic Medicine, Children’s National Medical Center, Washington, DC 20010, U.S.A. 4Department of Oncology and Physiology and Biophysics, Georgetown University School of Medicine, Washington, DC 20057, U.S.A.

Abstract

Genes mostly interact with each other to form transcriptional modules for performing single or multiple functions. It is important to unravel such transcriptional modules and to determine how disturbances in them may lead to disease. Here, we propose a non-negative independent component analysis (nICA) approach for transcriptional module discovery. nICA method utilizes the non-negativity constraint to enforce the independence of biological processes within the participated genes. In such, nICA decomposes the observed gene expression into positive independent components, which fi ts better to the reality of corresponding putative biological processes. In conjunction with nICA modeling, visual statistical data analyzer (VISDA) is applied to group genes into modules in latent variable space. We demonstrate the usefulness of the approach through the identification of composite modules from yeast data and the discovery of pathway modules in muscle regeneration.



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