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Bioinformatics Advance Access published online on December 22, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl640
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received September 18, 2006
Revised November 14, 2006
Accepted December 14, 2006

Gene expression

The discovery of transcriptional modules by a two-stage matrix decomposition approach

Huai Li 1, Yu Sun 1, and Ming Zhan 1 *

1 Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD 21224, USA

* To whom correspondence should be addressed.
Ming Zhan, E-mail: zhanmi{at}mail.nih.gov


  Abstract

Motivation: We address the problem of identifying gene transcriptional modules from gene expression data by proposing a new approach. Genes mostly interact with each other to form transcriptional modules for context-specific cellular activities or functions. Unraveling such transcriptional modules is important for understanding biological network, deciphering regulatory mechanisms, and identifying biomarkers.

Method: The proposed algorithm is based on two-stage matrix decomposition. We first model microarray data as nonlinear mixtures and adopt the nonlinear independent component analysis to reduce the nonlinear distortion and separate the data into independent latent components. We then apply the probabilistic sparse matrix decomposition approach to model the "hidden" expression profiles of genes across the independent latent components as linear weighted combinations of a small number of transcriptional regulator profiles. Finally, we propose a general scheme for identifying gene modules from the outcomes of the matrix decomposition.

Results: The proposed algorithm partitions genes into non-mutually exclusive transcriptional modules, independent from expression profile similarity measurement. The modules contain genes with not only similar but different expression patterns, and show the highest enrichment of biological functions in comparison with those by other methods. The usefulness of the algorithm was validated by a yeast microarray data analysis.

Availability: The software is available upon request to the authors.

Associate Editor: Golan Yona
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