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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
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Research Article

Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data

Huai Li and Ming Zhan*

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

*Corresponding Author:
Dr. Ming Zhan
National Institute on Aging, NIH
251 Bayview Blvd, Baltimore
MD 21224, USA
Tel: (410)-558- 8373
E-mail: [email protected]

Received Date: January 29, 2009; Accepted Date: March 11, 2009; Published Date: March 12, 2009

Citation: Huai L, Ming Z (2009) Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data. J Proteomics Bioinform 2:117-125. doi:10.4172/jpb.1000068

Copyright: © 2009 Huai L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Cross-species comparison of gene expression profiles allows deciphering fundamental and species-specific transcriptional programs of cells and offers insight into organization and evolution of the genome and genetic network. Here, we propose an algorithm for comparing microarray data from different species to unravel transcriptional modules that are conserved or divergent through evolution. The proposed algorithm is based on cross-species matrix decomposition that includes a nonlinear independent component analysis followed a generalized probabilistic sparse matrix factorization on microarray data from different species. The proposed algorithm captures transcriptional modularity that might result from highly nonlinear interactions among genes, and partitions genes into mutually non-exclusive transcriptional modules. The conserved transcriptional modules are identified by the latent variables that are associated with predominant biological prototypes shared across species. We illustrated the application of the proposed algorithm by an analysis of human and mouse embryonic stem cell (ESC) data. The analysis uncovered conserved and divergent transcriptional modules in the ESC transcriptomes, shedding light on the understanding of fundamental and species-specific regulatory mechanisms controlling ESC development.

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