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Open Access Highly Accessed Methodology article

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Ting Gong1, Jianhua Xuan1*, Li Chen1, Rebecca B Riggins2, Huai Li3, Eric P Hoffman4, Robert Clarke2 and Yue Wang1

Author Affiliations

1 Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA

2 Lombardi Comprehensive Cancer Center and Department of Oncology, Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA

3 Bioinformatics Unit, RRB, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA

4 Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA

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BMC Bioinformatics 2011, 12:82  doi:10.1186/1471-2105-12-82

Published: 22 March 2011

Abstract

Background

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.

Results

We propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.

Conclusion

We have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.