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

Ting Gong, Jianhua Xuan, Li Chen, Rebecca B. Riggins, Huai Li, Eric P. Hoffman, Robert Clarke, Yue Wang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Article number82
JournalBMC bioinformatics
StatePublished - Mar 22 2011
Externally publishedYes

Bibliographical note

Funding Information:
This study is supported by the National Institutes of Health under Grants (CA139246, CA149147, CA109872, CA149653, NS29525, EB000830 and CA096483) and the Department of Defense under Grant (BC030280). We thank Alan Zwart for his work in the acquisition of breast cancer cell line microarray data. We also thank the reviewers for their invaluable suggestions that lead to many improvements in the manuscript.


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