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

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dc.contributor.authorGong, Tingen
dc.contributor.authorXuan, Jianhuaen
dc.contributor.authorChen, Lien
dc.contributor.authorRiggins, Rebecca B.en
dc.contributor.authorLi, Huaien
dc.contributor.authorHoffman, Eric P.en
dc.contributor.authorClarke, Roberten
dc.contributor.authorWang, Yueen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2012-08-24T11:08:48Zen
dc.date.available2012-08-24T11:08:48Zen
dc.date.issued2011-03-22en
dc.date.updated2012-08-24T11:08:48Zen
dc.description.abstractBackground 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 modu≤ 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBMC Bioinformatics. 2011 Mar 22;12(1):82en
dc.identifier.doihttps://doi.org/10.1186/1471-2105-12-82en
dc.identifier.urihttp://hdl.handle.net/10919/18810en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderTing Gong et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleMotif-guided sparse decomposition of gene expression data for regulatory module identificationen
dc.title.serialBMC Bioinformaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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