ArticleBMC bioinformatics2011
Motif-guided sparse decomposition of gene expression data for regulatory module identification.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Inhibition of GPR137 expression reduces the proliferation and colony formation of malignant glioma cells.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2014Article
- Identifying context-specific transcription factor targets from prior knowledge and gene expression data.IEEE transactions on nanobioscience · 2013Article
- mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks.BMC systems biology · 2013Article
- Matrix Factorization for Transcriptional Regulatory Network Inference.IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology · 2012Article
- Antiestrogen Resistance and the Application of Systems Biology.Drug discovery today. Disease mechanisms · 2012Article
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8 authors.
Funding
Abstract
backgroundGenes 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.
resultsWe 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.
conclusionWe 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.
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