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Motif-guided sparse decomposition of gene expression data for regulatory module identification |
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| Authors: | Ting Gong Jianhua Xuan Li Chen Rebecca B Riggins Huai Li Eric P Hoffman Robert Clarke Yue Wang |
| Institution: | (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 |
| 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. |
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