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

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 coexpressed 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 i...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Ting Gong, Jianhua Xuan, Li Chen, Rebecca B. Riggins, Huai Li, Eric P. Hoffman, Robert Clarke, Yue Wang
• Keywords: Artificial Intelligence, BMCBI 2011, Gene, Regulatory Module, Transcription Factor