Sparse decomposition of gene expression data to infer transcriptional modules guided by motif information

  • Authors:

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

  • Affiliations:

  • Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Departments of Oncology and Physiology & Biophysics, Georgetown University, School of Medicine, Washington, DC;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Research Center for Genetic Medicine, Children's National Medical Center, Washington,;Departments of Oncology and Physiology & Biophysics, Georgetown University, School of Medicine, Washington, DC

  • Venue:
  • ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
  • Year:
  • 2008

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Abstract

An important topic in computational biology is to identify transcriptionalmodules through sequence analysis and gene expression profiling. Atranscriptional module is formed by a group of genes under control of one orseveral transcription factors (TFs) that bind to cis-regulatory elements in thepromoter regions of those genes. In this paper, we develop an integrative approach,namely motif-guided sparse decomposition (mSD), to uncover transcriptionalmodules by combining motif information and gene expression data.The method exploits the interplay of co-expression and co-regulation to findregulated gene patterns guided by TF binding information. Specifically, a motif-guided clustering method is first developed to estimate transcription factorbinding activities (TFBAs); sparse component analysis is then followed to furtheridentify TFs' target genes. The experimental results show that the mSD approachcan successfully help uncover condition-specific transcriptional modulesthat may have important implications in endocrine therapy of breast cancer.