Biomarker identification by knowledge-driven multilevel ICA and motif analysis

  • Authors:

  • Li Chen;Jianhua Xuan;Chen Wang;Yue Wang;Ie-Ming Shih;Tian-Li Wang;Zhen Zhang;Robert Clarke;Eric P. Hoffman

  • Affiliations:

  • Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203 VA, USA.;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203 VA, USA.;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203 VA, USA.;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203 VA, USA.;Department of Pathology, Gynecology and Oncology, The Johns Hopkins University, School of Medicine, Baltimore, 21231 MD, USA.;Department of Pathology, Gynecology and Oncology, The Johns Hopkins University, School of Medicine, Baltimore, 21231 MD, USA.;Department of Pathology, Gynecology and Oncology, The Johns Hopkins University, School of Medicine, Baltimore, 21231 MD, USA.;Department of Oncology and Physiology and Biophysics, Georgetown University, School of Medicine, Washington, 20057 DC, USA.;Research Center for Genetic Medicine, Children

  • Venue:
  • International Journal of Data Mining and Bioinformatics
  • Year:
  • 2009

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Abstract

Traditional statistical methods often fail to identify biologically meaningful biomarkers from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level Independent Component Analysis (ICA), to infer regulatory signals and identify biomarkers based on clustering results and partial prior knowledge. A statistical test is designed to evaluate significance of transcription factor enrichment for extracted gene set based on motif information. The experimental results on an Rsf-1 (HBXAP) induced microarray data set show that our method can successfully extract biologically meaningful biomarkers related to ovarian cancer compared to other gene selection methods with or without prior knowledge.