RSIR: regularized sliced inverse regression for motif discovery

  • Authors:

  • Wenxuan Zhong;Peng Zeng;Ping Ma;Jun S. Liu;Yu Zhu

  • Affiliations:

  • Department of Statistics, Harvard University Cambridge, MA 02138, USA;Department of Mathematics and Statistics, Auburn University Auburn, AL 36849, USA;Department of Statistics, University of Illinois at Urbana-Champaign Champaign, IL 61820, USA;Department of Statistics, Harvard University Cambridge, MA 02138, USA;Department of Statistics, Purdue University West Lafayette, IN 47907, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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Abstract

Motivation: Identification of transcription factor binding motifs (TFBMs) is a crucial first step towards the understanding of regulatory circuitries controlling the expression of genes. In this paper, we propose a novel procedure called regularized sliced inverse regression (RSIR) for identifying TFBMs. RSIR follows a recent trend to combine information contained in both gene expression measurements and genes' promoter sequences. Compared with existing methods, RSIR is efficient in computation, very stable for data with high dimensionality and high collinearity, and improves motif detection sensitivities and specificities by avoiding inappropriate model specification. Results: We compare RSIR with SIR and stepwise regression based on simulated data and find that RSIR has a lower false positive rate. We also demonstrate an excellent performance of RSIR by applying it to the yeast amino acid starvation data and cell cycle data. Availability: Matlab programs are available upon request from the authors. Contact:jliu@stat.harvard.edu; yuzhu@stat.purdue.edu