Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis

  • Authors:

  • Zoltan Kutalik;Jacqueline Inwald;Steve V. Gordon;R. Glyn Hewinson;Philip Butcher;Jason Hinds;Kwang-Hyun Cho;Olaf Wolkenhauer

  • Affiliations:

  • Control Systems Centre, Department of Electrical Engineering and Electronics, P.O. Box 88, UMIST, Manchester M60 1QD, UK,;Veterinary Laboratories Agency, Woodham Lane, New Haw, Addlestone, Surrey KT 15 3NB, UK,;Veterinary Laboratories Agency, Woodham Lane, New Haw, Addlestone, Surrey KT 15 3NB, UK,;Veterinary Laboratories Agency, Woodham Lane, New Haw, Addlestone, Surrey KT 15 3NB, UK,;Bacterial Microarray Group, St George's Hospital Medical School, London SW17 0RE, UK,;Bacterial Microarray Group, St George's Hospital Medical School, London SW17 0RE, UK,;School of Electrical Engineering, University of Ulsan, Ulsan 680-749, South Korea;Department of Computer Science, University of Rostock, Albert Einstein Str. 21, 18059 Rostock, Germany

  • Venue:
  • Bioinformatics
  • Year:
  • 2004

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Abstract

Motivation: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk's lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis. Results: We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others. Availability: Algorithms were implemented using the statistical programming language R and available on the author's web-page. Supplementary information: For additional material see http://www.sbi.uni-rostock.de/