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A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series

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Stegle, Oliver, Denby, Katherine J., Cooke, Emma J., Wild, David L., Ghahramani, Zoubin and Borgwardt, Karsten M. (2010) A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. In: Research in Computational Molecular Biology : Proceedings of the 13th Annual International Conference, RECOMB 2009, Tucson, AZ, USA, May 18-21, 2009. Lecture Notes in Computer Science, Volume 5541 . Berlin Heidelberg: Springer, pp. 201-216. ISBN 9783642020087

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Official URL: http://dx.doi.org/10.1007/978-3-642-02008-7_14

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Abstract

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.

Item Type: Book Item
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QD Chemistry
T Technology > TP Chemical technology
Q Science > QH Natural history > QH301 Biology
Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- )
Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Series Name: Lecture Notes in Computer Science
Journal or Publication Title: Lecture Notes in Computer Science
Publisher: Springer
Place of Publication: Berlin Heidelberg
ISBN: 9783642020087
ISSN: 0302-9743
Book Title: Research in Computational Molecular Biology : Proceedings of the 13th Annual International Conference, RECOMB 2009, Tucson, AZ, USA, May 18-21, 2009
Official Date: March 2010
Dates:
DateEvent
March 2010Published
Volume: Volume 5541
Number of Pages: 16
Page Range: pp. 201-216
DOI: 10.1089/cmb.2009.0175
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Cambridge Gates Trust, University of Warwick. MOAC Doctoral Training Centre, Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), European Union (EU), National Institutes of Health (U.S.) (NIH)
Grant number: BB/F005806/1 (BBSRC), IRG 46444 (EU), GM63208 (NIH)
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