The Library
A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series
Tools
Tools
Tools
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
Full text not available from this repository, contact author.
Official URL: http://dx.doi.org/10.1007/978-3-642-02008-7_14
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 > Life Sciences (2010- ) Faculty of Science > Centre for Systems Biology |
||||
| 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: |
|
||||
| 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) | ||||
| Related URLs: |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
