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Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana

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Kiddle, Steven J., Windram, Oliver P., McHattie, Stuart, Mead, A. (Andrew), Beynon, Jim, 1956-, Buchanan-Wollaston, Vicky, Denby, Katherine J. and Mukherjee, Sach. (2009) Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana. Bioinformatics, Vol.26 (No.3). pp. 355-362. ISSN 1367-4811

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btp673

Abstract

Motivation: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here.

Results: Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor–target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings.

Availability: Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.

Item Type:	Journal Article
Subjects:	Q Science > QK Botany Q Science > QH Natural history > QH426 Genetics
Divisions:	Faculty of Science > Centre for Systems Biology Faculty of Science > Statistics Faculty of Science > Life Sciences (2010- ) > Warwick HRI (2004-2010)
Library of Congress Subject Headings (LCSH):	Arabidopsis thaliana -- Genetics, Genetic transcription -- Regulation, Cluster analysis -- Research, Biological control systems -- Research
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4811
Official Date:	8 December 2009
Volume:	Vol.26
Number:	No.3
Number of Pages:	8
Page Range:	pp. 355-362
Identification Number:	10.1093/bioinformatics/btp673
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Grant number:	BB/F005806/1 (BBSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/3231