The Library

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana

Tools

Kiddle, Steven J., Windram, Oliver P., McHattie, Stuart, Mead, A., Beynon, Jim, 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

PDF
WRAP_Kiddle_kiddle_et_al_2009_revision_(3).pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (186Kb)

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

Dates:

Date	Event
8 December 2009	Published

Volume:

Vol.26

Number:

No.3

Number of Pages:

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)

References:

AbuQamar, S. et al. (2006) Expression profiling and mutant analysis reveals complex
regulatory networks involved in Arabidopsis response to Botrytis infection. The
Plant Journal, 48, 28-44.
Audenaert, K., DeMeyer, G.B., H¨ofte, M.M. (2002) Abscisic acid determines basal
susceptibility of tomato to Botrytis cinerea and suppresses salicylic acid-dependent
signaling mechanisms. Plant Physiol., 128 (2), 491-501.
Balasubramaniyan, R., H¨ullermeier, E.,Weskamp, N., K¨amper, J. (2004) Clustering of
gene expression data using a local shape-based similarity measure. Bioinformatics,
21 (7), 1069-1077.
Bar-Joseph, Z. et al. (2003) Computational discovery of gene modules and regulatory
networks. Nature Biotechnology, 22 (11), 1337-1342.
Eddy, S.R. (1998) Profile hidden Markov models. Bioinformatics, 14 (9), 755-63.
Frey, B. and Dueck, D. (2007) Clustering by Passing Messages Between Data Points.
Science, 315, 972-976.
Gasch, A.P. et al. (2000) Genomic expression programs in the response of yeast cells
to environmental changes. Mol. Biol. Cell., 11, 4241-4257.
Ghosh, D. and Chinnaiyan, A.M. (2007) Mixture modelling of gene expression data
from microarray experiments. Bioinformatics, 18 (2), 275-286.
G´utierrez, R.A., Shasha, D.E., Coruzzi, G.M. (2005) Systems biology for the virtual
plant. Plant Physiology, 138, 550-554.
Hartigan, J.A. (1972) Direct clustering of a data matrix. Journal of the American
Statistical Association, 67 (337), 123-129.
Hastie, T., Tibshirani, R. and Friedman, J.H. (2001) The Elements of Statistical
Learning. Springer-Verlag.
Heard, N.A. et al. (2005) Bayesian coclustering of Anopholes gene expression time
series: Study of immune defense response to multiple experimental challenges.
Proc. Nat. Acad. Sci., 102 (47), 16939-16944.
Lazzeroni, L. and Owen, A. (2002) Plaid models for gene expression data. Statistica
Sinica, 12 (2002), 61-86.
Locke, J.C.W. et al. (2006) Experimental validation of a predicted feedback loop in the
multi-oscillator clock of Arabidopsis thaliana. Molecular Systems Biology, 2006,
1-6.
Madeira, S.C. and Oliveira, A.L. (2005) A linear time biclustering algorithm for time
series gene expression data. Lecture Notes In Computer Science, 3692, 3806-3807.
Meng, J., Gao, S.J., Huang, Y. (2009) Enrichment constrained time-dependent
clustering analysis for finding meaningful temporal transcription modules.
Bioinformatics, 25 (12), 1521-1527.
Ng, A., Jordan, M.,Weiss, Y. (2002) On spectral clustering: analysis and an algorithm.
In T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural
Information Processing Systems 14. MIT Press.
Pr´e, M. et al. (2008) The AP2/ERF domain transcription factor ORA59 integrates
jasmonic acid and ethylene signals in plant defense. Plant Physiology, 147,
1347-1357.
Qian, J. et al. (2001) Beyond synnexpression relationships: local clustering of timeshifted
and inverted gene expression profiles identifies new biologically relevant
interactions. J. Mol. Biol., 314, 1053-1056.
Qian, J., Lin, J., Luscombe N.M., Yu, H., Gerstein, M. (2003) Prediction of regulatory
networks: genome-wide identification of transcription factor targets from gene
expression data. Bioinformatics, 22 (13), 1917-1926.
Rabiner, L.R. A tutorial on hidden Markov models and selected applications in
speech recognition. Proc. IEEE 77, 257?286 (1989)
Rabiner, L.R. (1989) A tutorial on hidden Markov models and selected applications in
speech recognition. Proc. IEEE, 77, 257-286 (1989).
Schmitt Jr, W.A., Raab, R.M., Stephanopoulos, G. (2004) Elucidation of gene
interaction networks through time-lagged correlation analysis of transcriptional
data. Genome Res., 2004 (14), 1654-1663.
Segal, E. et al. (2003) Module networks: identifying regulatory networks and their
condition specific regulators from gene expression data. Nat. Genet., 34, 166-176.
Shi, J. and Malik, J. (2000) Normalized cuts and image segmentation. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905.
Smith, A.S., Vollrath, A., Bradfield, C.A., Craven, M. (2009) Clustered alignments of
gene-expression data. Bioinformatics, 25 (12), 1521-1527.
Spellman, P.T.et al. (1998) Comprehensive identification of cell cycle-regulated genes
of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell.,
9, 3273-3297.
Swarbreck, D. et al. (2008) The Arabidopsis information resource (TAIR): gene
structure and function annotation. Nucleic Acids Research, 36, 1009-1014.
Thalamuthu, A., Mukhopadhyay, I., Zheng, X., Tseng, G.C. (2006) Evaluation and
comparison of gene clustering methods in microarray analysis. Bioinformatics, 22
(19), 2405-2412.
Walley, J.W. et al. (2007) Mechanical stress induces biotic and abiotic stress responses
via a novel cis-element. PLoS Genet., 3 (10), 1800-1812.
Yona,G., Dirks, W., Rahman, S., Lin, D.M. (2006) Effective similarity measures for
expression profiles. Bioinformatics, 22 (13), 1616-1622.