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DNA Research Advance Access originally published online on October 17, 2008
DNA Research 2008 15(6):367-374; doi:10.1093/dnares/dsn025
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© The Author 2008. Kazusa DNA Research Institute
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

SVD-based Anatomy of Gene Expressions for Correlation Analysis in Arabidopsis thaliana

Atsushi Fukushima1, Masayoshi Wada2, Shigehiko Kanaya1,2 and Masanori Arita1,3,4,*

1 RIKEN Plant Science Center, 1-7-22 Tsurumi, Yokohama, Kanagawa 230-0045, Japan
2 Department of Bioinformatics and Genomes, Graduate School of Information Science, Nara Institute of Science and Technology, Takayama, Ikoma, Nara 630-0101, Japan
3 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
4 Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan

Received 7 July 2008 ; accepted 19 September 2008.

Gene co-expression analysis has been widely used in recent years for predicting unknown gene function and its regulatory mechanisms. The predictive accuracy depends on the quality and the diversity of data set used. In this report, we applied singular value decomposition (SVD) to array experiments in public databases to find that co-expression linkage could be estimated by a much smaller number of array data. Correlations of co-expressed gene were assessed using two regulatory mechanisms (feedback loop of the fundamental circadian clock and a global transcription factor Myb28), as well as metabolic pathways in the AraCyc database. Our conclusion is that a smaller number of informative arrays across tissues can suffice to reproduce comparable results with a state-of-the-art co-expression software tool. In our SVD analysis on Arabidopsis data set, array experiments that contributed most as the principal components included stamen development, germinating seed and stress responses on leaf.

Key words: singular value decomposition; gene expression; gene correlation; Arabidopsis

* To whom correspondence should be addressed. E-mail: arita{at}k.u-tokyo.ac.jp

Edited by Katsumi Isono


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K. Kinoshita and T. Obayashi
Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis
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[Abstract] [Full Text] [PDF]


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