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Bioinformatics Advance Access published online on May 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm251
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Discovering gene expression patterns in Time Course Microarray Experiments by ANOVA-SCA

María José Nueda 1,a,b, Ana Conesa 2,6,a, Johan A. Westerhuis 3, Huub C.J. Hoefsloot 3, Age K. Smilde 3,4, Manuel Talón 2 and Alberto Ferrer 5

1Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain.
2Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain.
3Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV, Amsterdam, The Netherlands.
4TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands.
5Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022, Valencia, Spain.
6Bioinformatics Department. Centro de Investigación Príncipe Felipe. Autopista del Saler, 16, E46013, Valencia, Spain.

bCorresponding author. María José Nueda, E-mail: mj.nueda{at}ua.es and aconesa{at}cipf.es.


  Abstract

Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account.

Results: In this work we develop the application of the ANOVA-SCA (Smilde et al., 2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCAgenes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets.

Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm.

a These authors contributed equally to this work.

Associate Editor: Dr. Joaquin Dopazo

Received on February 15, 2007; revised on April 17, 2007; accepted on May 2, 2007

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