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Bioinformatics Advance Access published online on February 15, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl056
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received November 9, 2005
Revised February 1, 2006
Accepted February 10, 2006

Article

maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

Ana Conesa 1 * a, María José Nueda 2 a, Alberto Ferrer 3, and Manuel Talón 1

1 Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Valencia (Spain)
2 Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante (Spain)
3 Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Apartado 46022, Valencia (Spain)

* To whom correspondence should be addressed.
Ana Conesa, E-mail: aconesa{at}ivia.es


  Abstract

Motivation: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis.

Results: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression-steps approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes and secondly, a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray data set.

Availability: The method has been implemented in the statistical language R and is freely available from the Bioconductor contributed packages repository and from http://www.ivia.es/centrogenomica/bioinformatics.htm.

Associate Editor: David Rocke

a These authors contributed equally to this work.


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