Bioinformatics Advance Access originally published online on May 22, 2007
Bioinformatics 2007 23(14):1792-1800; doi:10.1093/bioinformatics/btm251
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Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA
1Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, 2Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, 3Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, 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 and 6Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain
*To whom correspondence should be addressed.
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Abstract |
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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 Analysis of variance–simultaneous component analysis (ANOVA–SCA) Smilde et al. Bioinformatics, (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 ASCA-genes. 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.
Contact: mj.nueda{at}ua.es and aconesa{at}cipf.es
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Joaquin Dopazo
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Received on February 15, 2007; revised on April 17, 2007; accepted on May 2, 2007
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