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Nucleic Acids Research 2006 34(2):e8; doi:10.1093/nar/gnj010
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Published online 23 January 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
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{at}oxfordjournals.org


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Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons

Bettina Harr* and Christian Schlötterer1

Institut für Genetik, Universität Köln Zülpicherstrasse 47, 50674 Köln, Germany 1Institut für Tierzucht und Genetik, Veterinärmedizinische Universität Wien Veterinärplatz 1, 1210 Wien, Austria

*To whom correspondence should be addressed. Tel: +49 221 470 2324; Fax: +49 221 470 5975; Email: harrb{at}uni-koeln.de

Received September 25, 2005. Revised November 9, 2005. Accepted January 3, 2006.

Oligonucleotide microarrays are an informative tool to elucidate gene regulatory networks. In order for gene expression levels to be comparable across microarrays, normalization procedures have to be invoked. A large number of methods have been described to correct for systematic biases in microarray experiments. The performance of these methods has been tested only to a limited extend. Here, we evaluate two different types of microarray analyses: (i) the same gene in replicate samples and (ii) different, but co-expressed genes in the same sample. The reliability of the latter analysis needs to be determined for the analysis of regulatory networks and our report is the first attempt to evaluate for the accuracy of different microarray normalization methods in this respect. Consistent with previous results we observed a large effect of the normalization method on the outcome of the expression analyses. Our analyses indicate that different normalization methods should be performed depending on whether a study is aiming to detect differential gene expression between independent samples or whether co-expressed genes should be identified. We make recommendations about the most appropriate method to use.


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