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As a service to the research community, Genome Biology used to publish non-peer-reviewed articles in a 'preprint' depository to which any research can be submitted and which all individuals can access free of charge.From January 2006 Genome Biology no longer publishes new articles in this section. Any article could be submitted by authors, who have sole responsibility for the article's content. The only screening process is to ensure relevance of the preprint to Genome Biology's scope and to avoid abusive, libellous or indecent articles. Articles in this section of the journal have not been peer-reviewed. Each preprint has a permanent URL, by which it can be cited. Research submitted to the preprint depository may be simultaneously or subsequently submitted to Genome Biology or any other publication for peer review; the only requirement is an explicit citation of, and link to, the preprint in the article that is eventually published. If possible, Genome Biology will provide a reciprocal link from the preprint depository to the published article.

Deposited research article

A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments

Qihua Tan¹ , Jesper Dahlgaard¹, Werner Vach², Basem M Abdallah³, Moustapha Kassem³ and Torben A Kruse¹

¹Department of Clinical Biochemistry and Genetics, Odense University Hospital, Denmark

²Department of Statistics, University of Southern Denmark, Denmark

³Department of Endocrinology, Odense University Hospital, Denmark

author email corresponding author email

Genome Biology 2005, 6:P5doi:10.1186/gb-2005-6-4-p5

This was the first version of this article to be made available publicly. This article was submitted to Genome Biology for peer review.

Subject areas: Bioinformatics, Methods, Genome studies

The electronic version of this article is the complete one and can be found online at: http://genomebiology.com/2005/6/4/P5

Received:	7 March 2005
Posted:	10 March 2005

© 2005 BioMed Central Ltd

Abstract

We introduce a non-parametric approach using bootstrap-assisted correspondence analysis to identify and validate genes that are differentially expressed in factorial microarray experiments. Model comparison showed that although both parametric and non-parametric methods capture the different profiles in the data, our method is less inclined to false positive results due to dimension reduction in data analysis.

Deposited research article

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