<p>A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments</p>

gb-2005-6-4-p5 GBJ Deposited research article <p>A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments</p> Tan Qihua qihua.tan@ouh.fyns-amt.dk Dahlgaard Jesper Vach Werner Abdallah M Basem Kassem Moustapha Kruse A Torben

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

Department of Statistics, University of Southern Denmark, Denmark

Department of Endocrinology, Odense University Hospital, Denmark

Genome Biology 1465-6906 2005 6 4 P5 http://genomebiology.com/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. 10.1186/gb-2005-6-4-p5 7 3 2005 10 3 2005 2005 BioMed Central Ltd

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

A non-parametric approach is prsented that uses bootstrap-assisted correspondence analysis to identify and validate genes that are differentially expressed in factorial microarray experiments which are characterized by simultaneous measurement of the effects of multiple experiment factors.

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.

Bioinformatics Methods Genome studies