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Bioinformatics Advance Access first published online on May 14, 2004
This version published online on May 21, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth319
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received July 11, 2003
Revised March 15, 2004
Accepted April 8, 2004

Article

The global error assessment (GEA) model for the selection of differentially expressed genes in microarray data

Robert Mansourian 1, David M. Mutch 2, Nicolas Antille 1, Jerome Aubert 3, Paul Fogel 4, Jean-Marc Le Goff 3, Julie Moulin 1, Anton Petrov 5, Andreas Rytz 1, Johannes J. Voegel 3, Matthew-Alan Roberts 1*

1 Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland
2 Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland; Institut de Biologie Animale, Université de Lausanne, CH-1015 Lausanne, Switzerland
3 Galderma Research & Development, 635, route des Lucioles - B.P.087, F-06902 Sophia Antipolis Cedex, France
4 Paul Fogel Consultant, 4 rue Le Goff, F-75005 Paris, France
5 BioDiscovery, Inc. 4640 Admiralty Way, Suite 710 Marina Del Rey, CA 90292

* To whom correspondence should be addressed. E-mail: mroberts{at}purina.com.


  Abstract

Motivation: Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where the k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining an intuitive and computationally efficient procedure.

Results: The present article describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray data sets, and was developed using an in vitro experiment that compared control and interferon-{gamma} treated skin cells. In this experiment, up to 9 replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared to both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness, and confidence in gene selection. A subset of the selected genes were validated by RT-PCR. All of these results suggest that GEA methodology is i) suitable for selection of differentially expressed genes in microarray data, ii) intuitive and computationally efficient, and iii) especially advantageous under conditions of low k.

Availability: The GEA code for R software is freely available upon request to authors.


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