Bioinformatics Advance Access originally published online on May 14, 2004
Bioinformatics 2004 20(16):2726-2737; doi:10.1093/bioinformatics/bth319
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Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data
1 Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland, 2 Galderma Research & Development, 635, route des Lucioles—B.P.087, F-06902 Sophia Antipolis Cedex, France, 3 Paul Fogel Consultant, 4 rue Le Goff, F-75005 Paris, France, 4 Center for Integrative Genomics, Université de Lausanne, CH-1015 Lausanne, Switzerland and 5 BioDiscovery, Inc. 4640 Admiralty Way, Suite 710 Marina Del Rey, CA 90292, USA
Received on March 15, 2004; accepted on April 8, 2004
Advance Access Publication May 14, 2004
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 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 as an intuitive and computationally efficient procedure.
Results: The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-
treated skin cells. In this experiment, up to nine 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 with 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 real-time reverse transcription–polymerase chain reaction (RT–PCR). All 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.
Contact: mroberts{at}purina.com
* To whom correspondence should be addressed.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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