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Open Access Highly Accessed Research article

Computational method for reducing variance with Affymetrix microarrays

Stephen Welle1*, Andrew I Brooks2 and Charles A Thornton3

Author Affiliations

1 Departments of Medicine, Pharmacology & Physiology, University of Rochester, Rochester, NY 14642, USA

2 Functional Genomics Center, Department of Environmental Medicine, University of Rochester, Rochester, NY 14642, USA

3 Department of Neurology, University of Rochester, Rochester, NY 14642, USA

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BMC Bioinformatics 2002, 3:23  doi:10.1186/1471-2105-3-23

Published: 30 August 2002

Abstract

Background

Affymetrix microarrays are used by many laboratories to generate gene expression profiles. Generally, only large differences (> 1.7-fold) between conditions have been reported. Computational methods to reduce inter-array variability might be of value when attempting to detect smaller differences. We examined whether inter-array variability could be reduced by using data based on the Affymetrix algorithm for pairwise comparisons between arrays (ratio method) rather than data based on the algorithm for analysis of individual arrays (signal method). Six HG-U95A arrays that probed mRNA from young (21–31 yr old) human muscle were compared with six arrays that probed mRNA from older (62–77 yr old) muscle.

Results

Differences in mean expression levels of young and old subjects were small, rarely > 1.5-fold. The mean within-group coefficient of variation for 4629 mRNAs expressed in muscle was 20% according to the ratio method and 25% according to the signal method. The ratio method yielded more differences according to t-tests (124 vs. 98 differences at P < 0.01), rank sum tests (107 vs. 85 differences at P < 0.01), and the Significance Analysis of Microarrays method (124 vs. 56 differences with false detection rate < 20%; 20 vs. 0 differences with false detection rate < 5%). The ratio method also improved consistency between results of the initial scan and results of the antibody-enhanced scan.

Conclusion

The ratio method reduces inter-array variance and thereby enhances statistical power.