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Nucleic Acids Research Advance Access originally published online on August 26, 2006
Nucleic Acids Research 2006 34(15):e105; doi:10.1093/nar/gkl435
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Nucleic Acids Research, 2006, Vol. 34, No. 15 e105
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

Non-linear analysis of GeneChip arrays

Diana Abdueva1,*, Dmitriy Skvortsov1 and Simon Tavaré1,2

1 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California Los Angeles, CA 9009-1340, USA 2 Department of Oncology, University of Cambridge Cambridge, UK

*To whom correspondence should be addressed. Tel: +1 213 281 2010; Fax: +1 213 740 8631; Email: abdueva{at}usc.edu

Received February 4, 2006. Revised May 20, 2006. Accepted May 31, 2006.

The application of microarray hybridization theory to Affymetrix GeneChip data has been a recent focus for data analysts. It has been shown that the hyperbolic Langmuir isotherm captures the shape of the signal response to concentration of Affymetrix GeneChips. We demonstrate that existing linear fit methods for extracting gene expression measures are not well adapted for the effect of saturation resulting from surface adsorption processes. In contrast to the most popular methods, we fit background and concentration parameters within a single global fitting routine instead of estimating the background before obtaining gene expression measures. We describe a non-linear multi-chip model of the perfect match signal that effectively allows for the separation of specific and non-specific components of the microarray signal and avoids saturation bias in the high-intensity range. Multimodel inference, incorporated within the fitting routine, allows a quantitative selection of the model that best describes the observed data. The performance of this method is evaluated on publicly available datasets, and comparisons to popular algorithms are presented.

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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