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Bioinformatics Advance Access published online on October 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti105
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 29, 2004
Revised June 10, 2004
Accepted September 9, 2004

Article

Normalization of two-channel microarray experiments: a semiparametric approach

J. E. Eckel 1*, C. Gennings 2, T. M. Therneau 1, L. D. Burgoon 3, D. R. Boverhof 4, and T. R. Zacharewski 4

1 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA
2 Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA
3 Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, 48824, USA; National Food Safety and Toxicology Center, Michigan State University, East Lansing, MI, 48824, USA; Center for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
4 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA; National Food Safety and Toxicology Center, Michigan State University, East Lansing, MI, 48824, USA; Center for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA

* To whom correspondence should be addressed.
J. E. Eckel, E-mail: eckel{at}mayo.edu


  Abstract

Motivation: An important underlying assumption of any experiment is that the experimental subjects are similar across levels of the treatment variable, so that changes in the response variable can be attributed to exposure to the treatment under study. This assumption is often not valid in the analysis of a microarray experiment due to systematic biases in the measured expression levels related to experimental factors such as spot location (often referred to as a print-tip effect), arrays, dyes, and various interactions of these effects. Thus, normalization is a critical initial step in the analysis of a microarray experiment, where the objective is to balance the individual signal intensity levels across the experimental factors, while maintaining the effect due to the treatment under investigation.

Results: Various normalization strategies have been developed including log-median centering, analysis of variance modeling, and local regression smoothing methods for removing linear and/or intensity-dependent systematic effects in two-channel microarray experiments. We describe a method that incorporates many of these into a single strategy, referred to as two-channel fastlo, and is derived from a normalization procedure that was developed for single-channel arrays. The proposed normalization procedure is applied to a two-channel dose-response experiment.

Availability: The SAS macro for two-channel fastlo is available from the authors upon request and the data used to test the methods is publicly available at http//www.bch.msu.edu/~zacharet/publications/supplementary/ee_dr.


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