Normalization of two-channel microarray experiments: A semiparametic approach

Department of Health Sciences Research, Mayo Clinic Rochester, MN 55905, USA.
Bioinformatics (Impact Factor: 4.98). 04/2005; 21(7):1078-83. DOI: 10.1093/bioinformatics/bti105
Source: PubMed


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.

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    • "Microarray expression data were analyzed on the log2 scale. Data quality was assessed via box and whisker plots along with residual and pair-wise MVA plots before and after normalization [29,30]. All arrays were normalized together using fastlo, a non-linear normalization similar to cyclic loess which runs in a fraction of the time [31]. "
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