Normalization of two-channel microarray experiments: A semiparametic approach

Department of Health Sciences Research, Mayo Clinic Rochester, MN 55905, USA.
Bioinformatics (Impact Factor: 4.62). 04/2005; 21(7):1078-83. DOI: 10.1093/bioinformatics/bti105
Source: PubMed

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.

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    • "In recent years, smoothing methods have been used in a wide range of studies to model a nonlinear exposure–response relationship. These include etiological investigations of air pollution [25], cancer risk assessment [6], nutrition [14], microarray studies [11], cardiovascular mortality [10], and occupational exposures [8] [18] [32]. In a study of lung cancer and occupational exposure to silica, the estimated log hazard ratio for silica exposure considered as a function of cumulative exposure referred to hereafter as the exposure–response was observed to increase over categories of exposures [5]. "
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    Journal of Applied Statistics 01/2015; 42(5). DOI:10.1080/02664763.2014.995607 · 0.45 Impact Factor
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    • "All data passed a quality assurance protocol (Burgoon et al., 2005) and deposited in TIMS dbZach data management system (Burgoon and Zacharewski, 2007). Microarray data were normalized using a semi-parametric approach (Eckel et al., 2005) and the posterior probability P1(t) values were calculated using an empirical Bayes method based on a per gene and dose basis using model-based t values (Eckel et al., 2004). Gene expression data were ranked and prioritized using |fold change| > 1.5 and statistical P1(t) value > 0.999 criteria to identify differentially expressed genes. "
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    Toxicology and Applied Pharmacology 04/2012; 262(2):124-38. DOI:10.1016/j.taap.2012.04.026 · 3.63 Impact Factor
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    • "Thus, we used a nonlinear model-based normalization that allows incorporation of known experimental effects, e.g. TLDA plate, to remove the nonlinear biases in the ΔC T values (Eckel et al., 2005). As with most commonly used global microarray normalization algorithms, this assumes that only a small portion of genes are differentially expressed between specimens normalized together, that the distribution of differentially expressed genes is approximately symmetric about identity, and that there are sufficient genes for estimation of bias without over-fitting. "
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