Normalization of two-channel microarray experiments: A semiparametic approach
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.
- SourceAvailable from: Ellen Eisen
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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 , cancer risk assessment , nutrition , microarray studies , cardiovascular mortality , and occupational exposures   . 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 . "
ABSTRACT: In this paper, we review available methods for determination of the functional form of the relation between a covariate and the log hazard ratio for a Cox model. We pay special attention to the detection of influential observations to the extent that they influence the estimated functional form of the relation between a covariate and the log hazard ratio. Our paper is motivated by a data set from a cohort study of lung cancer and silica exposure, where the nonlinear shape of the estimated log hazard ratio for silica exposure plotted against cumulative exposure and hereafter referred to as the exposure–response curve was greatly affected by whether or not two individuals with the highest exposures were included in the analysis. Formal influence diagnostics did not identify these two individuals but did identify the three highest exposed cases. Removal of these three cases resulted in a biologically plausible exposure–response curve.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. "
ABSTRACT: Continuous exposure to high concentrations of hexavalent chromium [Cr(VI)] in drinking water results in intestinal tumors in mice but not rats. Concentration-dependent gene expression effects were evaluated in female F344 rat duodenal and jejunal epithelia following 7 and 90 days of exposure to 0.3-520 mg/L (as sodium dichromate dihydrate, SDD) in drinking water. Whole-genome microarrays identified 3269 and 1815 duodenal, and 4557 and 1534 jejunal differentially expressed genes at 8 and 91 days, respectively, with significant overlaps between the intestinal segments. Functional annotation identified gene expression changes associated with oxidative stress, cell cycle, cell death, and immune response that were consistent with reported changes in redox status and histopathology. Comparative analysis with B6C3F1 mouse data from a similarly designed study identified 2790 differentially expressed rat orthologs in the duodenum compared to 5013 mouse orthologs at day 8, and only 1504 rat and 3484 mouse orthologs at day 91. Automated dose-response modeling resulted in similar median EC₅₀s in the rodent duodenal and jejunal mucosae. Comparative examination of differentially expressed genes also identified divergently regulated orthologs. Comparable numbers of differentially expressed genes were observed at equivalent Cr concentrations (μg Cr/g duodenum). However, mice accumulated higher Cr levels than rats at ≥ 170 mg/L SDD, resulting in a ~2-fold increase in the number of differentially expressed genes. These qualitative and quantitative differences in differential gene expression, which correlate with differences in tissue dose, likely contribute to the disparate intestinal tumor outcomes.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. "
ABSTRACT: We sought to determine the time and vaccinia virus dose combination that would maximize the number of acute immune response changes in response to vaccinia stimulation in preparation for a large gene expression microarray experiment. PBMCs from ten subjects were exposed to five vaccinia virus doses for three lengths of time. Gene expression was measured for 90 immune response genes via Taqman® Low Density Immune Arrays. Expression data were normalized via model-based non-linear normalization. Linear mixed effects model results were used to standardize changes across genes and determine the time/multiplicity of infection (MOI) combination with the largest number of changes. The greatest number of changes occurred with a MOI of 5.0 and exposure time of 48 h. Further inspection revealed that most changes had occurred earlier and faded at this combination. The second highest number of changes was found at a MOI of 0.5 PFU/cell and time of 18 h. We conclude a time of 18 h with a MOI of 0.5 PFU/cell is the optimal time/MOI combination for the full scale gene expression study. The strategy described herein is a general and resource efficient way to make critical decisions regarding experimental parameters for studies utilizing expensive assays that interrogate a large number of variables.Journal of immunological methods 03/2011; 366(1-2):69-78. DOI:10.1016/j.jim.2011.01.011 · 2.01 Impact Factor