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      Variance stabilization applied to microarray data calibration and to the quantification of differential expression.

      Bioinformatics

      Algorithms, Analysis of Variance, Calibration, standards, Data Interpretation, Statistical, Gene Expression Profiling, instrumentation, methods, Likelihood Functions, Models, Genetic, Models, Statistical, Oligonucleotide Array Sequence Analysis, Reference Standards, Reproducibility of Results, Sensitivity and Specificity, Software

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          Abstract

          We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.

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          12169536

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