Abstract
We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential gene expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic △h whose variance is approximately constant along the intensity range. The parametric form h(x) = arsinh(a+bx) is derived from a model of the varianceversus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation.
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von Heydebreck, A., Huber, W., Poustka, A., Vingron, M. (2002). Variance Stabilization and Robust Normalization for Microarray Gene Expression Data. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_97
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DOI: https://doi.org/10.1007/978-3-642-57489-4_97
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
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