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Title: Variance stabilization applied to stochastic modeling of DNA microarray measurements, calibration, and the quantification of differential expression
P67
Huber, Wolfgang (1); von Heydebreck, Anja (2); Sueltmann, Holger(1); Poustka, Annemarie (1); Vingron, Martin (2)

w.huber@dkfz.de
(1) Div. Molecular Genome Analysis, DKFZ, Heidelberg; (2) Dep. Computational Molecular Biology, MPI Molecular Genetics, Berlin

We introduce a stochastic 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 Delta h 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 Delta h 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-color cDNA arrays and a series of Affymetrix oligonucleotide arrays.
Software is freely available for academic use as an R package at http://www.dkfz.de/abt0840/whuber
[1] Variance stabilization applied to microarray data calibration and to the quantification of differential expression. W. Huber, A. von Heydebreck, H. Sueltmann, A. Poustka, M. Vingron. ISMB 2002.