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- Title
Variance stabilization applied to microarray data calibration and to the quantification of differential expression.
- Authors
Huber, Wolfgang; von Heydebreck, Anja; Sültmann, Holger; Poustka, Annemarie; Vingron, Martin
- 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.
- Publication
Bioinformatics (Oxford, England), 2002, Vol 18 Suppl 1, pS96
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/18.suppl_1.s96