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

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ID: 290468
2002
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Abstract
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 Δh 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 Δh 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. Availability: Software is freely available for academic use as an R package at http://www.dkfz.de/abt0840/whuber Contact: w.huber@dkfz.de
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Authors Wolfgang Huber, Anja von Heydebreck, Holger Sültmann, Annemarie Poustka, Martin Vingron
Journal BMC Bioinformatics
Year 2002
DOI
10.1093/bioinformatics/18.suppl_1.s96
URL
https://academic.oup.com/bioinformatics/article-pdf/18/suppl_1/S96/629729/18S096.pdf
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