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Bioinformatics. 2002;18 Suppl 1:S96-104.

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

Author information

  • 1Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de

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.

PMID:
12169536
[PubMed - indexed for MEDLINE]
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