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

Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology, S96-S104.

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 Creators:
Huber, Wolfgang, Author
von Heydebreck, Anja1, Author
Sültmann, Holger, Author
Poustka, Annemarie, Author
Vingron, Martin2, Author              
Affiliations:
1Max Planck Society, ou_persistent13              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 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 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 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-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.

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Language(s): eng - English
 Dates: 2002-07
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: eDoc: 28972
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Title: Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology
Source Genre: Issue
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: S96 - S104 Identifier: -

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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 18 (Supplement 1) Sequence Number: - Start / End Page: - Identifier: -