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Conference Paper

Variance stabilization and robust normalization for microarray gene expression data

MPS-Authors

von Heydebreck,  A.
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50470

Poustka,  A.
Evolution and Development (Albert Poustka), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  M.
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

von Heydebreck, A., Huber, W., Poustka, A., & Vingron, M. (2002). Variance stabilization and robust normalization for microarray gene expression data. In W. Härdle, & B. Rönz (Eds.), Proceedings in Computational Statistics (pp. 623-628). Heidelberg: Physika Verlag.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-8CB8-F
Abstract
We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of di erential gene expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a di erence statistic 4h whose variance is approximately constant along the intensity range. 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. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation.