ECCB 2002 Poster sorted by: Author | Number Next | Previous poster (in order of the view you have selected) |
Title: Variance stabilization applied to stochastic modeling of DNA microarray measurements, calibration, and the quantification of differential expression | P67 |
Huber, Wolfgang (1); von Heydebreck, Anja (2); Sueltmann, Holger(1); Poustka, Annemarie (1); Vingron, Martin (2) w.huber@dkfz.de (1) Div. Molecular Genome Analysis, DKFZ, Heidelberg; (2) Dep. Computational Molecular Biology, MPI Molecular Genetics, Berlin |
We introduce a stochastic 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 Delta 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 Delta 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-color cDNA arrays and a series of Affymetrix oligonucleotide arrays. Software is freely available for academic use as an R package at http://www.dkfz.de/abt0840/whuber |
[1] Variance stabilization applied to microarray data calibration and to the quantification of differential expression. W. Huber, A. von Heydebreck, H. Sueltmann, A. Poustka, M. Vingron. ISMB 2002. |