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

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 whos...

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Bibliographic Details
Published in:Bioinformatics 2002-07, Vol.18 (suppl-1), p.S96-S104
Main Authors: Huber, Wolfgang, von Heydebreck, Anja, Sültmann, Holger, Poustka, Annemarie, Vingron, Martin
Format: Article
Language:English
Subjects:
Algorithms
Analysis of Variance
Calibration - standards
Data Interpretation, Statistical
Gene Expression Profiling - instrumentation
Gene Expression Profiling - methods
Gene Expression Profiling - standards
Likelihood Functions
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis - instrumentation
Oligonucleotide Array Sequence Analysis - methods
Oligonucleotide Array Sequence Analysis - standards
Reference Standards
Reproducibility of Results
Sensitivity and Specificity
Software
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Description
Summary: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
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/18.suppl_1.S96