Variance stabilization applied to microarray data calibration and to the quantification of differential expression
Open accessAbstract
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
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Protein values were normalized using variance stabilizing normalization.
“then values were normalized using variance stabilizing normalization86.”
From APOE genotypes differentially remodel the astrocytic lipid droplet proteome to shape lipid droplet dynamics (2026) · cites this paper as [86]
Background-subtracted spot intensities were normalized using variance stabilization normalization.
“Background-subtracted spot intensities were normalized using variance stabilization normalization (Huber et al. 2002).”
From MicroRNA expression in the adult mouse central nervous system (2008) · cites this paper as Huber et al. 2002
All analyses were performed on raw data, quantile-normalized data, and variance stabilizing normalization (VSN) to account for normalization impact.
“Since the normalization may have an impact on the results, we performed all analyses on the raw data, normalized data by quantile normalization and by variance stabilizing normalization (6).”
From Distribution of miRNA expression across human tissues (2016) · cites this paper as [6]
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- Huber,W., von Heydebreck,A., Sültmann,H., Poustka,A. and Vin- gron,M. A model for the calibration of microarray data and the quantification of differential expression. Preprint available on request.
- Rousseuw,P.J. and Leroy,A.M. (1987) Robust Regression and Outlier Detection. Wiley.
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- Authors
- Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M.
- Affiliations
- German Cancer Research Center (DE)
- Max Planck Society (DE)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-07-01
- Volume
- 18
- Issue
- suppl_1
- Pages
- S96-S104
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2122598723
- Cited by
- 2,440
- References
- 0
- Citations by year
- 2026: 141 · 2025: 127 · 2024: 153 · 2023: 119 · 2022: 89 · 2021: 115 · 2020: 85 · 2019: 58 · 2018: 65 · 2017: 62 · 2016: 65 · 2015: 95 · 2014: 97 · 2013: 106 · 2012: 117
- FWCI
- 23.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- variance stabilizing transformation, measurement error, gene expression data, intensity transformation, mean-variance relationship, data preprocessing
- MeSH terms
- Algorithms, Analysis of Variance, Calibration, Data Interpretation, Statistical, Models, Genetic, Reference Standards, Sensitivity and Specificity, Software, Reproducibility of Results, Models, Statistical, Likelihood Functions, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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