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Centralization: A new method for the normalization of gene expression data

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zien, A., Aigner T, Zimmer, R., & Lengauer, T. (2001). Centralization: A new method for the normalization of gene expression data. Bioinformatics, 17, S323-S331. Retrieved from http://www.scai.fraunhofer.de/index.php?id=270L=1.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E264-6
Abstract
Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.