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Comparative study on normalization procedures for cluster analysis of gene expression datasets

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50127

Costa,  Ivan G.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

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

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Zitation

de Souto, M. C. P., de Araujo, D. S. A., Costa, I. G., Soares, R. G. F., Ludermir, T. B., & Schliep, A. (2008). Comparative study on normalization procedures for cluster analysis of gene expression datasets. Hong Kong: International Joint Conference on Neural Networks, Hong Kong), (2008).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7F03-C
Zusammenfassung
Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data. The results are presented in terms of the recovering of the true clusterstructure as found by five different clustering algorithms.