@article {bioinflmu-328,
title = {{Centralization: a new method for the normalization of gene expression data}},
journal = {Bioinformatics},
volume = {17},
number = {Suppl.1},
year = {2001},
pages = {S323-S331},
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. Contact: Alexander.Zien@gmd.de},
keywords = {expressionlab@lmu},
doi = {10.1093/bioinformatics/17.suppl_1.S323},
pdf = {PDF},
author = {Alexander Zien and Thomas Aigner and Ralf Zimmer and Thomas Lengauer}
}