@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 = {<a href="/files/oldwebfm//Publications/Centralization_a_new_method_for_the_normalization_of_gene_expression_data.pdf">PDF</a>},
	author = {Alexander Zien and Thomas Aigner and Ralf Zimmer and Thomas Lengauer}
}