Abstract
Normalizations of gene expression data are commonly used in practice. They are used for removing systematic variation which affects the measure of gene expression levels. But one can object to the using of normalized data for testing hypotheses. By using normalized data, tests can break nominal level of multiple testing on which we would like to test the hypotheses. It could bring a lot of false positives, which we would like to prevent. In this chapter, by simulating data with similar correlation structure as real data, we try to find out how quantile, global, and δ-sequence normalizations hold the nominal level of Bonferroni multiple testing procedure.
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References
Bolstad M, Irizarry R, Strand M, Speed T (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193
Klebanov L, Yakovlev A (2007) Diverse correlation structures in gene expression data and their utility in improving statistical inference. Ann Appl Stat 1(2):538–559
Acknowledgments
Author thanks Prof. Lev Klebanov for valuable comments, remarks, and overall help.
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© 2013 Springer Science+Business Media New York
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Bubelíny, P. (2013). Using of Normalizations for Gene Expression Analysis. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_5
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DOI: https://doi.org/10.1007/978-1-60327-337-4_5
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-60327-336-7
Online ISBN: 978-1-60327-337-4
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