Using of Normalizations for Gene Expression Analysis

  • Peter BubelínyEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 972)


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.

Key words

Gene expression Normalization δ-Sequence 



Author thanks Prof. Lev Klebanov for valuable comments, remarks, and overall help.


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    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):185–193PubMedCrossRefGoogle Scholar
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    Klebanov L, Yakovlev A (2007) Diverse correlation structures in gene expression data and their utility in improving statistical inference. Ann Appl Stat 1(2):538–559CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Probability and StatisticsCharles UniversityPragueCzech Republic

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