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Open Access Highly Accessed Research article

Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3

Ramona Schmid1, Patrick Baum1, Carina Ittrich1, Katrin Fundel-Clemens1, Wolfgang Huber2, Benedikt Brors3, Roland Eils34, Andreas Weith1, Detlev Mennerich15 and Karsten Quast1*

Author affiliations

1 Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach/Riss, Germany

2 EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

3 Theoretical Bioinformatics Department, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany

4 Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, University of Heidelberg, 69120 Heidelberg, Germany

5 Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT 06877, USA

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Citation and License

BMC Genomics 2010, 11:349  doi:10.1186/1471-2164-11-349

Published: 2 June 2010

Abstract

Background

Normalization of microarrays is a standard practice to account for and minimize effects which are not due to the controlled factors in an experiment. There is an overwhelming number of different methods that can be applied, none of which is ideally suited for all experimental designs. Thus, it is important to identify a normalization method appropriate for the experimental setup under consideration that is neither too negligent nor too stringent. Major aim is to derive optimal results from the underlying experiment. Comparisons of different normalization methods have already been conducted, none of which, to our knowledge, comparing more than a handful of methods.

Results

In the present study, 25 different ways of pre-processing Illumina Sentrix BeadChip array data are compared. Among others, methods provided by the BeadStudio software are taken into account. Looking at different statistical measures, we point out the ideal versus the actual observations. Additionally, we compare qRT-PCR measurements of transcripts from different ranges of expression intensities to the respective normalized values of the microarray data. Taking together all different kinds of measures, the ideal method for our dataset is identified.

Conclusions

Pre-processing of microarray gene expression experiments has been shown to influence further downstream analysis to a great extent and thus has to be carefully chosen based on the design of the experiment. This study provides a recommendation for deciding which normalization method is best suited for a particular experimental setup.