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BMC Genomics

, 11:349

First Online: 02 June 2010Received: 09 March 2010Accepted: 02 June 2010

Abstract

BackgroundNormalization 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.

ResultsIn 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.

ConclusionsPre-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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2164-11-349 contains supplementary material, which is available to authorized users.

Ramona Schmid, Patrick Baum contributed equally to this work.

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Autor: Ramona Schmid - Patrick Baum - Carina Ittrich - Katrin Fundel-Clemens - Wolfgang Huber - Benedikt Brors - Roland Eils - And

Fuente: https://link.springer.com/article/10.1186/1471-2164-11-349







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