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Bioinformatics Advance Access published online on July 5, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti570
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received February 11, 2005
Revised July 1, 2005
Accepted July 1, 2005

Article

Increased power of microarray analysis by use of an algorithm based on a multivariate procedure

K. Krohn 1, M. Eszlinger III2, R. Paschke III2, I. Roeder 3, and E. Schuster 3*

1 Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstraße 22, 04103 Leipzig, Germany
2 Medical Department, University of Leipzig, Ph.-Rosenthal-Straße 27, 04103 Leipzig, Germany
3 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstraße 16-18, 04107 Leipzig, Germany


  Abstract

Motivation: The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the "multiple testing problem", increasing the probability of obtaining false positive test results. To achieve more reliable results, it is therefore necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate (FDR). However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach.

Results: Here we discuss a multiplicity adjustment procedure applied to classical univariate as well as to recently proposed multivariate gene expression scores. All procedures strictly control the FWE. We demonstrate that the use of multivariate scores leads to a more efficient identification of differentially expressed genes than the widely used MAS5 approach provided by the Affymetrix software tools (Affymetrix Microarray Suite 5 or GeneChip Operating Software). The practical importance of this finding is successfully validated using real time quantitative PCR and data from spike-in experiments.


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