Research
Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis
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* Corresponding author: Reinhard Hoffmann r_hoffmann@m3401.mpk.med.uni-muenchen.de
1 Department of Bacteriology, Max von Pettenkofer Institut, Pettenkoferstrasse 9a, 80336 Munich, Germany
2 Section of Gene Function and Regulation, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, London SW3 6JB, UK
3 Department of Medical Informatics, Biometrics and Epidemiology, University of Munich, Marchioninistrasse 15, 81377 Munich, Germany
Genome Biology 2002, 3:research0033-research0033.11 doi:10.1186/gb-2002-3-7-research0033
Published: 14 June 2002Abstract
Background
Oligonucleotide microarrays measure the relative transcript abundance of thousands of mRNAs in parallel. A large number of procedures for normalization and detection of differentially expressed genes have been proposed. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined.
Results
We have employed four different normalization methods and all possible combinations with three different statistical algorithms for detection of differentially expressed genes on a prototype dataset. The number of genes detected as differentially expressed differs by a factor of about three. Analysis of lists of genes detected as differentially expressed, and rank correlation coefficients for probability of differential expression shows that a high concordance between different methods can only be achieved by using the same normalization procedure.
Conclusions
Normalization has a profound influence of detection of differentially expressed genes. This influence is higher than that of three subsequent statistical analysis procedures examined. Algorithms incorporating more array-derived information than gene-expression values alone are urgently needed.