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Bioinformatics Advance Access published online on July 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth442
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received December 23, 2004
Revised June 30, 2004
Accepted July 26, 2004

Article

The Baumgartner-Weiß-Schindler test for the detection of differentially expressed genes in replicated microarray experiments

Markus Neuhäuser 1* Roswitha Senske 1

1 Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin, New Zealand

* To whom correspondence should be addressed. E-mail: markus.neuhaeuser{at}medizin.uni-essen.de.


  Abstract

Motivation: An important application of microarray experiments is to identify differentially expressed genes. Because microarray data are often not distributed according to a normal distribution nonparametric methods were suggested for their statistical analysis. Here, the Baumgartner-Weiß-Schindler test, a novel and powerful test based on ranks, is investigated and compared with the parametric t test as well as with two other nonparametric tests (Wilcoxon rank sum test, Fisher-Pitman permutation test) recently recommended for the analysis of gene expression data.

Results: Simulation studies show that an exact permutation test based on the Baumgartner-Weiß-Schindler statistic B is preferable to the other three tests. It is less conservative than the Wilcoxon test and more powerful, in particular in case of asymmetric or heavily tailed distributions. When the underlying distribution is symmetric the differences in power between the tests are relatively small. Thus, the Baumgartner-Weiß-Schindler is recommended for the usual situation that the underlying distribution is a priori unknown.

Availability: SAS code available on request from the authors.


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