Open Access

The -Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis

EURASIP Journal on Bioinformatics and Systems Biology20062006:85769

https://doi.org/10.1155/BSB/2006/85769

Received: 31 January 2006

Accepted: 27 June 2006

Published: 14 September 2006

Abstract

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted -values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramér-von Mises two-sample test, based on a certain -distance between two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramér-von Mises test statistic in finite samples. However, the computation is very time- and space-consuming. An counterpart of the Cramér-von Mises test represents an appealing alternative. In this work, we present an efficient algorithm for computing exact quantiles of the -distance test statistic. The performance and power of the -distance test are compared with those of the Cramér-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The -distance test appears to be nearly as powerful as its counterpart. The lower computational intensity of the -distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramér-von Mises test.

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Authors’ Affiliations

(1)
Department of Biostatistics and Computational Biology, University of Rochester
(2)
Department of Mathematics and Statistics, Georgia State University
(3)
Department of Mathematics and Statistics, University of North Carolina at Charlotte

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Copyright

© Yuanhui Xiao et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.