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The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted [Image: see text]-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 tw...

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Detalles Bibliográficos
Autores principales: Xiao, Yuanhui, Gordon, Alexander, Yakovlev, Andrei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171322/
https://www.ncbi.nlm.nih.gov/pubmed/18427586
http://dx.doi.org/10.1155/BSB/2006/85769
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author Xiao, Yuanhui
Gordon, Alexander
Yakovlev, Andrei
author_facet Xiao, Yuanhui
Gordon, Alexander
Yakovlev, Andrei
author_sort Xiao, Yuanhui
collection PubMed
description Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted [Image: see text]-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 [Image: see text]-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 [Image: see text] 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 [Image: see text]-distance test statistic. The performance and power of the [Image: see text]-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 [Image: see text]-distance test appears to be nearly as powerful as its [Image: see text] counterpart. The lower computational intensity of the [Image: see text]-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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spelling pubmed-31713222011-09-13 The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis Xiao, Yuanhui Gordon, Alexander Yakovlev, Andrei EURASIP J Bioinform Syst Biol Research Article Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted [Image: see text]-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 [Image: see text]-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 [Image: see text] 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 [Image: see text]-distance test statistic. The performance and power of the [Image: see text]-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 [Image: see text]-distance test appears to be nearly as powerful as its [Image: see text] counterpart. The lower computational intensity of the [Image: see text]-distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramér-von Mises test. Springer 2006-09-14 /pmc/articles/PMC3171322/ /pubmed/18427586 http://dx.doi.org/10.1155/BSB/2006/85769 Text en Copyright © 2006 Yuanhui Xiao et al. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Research Article
Xiao, Yuanhui
Gordon, Alexander
Yakovlev, Andrei
The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title_full The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title_fullStr The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title_full_unstemmed The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title_short The [Image: see text]-Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis
title_sort [image: see text]-version of the cramér-von mises test for two-sample comparisons in microarray data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171322/
https://www.ncbi.nlm.nih.gov/pubmed/18427586
http://dx.doi.org/10.1155/BSB/2006/85769
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