The L1-version of the Cramér-von Mises test for two-sample comparisons in microarray data analysis.

EURASIP Journal on Bioinformatics & Systems Biology
Yuanhui XiaoAndrei Yakovlev

Abstract

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted p-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 L-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 L(1) 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 L(1)-distance test statistic. The performance and power of the L(1)-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 L(1)-distance test appears to be nearly as powerful as its L(2) counterpart. The lower computational intensity of the L(1)-distance test allows computation of exact quantiles of...Continue Reading

References

Nov 9, 2002·Bioinformatics·Olga G TroyanskayaRuss B Altman
Feb 3, 2006·BMC Bioinformatics·Xing QiuAndrei Yakovlev
May 2, 2006·Statistical Applications in Genetics and Molecular Biology·Ola Hössjer

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Citations

Jul 4, 2006·NeuroRx : the Journal of the American Society for Experimental NeuroTherapeutics·Anthony AlmudevarAndrei Y Yakovlev
Jan 28, 2016·IET Systems Biology·Henry Han, Ying Liu
Feb 18, 2010·Computational Statistics & Data Analysis·Alexander GordonAndrei Yakovlev

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