An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data

Zhang Shunpu
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Zhang Shunpu: University of Nebraska Lincoln

Statistical Applications in Genetics and Molecular Biology, 2007, vol. 5, issue 1, 25

Abstract: Previous nonparametric statistical methods on constructing the test and null statistics require having at least 4 arrays under each condition. In this paper, we provide an improved method of constructing the test and null statistics which only requires 2 arrays under one condition if the number of arrays under the other condition is at least 3. The conventional testing method defines the rejection region by controlling the probability of Type I error. In this paper, we propose to determine the critical values (or the cut-off points) of the rejection region by directly controlling the false discovery rate. Simulations were carried out to compare the performance of our proposed method with several existing methods. Finally, our proposed method is applied to the rat data of Pan et al. (2003). It is seen from both simulations and the rat data that our method has lower false discovery rates than those from the significance analysis of microarray (SAM) method of Tusher et al. (2001) and the mixture model method (MMM) of Pan et al. (2003).

Keywords: gene expression; microarray data; normal mixture; nonparametric method (search for similar items in EconPapers)
Date: 2007
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DOI: 10.2202/1544-6115.1246

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