A mixture model approach for the analysis of microarray gene expression data
Computational Statistics & Data Analysis
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Computational Statistics & Data Analysis
International Journal of Approximate Reasoning
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Many genome-wide association studies have been conducted to identify single nucleotide polymorphisms (SNPs) that are associated with particular diseases or other traits. The local false discovery rate (LFDR) estimated using semiparametric models has enjoyed success in simultaneous inference. However, semiparametric LFDR estimators can be biased because they tend to overestimate the proportion of the nonassociated SNPs. We address the problem by adapting a simple parametric mixture model (PMM) and by comparing this model to the semiparametric mixture model (SMM) behind an LFDR estimator that is known to be conservatively biased. Then, we also compare the PMM with a parametric nonmixture model (PNM). In our simulation studies, we thoroughly analyze the performances of the three models under different values of $(p_{1})$, a prior probability that is approximately equal to the proportion of SNPs that are associated with the disease. When $(p_{1} 10\%)$, the PMM generally performs better than the SMM. When $(p_{1}