Inference on the Limiting False Discovery Rate and the P-value Threshold Parameter Assuming Weak Dependence between Gene Expression Levels within Subject
Heller Glenn and
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Heller Glenn: Memorial Sloan-Kettering Cancer Center
Qin Jing: National Institute of Allergy and Infectious Diseases
Statistical Applications in Genetics and Molecular Biology, 2007, vol. 6, issue 1, 1-24
An objective of microarray data analysis is to identify gene expressions that are associated with a disease related outcome. For each gene, a test statistic is computed to determine if an association exists, and this statistic generates a marginal p-value. In an effort to pool this information across genes, a p-value density function is derived. The p-value density is modeled as a mixture of a uniform (0,1) density and a scaled ratio of normal densities derived from the asymptotic normality of the test statistic. The p-values are assumed to be weakly dependent and a quasi-likelihood is used to estimate the parameters in the mixture density. The quasi-likelihood and the weak dependence assumption enables estimation and asymptotic inference on the false discovery rate for a given rejection region, and its inverse, the p-value threshold parameter for a fixed false discovery rate. A false discovery rate analysis on a localized prostate cancer data set is used to illustrate the methodology. Simulations are performed to assess the performance of this methodology.
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