Inference on the limiting false discovery rate and the p-value threshold parameter assuming weak dependence between gene expression levels within subject.
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.
Heller
Glenn
G
Memorial Sloan-Kettering Cancer Center, USA. hellerg@mskcc.org
Qin
Jing
J
D015870:Gene Expression; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000032:analysis; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000601:surgery
D017430:Prostate-Specific Antigen