Empirical bayes methods and false discovery rates for microarrays.
In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.
Department of Statistics and Division of Biostatistics, Stanford University, Stanford, California 94305, USA.
Bradley
B
Efron
Robert
R
Tibshirani
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results