A Robust Method for Large-Scale Multiple Hypotheses Testing

作者：Han Seungbong; Andrei Adin Cristian^*; Tsui Kam Wah

来源：Biometrical Journal, 2010, 52(2): 222-232.

DOI：10.1002/bimj.200900177

• Bayesian framework
• Microarray data
• Mixture model
• Multiple testing
• Robustness

When drawing large-scale simultaneous inference, such as in genomics and imaging problems, multiplicity adjustments should be made, since, otherwise, one would be faced with an inflated type I error. Numerous methods are available to estimate the proportion of true null hypotheses no, among a large number of hypotheses tested. Many methods implicitly assume that the no is large, that is, close to 1. However, in practice, mid-range no values are frequently encountered and many of the widely used methods tend to produce highly variable or biased estimates of no. As a remedy in such situations, we propose a hierarchical Bayesian model that produces an estimator of no that exhibits considerably less bias and is more stable. Simulation studies seem indicative of good method performance even when low-to-moderate correlation exists among test statistics. Method performance is assessed in simulated settings and its practical usefulness is illustrated in an application to a type II diabetes study.

• 出版日期2010-4