False discovery rate regression: an application to neural synchrony detection in primary visual cortex.

ABSTRACT: Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. Th

SUBMITTER: Scott JG 

PROVIDER: S-EPMC4743052 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature