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. This poses many subtle issues at the interface
between inference and computation, and we investigate several variations of the
overall approach. Simulation evidence suggests that: (1) when covariate effects
are present, FDR regression improves power for a fixed false-discovery rate;
and (2) when covariate effects are absent, the method is robust, in the sense
that it does not lead to inflated error rates. We apply the method to neural
recordings from primary visual cortex. The goal is to detect pairs of neurons
that exhibit fine-time-scale interactions, in the sense that they fire together
more often than expected due to chance. Our method detects roughly 50% more
synchronous pairs versus a standard FDR-controlling analysis. The companion R
package FDRreg implements all methods described in the paper.

介绍了一种名为 “false-discovery-rate regression” 的方法，利用辅助信息直接提高测试结果的质量，进而增大了对细节精度的探测能力，虽然存在漏诊问题，但尽管如此，它具有广泛的适用性，且能为神经元电信号测试和分析等方面提供可能的解决方案。