False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. 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 article. Supplementary materials for this article are available online.本文介绍了错误发现率回归（false discovery rate regression），一种将协变量信息融入大规模多重检验问题的方法。该方法可估计检验层面协变量与单个观测为显著信号的先验概率之间的关联，并利用该估计得到的关联指导各检验的结果判定，将整体错误发现率（false discovery rate, FDR）控制在预设水平。该方法在统计推断与计算的交叉界面存在诸多微妙问题，本文对该整体方法的多种变体展开了探究。模拟研究结果显示：(1) 当存在协变量效应时，错误发现率回归可在固定FDR水平下提升检验效能；(2) 当不存在协变量效应时，该方法具有稳健性，不会引发误差率膨胀。本文将该方法应用于初级视觉皮层的神经记录（neural recordings）数据，旨在检测存在精细时间尺度交互作用的神经元对——即二者的同步发放频率高于随机预期水平。相较标准的FDR控制分析方法，本文方法可多检出约50%的同步神经元对。配套R软件包FDRreg实现了本文所述的全部方法。本文补充材料可在线获取。