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

Abstract

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.

• False discovery rate
• Empirical Bayes
• Multiple testing
• Neuroscience

Additional information

Notes on contributors

James G. Scott

James G. Scott is Associate Professor, McCombs School of Business, University of Texas at Austin, 1 University Station, B6500, Austin, TX 78712 (E-mail: [email protected]).

Ryan C. Kelly

Ryan C. Kelly, Google, New York, NY 10011 (E-mail: [email protected]).

Matthew A. Smith

Matthew A. Smith is Assistant Professor, Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213 (E-mail: [email protected]).

Pengcheng Zhou

Pengcheng Zhou is Graduate Student, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213 (E-mail: [email protected]).

Robert E. Kass

Robert E. Kass is Professor, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 (E-mail: [email protected]).