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We develop a new class of distribution--free multiple testing rules for falsediscovery rate (FDR) control under general dependence. A key element in ourproposal is a symmetrized data aggregation (SDA) approach to incorporating thedependence structure via sample splitting, data screening and informationpooling. The proposed SDA filter first constructs a sequence of rankingstatistics that fulfill global symmetry properties, and then chooses adata--driven threshold along the ranking to control the FDR. The SDA filtersubstantially outperforms the knockoff method in power under moderate to strongdependence, and is more robust than existing methods based on asymptotic$p$-values. We first develop finite--sample theory to provide an upper boundfor the actual FDR under general dependence, and then establish the asymptoticvalidity of SDA for both the FDR and false discovery proportion (FDP) controlunder mild regularity conditions. The procedure is implemented in the R package\texttt{SDA}. Numerical results confirm the effectiveness and robustness of SDAin FDR control and show that it achieves substantial power gain over existingmethods in many settings.

False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation