DART2: a robust multiple testing method to smartly leverage helpful or misleading ancillary information
This addresses the need for reliable multiple testing methods in fields like genomics, where ancillary information quality varies, though it is incremental as it builds on existing approaches.
The paper tackles the problem of multiple testing with ancillary information by developing DART2, a robust method that controls false discovery rate (FDR) and improves power when the information is helpful, while maintaining at least baseline power when it is misleading, as demonstrated in numerical studies and a gene association study with superior accuracy and robustness.
In many applications of multiple testing, ancillary information is available, reflecting the hypothesis null or alternative status. Several methods have been developed to leverage this ancillary information to enhance testing power, typically requiring the ancillary information is helpful enough to ensure favorable performance. In this paper, we develop a robust and effective distance-assisted multiple testing procedure named DART2, designed to be powerful and robust regardless of the quality of ancillary information. When the ancillary information is helpful, DART2 can asymptotically control FDR while improving power; otherwise, DART2 can still control FDR and maintain power at least as high as ignoring the ancillary information. We demonstrated DART2's superior performance compared to existing methods through numerical studies under various settings. In addition, DART2 has been applied to a gene association study where we have shown its superior accuracy and robustness under two different types of ancillary information.