Identifying replicable signals across different studies provides stronger scientific evidence and more powerful inference. Existing literature on high dimensional applicability analysis either imposes strong modeling assumptions or has low power. We develop a powerful and robust empirical Bayes approach for high dimensional replicability analysis. Our method effectively borrows information from different features and studies while accounting for heterogeneity. We show that the proposed method has better power than competing methods while controlling the false discovery rate, both empirically and theoretically. Analyzing datasets from the genome-wide association studies reveals new biological insights that otherwise cannot be obtained by using existing methods.

翻译：跨不同研究识别可复制信号能够提供更强的科学证据和更有效的推断。现有关于高维可重复性分析的文献要么施加了较强的模型假设，要么统计功效较低。我们提出了一种针对高维可重复性分析的高功效且稳健的经验贝叶斯方法。该方法在考虑异质性的同时，有效地利用了不同特征和不同研究间的信息。我们证明，无论在实证还是理论上，所提方法在控制错误发现率的同时，比竞争方法具有更高的统计功效。对全基因组关联研究数据集的分析揭示了现有方法无法获得的新的生物学见解。