Analysing data from large‐scale, multiexperiment studies requires scientists to both analyse each experiment and to assess the results as a whole. In this article, we develop double empirical Bayes testing (DEBT), an empirical Bayes method for analysing multiexperiment studies when many covariates are gathered per experiment. DEBT is a two‐stage method: in the first stage, it reports which experiments yielded significant outcomes and in the second stage, it hypothesises which covariates drive the experimental significance. In both of its stages, DEBT builds on the work of Efron, who laid out an elegant empirical Bayes approach to testing. DEBT enhances this framework by learning a series of black box predictive models to boost power and control the false discovery rate. In Stage 1, it uses a deep neural network prior to report which experiments yielded significant outcomes. In Stage 2, it uses an empirical Bayes version of the knockoff filter to select covariates that have significant predictive power of Stage 1 significance. In both simulated and real data, DEBT increases the proportion of discovered significant outcomes and selects more features when signals are weak. In a real study of cancer cell lines, DEBT selects a robust set of biologically plausible genomic drivers of drug sensitivity and resistance in cancer.

中文翻译：

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双经验贝叶斯检验

分析来自大规模、多实验研究的数据，需要科学家分析每个实验并从整体上评估结果。在本文中，我们开发了双经验贝叶斯检验 (DEBT)，这是一种经验贝叶斯方法，用于在每个实验收集许多协变量时分析多实验研究。DEBT 是一种双阶段方法：在第一阶段，它报告哪些实验产生了显着的结果，在第二阶段，它假设哪些协变量驱动实验意义。在这两个阶段中，DEBT 都建立在 Efron 的工作之上，Efron 提出了一种优雅的经验贝叶斯测试方法。DEBT 通过学习一系列黑盒预测模型来增强这个框架，以提高能力和控制错误发现率。在第一阶段，它在报告哪些实验产生了显着结果之前使用了深度神经网络。在第 2 阶段，它使用仿冒过滤器的经验贝叶斯版本来选择具有第 1 阶段显着性的显着预测能力的协变量。在模拟数据和真实数据中，DEBT 会增加发现的重要结果的比例，并在信号较弱时选择更多特征。在对癌细胞系的实际研究中，DEBT 选择了一组强大的生物学上合理的癌症药物敏感性和耐药性基因组驱动因素。DEBT 会增加发现的重要结果的比例，并在信号较弱时选择更多特征。在对癌细胞系的实际研究中，DEBT 选择了一组强大的生物学上合理的癌症药物敏感性和耐药性基因组驱动因素。DEBT 会增加发现的重要结果的比例，并在信号较弱时选择更多特征。在对癌细胞系的实际研究中，DEBT 选择了一组强大的生物学上合理的癌症药物敏感性和耐药性基因组驱动因素。