Covariate Adaptive False Discovery Rate Control With Applications to Omics-Wide Multiple Testing
Covariate Adaptive False Discovery Rate Control With Applications to Omics-Wide Multiple Testing
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DOI：
10.1080/01621459.2020.1783273
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发表时间：
2019-09
影响因子：
3.7
通讯作者：
Xianyang Zhang;Jun Chen
中科院分区：
文献类型：
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作者：
Xianyang Zhang;Jun Chen
摘要传统的多重检验方法通常假定不同特征的假设是可以互换的。然而，在许多科学应用中，关于信号和零点模式的附加协变量信息是可用的。在这篇文章中，我们介绍了一种在大规模推理问题中的FDR控制程序，它可以合并协变量信息。我们开发了一个快速算法来实现所提出的过程，并证明了它的渐近有效性，即使在基础似然比模型被错误指定并且p值是弱依赖的情况下(例如，强混合)。通过大量的仿真研究了该方法的有限样本性能，结果表明，该方法比现有的方法具有更好的灵活性、健壮性、强大的性能和计算效率。最后，我们将该方法应用于来自基因组学研究的几个组学数据集，目的是识别与某些临床和生物学表型相关的组学特征。我们证明了该方法在同类方法中是最有效的，特别是在信号稀疏的情况下。本文提出的协变量自适应多重检验方法在R包CAMT中得以实现。这篇文章的补充材料可以在网上找到。
Abstract Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in large-scale inference problem that can incorporate covariate information. We develop a fast algorithm to implement the proposed procedure and prove its asymptotic validity even when the underlying likelihood ratio model is misspecified and the p-values are weakly dependent (e.g., strong mixing). Extensive simulations are conducted to study the finite sample performance of the proposed method and we demonstrate that the new approach improves over the state-of-the-art approaches by being flexible, robust, powerful, and computationally efficient. We finally apply the method to several omics datasets arising from genomics studies with the aim to identify omics features associated with some clinical and biological phenotypes. We show that the method is overall the most powerful among competing methods, especially when the signal is sparse. The proposed covariate adaptive multiple testing procedure is implemented in the R package CAMT. Supplementary materials for this article are available online.