Abstract

The integrative analysis of multiple sequences of multiple tests has enjoyed increasing popularity in many applications, especially in large-scale genomics. In the context of large-scale multiple testing, the concept of signal classification has been developed recently for cases when the same features are involved in several independent studies, with the goal of classifying each feature into one of several classes. This paper considers the problem of such signal classification in a generalized compound decision-making framework, where the observed data are assumed to be generated from an underlying four-state Cartesian hidden Markov model. Two oracle procedures are proposed for the total and set-specific control of misclassification rates, respectively, while the number of correct classifications is maximized. Optimal data-driven procedures are also proposed, with their asymptotic properties derived. It is shown that signal-classification could be improved significantly by taking into account the dependence structure among features, and the proposed procedures could have a better performance than their competitors that ignore the dependence structure. The proposed methods are applied to a psychiatric genetics study for detecting genetic variants that affect either or both of bipolar disorder and schizophrenia.

摘要

多个测试的多个序列的综合分析在许多应用中越来越受欢迎，特别是在大规模基因组学中。在大规模多重测试的背景下，最近针对多个独立研究涉及相同特征的情况提出了信号分类的概念，其目标是将每个特征分类为多个类别之一。本文考虑了广义复合决策框架中的此类信号分类问题，其中假设观察到的数据是从基础四状态笛卡尔隐马尔可夫模型生成的。提出了两种预言程序，分别用于总体控制和特定集合的错误分类率控制，同时最大化正确分类的数量。还提出了最佳数据驱动程序，并导出了它们的渐近性质。结果表明，通过考虑特征之间的依赖结构可以显着改善信号分类，并且所提出的程序可以比忽略依赖结构的竞争对手具有更好的性能。所提出的方法应用于精神遗传学研究，用于检测影响双相情感障碍和精神分裂症之一或两者的遗传变异。