Signal Classification for the Integrative Analysis of Multiple Sequences of Large-Scale Multiple Tests
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AI 根据所提供的论文材料归类：摘要研究基因组多重检验的信号分类与误分类控制，未涉及所列经济计量方向，通用检验理论不足以细分归类。
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多组大规模多重检验整合分析中的信号分类
- 研究问题
- 研究同一组特征在多个独立实验中的检验结果如何联合分类，依据各实验的零假设与非零信号配置区分特征类别。
- 方法与思路
- 作者定义两类误分类率，分别构造控制它们并最大化期望正确分类数的理想程序，再提出相应的数据驱动程序。
- 材料陈述的贡献
- 摘要称数据驱动程序在一定条件下渐近有效且最优，数值实验接近理想程序的功效，并应用于两类精神疾病的遗传分析。
- 阅读关注点
- 应核对两种误分类率与通常错误发现率的区别、最优性的约束和目标，以及独立实验设定及数据驱动程序的条件。
- 阅读边界
- 仅据所给Crossref摘要，渐近最优条件和具体数值结果未说明；发现可能相关的遗传变异不能据此解释为因果作用。
原文摘要
Summary The integrative analysis of multiple data sets is becoming increasingly important in many fields of research. When the same features are studied in several independent experiments, it can often be useful to analyse jointly the multiple sequences of multiple tests that result. It is frequently necessary to classify each feature into one of several categories, depending on the null and non-null configuration of its corresponding test statistics. The paper studies this signal classification problem, motivated by a range of applications in large-scale genomics. Two new types of misclassification rate are introduced, and two oracle procedures are developed to control each type while also achieving the largest expected number of correct classifications. Corresponding data-driven procedures are also proposed, proved to be asymptotically valid and optimal under certain conditions and shown in numerical experiments to be nearly as powerful as the oracle procedures. In an application to psychiatric genetics, the procedures proposed are used to discover genetic variants that may affect both bipolar disorder and schizophrenia, as well as variants that may help to distinguish between these conditions.