Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (''negative result'') is expected. Motivated by a real proteomic dataset, we will present three promising and closely connected methods of using negative controls to assist simultaneous hypothesis testing. The first method uses negative controls to construct a permutation p-value for every hypothesis under investigation, and we give several sufficient conditions for such p-values to be valid and positive regression dependent on the set (PRDS) of true nulls. The second method uses negative controls to construct an estimate of the false discovery rate (FDR), and we give a sufficient condition under which the step-up procedure based on this estimate controls the FDR. The third method, derived from an existing ad hoc algorithm for proteomic analysis, uses negative controls to construct a nonparametric estimator of the local false discovery rate. We conclude with some practical suggestions and connections to some closely related methods that are propsed recently.

翻译：阴性对照是科学研究中的常用技术，泛指预期产生无效效应（"阴性结果"）的对照设置。受一个真实的蛋白质组学数据集启发，本文将介绍三种具有良好前景且密切关联的阴性对照辅助假设检验方法。第一种方法利用阴性对照为每个待检假设构建置换p值，我们给出了此类p值有效且满足真实原假设集合上正回归依赖性（PRDS）的若干充分条件。第二种方法利用阴性对照构建错误发现率（FDR）的估计量，并给出了该估计量基于逐步升序方法控制FDR的充分条件。第三种方法源于现有蛋白质组学分析中的特定启发式算法，利用阴性对照构建局部错误发现率的非参数估计量。最后，我们提出了一些实用建议，并探讨了与近期提出的若干相关方法的关联。