在多假设检验中,传统的错误发现率(FDR)控制方法通常基于检验统计量的零分布。然而,所有类型的空分布,包括理论,基于置换和经验的,有一些固有的缺点。例如,理论上的零可能会因为对样本分布的不当假设而失败。这里,我们提出了一种无零分布的FDR控制方法,用于病例对照研究中的多重假设检验。这种方法,命名为目标诱饵程序,简单地建立在通过一些统计或分数对测试进行排序的基础上,其零分布不需要知道。竞争性诱饵测试是根据原始样本的排列构建的,用于估计错误的目标发现。我们证明,当得分函数对称并且得分在不同测试之间独立时,这种方法可以控制FDR。模拟表明,它比两种流行的传统方法更稳定、更强大,即使存在依赖。还对两个真实数据集进行了评估,包括拟南芥基因组学数据集和COVID-19蛋白质组学数据集。
The traditional approaches to false discovery rate (FDR) control in multiple hypothesis testing are usually based on the null distribution of a test statistic. However, all types of null distributions, including the theoretical, permutation-based and empirical ones, have some inherent drawbacks. For example, the theoretical null might fail because of improper assumptions on the sample distribution. Here, we propose a null distribution-free approach to FDR control for multiple hypothesis testing in the case-control study. This approach, named target-decoy procedure, simply builds on the ordering of tests by some statistic or score, the null distribution of which is not required to be known. Competitive decoy tests are constructed from permutations of original samples and are used to estimate the false target discoveries. We prove that this approach controls the FDR when the score function is symmetric and the scores are independent between different tests. Simulation demonstrates that it is more stable and powerful than two popular traditional approaches, even in the existence of dependency. Evaluation is also made on two real datasets, including an arabidopsis genomics dataset and a COVID-19 proteomics dataset.