Null-free False Discovery Rate Control Using Decoy Permutations.

Null-free False Discovery Rate Control Using Decoy Permutations.
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DOI:
10.1007/s10255-022-1077-5
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发表时间:
2022
期刊:
Acta mathematicae applicatae Sinica (English series)
影响因子:
--
通讯作者:
Sun XM
Sun XM
中科院分区:
其他
文献类型:
--
作者:
He K;Li MJ;Fu Y;Gong FZ;Sun XM

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传统的多假设检验中的错误发现率(FDR)控制方法通常是基于检验统计量的零分布。然而,所有类型的零分布,包括理论的、基于置换的和经验的零分布,都有一些固有的缺点。例如,由于对样本分布的不正确假设,理论空值可能会失败。在这里,我们提出了一个零分布的方法来FDR控制多个假设检验的病例对照研究。这种方法被称为目标-诱饵过程,简单地建立在测试的排序上,通过一些统计量或分数,其空分布不需要已知。竞争性诱饵测试是从原始样本的排列构造的,并用于估计假目标发现。我们证明了这种方法控制FDR时,得分函数是对称的,不同的测试之间的分数是独立的。仿真结果表明,即使在存在依赖关系的情况下,该方法也比两种流行的传统方法更稳定、更强大。还对两个真实的数据集进行了评估,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.
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