Target-decoy FDR

Null-free False Discovery Rate Control Using Decoy Permutations



The current 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 large-scale two-groups hypothesis testing. 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 by permutations of original samples and are used to estimate the false target discoveries. We prove that this approach controls the FDR when the statistics are independent between different tests. Simulation demonstrates that it is more stable and powerful than two existing popular approaches. Evaluation is also made on a real dataset.


Source codes

The target-decoy FDR algorithm was implemented in R. The source codes can be freely downloaded here.



Kun He, Meng-jie Li, Yan Fu*, Fu-zhou Gong, Xiao-ming Sun. Null-free False Discovery Rate Control Using Decoy Permutations. Acta Mathematicae Applicatae Sinica, English Series, 38(2):235-253, 2022. [pdf][Supplementary Material]

Kun He, Mengjie Li, Yan Fu*, Fuzhou Gong, Xiaoming Sun. A direct approach to false discovery rates by decoy permutations. arXiv:1804.08222. 2018.

Kun He, Yan Fu*, Wen-Feng Zeng, Lan Luo, Hao Chi, Chao Liu, Lai-Yun Qing, Rui-Xiang Sun, and Si-Min He. A theoretical foundation of the target-decoy search strategy for false discovery rate control in proteomics. arXiv:1501.00537. 2015.

Kun He. Multiple hypothesis testing methods for large-scale peptide identification in computational proteomics. Master’s thesis, University of Chinese Academy of Sciences, 2013..



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