A Fuzzy Permutation Method for False Discovery Rate Control

作者：Yang Ya Hui; Lin Wan Yu; Lee Wen Chung^*

来源：Scientific Reports, 2016, 6(1): 28507.

DOI：10.1038/srep28507

Biomedical researchers often encounter the large-p-small-n situations-a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample size studies. The method introduces fuzziness into standard permutation analysis to produce randomized p-values, which are then converted into q-values for false discovery rate controls. Simple algebra shows that the fuzzy permutation method is at least as powerful as the standard permutation method under any alternative. Monte-Carlo simulations show that the proposed method has desirable statistical properties whether the study variables are normally or non-normally distributed. A real dataset is analyzed to illustrate its use. The proposed fuzzy permutation method is recommended for use in the large-p-small-n settings.

• 出版日期2016-6-22