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A Fuzzy Permutation Method for False Discovery Rate Control.

ABSTRACT: 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.

SUBMITTER: Yang YH

PROVIDER: S-EPMC4916423 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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A Fuzzy Permutation Method for False Discovery Rate Control.

Yang Ya-Hui YH Lin Wan-Yu WY Lee Wen-Chung WC

Scientific reports 20160622

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 lea ...[more]

PMID: 27328860

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