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Fast approximation of small p-values in permutation tests by partitioning the permutations
Brian D Segal1, Thomas Braun1, Michael R Elliott1
1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109-2029, U.S.A.
Biometrics
|March 16, 2018
Summary
Researchers developed efficient computational methods to estimate small permutation p-values for analyzing group differences in life sciences. These new algorithms speed up permutation tests, crucial for genetics and cancer research, by offering computational efficiency.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Permutation tests are widely used in life sciences for group comparisons.
- These tests offer exactness and applicability when distributions are complex.
- However, their computational intensity can be a limitation.
Purpose of the Study:
- To develop fast methods for estimating small permutation p-values (<10-6) in two-sample tests.
- To address the computational burden of traditional permutation tests.
- To provide efficient tools for analyzing differences in means.
Main Methods:
- Proposed an asymptotic approximation and a resampling algorithm.
- Methods are based on the distribution of test statistics within and across permutation partitions.
- Validated through simulations and application to cancer genomic data.
Main Results:
- The resampling algorithm demonstrates superior computational efficiency for extremely small p-values (<10-30).
- Methods successfully identified up- and down-regulated genes in cancer genomic data.
- The proposed techniques significantly reduce computational time for permutation tests.
Conclusions:
- The developed methods offer computationally efficient alternatives for estimating small permutation p-values.
- These approaches enhance the feasibility of permutation tests in large-scale genomic studies.
- The methods show promise for broader applications beyond the difference and ratio of means.
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