Evidence mapPaperPMID 39503929Full record

ArticleGenes & genomics2025

Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.

Iksoo Huh, Taesung Park

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Article in Genes & genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 authors.

Iksoo HuhCollege of Nursing and Research Institute of Nursing Science, Seoul National University, Seoul, 03080, Korea.
Taesung ParkDepartment of Statistics, Seoul National University, Seoul, 08826, Korea. tspark@stats.snu.ac.kr.ORCID http://orcid.org/0000-0002-8294-590X

Funding

Ministry of Health & Welfare HI16C2037National Research Foundation 2013M3A9C4078158
6 · The paper itself

Abstract

backgroundThe permutation test has been widely used to provide the p-values of statistical tests when the standard test statistics do not follow parametric null distributions. However, the permutation test may require huge numbers of iterations, especially when the detection of very small p-values is required for multiple testing adjustments in the analysis of datasets with a large number of features.

objectiveTo overcome this computational burden, we suggest a novel enhanced adaptive permutation test that estimates p-values using the negative binomial (NB) distribution. By the method, the number of permutations are differently determined for individual features according to their potential significance.

methodsIn detail, the permutation procedure stops, when test statistics from the permuted dataset exceed the observed statistics from the original dataset by a predefined number of times. We showed that this procedure reduced the number of permutations especially when there were many insignificant features. For significant features, we enhanced the reduction with Stouffer's method after splitting datasets.

resultsFrom the simulation study, we found that the enhanced adaptive permutation test dramatically reduced the number of permutations while keeping the precision of the permutation p-value within a small range, when compared to the ordinary permutation test. In real data analysis, we applied the enhanced adaptive permutation test to a genome-wide single nucleotide polymorphism (SNP) dataset of 327,872 features.

conclusionWe found the analysis with the enhanced adaptive permutation took a feasible time for genome-wide omics datasets, and successfully identified features of highly significant p-values with reasonable confidence intervals.

Indexed as

Computational BiologyGenome-Wide Association StudyGenomicsAlgorithmsBinomial DistributionComputer SimulationHumansConfidence intervalEnhanced adaptive permutation testGenome-wide omics datasetsNegative binomial distributionStouffer’s method

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