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

Iksoo Huh1, Taesung Park2

  • 1College of Nursing and Research Institute of Nursing Science, Seoul National University, Seoul, 03080, Korea.

Genes & Genomics
|November 6, 2024
PubMed
Summary

This study introduces an enhanced adaptive permutation test to efficiently calculate p-values for large datasets. The new method significantly reduces computational burden while maintaining accuracy, making complex analyses feasible.

Keywords:
Confidence intervalEnhanced adaptive permutation testGenome-wide omics datasetsNegative binomial distributionStouffer’s method

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Area of Science:

  • Statistical genetics
  • Computational biology
  • Bioinformatics

Background:

  • Permutation tests are crucial for non-parametric p-value calculation.
  • Standard permutation tests are computationally intensive for large datasets and multiple testing adjustments.
  • Detecting very small p-values requires extensive permutations.

Purpose of the Study:

  • To develop a computationally efficient permutation test for large-scale genomic data analysis.
  • To introduce an enhanced adaptive permutation test utilizing negative binomial (NB) distribution for p-value estimation.
  • To reduce the computational burden associated with traditional permutation tests.

Main Methods:

  • An adaptive permutation procedure that stops when permuted statistics exceed observed statistics a predefined number of times.
  • Feature-specific determination of permutation numbers based on potential significance.
  • Application of Stouffer's method for significant features after dataset splitting to enhance reduction.

Main Results:

  • The enhanced adaptive permutation test significantly reduced the number of permutations compared to the ordinary permutation test.
  • P-value precision was maintained within a small range.
  • The method was successfully applied to a genome-wide single nucleotide polymorphism (SNP) dataset with 327,872 features.

Conclusions:

  • The enhanced adaptive permutation test offers a feasible computational time for genome-wide omics datasets.
  • The method successfully identified highly significant features with confidence intervals.
  • This approach addresses the computational challenges in large-scale genomic data analysis.