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A U-statistics for integrative analysis of multilayer omics data.

Xiaqiong Wang1, Yalu Wen1

  • 1Department of Statistics, University of Auckland, Auckland, New Zealand.

Bioinformatics (Oxford, England)
|January 9, 2020
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Summary

We developed a novel U-statistics framework for analyzing multilayer omics data to find disease biomarkers. This method offers robust performance for complex diseases, outperforming existing approaches.

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Area of Science:

  • Genomics
  • Biomarker Discovery
  • Computational Biology

Background:

  • Multilayer omics data offer insights into complex diseases.
  • High dimensionality and complex etiologies present analytical challenges.

Purpose of the Study:

  • To develop a flexible framework for association analysis of multilayer omics data.
  • To address challenges posed by high dimensionality and complex disease models.

Main Methods:

  • Developed a U-statistics-based non-parametric framework.
  • Incorporated consensus and permutation-based weighting schemes for various disease models.
  • Designed for flexibility with different outcome types, making no distribution assumptions.

Main Results:

  • The framework demonstrates robust performance across various disease models.
  • Outperformed commonly used kernel regression-based methods in simulations and Alzheimer's disease data analysis.
  • The R-package "Uomic" is available for public use.

Conclusions:

  • The proposed U-statistics framework provides a powerful and flexible tool for multilayer omics data analysis.
  • This approach enhances biomarker detection for complex diseases.
  • The method offers improved performance compared to existing techniques.