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Testing for dependence on tree structures.

Merle Behr1, M Azim Ansari2,3, Axel Munk4,5,6

  • 1Department of Statistics, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method to test associations between sample data and tree structures in genomics. The method offers statistical guarantees for high power and controlled false positive rates, improving research reproducibility.

Keywords:
change-point detectionhypothesis testingsubgroup detectiontree structures

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Area of Science:

  • Genomic sciences
  • Biomedical sciences
  • Statistical genetics

Background:

  • Tree structures are common in genomic and biomedical studies, representing hierarchical relationships.
  • Assessing associations between sample response variables and tree-based latent structures is a frequent research question.
  • Current methods often require arbitrary decisions on tree pruning, lacking statistical rigor.

Purpose of the Study:

  • To develop a robust statistical method for testing associations between response variables and fixed tree structures.
  • To provide statistical guarantees for high power and control of the overall false positive error rate.
  • To enhance the reproducibility and robustness of findings in genomic and biomedical research.

Main Methods:

  • A novel statistical approach is presented to test for associations across all levels of a tree hierarchy.
  • The method accounts for the overall false positive error rate.
  • It is designed to work with a fixed tree structure and a measured response variable.

Main Results:

  • The proposed method demonstrates high statistical power in detecting associations.
  • It effectively controls the overall false positive error rate.
  • The approach offers statistical guarantees for its performance.

Conclusions:

  • The developed statistical method provides a rigorous framework for analyzing tree structures in relation to response variables.
  • This approach enhances the reliability and reproducibility of scientific findings in hierarchical data analysis.
  • It addresses limitations of current ad hoc methods by offering a statistically sound alternative.