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Related Experiment Video

Updated: Sep 23, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Closed testing with Globaltest, with application in metabolomics.

Ningning Xu1, Aldo Solari2, Jelle J Goeman1

  • 1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

Biometrics
|May 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiple testing method for the Globaltest in metabolomics. The method enables flexible feature set selection after data analysis while controlling error rates, with an available R package for implementation.

Keywords:
familywise error ratehigh-dimensional datapathway analysispost hoc inference

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Area of Science:

  • Metabolomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The Globaltest is widely used for pathway testing in metabolomics to assess associations between feature sets and a response.
  • Multiple testing correction is essential when evaluating numerous feature sets to avoid false discoveries.
  • Existing methods may lack flexibility in post hoc analysis of feature sets.

Purpose of the Study:

  • To develop a multiple testing procedure for the Globaltest that allows for post hoc selection of feature sets.
  • To control the familywise error rate across all possible feature sets.
  • To provide an efficient computational method for closed testing in metabolomics.

Main Methods:

  • A closed testing-based multiple testing method tailored for the Globaltest.
  • Derivation of a computational shortcut to overcome the exponential complexity of traditional closed testing.
  • Implementation of the shortcut procedure in an R package (ctgt).

Main Results:

  • The proposed method controls the familywise error rate simultaneously for all feature sets.
  • The shortcut enables exact closed testing on metabolomics data, significantly reducing computation time.
  • Demonstrated application of the method on real metabolomics datasets.

Conclusions:

  • The developed method offers a statistically rigorous and computationally efficient approach for multiple testing in Globaltest applications.
  • Researchers can now select feature sets of interest post-data analysis without compromising error control.
  • The ctgt R package facilitates the practical application of this advanced statistical technique in metabolomics research.