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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data.

Leqi Tian1,2, Zhenjiang Li3, Guoxuan Ma2,4

  • 1Shenzhen Research Institute of Big Data, Shenzhen 518712, China.

Bioinformatics (Oxford, England)
|May 31, 2022
PubMed
Summary

This study introduces metapo ne, an R package for untargeted metabolomics. It addresses metabolite matching uncertainty and integrates positive and negative mode data for robust pathway enrichment analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Metabolomics

Background:

  • Untargeted metabolomics data analysis requires pathway enrichment testing.
  • Existing algorithms face challenges with metabolite matching uncertainty, simultaneous analysis of positive and negative mode LC/MS data, and incomplete pathway databases.

Purpose of the Study:

  • To develop an innovative R/Bioconductor package for addressing key limitations in untargeted metabolomics pathway enrichment analysis.
  • To provide a tool that accounts for metabolite matching uncertainty and integrates multi-modal LC/MS data.

Main Methods:

  • Developed the R/Bioconductor package 'metapone'.
  • Implemented two novel statistical tests: a weighted gene set enrichment analysis-type test and a permutation-based weighted hypergeometric test, both considering matching uncertainty.
  • Integrated positive and negative ion mode data into a unified testing framework.
  • Curated comprehensive pathway information from KEGG, Mummichog, and SMPDB.

Main Results:

  • The 'metapone' package offers advanced statistical tests for pathway enrichment in untargeted metabolomics.
  • It successfully integrates positive and negative mode LC/MS data, overcoming a significant analytical limitation.
  • The package provides a more comprehensive set of pathways by combining multiple databases.

Conclusions:

  • 'metapone' provides a robust and comprehensive solution for pathway enrichment analysis in untargeted metabolomics.
  • The package enhances the reliability of metabolomics studies by addressing critical data analysis challenges.
  • It facilitates deeper biological insights through improved pathway testing methodologies.