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Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data.

  • Tian, Leqi1, 2
  • Li, Zhenjiang3
  • Ma, Guoxuan2, 4
  • Zhang, Xiaoyue3
  • Tang, Ziyin3
  • Wang, Siheng2
  • Kang, Jian4
  • Liang, Donghai3
  • Yu, Tianwei1, 2, 5
  • 1 Shenzhen Research Institute of Big Data.
  • 2 School of Data Science, The Chinese University of Hong Kong-Shenzhen. , (Hong Kong SAR China)
  • 3 Gangarosa Department of Environmental Health, Emory University.
  • 4 Department of Biostatistics, University of Michigan.
  • 5 Warshel Institute, Shenzhen, Guangdong, China. , (China)
Published Article
Bioinformatics (Oxford, England)
Publication Date
May 27, 2022
DOI: 10.1093/bioinformatics/btac364
PMID: 35639952


Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (1) matching uncertainty between data features and metabolites; (2) lacking of method to analyze positive mode and negative mode LC/MS data simultaneously on the same set of subjects; (3) the incompleteness of pathways in individual software packages. We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration - (1) a weighted GSEA-type test, and (2) a permutation-based weighted hypergeometric test. The package is capable of combining positive and negative ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: KEGG, Mummichog, and SMPDB. The package is available at Supplementary data are available at Bioinformatics online. © The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

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