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

• Published in: Bioinformatics Vol. 38; no. 14; pp. 3662 - 3664
• Main Authors: Tian, Leqi, Li, Zhenjiang, Ma, Guoxuan, Zhang, Xiaoyue, Tang, Ziyin, Wang, Siheng, Kang, Jian, Liang, Donghai, Yu, Tianwei
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 11.07.2022
• Subjects: Algorithms; Applications Notes; Genome; Humans; Metabolic Networks and Pathways; Metabolomics; Software
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btac364

Abstract Motivation 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: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography–mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages. Results 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—(i) a weighted gene set enrichment analysis-type test and (ii) 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: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database. Availability and implementation The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. Supplementary information Supplementary data are available at Bioinformatics online.