PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data

Yu-Han H Hsu, Claire Churchhouse, Tune H Pers, Josep M Mercader, Andres Metspalu, Krista Fischer, Kristen Fortney, Eric K Morgen, Clicerio Gonzalez, Maria E Gonzalez, Tonu Esko and Joel N Hirschhorn

PLOS Computational Biology, 2019, vol. 15, issue 1, 1-26

Abstract: Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.Author summary: Untargeted metabolomics can systematically profile thousands of metabolite signals in biological samples and is an increasingly popular approach for discovering biomarkers and predictors for human traits and diseases. However, currently, a significant portion of the measured signals cannot be identified as known metabolites or easily compared across datasets, and thus are usually excluded from downstream analyses. Here, we present PAIRUP-MS, a suite of computational methods designed to analyze unknown, unidentified signals across multiple mass spectrometry-based profiling datasets. Specifically, PAIRUP-MS contains a flexible imputation-based approach for pairing up unknown signals across datasets, allowing for meta-analysis of matched signals across studies that would otherwise be incompatible. PAIRUP-MS also offers a pathway annotation and enrichment analysis framework that links metabolite signals to plausible biological functions without using their chemical identities. Importantly, we validated both components of PAIRUP-MS using genetic data and applied them to study an example trait, body mass index. Overall, our results demonstrate that PAIRUP-MS enables previously infeasible analyses of unknown, unidentified signals across multiple datasets, thereby greatly improving power for discovery and biological inference.

Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006734 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 06734&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006734

DOI: 10.1371/journal.pcbi.1006734

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().