bioRxiv · 10.1101/209577
PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data
Abstract
Metabolomics is a powerful approach for discovering biomarkers and metabolic quantitative trait loci. While untargeted profiling methods can measure up to thousands of metabolite signals in a single experiment, many signals cannot be readily identified as known metabolites or compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To deal with these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets using an imputation-based approach and to generate pathway annotations for these signals. We performed meta and pathway analyses for both known and unknown signals in multiple datasets and then validated the results using genetic associations. Finally, we applied the methods to detect metabolite signals and pathways associated with body mass index, demonstrating that our framework is useful for analyzing unknown signals in a robust and biologically meaningful manner and for improving the power of untargeted metabolomics studies.
Source connections
Explore related subjects
Keep this discovery
Hsu, Y.-H. H., Churchhouse, C., Pers, T. H., Mercader, J. M., Metspalu, A., Fischer, K., Fortney, K., Morgen, E. K., Gonzalez, C., Gonzalez, M. E., Esko, T., Hirschhorn, J. N.. 2017-11-08. PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data. https://doi.org/10.1101/209577
Cite the original work for its findings. Save a collection to share your selection of sources.