Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.

Autor: Climaco Pinto R; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K., Karaman I; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K., Lewis MR; MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.; Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K., Hällqvist J; Centre for Translational Omics, Great Ormond Street Hospital, University College London, London WC1N 1EH, U.K.; Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K., Kaluarachchi M; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.; Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K., Graça G; Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K., Chekmeneva E; MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.; Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K., Durainayagam B; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K., Ghanbari M; Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands., Ikram MA; Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands., Zetterberg H; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, 431 41 Mölndal, Sweden.; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden.; Department of Neurodegenerative Disease, University College London, Queen Square, London WC1N 3BG, U.K.; UK Dementia Research Institute, University College London, London WC1N 3BG, U.K., Griffin J; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.; Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K., Elliott P; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K., Tzoulaki I; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 451 10 Ioannina, Greece., Dehghan A; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.; UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.; Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands., Herrington D; Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27101, United States., Ebbels T; Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.
Jazyk: angličtina
Zdroj: Analytical chemistry [Anal Chem] 2022 Apr 12; Vol. 94 (14), pp. 5493-5503. Date of Electronic Publication: 2022 Mar 31.
DOI: 10.1021/acs.analchem.1c03592
Abstrakt: Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio ( m / z ), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m / z , and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
Databáze: MEDLINE
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