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Gomari, D.P. ; Schweickart, A.* ; Cerchietti, L.* ; Paietta, E.* ; Fernandez, H.H.* ; Al-Amin, H.* ; Suhre, K.* ; Krumsiek, J.*

Variational autoencoders learn transferrable representations of metabolomics data.

Comm. Biol. 5:645 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 2399-3642
e-ISSN 2399-3642
Zeitschrift Communications Biology
Quellenangaben Band: 5, Heft: 1, Seiten: , Artikelnummer: 645 Supplement: ,
Verlag Springer
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen European Commission
National Institute for Health and Care Research
Medical Research Council
Wellcome Trust
National Cancer Institute
National Institute on Aging
National Institutes of Health
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