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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)
Publ. Version/Full Text Research data 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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 2399-3642
e-ISSN 2399-3642
Journal Communications Biology
Quellenangaben Volume: 5, Issue: 1, Pages: , Article Number: 645 Supplement: ,
Publisher Springer
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants 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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