Variational autoencoders learn transferrable representations of metabolomics data.
Commun Biol
; 5(1): 645, 2022 06 30.
Article
em En
| MEDLINE
| ID: mdl-35773471
ABSTRACT
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.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Diabetes Mellitus Tipo 2
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Commun Biol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Alemanha