Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Abstract Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging...

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Vydáno v:	Briefings in bioinformatics Ročník 25; číslo 6; s. 512
Hlavní autoři:	Li, Zuqi, Katz, Sonja, Saccenti, Edoardo, Fardo, David W, Claes, Peter, Martins dos Santos, Vitor A P, Van Steen, Kristel, Roshchupkin, Gennady V
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 23.09.2024 Oxford Publishing Limited (England)
Témata:	Algorithms Biological analysis Biological effects Cancer Cluster Analysis Clustering Computational Biology - methods Data integration Deep Learning Genomics - methods Heterogeneity Humans Life sciences Machine learning Multiomics Neoplasms - classification Neoplasms - genetics Precision medicine Problem Solving Protocol Regularization Sciences du vivant Training Unsupervised learning Unsupervised Machine Learning fairness multi-omics deep learning clustering autoencoder confounders
ISSN:	1467-5463, 1477-4054, 1477-4054
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Abstract Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders—external factors unrelated to the condition, e.g. batch effect or age—on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include (i) removal of latent features correlated with confounders, (ii) a conditional VAE, (iii) adversarial training, and (iv) adding a regularization term to the loss function. Using real-life multi-omics data from The Cancer Genome Atlas, we simulated various confounding effects (linear, nonlinear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 scopus-id:2-s2.0-85206648620 Zuqi Li and Sonja Katz contributed equally.
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbae512