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 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 la

SUBMITTER: Li Z 

PROVIDER: S-EPMC11483139 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature