Interpretable deep generative ensemble learning for single-cell omics with Hydra

ABSTRACT: Single-cell omics enable the dissection of cellular heterogeneity, yet the high dimensionality, inherent noise, and sparsity present significant challenges. These challenges are amplified for rare cell populations, which are often difficult to annotate reliably but can be central to development and disease. As single-cell assays increasingly capture multiple molecular layers, the integrative analysis of such multimodal data further increases complexity. Here, we propose Hydra, a deep generative framework based on an ensemble of variational autoencoders for effective learning of unimodal and multimodal single-cell omics data. Hydra implements interpretable modules for capturing cell type-specific molecular signatures. The ensemble of such interpretable modules enables reproducible feature s

SUBMITTER: Manoj M Wagle 

PROVIDER: S-SCDT-10_1038-S44320-026-00208-7 | biostudies-other |

REPOSITORIES: biostudies-other