Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

ABSTRACT: Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our var

SUBMITTER: Du JH 

PROVIDER: S-EPMC11406253 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature