CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.

ABSTRACT: With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting laten

SUBMITTER: Wu Y 

PROVIDER: S-EPMC11966612 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature