Single-cell disentangled representations for perturbation modeling and treatment effect estimation.

ABSTRACT: Dissecting cell-state-specific changes in gene regulation induced by perturbations is crucial for understanding biological mechanisms. However, single-cell sequencing provides only unmatched snapshots of cells under different conditions. This destructive measurement process hinders the estimation of individualized treatment effects (ITEs), which are essential for pinpointing these heterogeneous mechanistic responses. We develop scDRP, a generative framework that leverages disentangled representation learning with asymptotic correctness guarantees to separate perturbation-dependent and perturbation-independent latent variables via a sparsity regularized β -VAE. Assuming quantile-preserving effects of perturbations conditional on confounders, scDRP performs conditional optimal transport in t

SUBMITTER: Sun J 

PROVIDER: S-EPMC12699455 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature