PerturbBridge: Conditional Latent Schr\"{o}dinger Bridge for Single-Cell Perturbation Prediction

Abstract

Single-cell perturbation response prediction seeks to recover transcriptional responses under unseen perturbation conditions. Because RNA sequencing measurements are destructive, control and perturbed cells are typically observed as unpaired populations without cell-level correspondence, shifting the prediction objective from individual cellular outcomes to perturbation-specific population distributions. The Schr"odinger Bridge (SB) provides a principled framework for modeling these population-level transitions as stochastic transport, but learning bridge dynamics from high-dimensional, sparse gene-expression profiles remains challenging. We propose PerturbBridge, a conditional latent Schr\"odinger Bridge framework that reformulates stochastic population transport over high-dimensional, sparse gene-expression profiles as bridge learning in a compact cell latent space. This formulation enables an efficient approximation of SB-based stochastic population transport in single-cell perturbation prediction. PerturbBridge first learns a compact latent representation following the \beta-TCVAE paradigm \citep{chen2018isolating}, alleviating the challenges caused by high dimensionality and sparsity during bridge learning. An interpolation-consistency regularizer further encourages agreement between decoded latent interpolations and the corresponding expression-space interpolations. PerturbBridge then learns a perturbation-conditioned latent bridge using a tractable stochastic SB approximation between latent control and target populations, with endpoint MMD regularization promoting alignment between generated and observed target distributions. Experiments on the Norman CRISPR and Sci-Plex3 chemical perturbation benchmarks demonstrate competitive performance across all evaluation metrics. Notably, PerturbBridge achieves state-of-the-art performance in differential-expression recovery on both benchmarks, highlighting the effectiveness of latent stochastic transport modeling for population-level single-cell perturbation prediction.