Predicting Single-Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport.

Predicting how single cells respond to perturbations is a central problem in computational biology, with potential relevance to emerging artificial intelligence virtual cell (AIVC) research and drug-discovery efforts. However, substantial variation in perturbation responses across biological contexts and the limited generalizability of current models make prediction across cell types, patients, species, and other contexts particularly challenging. To address this challenge, we present single-cell perturbation inference via latent optimal transport (scPILOT), a query-conditioned framework for transferring responses to previously observed perturbations across biological contexts. scPILOT learns a generative latent representation through discriminator-assisted training and separates perturbation inference into cell-level response estimation from observed contexts and query-specific response transfer using latent optimal transport. Across held-out cell-type, patient, and species benchmarks, scPILOT achieved context-averaged R2 mean/MMD2 values of 0.945/0.137, 0.598/0.025, and 0.853/0.287, respectively. It also maintained strong population-average accuracy in a held-out cell-line benchmark, while complementary analyses indicated that performance was associated with dataset learnability and query-context match. With the continued expansion of single-cell perturbation datasets, scPILOT may provide a practical framework for transferring responses to previously observed perturbations across increasingly diverse biological contexts.