DeFo: Decomposed Dynamics for Forecasting Temporal Single-Cell Perturbation Responses

Forecasting future cellular trajectories for perturbations unseen during training from limited early-response observations is a fundamental challenge in single-cell perturbation modeling. Unlike conventional perturbation prediction that focuses on observed conditions or static endpoints, this task requires generalizing dynamic response patterns across perturbations while preserving shared cellular progression. We propose DeFo, a decomposed dynamics framework for held-out perturbation forecasting in temporal single-cell systems. DeFo introduces a control-referenced formulation that decomposes cellular evolution into a shared base field capturing perturbation-independent temporal progression and a perturbation-specific response component modeling deviations from the control trajectory. To infer future responses for unseen perturbations, we introduce Temporal Response Memory Attention (TRMA), a trajectory-valued memory retrieval mechanism that retrieves and softly composes temporal response patterns from known perturbations according to their early effects. Experiments on time-resolved chemical and genetic perturbation datasets demonstrate competitive recovery of population-level expression, control-referenced perturbation effects, and differential-expression directions. These results establish DeFo as a principled framework for learning transferable cellular dynamics and forecasting responses to unseen perturbations from limited temporal observations.