On Probability Misallocation in Single-Cell Perturbation Prediction

Single-cell perturbation prediction aims to model how interventions reshape cellular populations. Despite substantial progress across diverse modeling paradigms, little is known about the structure of the distributional errors that remain. We uncover a recurring failure across modeling paradigms: models misallocate probability across expression levels, even when mean expression is accurately predicted. We term the shared failure mode probability misallocation. In this work, we systematically characterize where misallocation occurs and show that the error can materially alter the biological conclusions that perturbation prediction is intended to support. Guided by this diagnosis, we propose several simple yet effective corrections that substantially reduce probability misallocation and improve perturbation prediction quality across modeling paradigms and datasets. Further analysis reveals why misallocation persists under existing objectives and how data characteristics shape the supervision required. Together, our work provides a new perspective on single-cell perturbation prediction and practical guidance for building more reliable virtual-cell models to accelerate biological discovery.