Diagnosing Representation and Prediction Errors in Virtual-Cell Models under Distribution Shift

Predicting transcriptional responses to unseen drugs and cellular contexts is a central challenge in virtual-cell research. Overall prediction errors do not distinguish limited response-space coverage from inaccurate prediction within the covered space. We present a diagnostic framework that separates these components using response spaces learned from training data and characterizes within-space errors through prediction direction and magnitude. Analyses of LINCS and MoABox show that substantial response coverage can coexist with limited prediction accuracy. On MoABox, the response space captures 81.9% of target energy, yet most error from a direct predictor occurs within this space. Cross-cell experiments further show that fixed magnitude shrinkage improves transfer on LINCS and OP3 over unscaled predictions while preserving direction. The framework provides a clearer account of where virtual-cell predictions fail and how their errors change under specific model adjustments.