Virtual Cell Models Make Systematic and Transferable Prediction Errors

Single-cell perturbation models predict transcriptional responses to unseen genetic or chemical interventions, but their prediction errors are commonly treated as residual noise. We present the first systematic study of whether these errors recur across perturbations and transfer to unseen interventions. Across diverse predictors and genetic, combinatorial, and drug perturbation datasets, error fingerprints estimated from training perturbations recur on held-out perturbations and anticipate which genes and biological pathways will be misestimated. The fingerprints are stable across training seeds, and much of their model-specific structure reflects persistent gene-wise over- and under-estimation. We also introduce Gap Transferability Correction (GTC), a lightweight post-hoc framework that reuses historical errors to improve unseen-perturbation predictions without retraining the base model. GTC improves differentially expressed gene recovery in all three perturbation settings. Experiments across multiple cell lines show that fingerprints transfer more reliably within a cell line than across cell lines, identifying cellular context as a boundary condition for error transfer. These results establish prediction error as a structured, transferable, and reusable property of virtual-cell models.