The recoverable resolution of cellular perturbation-response prediction

Abstract

Virtual cell models aim to predict how cells respond to perturbations, yet accurate prediction of post-perturbation gene expression can conceal failures to recover context-specific responses. Here we identify context geometry compression (CGC), in which models preserve cellular identity and shared perturbation effects while collapsing differences in how the same perturbation acts across contexts. Across biological systems, reproducible context-specific responses remained difficult to recover; in a large drug-response matrix, this persisted even with nearly complete perturbation coverage. The same drug-response predictions achieved higher normalized recovery in predefined pathway summaries than in gene-level response profiles, while coarse response programs were more predictable in human lymphoblastoid cell lines. Compact baseline transcriptomic representations retained most full-RNA predictive performance for patient-specific ex vivo drug responses. Together, these findings establish recoverable biological resolution, rather than measurement dimensionality, as a criterion for evaluating virtual cell predictions and matching model outputs to the biological detail supported by evidence.