PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
Jiabei Cheng, Changxi Chi, Jingbo Zhou, Hongyi Xin, Jun Xia
Reliable prediction of single-cell responses is crucial for both high-throughput screening and advancing biological understanding. Recent in-silico models have made much progress in prediction accuracy, but uncertainty metrics are rarely reported. When scientists apply these models to study specific perturbations, knowing how trustworthy individual predictions are is more practical than averaged accuracy.Therefore, we introduce \textbf{PRESCRIBE} (Predicting Single-Cell Responses with Bayesian Estimation), a novel model that not only predicts transcriptomic outcome but provides uncertainty estimates for each perturbation. PRESCRIBE employs the Natural Posterior Network to quantify uncertainty by modeling the energy distance between perturbed and control cellular states, while accounting for generalization difficulty. Our analysis demonstrates that PRESCRIBE effectively estimates prediction confidence, correlates well with its prediction accuracy, and enables the filtering of untrustworthy results. In practical applications, PRESCRIBE steadily achieves accuracy improvements of over 3\% against comparable baselines.