PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
This work addresses the challenge of reliable prediction in single-cell biology, particularly for stochastic gene perturbations, but it is incremental as it builds on existing uncertainty estimation methods.
The paper tackles the problem of predicting single-cell responses to gene perturbations unseen in training data by proposing PRESCRIBE, a Bayesian estimation framework that measures epistemic and aleatoric uncertainties to assign confidence scores, resulting in over 3% accuracy improvements compared to baselines.
In single-cell perturbation prediction, a central task is to forecast the effects of perturbing a gene unseen in the training data. The efficacy of such predictions depends on two factors: (1) the similarity of the target gene to those covered in the training data, which informs model (epistemic) uncertainty, and (2) the quality of the corresponding training data, which reflects data (aleatoric) uncertainty. Both factors are critical for determining the reliability of a prediction, particularly as gene perturbation is an inherently stochastic biochemical process. In this paper, we propose PRESCRIBE (PREdicting Single-Cell Response wIth Bayesian Estimation), a multivariate deep evidential regression framework designed to measure both sources of uncertainty jointly. Our analysis demonstrates that PRESCRIBE effectively estimates a confidence score for each prediction, which strongly correlates with its empirical accuracy. This capability enables the filtering of untrustworthy results, and in our experiments, it achieves steady accuracy improvements of over 3% compared to comparable baselines.