Generalizable single-cell perturbation response prediction using energy-guided flow matching
Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems. However, existing methods typically rely on fixed mappings learned during training, making it challenging to calibrate distribution shifts or adapt to novel perturbation conditions during inference. Here, we present scEGFlow, an energy-guided flow matching framework that dynamically bridges control and perturbed cellular states. scEGFlow models continuous transitions from control cell populations to perturbed states using conditional flow matching. It then applies condition-specific energy gradients to correct and steer these predictions, ...
| Authors | Jianan Wei, Jiajun Hong, Guikun Chen et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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