利用能量引导的流匹配实现单细胞扰动响应的泛化预测
Generalizable single-cell perturbation response prediction using energy-guided flow matching
- Zhejiang University（浙江大学）
- Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies（北京大学成都前沿交叉生物技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出scEGFlow，一种能量引导的流匹配框架，通过动态建模对照到扰动状态的连续转变并应用条件能量梯度校正，实现单细胞扰动响应的泛化预测，在基准测试中优于现有方法，并适应新条件。
中文摘要 AI 辅助
在单细胞分辨率下预测对扰动的表型和转录响应，为探测生物系统提供了强大的工具。然而，现有方法通常依赖于训练期间学到的固定映射，这使得在推理过程中校准分布偏移或适应新的扰动条件变得具有挑战性。在此，我们提出了scEGFlow，一种能量引导的流匹配框架，能够动态桥接对照和扰动细胞状态。scEGFlow使用条件流匹配对从对照细胞群体到扰动状态的连续转变进行建模。然后，它应用条件特定的能量梯度来校正和引导这些预测，从而在不重新训练流模型的情况下实现灵活调整。在涵盖成像表型和转录组谱的基准测试中进行的评估表明，scEGFlow在重建已见和未见扰动条件下的响应分布方面优于现有方法，忠实保留了细胞流形几何和群体异质性。这种优势在仅使用少量测量细胞适应新条件时尤为显著，持续提高了预测准确性。此外，scEGFlow准确重现了共识基因表达特征中扰动诱导的上调和下调模式，其中能量引导改善了预测与观察到的调控方向之间的一致性。最终，这些发现表明scEGFlow为单细胞扰动建模提供了一种模块化、可泛化且可引导的解决方案。通过将生成流建立在学习的生物景观之上，该架构为在计算机中导航和操纵细胞行为建立了新的计算范式。
英文摘要
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, enabling flexible adjustments without retraining the flow model. Evaluations across benchmarks spanning imaging phenotypes and transcriptomic profiles show that scEGFlow outperforms existing methods in reconstructing response distributions under both seen and unseen perturbation conditions, faithfully preserving cellular manifold geometry and population heterogeneity. This advantage is notable when adapting to new conditions with only a few measured cells, consistently improving prediction accuracy. Furthermore, scEGFlow accurately recapitulates perturbation-induced up- and down-regulation patterns across consensus gene expression signatures, where energy guidance improves the agreement between predicted and observed regulatory directions. Ultimately, these findings demonstrate that scEGFlow provides a modular, generalizable, and steerable solution for single-cell perturbation modeling. By grounding generative flows in learned biological landscapes, this architecture establishes a new computational paradigm for navigating and manipulating cellular behavior in silico.
