AI 中文总结
FlowGRN+在FlowGRN基础上，将样条拟合与流形投影整合入条件流匹配框架，提升了细胞轨迹的时间一致性与平滑度，在BEELINE基准上实现了有竞争力的GRN推断性能，为相关研究提供了实用框架。
AI 中文摘要
基因调控网络（GRN）是理解发育及疾病过程中细胞动态与潜在机制的基础。尽管单细胞RNA测序（scRNA-seq）技术已能收集大量单细胞分辨率的基因表达谱，但由于高维性和丢失事件，从scRNA-seq数据中推断GRN仍是重大挑战。近来，FlowGRN通过应用条件流匹配（CFM）学习细胞动态，在重建细胞轨迹和推断GRN方面展现出良好效果。然而FlowGRN仍存在重建动态的时间一致性问题，且依赖人工检查，阻碍了下游应用与可复现性。本文提出FlowGRN+，即FlowGRN的改进版本，其在CFM框架中整合样条拟合，以生成更稳定的参考轨迹用于训练，从而提升所学动态的时间一致性。为解决样条拟合中的过冲问题，我们进一步引入投影方案，将样条切线投影至数据流形的局部切空间。我们在BEELINE基准上评估FlowGRN+，结果显示其轨迹平滑度有所提升，且GRN推断性能具有竞争力。FlowGRN+为从scRNA-seq数据重建细胞轨迹和推断GRN提供了实用框架，本研究的见解也可能对其他基于CFM的细胞动态模型有所助益。
英文摘要
Gene regulatory networks (GRNs) are fundamental in understanding cellular dynamics and underlying mechanisms during development and disease. Although scRNA-seq technologies have enabled the collection of vast numbers of gene expression profiles at single-cell resolution, inferring GRNs from scRNA-seq data remains a significant challenge due to high dimensionality and dropout. Recently, FlowGRN has shown promising results in reconstructing cell trajectories and inferring GRNs by applying conditional flow matching (CFM) to learn the cell dynamics. However, FlowGRN still faces limitations in the temporal coherence of reconstructed dynamics and relies on human inspection, which hinders downstream applications and reproducibility. In this paper, we propose FlowGRN+, an improved version of FlowGRN that integrates spline fitting into the CFM framework to generate more stable reference trajectories for training, thereby improving the temporal coherence of the learned dynamics. To address overshooting in spline fitting, we further introduce a projection scheme that projects spline tangents onto the local tangent space of the data manifold. We evaluate FlowGRN+ on the BEELINE benchmark and show improved trajectory smoothness with a competitive GRN inference performance. FlowGRN+ provides a practical framework for reconstructing cell trajectories and inferring GRNs from scRNA-seq data, and the insights from this work may also be useful for other CFM-based models of cellular dynamics.
Comments10 pages. 3 figures, an extension of our publication at the CIBCB 2026
