这篇文章介绍了一种名为 STORIES 的新型计算方法，旨在利用 最优传输 (Optimal Transport, OT) 框架，从随时间变化的空间转录组学数据中推断细胞命运轨迹。STORIES 克服了现有梯度流学习方法在处理空间信息方面的挑战，通过使用 融合 Gromov-Wasserstein (Fused Gromov-Wasserstein, FGW) 扩展，学习一个考虑到空间上下文的 分化势能 (differentiation potential)。文章通过对蝾螈神经再生和小白鼠胶质细胞生成等多个大型时空图谱的基准测试，证明了 STORIES 在预测基因表达和保持空间一致性方面优于现有技术，并成功识别了这些生物过程中的 已知和推定的驱动基因及转录调节因子。References: Huizing G J, Samaran J, Capocefalo D, et al. STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport[J]. Nature Methods, 2025: 1-10.
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