用隐变量高斯过程与最优传输建模单细胞时序数据，提升轨迹推断精度。
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
- 引入异方差隐变量高斯过程建模群体趋势，结合希尔伯特空间近似。
- 在复杂插值与外推任务中达到当前最佳性能，准确捕捉细胞异质性。
- 适合研究发育、疾病等动态生物过程的科研人员使用。
单细胞RNA测序可在单细胞分辨率下揭示基因表达，但如何从这些静态快照中推断时序过程仍是根本挑战。现有基于神经微分方程和流的方法易过拟合，且未充分考虑生物变异性。本文提出一种生成式框架，采用隐变量异方差高斯过程（GP）并借助希尔伯特空间方法进行近似。为应对真实细胞轨迹缺失的问题，引入最优传输（OT）目标以对齐生成与观测的群体分布。通过引入细胞特异的隐变量时间及细胞类型条件，显式建模生物异质性，分离时序异步与不同细胞类型的分化路径。在复杂插值与外推基准上表现优于现有方法，并提出一种基于梯度的新策略用于推断扰动轨迹。
原文摘要 · Abstract (English)
Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods. To address the absence of genuine cell trajectories, we leverage an optimal transport (OT) objective that aligns generated and observed population distributions. Our method explicitly captures biological heterogeneity by incorporating cell-specific latent time and cell type conditioning to disentangle temporal asynchrony and trajectories to different cell types. We demonstrate state-of-the-art performance on complex interpolation and extrapolation benchmarks and introduce a novel gradient-based strategy for inferring perturbation trajectories.
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