从单细胞横断面数据中推断带增殖的随机动力学过程
Inferring stochastic dynamics with growth from cross-sectional data
- 基于拉格朗日形式的福克-普朗克方程，分离生长与噪声
- 在模拟和真实数据上准确还原细胞命运轨迹，精度优于现有方法
- 适合研究细胞分化、增殖等动态过程的生物学家与计算建模者
时序单细胞组学数据提供了高通量、全基因组的细胞状态测量，对反向解析细胞命运决定机制至关重要。但这类技术具有破坏性，仅能获取潜在随机动力系统的横断面数据，且细胞可能分裂或死亡。这给构建真实的生物物理模型带来巨大挑战。本文提出一种新方法——非平衡概率流推断，用于建模带增长的随机动力系统。通过采用福克-普朗克方程的拉格朗日形式，该方法可精确分离漂移、内在噪声与生长效应。我们在一系列模拟和真实单细胞RNA-seq数据集上验证了该方法的有效性，结果表明其相比多种现有方法具有更高准确性，并具备简单的两步训练流程。
原文摘要 · Abstract (English)
Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell fate. Such technologies are inherently destructive, allowing only cross-sectional measurements of the underlying stochastic dynamical system. Furthermore, cells may divide or die in addition to changing their molecular state. Collectively these present a major challenge to inferring realistic biophysical models. We present a novel approach, unbalanced probability flow inference, that addresses this challenge for biological processes modelled as stochastic dynamics with growth. By leveraging a Lagrangian formulation of the Fokker-Planck equation, our method accurately disentangles drift from intrinsic noise and growth. We showcase the applicability of our approach through evaluation on a range of simulated and real single-cell RNA-seq datasets. Comparing to several existing methods, we find our method achieves higher accuracy while enjoying a simple two-step training scheme.
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