Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Molecular Genetics, University of Toronto, Toronto, ON, Canada.
NPJ Syst Biol Appl. 2024 Aug 27;10(1):97. doi: 10.1038/s41540-024-00424-7.
Single-cell omics technologies can measure millions of cells for up to thousands of biomolecular features, enabling data-driven studies of complex biological networks. However, these high-throughput experimental techniques often cannot track individual cells over time, thus complicating the understanding of dynamics such as time trajectories of cell states. These "dynamical phenotypes" are key to understanding biological phenomena such as differentiation fates. We show by mathematical analysis that, in spite of high dimensionality and lack of individual cell traces, three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics. Moreover, we show through numerical simulations that an interaction matrix can be accurately determined with three or more time-points even in the presence of sampling and measurement noise typical of single-cell omics. Our results can guide the design of single-cell omics time-course experiments, and provide a tool for data-driven phase-space analysis.
单细胞组学技术可以测量多达数千个生物分子特征的数百万个细胞，从而能够对复杂的生物网络进行数据驱动的研究。然而，这些高通量的实验技术通常无法随时间跟踪单个细胞，从而使对细胞状态的时间轨迹等动态的理解变得复杂。这些“动态表型”是理解分化命运等生物学现象的关键。我们通过数学分析表明，尽管具有高维性和缺乏单个细胞轨迹，但单细胞组学数据的三个时间点在理论上是确定网络相互作用矩阵和相关动力学的必要且充分条件。此外，我们通过数值模拟表明，即使在存在单细胞组学中典型的采样和测量噪声的情况下，也可以通过三个或更多时间点准确地确定相互作用矩阵。我们的结果可以指导单细胞组学时间过程实验的设计，并为数据驱动的相空间分析提供工具。
