Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Genes (Basel). 2023 Jan 26;14(2):318. doi: 10.3390/genes14020318.
The recent advancement in single-cell RNA sequencing technologies enables the understanding of dynamic cellular processes at the single-cell level. Using trajectory inference methods, pseudotimes can be estimated based on reconstructed single-cell trajectories which can be further used to gain biological knowledge. Existing methods for modeling cell trajectories, such as minimal spanning tree or k-nearest neighbor graph, often lead to locally optimal solutions. In this paper, we propose a penalized likelihood-based framework and introduce a stochastic tree search (STS) algorithm aiming at the global solution in a large and non-convex tree space. Both simulated and real data experiments show that our approach is more accurate and robust than other existing methods in terms of cell ordering and pseudotime estimation.
单细胞 RNA 测序技术的最新进展使得人们能够在单细胞水平上理解动态细胞过程。使用轨迹推断方法，可以根据重建的单细胞轨迹估计伪时间，进一步用于获得生物学知识。现有的细胞轨迹建模方法，如最小生成树或 k-最近邻图，往往导致局部最优解。在本文中，我们提出了一个基于惩罚似然的框架，并引入了一种随机树搜索（STS）算法，旨在在大型非凸树空间中找到全局解。模拟和真实数据实验表明，在细胞排序和伪时间估计方面，我们的方法比其他现有方法更准确和稳健。
