Department of Computer Science, New Mexico State University, Las Cruces, USA.
Molecular Biology and Interdisciplinary Life Sciences Graduate Program, New Mexico State University, Las Cruces, USA.
BMC Bioinformatics. 2022 Aug 16;23(Suppl 8):340. doi: 10.1186/s12859-022-04875-9.
Cells progressing from an early state to a developed state give rise to lineages in cell differentiation. Knowledge of these lineages is central to developmental biology. Each biological lineage corresponds to a trajectory in a dynamical system. Emerging single-cell technologies such as single-cell RNA sequencing can capture molecular abundance in diverse cell types in a developing tissue. Many computational methods have been developed to infer trajectories from single-cell data. However, to our knowledge, none of the existing methods address the problem of determining the existence of a trajectory in observed data before attempting trajectory inference.
We introduce a method to identify the existence of a trajectory using three graph-based statistics. A permutation test is utilized to calculate the empirical distribution of the test statistic under the null hypothesis that a trajectory does not exist. Finally, a p-value is calculated to quantify the statistical significance for the presence of trajectory in the data.
Our work contributes new statistics to assess the level of uncertainty in trajectory inference to increase the understanding of biological system dynamics.
从早期状态发展到成熟状态的细胞在细胞分化中产生谱系。对这些谱系的了解是发育生物学的核心。每个生物谱系都对应于动力系统中的一条轨迹。新兴的单细胞技术，如单细胞 RNA 测序，可以在发育组织中的多种细胞类型中捕获分子丰度。已经开发出许多计算方法来从单细胞数据中推断轨迹。然而，据我们所知，现有的方法都没有在尝试轨迹推断之前解决确定观测数据中是否存在轨迹的问题。
我们介绍了一种使用基于图的三种统计量来识别轨迹存在的方法。使用置换检验计算不存在轨迹的零假设下检验统计量的经验分布。最后，计算 p 值来量化数据中轨迹存在的统计显著性。
我们的工作为评估轨迹推断中的不确定性水平提供了新的统计量，以增加对生物系统动力学的理解。
