Density-based detection of cell transition states to construct disparate and bifurcating trajectories.

ABSTRACT: Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting.

SUBMITTER: Lan T 

PROVIDER: S-EPMC9757071 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature