Inferring causal trajectories from spatial transcriptomics using CASCAT.

ABSTRACT: Spatial trajectory inference models cell differentiation and state dynamics within tissues by integrating spatial information. Existing spatial trajectory inference methods depend on similarity-based cell graphs constructed from spatial proximity, with less attention to the Markovian property in cell state transitions. In this study, we introduce CASCAT, a tree-shaped structural causal model with the Markovian property integrated to infer a unique cell differentiation trajectory, addressing challenges posed by Markov equivalence in high-dimensional and nonlinear data. CASCAT outperforms six state-of-the-art single-cell RNA sequencing (scRNA-seq)-oriented methods across 10 simulated and seven real scRNA-seq datasets and exceeds three leading spatial trajectory inference methods on 13 real s

SUBMITTER: Yu Y 

PROVIDER: S-EPMC12362255 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature