LoCoFlow: Local-Context Flow Matching for Single-Cell Dynamics and Fate Inference

Single-cell sequencing profiles cellular states at discrete time points, providing population-level snapshots from which developmental and disease-associated trajectories can be reconstructed. Existing optimal-transport and flow-matching approaches typically infer temporal couplings at the level of individual cells, with limited representation of the local manifold geometry of cell-state space and the uncertainty inherent in lineage diversification. Here, we introduce LoCoFlow, a population-level framework that represents each cell together with its local neighborhood as a point-cloud patch, thereby modeling cellular dynamics on local manifolds rather than between isolated states. This representation supports probabilistic temporal coupling while preserving local cell-state geometry, separates collective population displacement from within-neighborhood variation to capture both coordinated state transitions and cellular heterogeneity, and provides a natural distributional representation of fate uncertainty through changes in the shape and dispersion of local cell populations. We evaluate LoCoFlow across multiple longitudinal single-cell datasets, including the LARRY dataset with experimentally measured lineage-tracing information, and show that it captures complex cellular dynamics and fate uncertainty in both trajectory reconstruction and cell-fate inference tasks.