Forecasting Cellular and Population Dynamics via Multi-Horizon Flow Matching

Forecasting cellular populations from time-resolved single-cell RNA-seq requires predicting how cell states evolve and how these state-level changes reshape the population over time. We propose scHOMURA, a generative method for forecasting cellular and population dynamics from unpaired temporal snapshots. scHOMURA combines deterministic temporal displacement to capture shared state progression, conditional residual dynamics to model heterogeneous cellular outcomes, and a state-dependent growth field to model changes in relative population contribution. By directly learning transitions over multiple forecast horizons, scHOMURA reduces reliance on recursive one-step propagation and supports forecasting over both short and extended temporal intervals. Across five time-resolved single-cell datasets and seven forecasting splits, scHOMURA achieves strong distribution-, gene-expression-, and composition-level performance and remains stable as predictions extend beyond the observed temporal window. Beyond global population reconstruction, scHOMURA captures rare-state expansion, reaches future cellular states absent from the training period, and recovers developmental transitions consistent with known biological relationships. Together, scHOMURA provides a multi-horizon generative framework for forecasting cell-state dynamics and population remodeling from unpaired temporal snapshots.