Autoregressive forecasting of future single-cell state transitions

Abstract Existing methods for dynamic analysis of static single-cell RNA-sequencing data can reconstruct temporal structures covered by observed cells, but cannot forecast unobserved future state transitions. We propose a temporal generative AI model, CellTempo, to forecast future cellular dynamics by representing cells as learned semantic codes and training an autoregressive generation decoder to predict ordered code sequences. It can forecast long-range cell-state transition trajectories and landscapes from snapshot data. To train the model, we constructed a comprehensive single-cell trajectory dataset scBaseTraj by integrating RNA velocity, pseudotime, and inferred transition probabilities to compose multi-step cellular sequences. Experiments on multiple real datasets showed that CellTempo can forecast cell state evolutions from individual cells, and reconstruct nuanced cell-state potential landscapes and their varied progressions after genetic or chemical perturbations, all with high fidelity to biological truth. This work opens a route for forecasting unseen future dynamics of cell state transitions from static observations.