Mapping single cell differentiation fates with FateODE

Single-cell transcriptomics provides a powerful lens for deciphering cellular differentiation dynamics and understanding how phenotypic diversity emerges. However, because sequencing is inherently destructive, temporal progression cannot be tracked longitudinally within individual physical cells. Existing paradigms therefore diverge into two separate regimes: recovering macroscopic differentiation trends via trajectory inference, or approximating instantaneous dynamics through RNA velocity. Fundamentally, velocity and trajectory are the differential (local derivative) and integral (global path) manifestations of the same underlying dynamical system. While recent continuous dynamical frameworks (such as Neural ODEs) attempt to unify these perspectives, they exhibit three critical deficiencies: 1) failing to strictly enforce bidirectional consistency between velocity fields and trajectory integrals; 2) insufficiently accommodating technical measurement noise inherent to sequencing; and 3) inaccurate depiction of intrinsic stochasticity in cell fate bifurcations. Consequently, inferred dynamics frequently drift along biologically implausible off-manifold paths or overfit local noise, yielding erratic and overly tortuous trajectories. To overcome these limitations, we propose $\text{FateODE}$, a stochastic Neural ODE framework that enforces rigorous derivative-integral consistency while capturing manifold-constrained stochastic fate transitions. FateODE harmonizes macroscopic trajectories with splicing velocities by anchoring continuous integration across discrete waypoint observations and regularizing point-wise derivatives with high-confidence velocity vectors. To capture bifurcation stochasticity without sacrificing manifold geometry, FateODE introduces an inference-time diffusion term parameterized by a decoder-only variational architecture under Maximum Mean Discrepancy (MMD) supervision, faithfully simulating Brownian motion directly on the transcriptomic manifold. Beyond reconstructing smooth developmental vector fields, FateODE functions as a continuous "world model" capable of projecting cellular fates, prioritizing differentiation driver genes, and forecasting fate-landscape shifts under in-silico genetic perturbations. Extensive benchmarks across synthetic and real lineage-traced datasets demonstrate FateODE's superior dynamic modeling fidelity, accurately predicting terminal fate distributions under both unperturbed and perturbed conditions. https://anonymous.4open.science/r/FateODE-83D8/