Fig. 1: Overview of the spatiotemporal dynamical generative model (stDGM) framework.
From: Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data
A The analysis starts with raw data from single-cell sequencing, which is converted into a time-resolved, unpaired gene expression and then projected into a low-dimensional embedding. B stDGM model the evolution of the initial cell distribution to a target distribution over time. This approach accounts for key biological processes, including cell differentiation, growth/ death, stochastic effects, and cell-cell interactions. C The trained model enables a rich suite of downstream analyses, such as (a) visualizing velocity streamlines; (b) generating cell states and trajectories at unobserved time points; (c) identifying regions of high cellular proliferation; (d) mapping cell fate stability via a Waddington-like potential landscape; (e) inferring fate probabilities; and (f) constructing gene regulatory networks and predicting the effects of perturbations. The Figure was created in BioRender. Zhang, Z. (2025) under the license https://BioRender.com/f6etpat.
