Fig. 2: Historical evolution of mathematical foundations and algorithmic landscape for stDGM.
From: Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data
A Timeline traces the conceptual lineage from 18th-century static optimal transport to the recent unbalanced mean-field Schrödinger bridges, incrementally relaxing assumptions to match biological complexity. B The contemporary algorithmic zoo is compactly charted along three axes: data assumption, modeling strategy, and training method. The dense grid of named tools demonstrates a fast-evolving ecosystem where theoretical advances have already been packaged into practical, user-ready implementations. The algorithms are still in rapid expansion, and due to the limitation of the authors' scope, some relevant algorithms might not be included.
