arXiv:2605. 00545v2 Announce Type: replace-cross Abstract: Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis.
By Junda Ying, Yuxuan Wang, Bowen Yang, Peijie Zhou, Lei Zhang
arXiv:2607. 04738v1 Announce Type: cross Abstract: Reconstructing population dynamics is a central problem in the physical and data sciences.
By Markus Heinonen, Yair Shenfeld, Ricardo Baptista, Daniel Waxman, Dmitry Batenkov, Tim Cooijmans, Eli Bingham
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distributions driven by an energy functional.
Multiscale Supervised Unbalanced Optimal Transport Flow Matching (MUST‑FM) is a simulation‑free framework that scales unbalanced optimal transport (UOT) for single‑cell trajectory inference by exploiting hierarchical data structure. It optionally incorporates supervised transition priors, such as cell lineages, to guide displacement fields and mass variations. Experiments demonstrate reduced computational overhead and robust, biologically meaningful trajectory inference on atlas‑scale single‑cell datasets.
By Qiangwei Peng, Lezhi Chen, Peijie Zhou
PACE is a trajectory inference framework that reconstructs continuous transport dynamics from destructive single‑cell time‑course snapshots. It uses a state‑ and time‑dependent anisotropic Riemannian metric to guide transport along tangent directions, alternates between refining cross‑time couplings and fitting neural bridges, and distills the learned dynamics into a global velocity field. Across multiple datasets, PACE outperforms existing methods, reducing key distance metrics by an average of 23.7% and improving RNA‑velocity alignment by 15.4%.
By Chenglei Yu, Chuanrui Wang, Bangyan Liao, Tailin Wu
arXiv:2608. 15306v1 Announce Type: cross Abstract: High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time.
By Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran, Alejandra Castillo, Caroline Moosm\"uller, Shiying Li