Abstract:Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, regeneration and repair, disease progression, and response to treatment. We propose ContextFlow, a novel context-aware flow matching framework that incorporates prior knowledge to guide the inference of structural tissue dynamics from spatially resolved omics data. Specifically, ContextFlow integrates local tissue organization and ligand-receptor communication patterns into a transition plausibility matrix that regularizes the optimal transport objective. By embedding these contextual constraints, ContextFlow generates trajectories that are not only statistically consistent but also biologically meaningful, making it a generalizable framework for modeling spatiotemporal dynamics from longitudinal, spatially resolved omics data. Evaluated on three datasets, ContextFlow consistently outperforms state-of-the-art flow matching methods across multiple quantitative and qualitative metrics of inference accuracy and biological coherence. Our code is available at: \href{this https URL}{ContextFlow}
| Comments: | 42 pages, 21 figures, 30 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.02952 [cs.LG] |
| (or arXiv:2510.02952v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.02952 arXiv-issued DOI via DataCite |
Submission history
From: Santanu Rathod Mr. [view email]
[v1]
Fri, 3 Oct 2025 12:46:24 UTC (11,078 KB)
[v2]
Fri, 30 Jan 2026 15:03:43 UTC (12,097 KB)
[v3]
Thu, 14 May 2026 10:29:31 UTC (11,991 KB)