{"ID":2860928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02952","arxiv_id":"2510.02952","title":"ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data","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{https://github.com/santanurathod/ContextFlow}{ContextFlow}","short_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 t...","url_abs":"https://arxiv.org/abs/2510.02952","url_pdf":"https://arxiv.org/pdf/2510.02952v3","authors":"[\"Santanu Subhash Rathod\",\"Francesco Ceccarelli\",\"Sean B. Holden\",\"Pietro Liò\",\"Xiao Zhang\",\"Jovan Tanevski\"]","published":"2025-10-03T12:46:24Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":608773,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2860928,"paper_url":"https://arxiv.org/abs/2510.02952","paper_title":"ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data","repo_url":"https://github.com/santanurathod/ContextFlow","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}