ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data

• URL: http://arxiv.org/abs/2510.02952v1
• Date: Fri, 03 Oct 2025 12:46:24 GMT
• Title: ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data
• Authors: Santanu Subhash Rathod, Francesco Ceccarelli, Sean B. Holden, Pietro Liò, Xiao Zhang, Jovan Tanevski,
• Abstract summary: We propose ContextFlow, a context-aware flow matching framework.<n>It incorporates prior knowledge to guide the inference of structural tissue dynamics from spatially resolved omics data.<n>By embedding contextual constraints, ContextFlow generates trajectories that are not only statistically consistent but also biologically meaningful.
• Score: 12.454793927784642
• License: http://creativecommons.org/licenses/by/4.0/
• 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}

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