ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data
This addresses the challenge of modeling spatiotemporal dynamics in tissue development and disease for researchers in spatial omics, though it appears incremental as it builds on existing flow matching methods with added contextual constraints.
The paper tackled the problem of inferring trajectories from longitudinal spatially-resolved omics data to understand tissue dynamics, and the result was that ContextFlow, a context-aware flow matching framework, consistently outperformed state-of-the-art methods across multiple metrics on three datasets.
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}