Protocol for spatially resolved pathology scores using optimal transport on spatial transcriptomics data

<h2>Summary</h2> Spatial transcriptomics (ST) integrates gene expression with spatial context to study tissue pathology. We present SPaSE (spatially resolved pathology score), a protocol to quantify pathology by computing an optimal transport plan between ST spots of the diseased and the corresponding healthy tissue. We describe steps for assigning pathology scores to each spot and developing support vector regression models to link gene expression with pathological impact. We demonstrate the utility of SPaSE across multiple human and mouse ST datasets. For complete details on the use and execution of this protocol, please refer to Rahman et al.¹