Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture

Spatially resolved transcriptomics (SRT) measures gene expression within tissues. Existing methods that summarize the activity of functionally related genes often ignore relationships between neighboring locations and offer limited statistical testing, gene-level interpretation, or scalability. Here we present GESSO, a method that incorporates spatial information to estimate gene set activity within tissues, quantify how strongly each gene contributes to the resulting activity scores, and perform location-specific statistical tests for elevated pathway activity. In simulations and analyses of 13 datasets from five SRT platforms, GESSO better recovers known spatial patterns, provides well-calibrated statistical tests and interpretable gene-level contributions, and scales more efficiently than existing methods. Applications across tumor, embryonic, and lymphoid tissues reveal localized biological programs, including epithelial-to-mesenchymal transition at tumor-stroma interfaces, developmental signaling gradients, and coordinated immune pathways within germinal centers. Therefore, GESSO enables interpretable and scalable spatial mapping of gene set activity across diverse SRT technologies and biological settings. Spatial transcriptomics measures gene activity while preserving tissue structure. Here, the authors present GESSO, a scalable method that uses spatial information to quantify pathway activity and detect localized biological programs.