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Updated: Aug 6, 2026
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Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections
Published on: June 12, 2026
Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture
Andrew Yang1, Chichun Tan2,3, Ying Ma2,3
1The Warren Alpert Medical School of Brown University, Providence, RI 02903, USA.
Biorxiv : the Preprint Server for Biology
|July 17, 2026
Summary
GESSO is a new method for analyzing gene set activity in tissues. It accounts for spatial information, improving accuracy and interpretability for spatially resolved transcriptomics (SRT) data.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Spatially resolved transcriptomics (SRT) allows gene set activity measurement in tissues.
- Current methods ignore spatial dependencies, limiting discovery of region-specific activities and lacking significance inference.
- Existing tools struggle with scalability for large, high-resolution SRT datasets.
Purpose of the Study:
- To introduce GESSO (Gene sEt activity Score analysis with Spatial lOcation), a novel spatially informed gene set scoring method.
- To address limitations of existing methods including spatial incoherence, lack of significance testing, poor interpretability, and scalability issues.
- To provide a method adaptable to diverse SRT platforms and datasets.
Main Methods:
- GESSO utilizes a graph-regularized matrix decomposition algorithm to model gene set activity.
- It jointly infers spatially coherent gene set activity scores (GASs) and interpretable metagene weights.
- Includes a permutation-based local significance test and a stratified low-resolution approximation for scalability.
Main Results:
- GESSO demonstrated superior performance across 13 datasets from five SRT platforms in accuracy, calibration, interpretability, and scalability.
- Identified novel biological programs, including EMT activation at tumor-stroma interfaces and developmental signaling gradients.
- Revealed coordinated immune pathways within human lymph node germinal centers at subregional resolution.
Conclusions:
- GESSO offers a significant advancement in analyzing spatial gene set activity for SRT data.
- The method enhances biological discovery by capturing spatial context and providing interpretable results.
- GESSO's scalability and accuracy make it suitable for a wide range of modern high-resolution SRT applications.
