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1. Core Technology
Multi-modal Data Fusion:
Joint analysis of imaging (IHC/IF), point cloud (scRNA-seq), and vector (mask) data.
Full-scale Spatial Analytics:
Micro: Subcellular RNA spot registration.
Meso: Cell neighborhood density/morphology.
Macro: Tissue region statistics & cavity detection.
Smart Spatial Modules:
Geometric analysis (shape parameters), topological modeling (crypt structures), network inference (cell-cell proximity), dynamic edge correction (tumor margin processing).
2. Competitive Edge
| Dimension | Traditional Tools | Spa |
|---|---|---|
| Usability | Coding-dependent, steep learning curve | GUI-based, minutes to complete |
| Multi-modal Support | Fragmented tools, data conversion needed | Built-in fusion framework |
| Compatibility | Poor integration with Qupath | Direct PhenoCluster CSV input |
3. Key Performance
Code → Intuition: Complex analysis via GUI, no programming.
Fragments → Whole: First unified “Image-Omics-Morphology” platform.
Static → Dynamic: Real-time parameter tuning & collaborative annotation.
4. Use Cases
Cancer Research: Quantifying spatial immune cell distribution in TME.
Developmental Biology: Mapping cell fate and spatial trajectories in organogenesis.
