An Adaptive Spatial Graph is a flexible, data-driven structure used to model the relationships between different spatial regions in a tissue sample based on spatial proximity and gene expression patterns. The “nodes” of this graph represent different points or regions within the tissue, while the “edges” (connections between nodes) are adaptively assigned based on similarities in either the tissue’s spatial layout or molecular features like gene expression levels.

 

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Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs (ECCB)

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Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

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