The paper introduces CellNEST, a novel computational model utilizing Graph Attention Networks (GAT) and Deep Graph Infomax (DGI) to overcome challenges in detecting cell-cell communication (CCC) from spatial transcriptomics data, which is essential for understanding diseases like cancer. Existing methods often suffer from high false-positive rates and only detect single ligand-receptor pairs, while CellNEST accurately identifies both single CCC events and complex relay networks of communication by leveraging spatial context and pattern recognition. The paper demonstrates CellNEST's superior performance across various cancer and healthy tissues, including its ability to localize specific signals, like T cell homing in lymph nodes or aggressive communication in pancreatic ductal adenocarcinoma (PDAC), even predicting subtype-specific CCC in PDAC patients. Furthermore, the authors offer CellNEST-Interactive, a web-based tool for visualizing these complex spatial communication networks.
References:
Zohora F T, Paliwal D, Flores-Figueroa E, et al. CellNEST reveals cell–cell relay networks using attention mechanisms on spatial transcriptomics[J]. Nature Methods, 2025: 1-15.
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