MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics.

ABSTRACT: Accurately inferring cell-cell interactions from spatial transcriptomics data remains challenging due to tissue complexity and spatial heterogeneity. Recent deep learning models have started to combine different types of cellular information, such as spatial proximity, gene expression similarity, ligand-receptor signaling, and in some cases, gene regulatory networks. However, they often treat a cell's external interactions and internal gene regulation separately, merging them at the final step using simple concatenation or addition. This limits the model's ability to capture how cell-cell communication and internal molecular states are connected. In this paper, we present MAGNET (Multi-view Graph Autoencoder with Cell-Gene Attention Network), a framework that reconstructs cell-cell interac

SUBMITTER: Han C 

PROVIDER: S-EPMC12716704 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature