MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics
Fig 3
(A) Box plots summarizing the test performance of different models on three single-cell–resolution spatial transcriptomics datasets under the multi-view target adjacency matrix.
Results across varying train–test split ratios demonstrate the robustness and consistency of MAGNET after 300 training epochs. Statistical significance between MAGNET and TENET was evaluated using p-value comparisons across all metrics. (B) Bar plots comparing MAGNET with other representative cell–cell communication models (COMMOT, OT, and CellNEST) on simulated spatial datasets from the COMMOT benchmark under the multi-view setting. The evaluation based on Average Precision (AP), AUROC, and Balanced Accuracy highlights MAGNET’s superior and stable performance across datasets.