Fig 1.
Overview of the multi-view graph autoencoder with cell-gene attention network.
Table 1.
Performance comparison of MAGNET and baseline models on MERFISH, seqFISH, and STARmap datasets under Multi-View target adjacency matrix.
Table 2.
Performance comparison of MAGNET and baseline models on three spatial transcriptomics datasets (seqFISH, MERFISH, and STARmap) using single-view target adjacency matrices at split 0.3.
Fig 2.
Training trends of different methods on three single-cell resolution spatial transcriptomics datasets (MERFISH, seqFISH, and STARmap), evaluated using Average Precision (AP), AUROC, and AUC over 120 training epochs.
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.
Fig 4.
Performance impact of MAGNET’s core modules assessed via ablation studies.
Each radar chart visualizes the model’s performance on a specific dataset after the removal of a key architectural component. The axes represent different evaluation metrics or datasets, and the area of the polygon indicates overall performance, demonstrating the critical contribution of each module.
Table 3.
Ablation study of graph type usage on different datasets.
Fig 5.
Hyperparameter sensitivity analysis of MAGNET.
(A–D) Effects of ,
, τ, and m on average precision (AP). (E–F) Quantitative summaries showing the variation range (<1) and corresponding p-values for each parameter, all remaining above 0.05, indicating no statistically significant difference across settings. (G) Evaluation of the neighborhood size k (the number of nearest neighboring cells) in graph construction across spatial, ligand–receptor, and transcriptional graphs. MAGNET maintains stable AP, ROC-AUC, and F1 scores when k varies from 3 to 10, confirming robustness to local connectivity settings.
Fig 6.
This Fig presents the MAGNET reconstructed cell-cell interaction network derived from spatial transcriptomic cancer data, highlighting potential roles in the tumor microenvironment.
It visualizes ligand–receptor interactions within individual clusters(A), while highlighting functional interactions between different clusters (B), and provides detailed insights into interactions among specific cell clusters at the level of Gene Expression Modules (GEMs) (C). The analysis is based on GEMs, showing the number of times and the flow values with which different clusters are connected through each GEM (D). Functional insights are provided through Gene Set Enrichment Analysis (GSEA) of key biological pathways (E). (F) shows representative gene signatures for each GEM.
Fig 7.
MAGNET reconstructs spatially organized intercellular communication in the mouse brain.
(A) Manual annotation of major anatomical regions (CTX, HPF, CP, TH, HY, NFT). (B) Reconstructed ligand–receptor interaction networks showing intracluster (left) and intercluster (right) signaling patterns. (C) Spatial distance distribution of interaction edges. (D) Edge counts by distance bin and the cumulative distribution function (CDF) of distances. (E) Node-level topology: distributions of cell degree and closeness centrality, highlighting local communication hubs. (F) Cell-type composition of hubs (degree > 10) and non-hubs. (G) Per-cluster comparison of five hubs and five non-hubs. Left: for each cluster, we compare the distribution of linked neighbor clusters for hubs versus non-hubs. Right: for each cluster, we compare normalized gene-expression between hubs and non-hubs.
Fig 8.
Attention–gating analysis of MAGNET.
(A) Mean normalized attention per head across spatial distance bins. (B) Cell-level heatmap and cell-type summaries showing how attention scores are distributed across cells and across cell types. (C) Gating maps for the three views (W1, W2, W3). (D) Ligand–receptor expression visuals: spatial map of LR-pair intensity (left), high-expression spots (middle), and an example pair (COL1A1–DDR1) overlaid with spatial gate W1 (right). (E) Cancer-enriched regions focusing on cluster 5 and cluster 9: spatial locations of the two clusters; summaries of W1, W2, and W3 by cluster and by cell type; and spatial maps of W1, W2, and W3 within cluster 5 and cluster 9.