STARGATE: Spatial Transcriptomic Analysis with Recurrent and Graph Attention Techniques using Ensemble Learning

Previous research has identified hematoma as the primary cause of mortality and disability in intraventricular hemorrhage (IVH), yet neurologic outcomes post-IVH have not significantly improved following hematoma evacuation. Therefore, utilizing spatial transcriptomics sequencing technology to acquire and analyze IVH data is of significant importance for the early diagnosis and treatment of acute cerebral hemorrhage. However, existing spatial transcriptomics clustering algorithms often overlook local information, fail to integrate multimodal data, and suffer from performance instability. To address these issues, we developed a multimodal spatial transcriptomics clustering algorithm named STARGATE, which initially captures the global spatial topology and local long-sequence gene set features of cells by integrating Graph Convolutional Networks (GCNs) based on self-attention mechanisms and Long Short-Term Memory networks (LSTMs), subsequently fuses gene expression, tissue imagery, morphological, and spatial coordinate features using Gated Recurrent Units (GRUs), and finally enhances the accuracy and robustness of predictions through ensemble learning methods. Our key experimental results include: (1) In clustering experiments on our proprietary mouse IVH spatial transcriptomics dataset, the STARGATE model achieved Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Fowlkes-Mallows Index (FMI) scores of 0.80, 0.84, and 0.82, respectively, all superior to classic algorithms such as stLearn and CCST. (2) In clustering experiments on the mouse IVH dataset, the STARGATE model reached an accuracy of 89.63%, with an excellent confusion matrix, demonstrating outstanding accuracy and stability. (3) In clustering experiments on the public dataset DLFPC, which is known for its high data complexity leading to generally lower clustering metrics, the STARGATE model's ARI, NMI, and FMI indicators still outperformed classic algorithms such as SpaGCN and Seurat.