Memory Usage (MB).

Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structured domains, which involves analyzing transcriptomic data to find clusters of cells with similar expression patterns and their spatial distribution. To address these challenges, we propose a novel deep-learning method called DMGCN for domain identification. The process begins with preprocessing that constructs two types of graphs: a spatial graph based on Euclidean distance and a feature graph based on Cosine distance. These graphs represent spatial positions and gene expressions, respectively. The embeddings of both graphs are generated using a multi-view graph convolutional encoder with an attention mechanism, enabling separate and co-convolution of the graphs, as well as corrupted feature convolution for contrastive learning. Finally, a fully connected network (FCN) decoder is employed to generate domain labels and reconstruct gene expressions for downstream analysis. Experimental results demonstrate that DMGCN consistently outperforms state-of-the-art methods in various tasks, including spatial clustering, trajectory inference, and gene expression broadcasting.

空间转录组学（Spatial transcriptomics）是快速发展的单细胞基因组学分支领域，可在获取组织内空间位置信息的同时定量检测基因表达水平。该领域的核心挑战之一是识别空间结构域，即通过分析转录组数据，筛选出表达模式相似的细胞簇并明确其空间分布特征。为解决此类挑战，我们提出了一种名为DMGCN的新型深度学习方法用于结构域识别。该方法首先通过预处理构建两类图：一类是基于欧氏距离构建的空间图，用于表征组织的空间位置；另一类是基于余弦距离构建的特征图，用于表征基因表达特征。随后，我们采用带有注意力机制的多视角图卷积编码器生成两类图的嵌入表示，实现图的独立卷积与联合卷积，并引入带噪特征卷积以支持对比学习。最后，通过全连接网络（FCN）解码器生成结构域标签，并重构基因表达以支撑下游分析任务。实验结果表明，在空间聚类、轨迹推断与基因表达展示等各类任务中，DMGCN始终优于当前顶尖方法。