School of Computer Science and Technology, Hainan University, Haikou, China.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
PLoS Comput Biol. 2024 Sep 5;20(9):e1012409. doi: 10.1371/journal.pcbi.1012409. eCollection 2024 Sep.
Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.
空间转录组技术可以解析空间水平的转录组数据，以检测高通量基因表达，并保留组织空间结构的信息。识别空间域，即识别基因表达和组织学相似的区域，是空间转录组数据分析最基本和关键的方面。目前大多数方法仅通过单一视图来识别空间域，这可能会掩盖某些重要信息，从而无法充分利用空间转录组数据中嵌入的信息。因此，我们提出了一种基于多视图图卷积网络（MVST）的无监督聚类框架，通过多视图图卷积网络学习从基因表达信息、空间位置信息和组织病理学图像信息构建的邻域图的图嵌入特征，实现准确的空间域识别。通过从多个视图探索空间转录组数据，MVST 能够全面而充分地利用空间转录组的所有部分的数据，以获得更准确的空间表达模式。我们在真实的空间转录组数据集上验证了 MVST 的有效性，在一些模拟数据集上验证了 MVST 的稳健性，以及在消融实验中验证了 MVST 框架结构的合理性，从实验结果可以清楚地看出，与当前更先进的方法相比，MVST 可以实现更准确的空间域识别。总之，MVST 是一种用于空间转录组研究的强大工具，可以提高空间域识别能力。
