School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, 138648, Singapore.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad278.
Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging.
To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.
最近在空间分辨转录组学（ST）技术方面的进展使得能够在保留细胞空间上下文的情况下测量基因表达谱。将细胞的基因表达与其空间分布联系起来对于更好地理解组织微环境和生物学进展至关重要。然而，有效地将基因表达数据与空间信息相结合以识别空间域仍然具有挑战性。
为了解决上述问题，在本文中，我们提出了一种名为 STMGCN 的新颖无监督学习框架，用于使用多视图图卷积网络（MGCN）识别空间域。具体来说，为了充分利用空间信息，我们首先基于空间坐标构建了多个具有不同相似性度量的邻接图（视图）。然后，STMGCN 通过图卷积网络将基因表达与每个邻接图相结合，学习多个视图特定的嵌入。最后，为了捕捉不同图的重要性，我们进一步引入注意力机制自适应地融合视图特定的嵌入，从而得出最终的点嵌入。STMGCN 允许有效地利用空间上下文，通过多次图卷积来增强潜在嵌入的表达能力。我们在两个模拟数据集和五个具有不同分辨率的不同平台的真实空间转录组学数据集上应用 STMGCN。实验结果表明，与五种最先进的方法（包括空间和非空间方法）相比，STMGCN 在空间域识别方面获得了有竞争力的结果。此外，STMGCN 可以检测到在识别的域中具有丰富表达模式的空间可变基因。总体而言，STMGCN 是一种用于识别空间转录组学数据中空间域的强大而有效的计算框架。
