Wu Hanwen, Gao Jie
Jiangnan University, Wuxi, Jiangsu 214122, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Apr 25;41(2):246-252. doi: 10.7507/1001-5515.202304030.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.
由于数据的高维度和复杂性，空间转录组数据的分析一直是一个具有挑战性的问题。同时，聚类分析是空间转录组数据分析的核心问题。在本文中，提出了一种基于图注意力网络的深度学习方法用于空间转录组数据的聚类分析。我们的方法首先增强空间转录组数据，然后使用图注意力网络从节点中提取特征，最后使用 Leiden 算法进行聚类分析。与传统的非空间和空间聚类方法相比，我们的方法通过聚类评估指标在数据分析中具有更好的性能。实验结果表明，所提出的方法可以有效地对空间转录组数据进行聚类并识别不同的空间区域，为研究空间转录组数据提供了一种新工具。
