Identifying spatial domains from spatial transcriptome by graph attention network

由于数据的高维和复杂性，空间转录组数据的分析一直是一个具有挑战性的问题，而聚类分析则是空间转录组数据分析的核心问题。本文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。首先，对空间转录组数据进行增强，然后使用图注意力网络对节点进行特征提取，最后使用莱顿（Leiden）算法进行聚类分析。通过聚类的评价指标发现，与传统的非空间及空间聚类方法相比，本文提出的方法具有更好的数据分析性能。实验结果表明，本文所提方法可以有效地聚类空间转录组数据，从而能够识别不同的空间区域，为研究空间转录组数据提供了新的工具。.; 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.