Zhang Lin, Xiang Haiping, Wang Feng, Chen Zepeng, Shen Mo, Ma Jiani, Liu Hui, Zheng Hongdang
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; Department of Veterinary Biosciences, Melbourne Veterinary School, the University of Melbourne, Parkville, Victoria 3010, Australia.
Methods. 2024 Sep;229:115-124. doi: 10.1016/j.ymeth.2024.06.010. Epub 2024 Jun 29.
Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: https://github.com/labiip/scGAAC.
单细胞RNA测序（scRNA-seq）能够研究控制细胞异质性和多样性的复杂机制。聚类分析仍然是scRNA-seq中识别细胞类型的关键工具。然而，单细胞数据中的噪声、高维度和数据丢失等问题一直存在。尽管scRNA-seq聚类方法不断涌现，但这些方法往往侧重于从单个细胞表达数据中提取特征，而忽略了潜在的细胞间关系。为了克服这一局限性，我们引入了scGAAC，一种基于注意力的图卷积自动编码器的新型聚类方法。通过图注意力自动编码器利用细胞之间的结构信息，scGAAC在从单细胞基因表达模式中提取特征信息的同时，揭示潜在关系。一个注意力融合模块通过注意力权重将图注意力自动编码器和自动编码器学到的特征进行融合。最终，一个自监督学习策略指导模型优化。scGAAC是一个无假设框架，在四个真实的scRNA-seq数据集上的表现优于大多数现有方法。scGAAC的实现可在Github上公开获取：https://github.com/labiip/scGAAC 。
