Abstract：
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.
摘要：
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中用于辨别细胞类型的关键工具。然而,持续的挑战来自噪音，高维，并在单细胞数据中退出。尽管scRNA-seq聚类方法的增殖，这些通常专注于从单个细胞表达数据中提取表示，忽略潜在的细胞间关系。为了克服这个限制,我们介绍一下scGAAC,一种基于注意力图卷积自动编码器的新聚类方法。通过图形注意力自动编码器利用单元之间的结构信息，scGAAC揭示潜在的关系，同时从单细胞基因表达模式中提取表征信息。注意力融合模块通过注意力权重来合并图形注意力自动编码器和自动编码器的学习特征。最终,自监督学习策略指导模型优化。scGAAC,一个无假设的框架，在四个真实的scRNA-seq数据集上比大多数最先进的方法表现更好。scGAAC实现在Github上公开可用，网址为：https://github.com/labiip/scGAAC。
