In recent years, the field of single-cell RNA sequencing has seen a surge in the development of clustering methods. These methods enable the identification of cell subpopulations, thereby facilitating the understanding of tumor microenvironments. Despite their utility, most existing clustering algorithms primarily focus on the attribute information provided by the cell matrix or the network structure between cells, often neglecting the network between genes. This oversight could lead to loss of information and clustering results that lack clinical significance. To address this limitation, we develop an advanced single-cell clustering model incorporating dual-graph alignment, which integrates gene network information into the clustering process based on self-supervised and unsupervised optimization. Specifically, we designed a graph-based autoencoder enhanced by an attention mechanism to effectively capture relationships between cells. Moreover, we performed the node2vec method on Protein-Protein Interaction (PPI) networks to derive the gene network structure and maintained this structure throughout the clustering process. Our proposed method has been demonstrated to be effective through experimental results, showcasing its ability to optimize clustering outcomes while preserving the original associations between cells and genes. This research contributes to obtaining accurate cell subpopulations and generates clustering results that more closely resemble real-world biological scenarios. It provides better insights into the characteristics and distribution of diseased cells, ultimately building a foundation for early disease diagnosis and treatment.

近年来，单细胞RNA测序领域的聚类方法得到了大量发展，然而大多数已有的聚类算法往往忽略基因之间的网络结构，并以属性信息为主要依据，本文提出了一种先进的单细胞聚类模型，通过双图对齐的方式整合了基因网络信息，优化聚类结果并保留了细胞与基因之间的关联，从而更准确地获得细胞亚群，并生成更接近真实生物场景的聚类结果，为早期疾病诊断和治疗奠定了基础。

通过双图对齐进行单细胞聚类