Title: scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data Show Chinese title

Title: scSiameseClu：一种用于解释单细胞RNA测序数据的孪生聚类框架

Abstract: Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially graph neural networks (GNNs)-based methods, have significantly improved clustering performance. However, the analysis of scRNA-seq data remains challenging due to noise, sparsity, and high dimensionality. Compounding these challenges, GNNs often suffer from over-smoothing, limiting their ability to capture complex biological information. In response, we propose scSiameseClu, a novel Siamese Clustering framework for interpreting single-cell RNA-seq data, comprising of 3 key steps: (1) Dual Augmentation Module, which applies biologically informed perturbations to the gene expression matrix and cell graph relationships to enhance representation robustness; (2) Siamese Fusion Module, which combines cross-correlation refinement and adaptive information fusion to capture complex cellular relationships while mitigating over-smoothing; and (3) Optimal Transport Clustering, which utilizes Sinkhorn distance to efficiently align cluster assignments with predefined proportions while maintaining balance. Comprehensive evaluations on seven real-world datasets demonstrate that scSiameseClu outperforms state-of-the-art methods in single-cell clustering, cell type annotation, and cell type classification, providing a powerful tool for scRNA-seq data interpretation. Show Chinese abstract

Abstract: 单细胞RNA测序（scRNA-seq）揭示了细胞的异质性，其中细胞聚类在识别细胞类型和标记基因中起着关键作用。 最近的进展，特别是基于图神经网络（GNNs）的方法，显著提高了聚类性能。 然而，由于噪声、稀疏性和高维度，scRNA-seq数据的分析仍然具有挑战性。 加上这些挑战，GNNs常常受到过度平滑的影响，限制了它们捕捉复杂生物信息的能力。 为了解决这些问题，我们提出了scSiameseClu，一种用于解释单细胞RNA-seq数据的新颖Siamese聚类框架，包括三个关键步骤： (1) 双增强模块，该模块对基因表达矩阵和细胞图关系应用生物学相关的扰动，以增强表示的鲁棒性；(2) Siamese融合模块，该模块结合交叉相关性细化和自适应信息融合，以捕捉复杂的细胞关系并减轻过度平滑；以及(3) 最优传输聚类，该模块利用Sinkhorn距离高效地将聚类分配与预定义比例对齐，同时保持平衡。 在七个真实数据集上的综合评估表明，scSiameseClu在单细胞聚类、细胞类型注释和细胞类型分类方面优于最先进的方法，为scRNA-seq数据解释提供了一个强大的工具。