This paper proposes scSiameseClu, a novel Siamese Clustering framework for single-cell RNA sequencing (scRNA-seq) data analysis. scSiameseClu aims to address the challenging task of analyzing scRNA-seq data due to noise, sparsity, high dimensionality, and over-smoothing problems of graph neural networks (GNNs). scSiameseClu comprises three main steps: the Dual Augmentation Module, the Siamese Fusion Module, and Optimal Transport Clustering. The framework utilizes biologically informative perturbations to enhance representation robustness, captures complex cellular relationships while mitigating over-smoothing, and efficiently aligns cluster assignments to a predefined ratio. 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.
