scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data

TL;DR

scSiameseClu is a novel framework that improves clustering and interpretation of single-cell RNA sequencing data by combining biologically informed data augmentation, a Siamese fusion approach, and optimal transport clustering.

Contribution

The paper introduces scSiameseClu, a new Siamese clustering framework that addresses over-smoothing and enhances biological interpretation in scRNA-seq data analysis.

Findings

Outperforms existing methods in clustering accuracy

Effectively captures complex cellular relationships

Provides robust cell type annotation and classification

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.