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

arXiv: https://arxiv.org/abs/2505.12626 · HTML: https://mlsanity.com/paper/2505.12626/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 15 of 15)

- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [Deep Fusion Clustering Network](https://mlsanity.com/paper/2012.09600/) (2020)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Graph Soft-Contrastive Learning via Neighborhood Ranking](https://mlsanity.com/paper/2209.13964/) (2022)
- [LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding](https://mlsanity.com/paper/2102.10826/) (2021)
- [scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding](https://mlsanity.com/paper/2404.06167/) (2024)
- [FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization](https://mlsanity.com/paper/2405.06312/) (2024)
- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition](https://mlsanity.com/paper/2209.13645/) (2022)
- [scVGAE: A ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation](https://mlsanity.com/paper/2403.08959/) (2024)

## Built on by (most cited first, 6 of 6)

- [scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing](https://mlsanity.com/paper/2512.02471/) (2025)
- [Soft Graph Clustering for single-cell RNA Sequencing Data](https://mlsanity.com/paper/2507.09890/) (2025)
- [scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis](https://mlsanity.com/paper/2509.25884/) (2025)
- [SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM](https://mlsanity.com/paper/2508.20514/) (2025)
- [scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing](https://mlsanity.com/paper/2606.13007/) (2026)
- [scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering](https://mlsanity.com/paper/2606.18672/) (2026)

## Abstract (arXiv)

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.