---
title: "scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering"
authors:
  - "Jinke Wu"
  - "Yifan Wang"
  - "Siyu Yi"
  - "Caiyang Yu"
  - "Ziyue Qiao"
  - "Nan Yin"
  - "Jiancheng Lv"
  - "Wei Ju"
doi: "10.24963/ijcai.2026/346"
url: "https://ijcai.org/proceedings/2026/346"
pdf: "https://ijcai.org/proceedings/2026/0346.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3115-3123"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: Bioinformatics"
  - "Data Mining: Mining graphs"
---

# scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu, Ziyue Qiao, Nan Yin, Jiancheng Lv, Wei Ju

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3115-3123. DOI: [10.24963/ijcai.2026/346](https://doi.org/10.24963/ijcai.2026/346)

## Abstract

Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.