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

• URL: http://arxiv.org/abs/2606.18672v1
• Date: Wed, 17 Jun 2026 04:17:49 GMT
• Title: scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
• Abstract summary: Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level.<n>We propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN)<n>Our scGTN consistently outperforms existing methods.
• Score: 35.886337545978364
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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