Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer

Chu Xuan Zhang; Yue Chao Li; Xin Yuan Li; Zhu Hong You; Yu An Huang

doi:10.1109/BIBM66473.2025.11357177

Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer

Chu Xuan Zhang
, Yue Chao Li
, Xin Yuan Li
, Zhu Hong You
, Yu An Huang

School of Computer Science

Northwestern Polytechnical University Xian
City University of Hong Kong (Dongguan)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Single-cell RNA sequencing (scRNA-seq) technology reveals cellular heterogeneity and functional diversity, but its high dimensionality and sparsity pose challenges for analysis. Cell clustering is a crucial task in scRNA-seq data analysis. While supervised methods rely on extensive annotations, traditional unsupervised clustering methods often ignore intercellular relationships between cells. Recent works have shown that pathway-aware, multi-view graph constructions improve robustness and annotation accuracy across platforms and tissues [1] [2]. We propose scMGTC, a novel unsupervised multi-graph transformer-based cell clustering model. scMGTC integrates biological prior knowledge into representation learning by extracting gene sets from KEGG pathways and constructing distinct cell-cell graphs for each pathway. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scMGTC achieves promising performance in scRNA-seq clustering.

Original language	English
Title of host publication	Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025
Editors	Juan Liu, Jingshan Huang, Xiaowo Wang, Fa Zhang, Xiufen Zou, Tian Tian, Xiaohua Hu, Bin Hu, Yi Xiong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1947-1950
Number of pages	4
ISBN (Electronic)	9798331515577
DOIs	https://doi.org/10.1109/BIBM66473.2025.11357177
State	Published - 2025
Event	2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 - Wuhan, China Duration: 15 Dec 2025 → 18 Dec 2025

Publication series

Name	Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

Conference

Conference	2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025
Country/Territory	China
City	Wuhan
Period	15/12/25 → 18/12/25

Keywords

Cell clustering
Graph neural network algorithm
Multi-Graph Transformer architecture
Single-cell RNA sequencing

Access to Document

10.1109/BIBM66473.2025.11357177

Cite this

Zhang, C. X., Li, Y. C., Li, X. Y., You, Z. H., & Huang, Y. A. (2025). Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer. In J. Liu, J. Huang, X. Wang, F. Zhang, X. Zou, T. Tian, X. Hu, B. Hu, & Y. Xiong (Eds.), Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 (pp. 1947-1950). (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM66473.2025.11357177

Zhang, Chu Xuan ; Li, Yue Chao ; Li, Xin Yuan et al. / Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer. Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. editor / Juan Liu ; Jingshan Huang ; Xiaowo Wang ; Fa Zhang ; Xiufen Zou ; Tian Tian ; Xiaohua Hu ; Bin Hu ; Yi Xiong. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 1947-1950 (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025).

@inproceedings{ecdb54532d954b488af4ef9a6a01f62b,

title = "Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer",

abstract = "Single-cell RNA sequencing (scRNA-seq) technology reveals cellular heterogeneity and functional diversity, but its high dimensionality and sparsity pose challenges for analysis. Cell clustering is a crucial task in scRNA-seq data analysis. While supervised methods rely on extensive annotations, traditional unsupervised clustering methods often ignore intercellular relationships between cells. Recent works have shown that pathway-aware, multi-view graph constructions improve robustness and annotation accuracy across platforms and tissues [1] [2]. We propose scMGTC, a novel unsupervised multi-graph transformer-based cell clustering model. scMGTC integrates biological prior knowledge into representation learning by extracting gene sets from KEGG pathways and constructing distinct cell-cell graphs for each pathway. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scMGTC achieves promising performance in scRNA-seq clustering.",

keywords = "Cell clustering, Graph neural network algorithm, Multi-Graph Transformer architecture, Single-cell RNA sequencing",

author = "Zhang, \{Chu Xuan\} and Li, \{Yue Chao\} and Li, \{Xin Yuan\} and You, \{Zhu Hong\} and Huang, \{Yu An\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 ; Conference date: 15-12-2025 Through 18-12-2025",

year = "2025",

doi = "10.1109/BIBM66473.2025.11357177",

language = "英语",

series = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1947--1950",

editor = "Juan Liu and Jingshan Huang and Xiaowo Wang and Fa Zhang and Xiufen Zou and Tian Tian and Xiaohua Hu and Bin Hu and Yi Xiong",

booktitle = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",

}

Zhang, CX, Li, YC, Li, XY, You, ZH & Huang, YA 2025, Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer. in J Liu, J Huang, X Wang, F Zhang, X Zou, T Tian, X Hu, B Hu & Y Xiong (eds), Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025, Institute of Electrical and Electronics Engineers Inc., pp. 1947-1950, 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025, Wuhan, China, 15/12/25. https://doi.org/10.1109/BIBM66473.2025.11357177

Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer. / Zhang, Chu Xuan; Li, Yue Chao; Li, Xin Yuan et al.
Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. ed. / Juan Liu; Jingshan Huang; Xiaowo Wang; Fa Zhang; Xiufen Zou; Tian Tian; Xiaohua Hu; Bin Hu; Yi Xiong. Institute of Electrical and Electronics Engineers Inc., 2025. p. 1947-1950 (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer

AU - Zhang, Chu Xuan

AU - Li, Yue Chao

AU - Li, Xin Yuan

AU - You, Zhu Hong

AU - Huang, Yu An

PY - 2025

Y1 - 2025

N2 - Single-cell RNA sequencing (scRNA-seq) technology reveals cellular heterogeneity and functional diversity, but its high dimensionality and sparsity pose challenges for analysis. Cell clustering is a crucial task in scRNA-seq data analysis. While supervised methods rely on extensive annotations, traditional unsupervised clustering methods often ignore intercellular relationships between cells. Recent works have shown that pathway-aware, multi-view graph constructions improve robustness and annotation accuracy across platforms and tissues [1] [2]. We propose scMGTC, a novel unsupervised multi-graph transformer-based cell clustering model. scMGTC integrates biological prior knowledge into representation learning by extracting gene sets from KEGG pathways and constructing distinct cell-cell graphs for each pathway. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scMGTC achieves promising performance in scRNA-seq clustering.

AB - Single-cell RNA sequencing (scRNA-seq) technology reveals cellular heterogeneity and functional diversity, but its high dimensionality and sparsity pose challenges for analysis. Cell clustering is a crucial task in scRNA-seq data analysis. While supervised methods rely on extensive annotations, traditional unsupervised clustering methods often ignore intercellular relationships between cells. Recent works have shown that pathway-aware, multi-view graph constructions improve robustness and annotation accuracy across platforms and tissues [1] [2]. We propose scMGTC, a novel unsupervised multi-graph transformer-based cell clustering model. scMGTC integrates biological prior knowledge into representation learning by extracting gene sets from KEGG pathways and constructing distinct cell-cell graphs for each pathway. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scMGTC achieves promising performance in scRNA-seq clustering.

KW - Cell clustering

KW - Graph neural network algorithm

KW - Multi-Graph Transformer architecture

KW - Single-cell RNA sequencing

UR - https://www.scopus.com/pages/publications/105033562717

U2 - 10.1109/BIBM66473.2025.11357177

DO - 10.1109/BIBM66473.2025.11357177

M3 - 会议稿件

AN - SCOPUS:105033562717

T3 - Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

SP - 1947

EP - 1950

BT - Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

A2 - Liu, Juan

A2 - Huang, Jingshan

A2 - Wang, Xiaowo

A2 - Zhang, Fa

A2 - Zou, Xiufen

A2 - Tian, Tian

A2 - Hu, Xiaohua

A2 - Hu, Bin

A2 - Xiong, Yi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025

Y2 - 15 December 2025 through 18 December 2025

ER -

Zhang CX, Li YC, Li XY, You ZH , Huang YA. Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer. In Liu J, Huang J, Wang X, Zhang F, Zou X, Tian T, Hu X, Hu B, Xiong Y, editors, Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 1947-1950. (Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025). doi: 10.1109/BIBM66473.2025.11357177

Unsupervised Cell Clustering in Single-Cell RNA Sequencing Data Using a Multi-Graph Transformer

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