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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Single-cell multi-omics data multi-scale dual-alignment deep clustering

Jin Zhichenga,b
Zhang Yia,b
Li Yurua,b
Su Chena,b
Tian Yea,b
Wang Yina,b
Feng Xia,b
a. School of Computer Science & Engineering, b. Guangxi key Laboratory of Embedded Technology & Intelligent System, Guilin University of Technology, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0204

Abstract

Single-cell clustering analysis plays a crucial role in dissecting cellular heterogeneity. Existing methods for integrating multi-omics data face several challenges, including insufficient modeling of local and global inter-omics relationships, feature redundancy, noise interference, and difficulties in constructing a consensus clustering space. To address these issues, this study proposed a novel single-cell multi-omics clustering method called Multi-scale Dual-alignment Deep Clustering (scMDDC) . scMDDC captures both local and global relationships between cells through a multi-scale fusion strategy, which effectively extracts complex intercellular interaction patterns. Furthermore, it reduces inter-omics redundant information and highlights modality-specific signals via contrastive alignment and cell alignment. The method then iteratively treats different omics modalities as anchors to guide the clustering of other modalities using a multi-omics co-clustering strategy, thereby achieving inter-modality complementarity and enhancing consensus. Extensive experiments on multiple real-world datasets show that scMDDC significantly outperforms eight benchmark models on various clustering evaluation metrics, including clustering accuracy and the adjusted Rand index. This demonstrates that scMDDC not only provides a new and effective approach for single-cell multi-omics analysis but also substantially improves the precision of cell type identification.

Foundation Support

国家自然科学基金资助项目(62166014)
广西自然科学基金面上项目(2025GXNSFAA069627)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0204
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-17] Accepted Paper

Cite This Article

靳志成, 张奕, 李玉茹, 等. 单细胞多组学数据的多尺度双对齐深度聚类方法 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0204. (Jin Zhicheng, Zhang Yi, Li Yuru, et al. Single-cell multi-omics data multi-scale dual-alignment deep clustering [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0204. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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