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Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering

Pengcheng Yang Orcid Logo, Fei Lu, Qianwen Xue, Weimin Ma, Qianwen Liu, Qiang Li, Yulin Zhang Orcid Logo, Cheng Cheng Orcid Logo

IET Systems Biology, Volume: 20, Issue: 1, Start page: e70070

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1049/syb2.70070

Abstract

Traditional bulk RNA sequencing often masks cell-to-cell variability, leading to a loss of individual heterogeneity information. Single-cell RNA sequencing (scRNA-seq) preserves cellular heterogeneity by reverse-transcribing, amplifying, as well as sequencing mRNA molecules from individual cells, en...

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Published in: IET Systems Biology
ISSN: 1751-8849 1751-8857
Published: Wiley 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71855
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However, scRNA-seq data are inherently high-dimensional and noisy with prevalent dropout events, posing challenges for accurate clustering and subtype identification. To address these issues, this study proposes an Adaptive Fractional-Order Total Variation Regularised Low-Rank Representation (AFTV-LRR) model that integrates adaptive fractional-order total variation into the low-rank representation framework. The proposed method reconstructs low-rank subspace structures to learn cell similarities while preserving fine-grained cellular features through fractional-order gradient learning. The optimisation problem is efficiently solved using the Alternating Direction Method of Multipliers (ADMM), and spectral clustering is applied to the learnt similarity matrix for accurate cell type identification. Extensive experiments on 11 publicly available scRNA-seq datasets demonstrate that AFTV-LRR achieves competitive and often superior performance compared with eight representative single-cell clustering algorithms in terms of Adjusted Rand Index (ARI) and Normalised Mutual Information (NMI). Visualisation with t-SNE further confirms that the proposed model yields clearer inter-cluster separations and higher intra-cluster compactness. Moreover, marker gene analysis using the mouse embryo dataset supports the biological interpretability and robustness of the clustering results. 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spelling 2026-06-18T13:36:37.0151198 v2 71855 2026-05-06 Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2026-05-06 MACS Traditional bulk RNA sequencing often masks cell-to-cell variability, leading to a loss of individual heterogeneity information. Single-cell RNA sequencing (scRNA-seq) preserves cellular heterogeneity by reverse-transcribing, amplifying, as well as sequencing mRNA molecules from individual cells, enabling in-depth studies of cell development, differentiation, and disease mechanisms. However, scRNA-seq data are inherently high-dimensional and noisy with prevalent dropout events, posing challenges for accurate clustering and subtype identification. To address these issues, this study proposes an Adaptive Fractional-Order Total Variation Regularised Low-Rank Representation (AFTV-LRR) model that integrates adaptive fractional-order total variation into the low-rank representation framework. The proposed method reconstructs low-rank subspace structures to learn cell similarities while preserving fine-grained cellular features through fractional-order gradient learning. The optimisation problem is efficiently solved using the Alternating Direction Method of Multipliers (ADMM), and spectral clustering is applied to the learnt similarity matrix for accurate cell type identification. Extensive experiments on 11 publicly available scRNA-seq datasets demonstrate that AFTV-LRR achieves competitive and often superior performance compared with eight representative single-cell clustering algorithms in terms of Adjusted Rand Index (ARI) and Normalised Mutual Information (NMI). Visualisation with t-SNE further confirms that the proposed model yields clearer inter-cluster separations and higher intra-cluster compactness. Moreover, marker gene analysis using the mouse embryo dataset supports the biological interpretability and robustness of the clustering results. Overall, this work provides an adaptive computational framework for improving the accuracy and reliability of single-cell clustering analysis. Journal Article IET Systems Biology 20 1 e70070 Wiley 1751-8849 1751-8857 biocomputing, bioinformatics, biology computing 31 12 2026 2026-12-31 10.1049/syb2.70070 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) UKRI (EP/W020408/1). 2026-06-18T13:36:37.0151198 2026-05-06T10:17:32.0013504 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Pengcheng Yang 0009-0004-1066-6698 1 Fei Lu 2 Qianwen Xue 3 Weimin Ma 4 Qianwen Liu 5 Qiang Li 6 Yulin Zhang 0000-0002-9125-5273 7 Cheng Cheng 0000-0003-0371-9646 8 71855__36939__33310adba5cb4cdc9bced17217f66f75.pdf 71855.VOR.pdf 2026-06-11T13:32:11.3764846 Output 4657229 application/pdf Version of Record true © 2026 The Author(s). IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
spellingShingle Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
Cheng Cheng
title_short Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
title_full Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
title_fullStr Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
title_full_unstemmed Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
title_sort Fractional Order Total Variation Low‐Rank Representation on Single‐Cell RNA Sequencing Clustering
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Pengcheng Yang
Fei Lu
Qianwen Xue
Weimin Ma
Qianwen Liu
Qiang Li
Yulin Zhang
Cheng Cheng
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publishDate 2026
institution Swansea University
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doi_str_mv 10.1049/syb2.70070
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Traditional bulk RNA sequencing often masks cell-to-cell variability, leading to a loss of individual heterogeneity information. Single-cell RNA sequencing (scRNA-seq) preserves cellular heterogeneity by reverse-transcribing, amplifying, as well as sequencing mRNA molecules from individual cells, enabling in-depth studies of cell development, differentiation, and disease mechanisms. However, scRNA-seq data are inherently high-dimensional and noisy with prevalent dropout events, posing challenges for accurate clustering and subtype identification. To address these issues, this study proposes an Adaptive Fractional-Order Total Variation Regularised Low-Rank Representation (AFTV-LRR) model that integrates adaptive fractional-order total variation into the low-rank representation framework. The proposed method reconstructs low-rank subspace structures to learn cell similarities while preserving fine-grained cellular features through fractional-order gradient learning. The optimisation problem is efficiently solved using the Alternating Direction Method of Multipliers (ADMM), and spectral clustering is applied to the learnt similarity matrix for accurate cell type identification. Extensive experiments on 11 publicly available scRNA-seq datasets demonstrate that AFTV-LRR achieves competitive and often superior performance compared with eight representative single-cell clustering algorithms in terms of Adjusted Rand Index (ARI) and Normalised Mutual Information (NMI). Visualisation with t-SNE further confirms that the proposed model yields clearer inter-cluster separations and higher intra-cluster compactness. Moreover, marker gene analysis using the mouse embryo dataset supports the biological interpretability and robustness of the clustering results. Overall, this work provides an adaptive computational framework for improving the accuracy and reliability of single-cell clustering analysis.
published_date 2026-12-31T05:33:29Z
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