A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering
Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells wit...
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| Veröffentlicht in: | BMC genomics Jg. 26; H. 1; S. 749 - 13 |
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BioMed Central
18.08.2025
BioMed Central Ltd Springer Nature B.V BMC |
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| Abstract | Background
Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.
Results
Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at:
http://github.com/gladex/scCAGN
.
Conclusion
scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. |
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| AbstractList | Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.
Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN .
scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.BACKGROUNDSingle-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN .RESULTSHere, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN .scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity.CONCLUSIONscCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering. Results Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: Conclusion scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. Keywords: ScRNA-seq, Self-supervised clustering, GCN, Adversarial AE, Cross-attention mechanism Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering. Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN. scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. Abstract Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering. Results Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN . Conclusion scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering. Results Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN . Conclusion scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. BackgroundSingle-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.ResultsHere, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes adversarial autoencoders to augment encoder capabilities. Graph feature representations obtained via a GCN were integrated using a dynamic information fusion mechanism, yielding enhanced feature representations. In addition, scCAGN combines three different loss functions to optimize clustering performance through a joint clustering approach. By leveraging a unique information fusion and joint mechanism, scCAGN extracts deep cell features without labeled information, thus improving cell classification efficiency. Our findings show that scCAGN surpasses the existing methods in clustering performance across eight typical scRNA-seq datasets, achieving a maximum Normalized Mutual Information (NMI) improvement of 11.94%, notably reaching an NMI of 0.9732 in the QS_diaphragm dataset. It showed an average NMI improvement of 13% across the eight benchmark datasets, surpassing the lowest-performing method. Further ablation and hyperparameter analyses validated the robustness of the proposed method. The code is available at: http://github.com/gladex/scCAGN.ConclusionscCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity. |
| ArticleNumber | 749 |
| Audience | Academic |
| Author | Tang, Binhua Gao, Xinyu Feng, Yingying |
| Author_xml | – sequence: 1 givenname: Binhua surname: Tang fullname: Tang, Binhua email: bh.tang@hhu.edu.cn organization: Key Laboratory of Maritime Intelligent Cyberspace Technology (Ministry of Education of China), Hohai University, Shanghai Key Laboratory of Data Science, Fudan University, BGI Research – sequence: 2 givenname: Yingying surname: Feng fullname: Feng, Yingying organization: Key Laboratory of Maritime Intelligent Cyberspace Technology (Ministry of Education of China), Hohai University – sequence: 3 givenname: Xinyu surname: Gao fullname: Gao, Xinyu organization: Key Laboratory of Maritime Intelligent Cyberspace Technology (Ministry of Education of China), Hohai University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40826008$$D View this record in MEDLINE/PubMed |
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| Keywords | ScRNA-seq GCN Self-supervised clustering Adversarial AE Cross-attention mechanism |
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| PublicationTitleAlternate | BMC Genomics |
| PublicationYear | 2025 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
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| References | Y Gan (11941_CR9) 2023; 24 M Ciortan (11941_CR8) 2021; 38 T Liu (11941_CR16) 2024; 25 E Papalexi (11941_CR1) 2018; 18 Z Yu (11941_CR26) 2022; 36 X Li (11941_CR13) 2020; 11 H Gao (11941_CR10) 2023; 24 J Wang (11941_CR11) 2021; 12 D Tan (11941_CR20) 2024; 25 Z Fang (11941_CR24) 2024; 40 M Brendel (11941_CR7) 2022; 20 11941_CR14 R Petegrosso (11941_CR5) 2019; 21 X Wang (11941_CR18) 2023; 39 H-Y Wang (11941_CR21) 2023; 24 X Feng (11941_CR23) 2024; 25 B Van de Sande (11941_CR2) 2023; 22 B Tran (11941_CR22) 2022; 12 T Tian (11941_CR17) 2021; 12 11941_CR6 W Yang (11941_CR15) 2023; 39 Y Wang (11941_CR19) 2023; 39 Z Luo (11941_CR12) 2021; 11 Z Zhang (11941_CR27) 2024; 25 Z Liu (11941_CR25) 2024; 25 P Qiu (11941_CR3) 2020; 11 SV Stassen (11941_CR4) 2020; 36 |
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| Snippet | Background
Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell... Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell... Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell... BackgroundSingle-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell... Abstract Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a... |
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| SubjectTerms | Ablation Adversarial AE Algorithms Animal Genetics and Genomics Artificial neural networks Autoencoder Biomedical and Life Sciences Cluster Analysis Clustering Cross-attention mechanism Data analysis Data integration Datasets Electronic data processing GCN Gene expression Gene sequencing Genomics of isolated human populations Graphical representations Heterogeneity Humans Learning Life Sciences Machine learning Methods Microarrays Microbial Genetics and Genomics Neural networks Normal distribution Plant Genetics and Genomics Proteomics RNA sequencing RNA-Seq - methods ScRNA-seq Self-supervised clustering Sensory integration Single-Cell Gene Expression Analysis - methods |
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| Title | A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering |
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