Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation
Abstract Background Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a l...
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BMC
2025-01-01
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| Series: | BMC Biology |
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| Online Access: | https://doi.org/10.1186/s12915-025-02128-8 |
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| author | Yu-An Huang Yue-Chao Li Zhu-Hong You Lun Hu Peng-Wei Hu Lei Wang Yuzhong Peng Zhi-An Huang |
| author_facet | Yu-An Huang Yue-Chao Li Zhu-Hong You Lun Hu Peng-Wei Hu Lei Wang Yuzhong Peng Zhi-An Huang |
| author_sort | Yu-An Huang |
| collection | DOAJ |
| description | Abstract Background Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks. Results We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations. Conclusions scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell–cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness. |
| format | Article |
| id | doaj-art-6dcbaecea40a49119d0a387f9e7dae1d |
| institution | Kabale University |
| issn | 1741-7007 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-6dcbaecea40a49119d0a387f9e7dae1d2025-01-26T12:52:38ZengBMCBMC Biology1741-70072025-01-0123112110.1186/s12915-025-02128-8Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotationYu-An Huang0Yue-Chao Li1Zhu-Hong You2Lun Hu3Peng-Wei Hu4Lei Wang5Yuzhong Peng6Zhi-An Huang7School of Computer Science, Northwestern Polytechnical UniversitySchool of Computer Science, Northwestern Polytechnical UniversitySchool of Electronic Information, Xijing UniversityXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of ScienceXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of ScienceGuangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of SciencesGuangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal UniversityResearch Office, City University of Hong Kong (Dongguan)Abstract Background Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks. Results We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations. Conclusions scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell–cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.https://doi.org/10.1186/s12915-025-02128-8Single-cell RNA sequencingCell type annotationConsensus graphCellular communicationPathway integration |
| spellingShingle | Yu-An Huang Yue-Chao Li Zhu-Hong You Lun Hu Peng-Wei Hu Lei Wang Yuzhong Peng Zhi-An Huang Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation BMC Biology Single-cell RNA sequencing Cell type annotation Consensus graph Cellular communication Pathway integration |
| title | Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation |
| title_full | Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation |
| title_fullStr | Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation |
| title_full_unstemmed | Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation |
| title_short | Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation |
| title_sort | consensus representation of multiple cell cell graphs from gene signaling pathways for cell type annotation |
| topic | Single-cell RNA sequencing Cell type annotation Consensus graph Cellular communication Pathway integration |
| url | https://doi.org/10.1186/s12915-025-02128-8 |
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