Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation

• Published in: BMC biology Vol. 23; no. 1; pp. 23 - 21
• Main Authors: Huang, Yu-An, Li, Yue-Chao, You, Zhu-Hong, Hu, Lun, Hu, Peng-Wei, Wang, Lei, Peng, Yuzhong, Huang, Zhi-An
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.01.2025; BioMed Central; BMC
• Subjects: Accuracy; Annotations; Automation; Cell type annotation; Cells; Cellular communication; Cellular signal transduction; Computational Biology - methods; Consensus graph; Datasets; Deep learning; Gene expression; Gene Regulatory Networks; Gene sequencing; Genetic research; Graph representations; Graphical representations; Graphs; Humans; Identification and classification; Integrated approach; Knowledge representation; Machine learning; Neural networks; Pathway integration; Predictions; RNA sequencing; Robustness; Signal Transduction - genetics; Single-Cell Analysis - methods; Single-cell RNA sequencing; Spatial data; China; Pathway integration; Cell type annotation; Cellular communication; Single-cell RNA sequencing; Consensus graph
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-025-02128-8

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. 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. 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.