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

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...