scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data

• Published in: Scientific reports 2024-09, Vol.14 (1), p.22308-15, Article 22308
• Main Authors: Kong, Tianci, Yu, Tiancheng, Zhao, Jiaxin, Hu, Zhenhua, Xiong, Neal, Wan, Jian, Dong, Xiaoliang, Pan, Yi, Zheng, Huilin, Zhang, Lei
• Format: Article
• Language: English
• Subjects: 631/114; 631/114/1386; 631/114/2785; 631/1647/48; Algorithms; Annotations; Computational Biology - methods; Humanities and Social Sciences; Humans; Molecular Sequence Annotation; multidisciplinary; RNA-Seq - methods; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-73356-1

Single-cell RNA sequencing (scRNA-seq) is a key technology for investigating cell development and analysing cell diversity across various diseases. However, the high dimensionality and extreme sparsity of scRNA-seq data pose great challenges for accurate cell type annotation. To address this, we developed a new cell-type annotation model called scGAA (general gated axial-attention model for accurate cell-type annotation of scRNA-seq). Based on the transformer framework, the model decomposes the traditional self-attention mechanism into horizontal and vertical attention, considerably improving computational efficiency. This axial attention mechanism can process high-dimensional data more efficiently while maintaining reasonable model complexity. Additionally, the gated unit was integrated into the model to enhance the capture of relationships between genes, which is crucial for achieving an accurate cell type annotation. The results revealed that our improved transformer model is a promising tool for practical applications. This theoretical innovation increased the model performance and provided new insights into analytical tools for scRNA-seq data.