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

ABSTRACT: 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 i

SUBMITTER: Kong T 

PROVIDER: S-EPMC11436728 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature