ScTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data.

ABSTRACT: Cell type annotation is crucial in single-cell RNA sequencing data analysis because it enables significant biological discoveries and deepens our understanding of tissue biology. Given the high-dimensional and highly sparse nature of single-cell RNA sequencing data, most existing annotation tools focus on highly variable genes to reduce dimensionality and computational load. However, this approach inevitably results in information loss, potentially weakening the model's generalization performance and adaptability to novel datasets. To mitigate this issue, we developed scTrans, a single cell Transformer-based model, which employs sparse attention to utilize all non-zero genes, thereby effectively reducing the input data dimensionality while minimizing information loss. We validated the spee

SUBMITTER: Zou Z 

PROVIDER: S-EPMC11970913 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature