scSwinTNet: A Cell Type Annotation Method for Large-Scale Single-Cell RNA-Seq Data Based on Shifted Window Attention.

Abstract

The annotation of cell types based on single-cell RNA sequencing (scRNA-seq) data is a critical downstream task in single-cell analysis, with significant implications for a deeper understanding of biological processes. Most analytical methods cluster cells by unsupervised clustering, which requires manual annotation for cell type determination. This procedure is time-overwhelming and non-repeatable. To accommodate the exponential growth of sequencing cells, reduce the impact of data bias, and integrate large-scale datasets for further improvement of type annotation accuracy, we proposed scSwinTNet. It is a pre-trained tool for annotating cell types in scRNA-seq data, which uses self-attention based on shifted windows and enables intelligent information extraction from gene data. We demonstrated the effectiveness and robustness of scSwinTNet by using 399 760 cells from human and mouse tissues. To the best of our knowledge, scSwinTNet is the first model to annotate cell types in scRNA-seq data using a pre-trained shifted window attention-based model. It does not require a priori knowledge and accurately annotates cell types without manual annotation.