Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that profiles genome-wide gene expression at the single-cell level,and can efficiently resolve cellular heterogeneity.It is widely applied in fields such as developmental biology and disease research. However,scRNA-seq data often exhibit characteristics such as high noise,high dimensionality, and high sparsity,which pose significant challenges to traditional data analysis methods.In recent years,deep learning models,represented by autoencoders and generative adversarial networks, have been extensively applied to scRNA-seq data analysis tasks,including expression imputation, batch effect correction,dimensionality reduction,cell clustering,and cell type annotation. These applications demonstrate the power of deep learning. Notably, Transformer-based deep learning models, leveraging self-attention mechanisms to capture implicit dependencies among genes and associations between gene expression and cells, offer a novel strategy and direction for scRNA-seq data analysis, and provide innovative solutions with promising applications for the integration of multimodal omics data.