Cell-Specific Highly Correlated Network for Self-Supervised Distillation in Cell Type Annotation

Self-supervised learning has succeeded significantly in cell type annotation based on transcriptomic data. However, existing methods encode transcriptomic data as gene feature representations, limiting self-supervised learning to the gene level. This paper proposes a novel self-supervised distillation learning framework, scTCHCN, which designs a cell-specific highly correlated network. Firstly, this framework integrates the cell-specific highly correlated network with single-cell transcriptomic data to extract cell and gene features. ScTCHCN promotes interactions between the network and the transcriptomic data features during pre-training by constructing the cell-specific highly correlated network. Additionally, a self-supervised paradigm with distillation learning is proposed to enhance the global feature correlation between the two. Ultimately, the pre-trained student model aggregates deep feature constraints and complementary information at the single-cell and gene levels. It combines them with the prediction layer to construct the final cell type prediction model. Experimental results demonstrate that scTCHCN excels in cell type annotation and rare cell identification tasks, showcasing its potential for other applications. The source code and additional information for this study are available at https://github.com/ZhangLab312/scTCHCN.