PEGCN: A Single-Cell Type Annotation model based on GCN with Pseudo Labels and Ensemble Learning

Single-cell type annotation helps to identify specific subpopulations of cells associated with a particular disease, thus providing therapeutic targets for precision medicine. Previous tasks on single-cell type annotation have been performed on the basis of RNA-seq sequencing data, and gradually some studies are now being conducted on scATAC-seq sequencing data as well. Although the researchs in this area has achieved considerable results, it is still constrained by the high dimensionality, high noise, and uneven sample distribution inherent in scATAC-seq datasets. In this paper, we propose a graph convolution model that incorporates ensemble learning. The utilization of Pseudo Labels for cell graph construction significantly mitigates the excessively high anisotropy index in the graph caused by noise, presenting a novel approach to enhancing graph quality. Furthermore, our method introduces a second innovation by effectively addressing the challenge of high-dimensional features in ATAC data through guided feature selection facilitated by ensemble learning, ultimately improving the overall performance and accuracy of the analysis. The model effectively reduces the impact of the previously mentioned problems on the scATAC-seq dataset, and improves the predictive performance of the model. The single-cell type annotation results exceed 90% of the ACC in different datasets and the Macro-average ACC exceeds that of other models.