ABSTRACT
Human blood expression data has played an important role in the study of molecular regulatory mechanisms of diseases. The identification and automatic annotation of cell subtypes can be used to explore disease subtypes and to model the short-term evolution mechanisms of diseases. Due to the weak signal pattern, automatic annotation based on blood expression data is computationally difficult, and effective automatic annotation algorithms are urgently needed to support it. In this paper, human peripheral blood expression data (PBMC) datasets have been selected as benchmark data to validate the performance of cell type detection approaches. In this study, ReconSAGE is proposed to capture the topological structures between cells. This method can better reveal cell subpopulations and improve the accuracy and robustness of cell clustering.
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Index Terms
- ReconSAGE: A Graph Neural Network for Single Cell Annotation Based on Autoencoder
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