Mengyuan Zhao1,2, Jiawei Li3, Xiaoyi Liu4, Ke Ma5, Jijun Tang1 and Fei Guo6 1College of Computer Science and Control Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; 2University of Chinese Academy of Sciences, Beijing 100190, China; 3College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; 4Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29208, USA; 5College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China; 6School of Computer Science and Engineering, Central South University, Changsha 410083, China Corresponding authors: guofeicsu.edu.cn, jj.tangsiat.ac.cn Abstract

Cell identity annotation for single-cell transcriptome data is a crucial process for constructing cell atlases, unraveling pathogenesis, and inspiring therapeutic approaches. Currently, the efficacy of existing methodologies is contingent upon specific data sets. Nevertheless, such data are often sourced from various batches, sequencing technologies, tissues, and even species. Notably, the gene regulatory relationship remains unaffected by the aforementioned factors, highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse data sources while establishing distant cellular connections, yielding valuable biological insights. Experiments involving 22 scenarios demonstrate that scHGR precisely and consistently annotates cell identities, benchmarked against state-of-the-art methods. Crucially, scHGR uncovers novel subtypes within peripheral blood mononuclear cells, specifically from CD4+ T cells and cytotoxic T cells. Furthermore, by characterizing a cell atlas comprising 56 cell types for COVID-19 patients, scHGR identifies vital factors like IL1 and calcium ions, offering insights for targeted therapeutic interventions.

Received August 28, 2023. Accepted July 23, 2024.