A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data

Zhao, Mengyuan1,2; Li, Jiawei3; Liu, Xiaoyi4; Ma, Ke5; Tang, Jijun1; Guo, Fei6

2024-07-01

10.1101/gr.278439.123

GENOME RESEARCH   影响因子和分区

1088-9051

1549-5469

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.

SCI

英语

其他

National Natural Science Foundation of China (NSFC)["62322215","62172296"] ; Shenzhen Science and Technology Program[KQTD20200820113106007] ; Excellent Young Scientists Fund in Hunan Province[2022JJ20077] ; High-performance computing clusters of Shenzhen Institutes of Advanced Technology[PL-17161]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Genetics & Heredity

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Genetics & Heredity

WOS:001303143700001

COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT

MOLECULAR BIOLOGY & GENETICS

Web of Science

1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Coll Comp Sci & Control Engn, Shenzhen 518055, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China
4.Univ South Carolina, Comp Sci & Engn, Columbia, SC 29208 USA
5.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China
6.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China

Zhao, Mengyuan,Li, Jiawei,Liu, Xiaoyi,et al. A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data[J]. GENOME RESEARCH,2024,34(7).

Zhao, Mengyuan,Li, Jiawei,Liu, Xiaoyi,Ma, Ke,Tang, Jijun,&Guo, Fei.(2024).A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.GENOME RESEARCH,34(7).

Zhao, Mengyuan,et al."A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data".GENOME RESEARCH 34.7(2024).