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

AI Article Synopsis

  • Cell identity annotation is vital for understanding disease and developing treatments, but existing methods depend on specific data sets that can vary widely.
  • The new tool, scHGR, automates the annotation process by using gene regulatory relationships to create communication graphs from single-cell transcriptome data, which helps to reduce data noise and clarify cellular connections.
  • Experiments show that scHGR accurately identifies cell identities and discovers new subtypes in immune cells, providing valuable insights for COVID-19 research and potential therapies.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368180PMC
http://dx.doi.org/10.1101/gr.278439.123DOI Listing

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