ArticleMethodsX2023
A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data.
Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Elovl1 inhibition reduced very long chain fatty acids in a mouse model of adrenoleukodystrophy.iScience · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- ENACT: End-to-End Analysis of Visium High Definition (HD) Data.Bioinformatics (Oxford, England) · 2025Article
- Morphomolecular Pathology and Genomic Insights into the Cells of Origin of Cholangiocarcinoma and Combined Hepatocellular-Cholangiocarcinoma.The American journal of pathology · 2025Review
- The IL-4-IL-4Rα axis modulates olfactory neuroimmune signaling to induce loss of smell.Allergy · 2025Article
- Single-cell RNA-seq data augmentation using generative Fourier transformer.Communications biology · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- starTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis.Communications biology · 2024Article
- irGSEA: the integration of single-cell rank-based gene set enrichment analysis.Briefings in bioinformatics · 2024Article
- Scaling up single-cell RNA-seq data analysis with CellBridge workflow.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) experiments provide opportunities to peer into complex tissues at single-cell resolution. However, insightful biological interpretation of scRNA-seq data relies upon precise identification of cell types. The ability to identify the origin of a cell quickly and accurately will greatly improve downstream analyses. We present Sargent, a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers. We demonstrate Sargent's high accuracy by annotating simulated datasets. Further, we compare Sargent performance against expert-annotated scRNA-seq data from human organs including PBMC, heart, kidney, and lung. We demonstrate that Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation. Additionally, the automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.•Sargent is a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers.•Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation.•Automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.
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