ArticleBiomolecules2022
Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AICellType: a large language model-based platform for accurate cell type annotation.Briefings in bioinformatics · 2026Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.Nature communications · 2025Article
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.bioRxiv : the preprint server for biology · 2025Article
- Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.Cell reports. Medicine · 2024Article
- TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.Briefings in bioinformatics · 2023Article
- Integrative Analysis of Single-Cell and Bulk Sequencing Data Depicting the Expression and Function of P2ry12 in Microglia Post Ischemia-Reperfusion Injury.International journal of molecular sciences · 2023Article
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2 authors.
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Abstract
Recent advancement in single-cell RNA sequencing (scRNA-seq) technology is gaining more and more attention. Cell type annotation plays an essential role in scRNA-seq data analysis. Several computational methods have been proposed for automatic annotation. Traditional cell type annotation is to first cluster the cells using unsupervised learning methods based on the gene expression profiles, then to label the clusters using the aggregated cluster-level expression profiles and the marker genes' information. Such procedure relies heavily on the clustering results. As the purity of clusters cannot be guaranteed, false detection of cluster features may lead to wrong annotations. In this paper, we improve this procedure and propose an Automatic Cell type Annotation Method (ACAM). ACAM delineates a clear framework to conduct automatic cell annotation through representative cluster identification, representative cluster annotation using marker genes, and the remaining cells' classification. Experiments on seven real datasets show the better performance of ACAM compared to six well-known cell type annotation methods.
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