ReviewBriefings in bioinformatics2025
An overview of computational methods in single-cell transcriptomic cell type annotation.
Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- scKanFormer: A Transformer-KAN framework with biologically informed attention for cell type annotation in large-scale scRNA-seq data.PLoS computational biology · 2026Article
- Neural crest cell signatures drive tumorigenesis in tuberous sclerosis complex and lymphangioleiomyomatosis.JCI insight · 2026Article
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Multi-omics integration and clinical validation identify CKAP2 as a diagnostic biomarker for bladder cancer.World journal of surgical oncology · 2026Article
- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer's Disease.International journal of molecular sciences · 2026Review
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq.PLoS computational biology · 2026Article
- Beyond the mean: genetic control of gene expression fidelity and dispersion.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data.Nucleic acids research · 2026Article
- Understanding the immune microenvironment of ovarian cancer.Frontiers in oncology · 2026Review
- Identity crisis: exploring the boundaries of cell type identification in the age of single-cell transcriptomics.Frontiers in cellular neuroscience · 2026Article
- Diverse infections transcriptionally reprogram the intestinal epithelium and epithelial-immune cell interactions.bioRxiv : the preprint server for biology · 2025Article
- Cancer Reversion Therapy: Prospects, Progress and Future Directions.Current issues in molecular biology · 2025Review
- Single-cell multi-omics data reveal heterogeneity in liver tissue microenvironment induced by hypertension.Molecular therapy. Nucleic acids · 2025Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers can accurately infer cell types, sparking the development of numerous innovative annotation methods. These methods utilize a range of strategies, including marker genes, correlation-based matching, and supervised learning, to classify cell types. In this review, we systematically examine these annotation approaches based on transcriptomics-specific gene expression profiles and provide a comprehensive comparison and categorization of these methods. Furthermore, we focus on the main challenges in the annotation process, especially the long-tail distribution problem arising from data imbalance in rare cell types. We discuss the potential of deep learning techniques to address these issues and enhance model capability in recognizing novel cell types within an open-world framework.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.