ReviewComputational and structural biotechnology journal2021
Automatic cell type identification methods for single-cell RNA sequencing.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- TORC: Target-Oriented Reference Construction for Supervised Cell-Type Identification in scRNA-seq.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Statistically principled feature selection for single cell transcriptomics.BMC bioinformatics · 2025Article
- Self-Supervised Graph Representation Learning for Single-Cell Classification.Interdisciplinary sciences, computational life sciences · 2025Article
- TORC: Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.Genome biology · 2025Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Statistically principled feature selection for single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.Genome biology · 2024Article
- Coding and Non-Coding Transcriptomic Landscape of Aortic Complications in Marfan Syndrome.International journal of molecular sciences · 2024Review
- CAraCAl: CAMML with the integration of chromatin accessibility.BMC bioinformatics · 2024Article
- Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases.Biomedicines · 2024Review
- Article
- Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights.PLoS genetics · 2024Article
- Partial label learning for automated classification of single-cell transcriptomic profiles.PLoS computational biology · 2024Article
- Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics.The Plant cell · 2024Article
- HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many research disciplines due to its ability to elucidate the heterogeneous and complex cell-type compositions of different tissues and cell populations. Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. By contrast, automatic cell-type identification methods may have the advantages of being fast, accurate, and more user friendly. Here, we discuss and evaluate thirty-two published automatic methods for scRNA-seq data analysis in terms of their prediction accuracy, F1-score, unlabeling rate and running time. We highlight the advantages and disadvantages of these methods and provide recommendations of method choice depending on the available information. The challenges and future applications of these automatic methods are further discussed. In addition, we provide a free scRNA-seq data analysis package encompassing the discussed automatic methods to help the easy usage of them in real-world applications.
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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.