ArticleBriefings in bioinformatics2022
A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.bioRxiv : the preprint server for biology · 2026Article
- Investigating Spectral Biomarker Candidates for Migratory Potential in Cancer Cells Using Micro-FTIR and O‑PTIR Spectroscopy.ACS measurement science au · 2026Article
- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Article
- LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.Genomics, proteomics & bioinformatics · 2025Article
- TORC: Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.Genome biology · 2025Article
- ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics.Briefings in bioinformatics · 2025Article
- Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics.Briefings in bioinformatics · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- Circulating CD8 T cells from patients with mild-to-moderate psoriasis are functionally impaired.Frontiers in immunology · 2025Article
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets.Computational and structural biotechnology journal · 2024Article
- A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.Briefings in bioinformatics · 2024Article
- Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes.bioRxiv : the preprint server for biology · 2024Article
- SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition.Briefings in bioinformatics · 2024Article
- Defining and benchmarking open problems in single-cell analysis.Research square · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The cell type identification is among the most important tasks in single-cell RNA-sequencing (scRNA-seq) analysis. Many in silico methods have been developed and can be roughly categorized as either supervised or unsupervised. In this study, we investigated the performances of 8 supervised and 10 unsupervised cell type identification methods using 14 public scRNA-seq datasets of different tissues, sequencing protocols and species. We investigated the impacts of a number of factors, including total amount of cells, number of cell types, sequencing depth, batch effects, reference bias, cell population imbalance, unknown/novel cell type, and computational efficiency and scalability. Instead of merely comparing individual methods, we focused on factors' impacts on the general category of supervised and unsupervised methods. We found that in most scenarios, the supervised methods outperformed the unsupervised methods, except for the identification of unknown cell types. This is particularly true when the supervised methods use a reference dataset with high informational sufficiency, low complexity and high similarity to the query dataset. However, such outperformance could be undermined by some undesired dataset properties investigated in this study, which lead to uninformative and biased reference datasets. In these scenarios, unsupervised methods could be comparable to supervised methods. Our study not only explained the cell typing methods' behaviors under different experimental settings but also provided a general guideline for the choice of method according to the scientific goal and dataset properties. Finally, our evaluation workflow is implemented as a modularized R pipeline that allows future evaluation of new methods. Availability: All the source codes are available at https://github.com/xsun28/scRNAIdent.
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