ReviewBriefings in bioinformatics2020
Evaluation of single-cell classifiers for single-cell RNA sequencing data sets.
Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed.
- CYBB regulates synovial fibroblast inflammatory activation and sEV-mediated synovial-cartilage communication in osteoarthritis.Arthritis research & therapy · 2026Article
- Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.Clinical and experimental medicine · 2026Article
- SPP1 in notochord cells modulates intervertebral disc degeneration through CD44 recognition by macrophages based on single-cell transcriptome analysis.International journal of surgery (London, England) · 2026Article
- Identifying NDUFB2 as a prognostic biomarker for glioblastoma through an exploratory analysis of an anesthesia-related gene signature.BMC cancer · 2025Article
- Article
- Decoding heart failure subtypes with neural networks via differential explanation analysis.Briefings in bioinformatics · 2025Article
- Casein kinase 1 family member CSNK1E can regulate proliferation and migration in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2025Article
- scKAN: interpretable single-cell analysis for cell-type-specific gene discovery and drug repurposing via Kolmogorov-Arnold networks.Genome biology · 2025Article
- Self-Supervised Graph Representation Learning for Single-Cell Classification.Interdisciplinary sciences, computational life sciences · 2025Article
- Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.BMC bioinformatics · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.Bioinformatics (Oxford, England) · 2025Article
- scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules.Briefings in bioinformatics · 2025Article
- Machine learning-based identification of leptin-associated biomarkers and prognostic prediction models in sepsis.Frontiers in cellular and infection microbiology · 2025Article
- Identify GDPD3 as a key regulator of epithelial-mesenchymal transition and prostate adenocarcinoma progression via the LPA/LPAR1/AKT axis: transcriptomic and experimental study.Frontiers in immunology · 2025Article
- High resolution tissue and cell type identification via single cell transcriptomic profiling.PloS one · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- From DNA to Big Data: NGS Technologies and Their Applications.Methods in molecular biology (Clifton, N.J.) · 2025Review
- CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer.Current genomics · 2025Article
- The Role of Immunocyte Infiltration Regulatory Network Based on hdWGCNA and Single-Cell Bioinformatics Analysis in Intervertebral Disc Degeneration.Inflammation · 2024Article
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) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning 'unassigned' labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
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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.