ReviewRNA (New York, N.Y.)2023
Review of single-cell RNA-seq data clustering for cell-type identification and characterization.
Review in RNA (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
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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
75 citing papers in PubMed.
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.Molecular systems biology · 2026Article
- Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.Nature communications · 2026Article
- CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.PLoS computational biology · 2026Article
- Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.Interdisciplinary sciences, computational life sciences · 2026Article
- Immune context governs stage-specific responses to depleting anti-PD-1 antibody in type 1 diabetes.Cell biomaterials · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.Briefings in bioinformatics · 2026Article
- Linking Targeted Pancreatic Cancer Genes With Metabolic Disorders: A Cross-Species Translational Pathway.Cancer medicine · 2026Article
- Cell-Type-Specific and Variety-Specific Responses to Salt Stress in Wheat Root Revealed by Single-Cell Transcriptomics.Plant biotechnology journal · 2026Article
- Review
- Direct detection of rare circulating tumor cells in peripheral blood mononuclear cells by scRNA seq: Spike-in strategy based feasibility study.The journal of liquid biopsy · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Adaptive multi-view information bottleneck for multi-omics data clustering.Briefings in bioinformatics · 2026Article
- Uncovering the role of integrated stress in Alzheimer's disease through single-cell and transcriptomic analysis.Scientific reports · 2026Article
- gLeiden: accelerated community detection algorithms using directed and undirected graphs on GPUs.Bioinformatics advances · 2026Article
- BIOGEN: evidence-grounded multi-agent reasoning framework for transcriptomic interpretation in antimicrobial resistance.Frontiers in bioinformatics · 2026Article
- CORTADO: hill climbing optimization for cell-type specific marker gene discovery and clustering accuracy improvement.Bioinformatics advances · 2026Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.
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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.