ReviewNature reviews. Genetics2019
Challenges in unsupervised clustering of single-cell RNA-seq data.
Review in Nature reviews. Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 603 papers.
What it found
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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
603 citing papers in PubMed.
- MIGC: Pseudotime differentially expressed genes detection with mutual information.Quantitative biology (Beijing, China) · 2026Article
- The misplaced mouse Pax6 interneuron subclass: A cross-species transcriptomic reassignment.iScience · 2026Article
- Single-cell spatial transcriptomics identifies SERPINE1 as a vascular endothelial mediator of cisplatin resistance in head and neck squamous cell carcinoma.Translational oncology · 2026Article
- Donor-aware integrative single-cell analysis distinguishes consistent memory B-cell programmes from donor-sensitive state boundaries in untreated rheumatoid arthritis.Functional & integrative genomics · 2026Article
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- RAGCell: retrieval-augmented generation as supervision for versatile single-cell analysis.Bioinformatics (Oxford, England) · 2026Article
- PRISM-GEP: Viewing single-cell expression through the lens of topic modeling.Bioinformatics (Oxford, England) · 2026Article
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.PLoS computational biology · 2026Article
- HPRT1 drives mitochondrial metabolic reprogramming and immunosuppressive microenvironment in oral squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2026Article
- Article
- CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.Metabolic brain disease · 2026Review
- TopClust: Topological-based analysis of scRNA-seq data for data-driven identification of clusters and core cluster cells.Biophysical journal · 2026Article
- Dynamic control of cell state transitions during tissue morphogenesis.The EMBO journal · 2026Review
- Hidden causal inference delineates dynamic lncRNA regulation in autism spectrum disorder.Briefings in bioinformatics · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data.Nature biotechnology · 2026Article
- Integrating machine learning and multi-omics analysis to identify and validate key genes associated with periodontitis.Acta odontologica Scandinavica · 2026Article
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
543 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.
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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.