ArticleRNA biology2020
Single-cell RNA-seq clustering: datasets, models, and algorithms.
Article in RNA biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis.BioMed research international · 2021Pooled it
- Single cell analysis of muscle contracture in cerebral palsy reveals profibrotic and antimyogenic stem cell populations with altered cell-cell interactions.American journal of physiology. Cell physiology · 2026Article
- DGM: deep graph clustering with mincut for analysis of single-cell transcriptomics.BMC genomics · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Epitranscriptomic sculpting: the role of mMedical oncology (Northwood, London, England) · 2025Review
- OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.Nature communications · 2024Article
- Identifying cell states in single-cell RNA-seq data at statistically maximal resolution.PLoS computational biology · 2024Article
- Traumatic brain injury promotes neurogenesis at the cost of astrogliogenesis in the adult hippocampus of male mice.Nature communications · 2024Article
- scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder.Briefings in bioinformatics · 2024Article
- scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.Life science alliance · 2023Article
- Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value.Blood science (Baltimore, Md.) · 2023Review
- Improving the performance of single-cell RNA-seq data mining based on relative expression orderings.Briefings in bioinformatics · 2023Article
- Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.Frontiers in microbiology · 2023Article
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- How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data.Briefings in bioinformatics · 2022Article
- Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity.Nature communications · 2022Article
- Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes.Frontiers in oncology · 2022Article
- D3K: The Dissimilarity-Density-Dynamic Radius K-means Clustering Algorithm for scRNA-Seq Data.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgroups and cell dynamics at the group level. Two important aspects of scRNA-seq data analysis were introduced and discussed in the present review: relevant datasets and analytical tools. In particular, we reviewed popular scRNA-seq datasets and discussed scRNA-seq clustering models including K-means clustering, hierarchical clustering, consensus clustering, and so on. Seven state-of-the-art scRNA clustering methods were compared on five public available datasets. Two primary evaluation metrics, the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI), were used to evaluate these methods. Although unsupervised models can effectively cluster scRNA-seq data, these methods also have challenges. Some suggestions were provided for future research directions.
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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.