ReviewBriefings in bioinformatics2020
Clustering and classification methods for single-cell RNA-sequencing data.
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 93 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- A systematic single-cell analysis of enhancer RNAs across human and mouse.Nucleic acids research · 2026Article
- scKanFormer: A Transformer-KAN framework with biologically informed attention for cell type annotation in large-scale scRNA-seq data.PLoS computational biology · 2026Article
- Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- Psoriasis-Obesity-Related Gene COX7C Promotes Keratinocyte Proliferation and Positively Regulates Inflammatory Responses in Psoriasis.Journal of cellular and molecular medicine · 2026Article
- scIMGCN: an Automatic Single-Cell Type Annotation Method Based on Interpretable Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.Interdisciplinary sciences, computational life sciences · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- Dynamic gut responses to sepsis uncovered by multi-omics profiling in a rodent model.Communications biology · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data.Nucleic acids research · 2026Article
- A clustering method for single-cell RNA sequencing data based on denoising and masking learning.Frontiers in bioinformatics · 2026Article
- Single-cell multi-omics data reveal heterogeneity in liver tissue microenvironment induced by hypertension.Molecular therapy. Nucleic acids · 2025Article
- Consensus spectral clustering with weighted similarity functions for single-cell RNA sequencing data.Communications in statistics: Simulation and computation · 2025Article
- SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation.PLoS computational biology · 2025Article
- Macrophage marker gene-driven prognostic models for esophageal cancer: integrating multi-omics analysis and therapeutic strategies.3 Biotech · 2025Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- Analysis of Multiple Programmed Cell Death Patterns and Functional Validations of Apoptosis-Associated Genes in Lung Adenocarcinoma.Annals of surgical oncology · 2025Article
- Molecular Subtypes and Risk Prediction Model Based on Malignant Cell Differentiation Trajectories in Breast Cancer.Journal of cellular and molecular medicine · 2025Article
33 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
4 authors.
Funding
Abstract
Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.
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What OpenQuestion holds
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.