ArticleBriefings in bioinformatics2023
Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- ScEnsemble: weighted hypergraph ensemble clustering for single-cell RNA sequencing.BMC bioinformatics · 2026Article
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- Application of hierarchical self-supervised contrastive learning in domain adaptation matching of multimodal remote sensing image.Scientific reports · 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
- Single Cell RNA Sequencing in Colorectal Cancer Immunology: Recent Updates, Application, and Emerging Challenges.Current molecular medicine · 2026Review
- circPVT1 regulates EMT and induces macrophage polarization to promotes the progression of renal cell carcinoma.Frontiers in immunology · 2026Article
- Deep clustering of single-cell RNA-seq using adversarial graph contrastive learning.Briefings in bioinformatics · 2025Article
- Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2025Article
- Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?Annals of hematology · 2025Review
- scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.BMC bioinformatics · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- Article
- Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2024Article
- Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.Briefings in bioinformatics · 2024Article
- scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.Briefings in bioinformatics · 2024Article
- Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.
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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.