ArticleBriefings in bioinformatics2021
Consensus clustering of single-cell RNA-seq data by enhancing network affinity.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering.PLoS computational biology · 2026Article
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- SpatialCVGAE: Consensus Clustering Improves Spatial Domain Identification of Spatial Transcriptomics Using VGAE.Interdisciplinary sciences, computational life sciences · 2025Article
- scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation.Nature communications · 2025Article
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data.Genome research · 2025Article
- Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering.Computational and structural biotechnology journal · 2024Article
- CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.BMC bioinformatics · 2023Article
- MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification.Scientific reports · 2023Article
- Comprehensive analysis identifies DNA damage repair-related gene HCLS1 associated with good prognosis in lung adenocarcinoma.Translational cancer research · 2023Article
- Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.Briefings in bioinformatics · 2023Article
- ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data.Genome biology · 2023Article
- Decoding brain memory formation by single-cell RNA sequencing.Briefings in bioinformatics · 2022Article
- Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance.Biomolecules · 2022Article
- scMelody: An Enhanced Consensus-Based Clustering Model for Single-Cell Methylation Data by Reconstructing Cell-to-Cell Similarity.Frontiers in bioengineering and biotechnology · 2022Review
- Article
- Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations.Vascular health and risk management · 2021Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.
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