ArticleScientific reports2022
scCAN: single-cell clustering using autoencoder and network fusion.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SAKURA: a knowledge-guided approach to recovering important, rare signals from single-cell data.Genome biology · 2026Article
- A knowledge-guided approach to recovering important rare signals from high-dimensional single-cell data.bioRxiv : the preprint server for biology · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.Briefings in bioinformatics · 2025Article
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.Scientific reports · 2025Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.GigaScience · 2025Article
- ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.Genome biology · 2024Article
- scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
- CASCC: a co-expression-assisted single-cell RNA-seq data clustering method.Bioinformatics (Oxford, England) · 2024Article
- scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.Bioinformatics (Oxford, England) · 2024Article
- Integrative single-cell analysis: dissecting CD8 + memory cell roles in LUAD and COVID-19 via eQTLs and Mendelian Randomization.Hereditas · 2024Article
- Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.Nature communications · 2024Article
- Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.Briefings in bioinformatics · 2023Article
- A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.BMC bioinformatics · 2023Article
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
- Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data.Frontiers in medicine · 2022Article
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5 authors.
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Abstract
Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high-dimensionality of the data (tens of thousands of genes), and the high dropout rates all present substantial challenges in single-cell analysis. Here we introduce a new method, named single-cell Clustering using Autoencoder and Network fusion (scCAN), that can overcome these challenges to accurately segregate different cell types in large and sparse scRNA-seq data. In an extensive analysis using 28 real scRNA-seq datasets (more than three million cells) and 243 simulated datasets, we validate that scCAN: (1) correctly estimates the number of true cell types, (2) accurately segregates cells of different types, (3) is robust against dropouts, and (4) is fast and memory efficient. We also compare scCAN with CIDR, SEURAT3, Monocle3, SHARP, and SCANPY. scCAN outperforms these state-of-the-art methods in terms of both accuracy and scalability. The scCAN package is available at https://cran.r-project.org/package=scCAN . Data and R scripts are available at http://sccan.tinnguyen-lab.com/.
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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.