ArticleNature communications2021
GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- BayesRare: Bayesian mixture model for population-level rare cell type detection in multi-subject single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- SLECA: A Single-Cell Atlas of Systemic Lupus Erythematosus Enabling Rare-Cell Discovery Using Graph Transformer.Computational and structural biotechnology journal · 2026Article
- Revealing the Best Strategies for Rare Cell Type Detection in Multi-Sample Single-Cell Datasets.Genes · 2025Article
- A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.Genome biology · 2025Article
- Advancing biological understanding of cellular senescence with computational multiomics.Nature genetics · 2025Review
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data.Computational and structural biotechnology journal · 2024Article
- FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics.Nature protocols · 2024Review
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.Nature communications · 2024Article
- aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph.Genome biology · 2024Article
- MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.Nature communications · 2024Article
- TTD: Therapeutic Target Database describing target druggability information.Nucleic acids research · 2024Article
- Rarity: discovering rare cell populations from single-cell imaging data.Bioinformatics (Oxford, England) · 2023Article
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8 authors.
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Abstract
Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.
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