ArticleGenome biology2024
aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- MINGL Quantifies Borders, Gradients, and Heterogeneity in Multicellular Tissue Organization.bioRxiv : the preprint server for biology · 2026Article
- Predicting complications and mortality in myocardial infarction patients using a graph neural network model.Scientific reports · 2026Article
- Microenvironment-aware spatial modeling for accurate inference of cell identity.Nucleic acids research · 2026Article
- Integrated bulk and single-cell transcriptomics with experimental validation to identify calmodulin-related prognostic genes in lung adenocarcinoma.American journal of cancer research · 2026Article
- JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification.PeerJ · 2026Article
- Dipeptidase-1-knockout mice develop invasive tumors with features of microsatellite-unstable colorectal cancer.JCI insight · 2025Article
- Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.GigaScience · 2025Article
- Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.Frontiers in immunology · 2025Review
- aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph.Genome biology · 2024Article
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Abstract
Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.
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