ArticleNature communications2024
scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.
Article in Nature communications, 2024. 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.
- Specificity-driven graph learning for the identification of rare cell states.Advanced biotechnology · 2026Article
- 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
- PalmaClust: A graph-fusion framework leveraging the Palma ratio for robust ultra-rare cell type detection in scRNA-seq data.bioRxiv : the preprint server for biology · 2026Article
- scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 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
- From microscopes to maps: enabling a new era of open and reproducible data sharing in intravital imaging.The EMBO journal · 2026Article
- A deep learning representation and spatial Bayesian cell-type deconvolution for spatial transcriptomics.PeerJ · 2026Article
- Revealing the Best Strategies for Rare Cell Type Detection in Multi-Sample Single-Cell Datasets.Genes · 2025Article
- Non-canonical NOTCH1 signaling regulates ferroptosis vulnerability in dormant lung cancer cells with stable resistance.Cell death & disease · 2025Article
- A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.Genome biology · 2025Article
- Single-cell sequencing and organoids: applications in organ development and disease.Molecular biomedicine · 2025Review
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction.Interdisciplinary sciences, computational life sciences · 2025Article
- Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions.Analytical chemistry · 2025Review
- Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.Briefings in bioinformatics · 2024Article
- scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.Nature communications · 2024Article
- Review
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7 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.
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