ArticleCell reports methods2025
Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- FFPE-CUTAC: A Single Assay, Multiple Layers.bioRxiv : the preprint server for biology · 2026Article
- A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.Nature reviews. Cancer · 2026Review
- Beyond chromatin accessibility: bulk ATAC-seq as an integrative assay to portray genomes and epigenomes.NAR genomics and bioinformatics · 2026Article
- Spatial-aware detection of copy number alterations from spatial transcriptomics using SpaCNA.Nature communications · 2026Article
- scCirclehunter delineates ecDNA-containing cells using single-cell ATAC-seq, with a focus on glioblastoma.Cell discovery · 2025Article
- A hierarchical, count-based model highlights challenges in scATAC-seq data analysis and points to opportunities to extract finer-resolution information.Genome biology · 2025Article
- Discovery of candidate functional non-coding mutations in acute myeloid leukemia using single-cell chromatin accessibility sequencing.Communications biology · 2025Article
- TGF-β-driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states.Frontiers in immunology · 2025Review
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Authors and funding
4 authors.
Funding
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Abstract
Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.
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