ArticlePLoS computational biology2020
Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 55 citations in OpenAlex.
- CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- High-resolution detection of copy number alterations in single cells with HiScanner.Nature communications · 2025Article
- High-resolution detection of copy number alterations in single cells with HiScanner.bioRxiv : the preprint server for biology · 2025Article
- CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution.PLoS computational biology · 2025Article
- Single-cell copy number calling and event history reconstruction.Bioinformatics (Oxford, England) · 2025Article
- DICE: fast and accurate distance-based reconstruction of single-cell copy number phylogenies.Life science alliance · 2025Article
- SCGclust: Single Cell Graph clustering using graph autoencoders integrating SNVs and CNAs.bioRxiv : the preprint server for biology · 2025Article
- Single-cell somatic copy number variants in brain using different amplification methods and reference genomes.Communications biology · 2024Article
- Detection of mosaic and population-level structural variants with Sniffles2.Nature biotechnology · 2024Article
- SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics.Bioinformatics (Oxford, England) · 2024Article
- NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model.Algorithms for molecular biology : AMB · 2024Article
- CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution.bioRxiv : the preprint server for biology · 2024Article
- scAbsolute: measuring single-cell ploidy and replication status.Genome biology · 2024Article
- Computational immunogenomic approaches to predict response to cancer immunotherapies.Nature reviews. Clinical oncology · 2024Review
- Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data.Genome research · 2023Article
- Single-cell somatic copy number variants in brain using different amplification methods and reference genomes.bioRxiv : the preprint server for biology · 2023Article
- epiAneufinder identifies copy number alterations from single-cell ATAC-seq data.Nature communications · 2023Article
- Review
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.
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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.