ArticleNature communications2025
Benchmarking scRNA-seq copy number variation callers.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spatial Integration of Protein and Chromosomal States Reveals Early Copy-Number Changes and Genotype-Associated Immune Neighborhoods in Serous Ovarian Cancer Evolution.Cancer discovery · 2026Article
- HisToSpatialCNV: an interpretable deep learning method predicting spatial copy number variations from histopathology images.Nature biomedical engineering · 2026Article
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.Nature communications · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant.bioRxiv : the preprint server for biology · 2026Article
- High-resolution single-cell sequencing of trans-spliced mRNA.Nature protocols · 2026Review
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.bioRxiv : the preprint server for biology · 2026Article
- Quantitative coupling of clonal CNV evolution and spatially restricted malignant states identifies MFGE8 as a candidate late-state-associated target in breast cancer.Journal of translational medicine · 2026Article
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Benchmarking scRNA-seq copy number variation callers.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Copy number variations (CNVs), the gain or loss of genomic regions, are associated with disease, especially cancer. Single cell technologies offer new possibilities to capture within-sample heterogeneity of CNVs and identify subclones relevant for tumor progression and treatment outcome. Several computational tools have been developed to identify CNVs from scRNA-seq data. However, an independent benchmarking of them is lacking. Here, we evaluate six popular methods in their ability to correctly identify ground truth CNVs, euploid cells and subclonal structures in 21 scRNA-seq datasets. We discover dataset-specific factors influencing the performance, including dataset size, the number and type of CNVs in the sample and the choice of the reference dataset. Methods which include allelic information perform more robustly for large droplet-based datasets, but require higher runtime. Furthermore, the methods differ in their additional functionalities. We offer a benchmarking pipeline to identify the optimal method for new datasets, and improve methods' performance.
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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.