ArticleBriefings in bioinformatics2025
Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets.
Article in Briefings in bioinformatics, 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.
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- STGBench: sequencing-level spatial DNA-RNA simulation for multimodal and virtual cell-oriented benchmarking of genomic alterations.Briefings in bioinformatics · 2026Article
- Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- JAG1 expression in papillary thyroid cancer stem-like cells predicts poor prognosis and implicates angiogenesis.Discover oncology · 2026Article
- The dual role of whole-genome duplication: biological mechanisms, functional consequences, and detection advances.Acta biochimica et biophysica Sinica · 2025Review
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
Copy number alterations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor versus normal cell classification; CNAs profile accuracy, tumor subclone inference, and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.
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