Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets
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Background
Copy number aberrations (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.
Results
Herein, we evaluated five state-of-the-art methods based on their performance in tumor vs 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. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools.
Conclusions
In summary, our study evaluated five state-of-the-art methods and found that Numbat outperformed others across most evaluation criteria. This study provides a valuable guideline for researchers to select and use the methods appropriately for their datasets.
