A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data

Abstract

Background: Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, several scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically.

Methods: We benchmarked five commonly used scCNV inference methods HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms using data from our previous multicenter study. We evaluated scCNV performance further using scRNA-seq datasets derived from mixed samples consisting of five human lung adenocarcinoma cell lines and also sequenced tissues from a small cell lung cancer patient and used the data to validate our findings with a clinical scRNA-seq dataset.

Results: We found that the sensitivity and specificity of the five scCNV inference methods varied, depending on the selection of reference data, sequencing depth, and read length. CopyKAT and CaSpER outperformed other methods overall, while inferCNV, sciCNV, and CopyKAT performed better than other methods in subclone identification. We found that batch effects significantly affected the performance of subclone identification in mixed datasets in most methods we tested.

Conclusion: Our benchmarking study revealed the strengths and weaknesses of each of these scCNV inference methods and provided guidance for selecting the optimal CNV inference method using scRNA-seq data.

Keywords

scRNA-seq / RNA-seq / copy number variation (CNV) inference / scRNA-seq CNV methods / benchmarking

Acknowledgements

The authors would like to thank Ms. Diana Ho and Ms. Adriana Lopez of the LLU Center for Genomics for their administrative support, particularly in coordinating the Zoom conference calls for the project. The authors would like to thank Dr. Feng Zeng for his help with editing Figure 1 and Dr. Lijuan Song for her assistance with reference citation proof-reading. The genomic work carried out at the LLU Center for Genomics was funded in part by the Ardmore Institute of Health grant 2150141 (CW) and Dr. Charles A. Sims' gift to LLU Center for Genomics. The work of Chunlin Xiao was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health.

Author contributions

Xin Chen (Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Writing - review & editing), Li Tai Fang (Investigation, Validation, Writing - review & editing), Zhong Chen (Writing - review & editing), Wanqiu Chen (Writing - review & editing), Hongjin Wu (Writing - review & editing), Bin Zhu (Writing - review & editing), Malcolm Moos Jr (Writing - review & editing), Andrew Farmer (Writing - review & editing), Xiaowen Zhang (Writing - review & editing), Wei Xiong (Writing - review & editing), Shusheng Gong (Writing - review & editing), Wendell Jones (Writing - review & editing), Christopher E. Mason (Writing - review & editing), Shixiu Wu (Writing - review & editing), Chunlin Xiao (Writing - review & editing), Charles Wang (Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing - original draft, Writing - review & editing)

Supplementary data

Supplementary data is available at PCMEDI online.

Conflicts of interest

Andrew Farmer is an employee of Takara Bio USA, Inc., and Wendell Jones is an employee of IQVIA Laboratories Genomics. All other authors claim there are no conflicts of interest. The views presented in this article do not necessarily reflect the current or future opinion or policy of the US Food and Drug Administration. Any mention of commercial products is for clarification and not intended as an endorsement. In addition, as the Editorial Board Member of Precision Clinical Medicine, the author Christopher E Mason and the corresponding author Charles Wang were blinded from reviewing and making decisions on this manuscript.

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