A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data
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.
Authors
- Malcolm Moos (ORCID: https://orcid.org/0000-0002-9575-9938)
- Wanqiu Chen (ORCID: https://orcid.org/0000-0003-3706-7834)
- Shixiu Wu (ORCID: https://orcid.org/0000-0001-8286-6673)
- Hongjin Wu (ORCID: https://orcid.org/0000-0002-2053-7441)
- Li Tai Fang (ORCID: https://orcid.org/0000-0003-3201-5162)
- Andrew A. Farmer (ORCID: https://orcid.org/0000-0002-6170-4402)
- Wendell Jones (ORCID: https://orcid.org/0000-0002-9676-5387)
- Chunlin Xiao
- Bin Zhu (ORCID: https://orcid.org/0000-0003-0172-5516)
- Christopher E. Mason (ORCID: https://orcid.org/0000-0002-1850-1642)
- Xin Chen (ORCID: https://orcid.org/0000-0002-5310-5426)
- Zhong Chen (ORCID: https://orcid.org/0000-0003-2444-8216)
- Charles Wang
- Xiaowen Zhang (ORCID: https://orcid.org/0000-0001-9016-8947)
- Wei Xiong (ORCID: https://orcid.org/0000-0002-2932-6886)
- Shusheng Gong (ORCID: https://orcid.org/0000-0002-1429-2118)
Institutions
- National Institutes of Health (US)
- Center for Biologics Evaluation and Research (US)
- Capital Medical University (CN)
- Cornell University (US)
- Loma Linda University (US)
- AbbVie (United States) (US)
- Quzhou City People's Hospital (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- IQVIA (United States) (US)
- National Center for Biotechnology Information (US)
- Takara (Sweden) (SE)
- Quzhou University (CN)
- Freenome (United States) (US)
- Takara (United States) (US)
- National Cancer Institute (US)
- Beijing Friendship Hospital (CN)
- Department of Health and Human Services (US)
- Weill Cornell Medicine (US)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Precision Clinical Medicine
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1093/pcmedi/pbaf011
- Citations
- 3
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 2.81
Funders
- National Institutes of Health
- U.S. National Library of Medicine