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.

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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

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article

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

Malcolm Moos, Wanqiu Chen, Shixiu Wu, Hongjin Wu et al.
3 citations
Precision Clinical Medicine
Cancer Genomics and Diagnostics
2.81
article

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

Malcolm Moos, Wanqiu Chen, Shixiu Wu, Hongjin Wu, Li Tai Fang, Andrew A. Farmer, Wendell Jones, Chunlin Xiao, Bin Zhu, Christopher E. Mason, Xin Chen, Zhong Chen, Charles Wang, Xiaowen Zhang, Wei Xiong, Shusheng Gong
article en
3 citations

Abstract

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.

Precision Clinical MedicineVol. 8(2)
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)
National Institutes of Health, U.S. National Library of Medicine
Openalex Percentile: Top 9%
Cancer Genomics and Diagnostics
2.81
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