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Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets.

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  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
    • Publication Information:
      Publication: Oxford : Oxford University Press
      Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
    • Subject Terms:
      Single-Cell Analysis*/methods ; Neoplasms*/genetics ; Computational Biology*/methods ; Genomics*/methods ; DNA Copy Number Variations*; Humans ; Benchmarking ; Multiomics
    • 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.
      (© The Author(s) 2025. Published by Oxford University Press.)
    • References:
      Nature. 2022 Dec;612(7938):141-147. (PMID: 36352227)
      Nat Commun. 2024 Aug 6;15(1):6684. (PMID: 39107346)
      Nat Commun. 2022 Oct 20;13(1):6206. (PMID: 36266286)
      Nat Commun. 2023 Feb 25;14(1):1074. (PMID: 36841879)
      Nat Commun. 2020 Jan 3;11(1):89. (PMID: 31900397)
      Gigascience. 2021 Feb 16;10(2):. (PMID: 33590861)
      Nat Commun. 2017 May 05;8:15081. (PMID: 28474673)
      Cell. 2021 Apr 15;184(8):2239-2254.e39. (PMID: 33831375)
      Cancer Discov. 2021 Apr;11(4):916-932. (PMID: 33811124)
      Clin Transl Med. 2022 Mar;12(3):e694. (PMID: 35352511)
      Sci Adv. 2023 Jan 4;9(1):eabp8901. (PMID: 36598983)
      Neuro Oncol. 2021 Nov 2;23(11):1859-1871. (PMID: 33908609)
      Innovation (Camb). 2022 Oct 18;3(6):100342. (PMID: 36353677)
      Genome Res. 2016 Mar;26(3):376-84. (PMID: 26772196)
      Bioinformatics. 2007 Mar 15;23(6):657-63. (PMID: 17234643)
      Genome Biol. 2021 Feb 23;22(1):70. (PMID: 33622385)
      Ann Oncol. 2015 Jan;26(1):64-70. (PMID: 25319062)
      Cell Rep. 2014 Sep 11;8(5):1280-9. (PMID: 25159146)
      Nat Biotechnol. 2023 Mar;41(3):417-426. (PMID: 36163550)
      Nature. 2024 Sep;633(8028):127-136. (PMID: 39112709)
      Nature. 2022 Aug;608(7924):795-802. (PMID: 35978189)
      Nat Methods. 2015 Nov;12(11):1058-60. (PMID: 26344043)
      Nat Protoc. 2021 Jan;16(1):1-9. (PMID: 33288955)
      Bioinformatics. 2016 Sep 15;32(18):2847-9. (PMID: 27207943)
      Nature. 2022 Aug;608(7922):360-367. (PMID: 35948708)
      Annu Rev Immunol. 2021 Apr 26;39:583-609. (PMID: 33637019)
      Curr Genomics. 2010 Sep;11(6):387-96. (PMID: 21358982)
      Brief Bioinform. 2022 Jul 18;23(4):. (PMID: 35801503)
      Genome Res. 2015 May;25(5):714-24. (PMID: 25858951)
      Cell. 2021 Jan 21;184(2):404-421.e16. (PMID: 33357445)
      Nat Biotechnol. 2021 May;39(5):599-608. (PMID: 33462507)
      Cell Res. 2019 Sep;29(9):725-738. (PMID: 31273297)
      Cell Syst. 2022 Feb 16;13(2):183-193.e7. (PMID: 34731645)
      Bioinformatics. 2005 Nov 15;21(22):4084-91. (PMID: 16159913)
      Mol Cell. 2020 Nov 5;80(3):541-553.e5. (PMID: 33068522)
      Cell. 2017 Dec 14;171(7):1611-1624.e24. (PMID: 29198524)
      Cancer Cell. 2020 Dec 14;38(6):818-828.e5. (PMID: 33096021)
      Science. 2016 Apr 8;352(6282):189-96. (PMID: 27124452)
    • Grant Information:
      No.117005-AC2106/002 Zhejiang lab Development of Novel Functional Proteins Based on Databases and Artificial Intelligence; No. 31871332 National Natural Science Foundation of China
    • Contributed Indexing:
      Keywords: copy number aberrations; copy number alteration; copy number variations; loss of heterozygosity; single cell multi-omics; single-cell RNA sequencing
    • Publication Date:
      Date Created: 20250304 Date Completed: 20250511 Latest Revision: 20250511
    • Publication Date:
      20260130
    • Accession Number:
      PMC11879432
    • Accession Number:
      10.1093/bib/bbaf076
    • Accession Number:
      40037644