Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

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dc.citation.articleNumbere1008012
dc.citation.issueNumber7
dc.citation.journalTitlePLoS Computational Biology
dc.citation.volumeNumber16
dc.contributor.authorMallory, Xian F.
dc.contributor.authorEdrisi, Mohammadamin
dc.contributor.authorNavin, Nicholas
dc.contributor.authorNakhleh, Luay
dc.date.accessioned2020-11-04T18:47:51Z
dc.date.available2020-11-04T18:47:51Z
dc.date.issued2020
dc.description.abstractSingle-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods–Ginkgo, HMMcopy, and CopyNumber–on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.
dc.identifier.citationMallory, Xian F., Edrisi, Mohammadamin, Navin, Nicholas, et al.. "Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data." <i>PLoS Computational Biology,</i> 16, no. 7 (2020) Public Library of Science: https://doi.org/10.1371/journal.pcbi.1008012.
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1008012
dc.identifier.urihttps://hdl.handle.net/1911/109504
dc.language.isoeng
dc.publisherPublic Library of Science
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAssessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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