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

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dc.citation.articleNumbere1008012en_US
dc.citation.issueNumber7en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber16en_US
dc.contributor.authorMallory, Xian F.en_US
dc.contributor.authorEdrisi, Mohammadaminen_US
dc.contributor.authorNavin, Nicholasen_US
dc.contributor.authorNakhleh, Luayen_US
dc.date.accessioned2020-11-04T18:47:51Zen_US
dc.date.available2020-11-04T18:47:51Zen_US
dc.date.issued2020en_US
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.en_US
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.en_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1008012en_US
dc.identifier.urihttps://hdl.handle.net/1911/109504en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleAssessing the performance of methods for copy number aberration detection from single-cell DNA sequencing dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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