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

Single-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 eit...

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Bibliographic Details
Published in:PLoS computational biology 2020-07, Vol.16 (7), p.e1008012-e1008012
Main Authors: Mallory, Xian F, Edrisi, Mohammadamin, Navin, Nicholas, Nakhleh, Luay
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Artificial chromosomes
Biology and Life Sciences
Cancer
Computer and Information Sciences
Computer science
Computer simulation
Copy number
Copy number variations
Datasets
Deoxyribonucleic acid
Development and progression
DNA
DNA sequencing
Gene sequencing
Genetic aspects
Genomes
Ground truth
Haplotypes
Methods
Mutation
Performance assessment
Phylogeny
Physical Sciences
Research and Analysis Methods
Risk factors
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Summary:Single-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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008012