Abstract
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.
Highlights
Acquired mutations are the main causes of cancer [1,2,3]
Copy number aberrations (CNAs), refer to evolutionary events that act on cancer genomes by deleting segments of the genomes or introducing new copies of existing segments
We simulated single cells whose coverage variabilities mimic those produced by three single-cell sequencing technologies (MALBAC, DOP-PCR and TnBC) [39] that have been used for CNA detection
Summary
Acquired mutations are the main causes of cancer [1,2,3]. Copy number aberrations (CNAs) are one such type of acquired mutations and have been implicated in over-regulating oncogenes or down-regulating tumor suppressor genes [4]. In the past two decades, a wide array of DNA technologies have been used to detect CNAs, among which the three most widely used are array Comparative Genomic Hybridization (aCGH), Generation Sequencing (NGS), and single-cell sequencing [5]. Array CGH [6] is a cytogenetic approach that uses fluorescent dyes on the test (tumor) and reference (normal) samples, and applies them to a microarray, which is an array of probes. HMMcopy [8] was designed to ameliorate the problem of outliers and uses a Hidden Markov Model (HMM) to divide the genome into piecewise fixed segments in order to make inferences on CNAs. since aCGH data is limited in resolution and throughput [11], as well as suffers from a hybridization bias problem, it is not the optimal technology to detect CNAs for cancer samples
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