Performance comparison of Accucopy, Sequenza, and ControlFreeC

Abstract

Copy number alterations (CNAs) are an important type of genomic aberrations. It plays an important role in tumor pathogenesis and progression of cancer. It is important to detect regions of the cancer genome where copy number changes occur, which may provide clues that drive cancer progression. Deep sequencing technology provides genomic data at single-nucleotide resolution and is considered a better technique for detecting CNAs. There are currently many CNA-detection algorithms developed for whole genome sequencing (WGS) data. However, their detection capabilities have not been systematically investigated. Therefore, we selected three algorithms: Accucopy, Sequenza, and ControlFreeC, and applied them to data simulated under different settings. The results indicate that: 1) the correct inference of tumor sample purity is crucial to the inference of CNAs. If the tumor purity is wrongly inferred, the CNA detection will fail. 2) Higher sequencing depth and abundance of CNAs can improve performance. 3) Under the settings tested (sequencing depth at 5X or 30X, purity from 0.1 to 0.9, existence of subclones or not), Accucopy is the best-performing algorithm overall. For coverage=5X samples, ControlFreeC requires tumor purity to be above 50% to perform well. Sequenza can only perform well in high-coverage and more-CNA samples.