Critical evaluation of CNA estimators for DNA data using matching confidence masks and WGS technology

A big number of different estimators have been proposed during decades to detect the Copy Number Alterations (CNA) in Deoxyribonucleic Acid (DNA) structures and indicate various diseases, including cancer. Due to the different cost functions and wide variations in DNA probes, the estimates in some cases turn out to be incompatible with each other. Therefore, each DNA profile is often supplied with a 'gold standard' provided by medical experts according to their expertise. In this paper, we test various CNAs estimators based on the Whole Genome Sequencing (WGS) and select the best one using probabilistic confidence masks. The masks are created for the segmental Gaussian noise and asymmetric exponential power distribution in the breakpoints. The estimates produced by the algorithms are compared at several probability levels. A complete statistical analysis of the CNA structures is carried out by varying the probability of the breakpoint existence. The best estimator is selected for each of the DNA profiles.