Matching Confidence Masks with Experts Annotations for Estimates of Chromosomal Copy Number Alterations

Structural aberrations (SAs), gains or losses in large segments of genomes, are associated with several genetic disorders. The SAs are commonly called the copy number alterations (CNAs) and their identification/classification is required to identify diseases. Many methods have been proposed to estimate the breakpoints and segmental constants in the CNAs with highest precision using the most powerful technologies of hybridization. However, locations and lengths of CNAs estimated using well-elaborated methods are often contradictory due to extensive variability of measurements and performance of the algorithms. Still much less attention is given to the estimation accuracy and it is difficult to select the best estimator. In this work, we propose to modify the confidence masks replacing the skew Laplace distribution with the asymmetric exponential power distribution (AEP) to approximate the jitter distribution in CNAs. Next, the estimates obtained using different algorithms are matched with the annotations made by experts employing the improved masks. Finally, we specify the match confidence probability of each CNAs detector algorithm respect the experts estimates.