Correcting estimates of DNA CNAs using improved confidence masks tuned to gold standard

Correcting estimates of DNA CNAs using improved confidence masks tuned to gold standard

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Copy number alterations (CNAs) are hallmarks of cancer, which are now been routinely measured by different techniques and used for diagnostic and prognostic purpose. Efficient and accurate detection of the breakpoint positions in heterogeneous cancer sample measured with intrinsic random noise and subjected to technical and biological biases is a challenging practical and methodological problem. To improve the CNA estimates, the authors present the probabilistic approach for breakpoints detection that gives confidence masks (the system of local segmentation profiles with confidence probabilities) tuned using experts estimates. The authors show that the asymmetric exponential power distribution matches well the uncertainties (jitter) in the breakpoint locations. The confidence upper and lower boundary masks for the breakpoint location are built using this function. The confidence masks are then tuned based on the medical expert annotations of the training set of the breakpoints obtained by the standard circular binary segmentation (CBS) algorithm. Comparison of modified confidence masks and experts annotations on the testing set of CNA profiles of neuroblastoma showed improvement of the CNA estimates.

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