Abstract
Jitter is inherent to the breakpoints of measured genome somatic copy number alterations (SCNAs). Therefore, an analysis of jitter is required to reduce errors in the SCNA estimation. The SCNA measurements are accompanied with intensive noise that may cause errors and ambiguities in the breakpoint detection with low signal-to-noise ratios (SNRs). We show that the asymmetric exponential power distribution (AEPD) provides much better approximation to the jitter distribution than the earlier proposed discrete skew Laplace distribution. Furthermore, we confirm that (AEP) distribution its suitable for computing the confidence upper and lower boundary limits used to guarantee an existence of genomic changes with a required probability. We test some simulated SCNAs measurements by the upper and lower confidence bound masks with several probabilities.
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Muñoz–Minjares, J.U., Shmaliy, Y.S., Morales–Mendoza, L.J., Vite–Chavez, O. (2018). Jitter Approximation and Confidence Masks in Simulated SCNA Using AEP Distribution. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_27
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DOI: https://doi.org/10.1007/978-3-030-02840-4_27
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