Power calculator for detecting allelic imbalance using hierarchical Bayesian model

Abstract Objective Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between c...

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Autores principales: Katrina Sherbina, Luis G. León-Novelo, Sergey V. Nuzhdin, Lauren M. McIntyre, Fabio Marroni
Formato: article
Lenguaje:EN
Publicado: BMC 2021
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R
Acceso en línea:https://doaj.org/article/67038a5f99e64e0bba53a5bbb18ed566
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Sumario:Abstract Objective Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates? Results We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.