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
Materias:	Allelic imbalance Type I error Power Simulation Allele specific reads Biological replicates Medicine R Biology (General) QH301-705.5 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/67038a5f99e64e0bba53a5bbb18ed566
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spelling	oai:doaj.org-article:67038a5f99e64e0bba53a5bbb18ed5662021-11-28T12:25:14ZPower calculator for detecting allelic imbalance using hierarchical Bayesian model10.1186/s13104-021-05851-x1756-0500https://doaj.org/article/67038a5f99e64e0bba53a5bbb18ed5662021-11-01T00:00:00Zhttps://doi.org/10.1186/s13104-021-05851-xhttps://doaj.org/toc/1756-0500Abstract 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.Katrina SherbinaLuis G. León-NoveloSergey V. NuzhdinLauren M. McIntyreFabio MarroniBMCarticleAllelic imbalanceType I errorPowerSimulationAllele specific readsBiological replicatesMedicineRBiology (General)QH301-705.5Science (General)Q1-390ENBMC Research Notes, Vol 14, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Allelic imbalance Type I error Power Simulation Allele specific reads Biological replicates Medicine R Biology (General) QH301-705.5 Science (General) Q1-390
spellingShingle	Allelic imbalance Type I error Power Simulation Allele specific reads Biological replicates Medicine R Biology (General) QH301-705.5 Science (General) Q1-390 Katrina Sherbina Luis G. León-Novelo Sergey V. Nuzhdin Lauren M. McIntyre Fabio Marroni Power calculator for detecting allelic imbalance using hierarchical Bayesian model
description	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.
format	article
author	Katrina Sherbina Luis G. León-Novelo Sergey V. Nuzhdin Lauren M. McIntyre Fabio Marroni
author_facet	Katrina Sherbina Luis G. León-Novelo Sergey V. Nuzhdin Lauren M. McIntyre Fabio Marroni
author_sort	Katrina Sherbina
title	Power calculator for detecting allelic imbalance using hierarchical Bayesian model
title_short	Power calculator for detecting allelic imbalance using hierarchical Bayesian model
title_full	Power calculator for detecting allelic imbalance using hierarchical Bayesian model
title_fullStr	Power calculator for detecting allelic imbalance using hierarchical Bayesian model
title_full_unstemmed	Power calculator for detecting allelic imbalance using hierarchical Bayesian model
title_sort	power calculator for detecting allelic imbalance using hierarchical bayesian model
publisher	BMC
publishDate	2021
url	https://doaj.org/article/67038a5f99e64e0bba53a5bbb18ed566
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Power calculator for detecting allelic imbalance using hierarchical Bayesian model

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