scholarly journals Power calculator for detecting allelic imbalance using hierarchical Bayesian model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Katrina Sherbina ◽  
Luis G. León-Novelo ◽  
Sergey V. Nuzhdin ◽  
Lauren M. McIntyre ◽  
Fabio Marroni
Keyword(s):  
Differential Expression ◽  
Bayesian Model ◽  
Allelic Imbalance ◽  
Type I Error ◽  
Type I ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Allele Specific ◽  
Python Package

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.

scholarly journals Power Calculator for Detecting Allelic Imbalance Using Hierarchical Bayesian Model

2021 ◽  
Author(s):  
Katrina Sherbina ◽  
Luis G Leon-Novelo ◽  
Sergey V Nuzhdin ◽  
Lauren M McIntyre ◽  
Fabio Marroni
Keyword(s):  
Simulation Study ◽  
Bayesian Model ◽  
Allelic Imbalance ◽  
Type I Error ◽  
Type I ◽  
Type Ii ◽  
Hierarchical Bayesian Model ◽  
Type Ii Error ◽  
Allele Specific ◽  
Python Package

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Statistical methods for testing in this area exist, with impacts of explosive type I error in the presence of bias well understood. However, for study design, the more important and understudied problem is the type II error and power. As the biological questions for this type of study explode, and the costs of the technology plummet, what is more important: reads or replicates? How small of an interaction can be detected while keeping the type I error at bay? Here we present a simulation study that demonstrates that the proper model can control type I error below 5% for most scenarios. 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 is needed to detect a 20% or 30% difference in AI between conditions with comparable power but these reads need to be divided amongst 8 replicates. 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 tailored to their own specific study needs.

scholarly journals Integrating spatial synchrony/asynchrony of population distribution into stock assessment models: a spatial hierarchical Bayesian statistical catch-at-age approach

2016 ◽  
Vol 73 (7) ◽  
pp. 1725-1738 ◽  
Author(s):  
Yan Jiao ◽  
Rob O'Reilly ◽  
Eric Smith ◽  
Don Orth ◽  
Keyword(s):  
Relative Abundance ◽  
Bayesian Model ◽  
Population Distribution ◽  
Stock Assessment ◽  
Spatial Models ◽  
Reference Points ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Spatial Synchrony ◽  
Abundance Indices

Abstract In many marine fisheries assessments, population abundance indices from surveys collected by different states and agencies do not always agree with each other. This phenomenon is often due to the spatial synchrony/asynchrony. Those indices that are asynchronous may result in discrepancies in the assessment of temporal trends. In addition, commonly employed stock assessment models, such as the statistical catch-at-age (SCA) models, do not account for spatial synchrony/asynchrony associated with spatial autocorrelation, dispersal, and environmental noise. This limits the value of statistical inference on key parameters associated with population dynamics and management reference points. To address this problem, a set of geospatial analyses of relative abundance indices is proposed to model the indices from different surveys using spatial hierarchical Bayesian models. This approach allows better integration of different surveys with spatial synchrony and asynchrony. We used Atlantic weakfish (Cynoscion regalis) as an example for which there are state-wide surveys and expansive coastal surveys. We further compared the performance of the proposed spatially structured hierarchical Bayesian SCA models with a commonly used Bayesian SCA model that assumes relative abundance indices are spatially independent. Three spatial models developed to mimic different potential spatial patterns were compared. The random effect spatially structured hierarchical Bayesian model was found to be better than the commonly used SCA model and the other two spatial models. A simulation study was conducted to evaluate the uncertainty resulting from model selection and the robustness of the recommended model. The spatially structured hierarchical Bayesian model was shown to be able to integrate different survey indices with/without spatial synchrony. It is suggested as a useful tool when there are surveys with different spatial characteristics that need to be combined in a fisheries stock assessment.

scholarly journals DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates

2017 ◽  
Vol 33 (19) ◽  
pp. 3018-3027
Author(s):  
Hao Peng ◽  
Yifan Yang ◽  
Shandian Zhe ◽  
Jian Wang ◽  
Michael Gribskov ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Differentially Expressed ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model
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