Power Calculator for Detecting Allelic Imbalance Using Hierarchical Bayesian Model

Abstract

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.