magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing.

• Published in: PLoS Computational Biology
• Main Authors: Zhenxing Guo, Daoyu Duan, Wen Tang, Julia Zhu, William S Bush, Liangliang Zhang, Xiaofeng Zhu, Fulai Jin, Hao Feng
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2024-02-01
• Online Access: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011875&type=printable

Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this work, we propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics.