التفاصيل البيبلوغرافية
| العنوان: |
magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing. |
| المؤلفون: |
Guo, Zhenxing, Duan, Daoyu, Tang, Wen, Zhu, Julia, Bush, William S., Zhang, Liangliang, Zhu, Xiaofeng, Jin, Fulai, Feng, Hao |
| المصدر: |
PLoS Computational Biology; 2/12/2024, Vol. 20 Issue 2, p1-13, 13p |
| مصطلحات موضوعية: |
RNA methylation, RNA analysis, NUCLEOTIDE sequencing, MAGPIES, RNA modification & restriction |
| مستخلص: |
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. Author summary: Sample size and sequencing depth are two essential quantitative factors determined prior to high throughput sequencing experiments, for statistical power maximization with limited budget. Due to the complex structure of data from m6A RNA methylation sequencing, analytical derivations for both quantities remain challenging in experimental designs. In response to this challenge, we propose a simulation-based statistical framework, together with a user-friendly R/Bioconductor package magpie, to comprehensively assess the power of the differential m6A methylation detection at varied sample sizes, effect sizes, baseline expression levels, and sequencing depths. Using in-silico synthetic data that mimic real data well, magpie provides several major evaluation metrics to assist users in study design and statistical power evaluation. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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