title: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data.
abstract: As RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study's optimal sample size is now a vital step in experimental design. Current methods for calculating a study's required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.
authors.forename: Chung-I
authors.initials: CI
authors.lastname: Li
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
mesh_terms: D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016014:Linear Models; D040901:Proteomics; D012313:RNA / Q000737:chemistry; D018401:Sample Size; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA