title: Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments.
abstract: BACKGROUND
  RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.
  
  
  RESULTS
  In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method.
  
  
  CONCLUSIONS
  Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network ( http://cran.r-project.org ).
authors.affiliation: Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa, 50011, USA.
authors.forename: Ran
authors.initials: R
authors.lastname: Bi
authors.affiliation: Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa, 50011, USA. pliu@iastate.edu.
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D020407:Internet; D012313:RNA / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D018401:Sample Size; D017423:Sequence Analysis, RNA; D059467:Transcriptome; D014584:User-Computer Interface
chemical_list: D012313:RNA
keywords: Experimental design; FDR; Power analysis; RNA-seq; Sample size calculation