Abstract
BackgroundOne of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes.ResultsTo solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at https://cqs.app.vumc.org/shiny/RnaSeqSampleSize/.ConclusionsRnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.
Highlights
One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation
In earlier RNA-Seq studies, the analysis was based on Poisson distribution, because RNA-Seq data can be represented as read counts [4,5,6]
The detailed feature list of RnaSeqSampleSize package can be observed in Fig. 1: Sample size estimation with single average read count and dispersion RnaSeqSampleSize was developed based on the sample size and power estimation methods described in the previous study [10], and it greatly improved the compatibility and efficiency of older methods
Summary
We developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/ RnaSeqSampleSize/
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