RnaSeqSampleSize: Real data based sample size estimation for RNA-Seq with complex design
Shilin Zhao,Yu Shyr Vanderbilt University Medical Center
Abstract
One of the most important components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. We developed a sample size and power estimation package named RnaSeqSampleSize, which utilized the genes expression patterns in real RNA-seq data with similar conditions to provide a more accurate and reliable estimation. The package was published two years ago and widely used by investigators in different areas. To extend its application in complex experiments such as multiple treatment conditions or designing with covariates, we proposed generalized linear model in the package. We also integrated TCGA and recount2 datasets to provide better and more comprehensive reference datasets. At last, parallel computing is now supported by the package to improve performance in large dataset or complex design. In summary, RnaSeqSampleSize provides an easy-to-use and reliable tool for real-data based sample size estimation for RNA-Seq experiments with complex design.
Keywords: RNA-Seq,Power estimation,Sample Size,Bioinformatics