Sample Size Calculation of RNA-sequencing Experiment-A Simulation-Based Approach of TCGA DataDerek Shyr1 and Chung-I Li2*
- *Corresponding Author:
- Chung-I Li
Department of Applied Mathematics, National Chiayi University
Chiayi, Taiwan 60004, ROC
E-mail: [email protected]
Received date: May 22, 2014; Accepted date: June 26, 2014; Published date: June 30, 2014
Citation: Shyr D, Li CI (2014) Sample Size Calculation of RNA-sequencing Experiment-A Simulation-Based Approach of TCGA Data. J Biomet Biostat 5: 198. doi:10.4172/2155-6180.1000198
Copyright: © 2014 Shyr D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
Power and sample size calculation is an essential component of experimental design in biomedical research. For RNA-sequencing experiments, sample size calculations have been proposed based on mathematical models such as Poisson and negative binomial; however, RNA-seq data has exhibited variations, i.e. over-dispersion, that has caused past calculation methods to be under- or over-power. Because of this issue and the field’s lack of a simulation-based sample size calculation method for assessing differential expression analysis of RNA-seq data, we developed this method and applied it to three cancer sites from the Tumor Cancer Genome Atlas. Our results showed that each cancer site had its own unique dispersion distribution, which influenced the power and sample size calculation.