Journal Article

Power analysis for RNA-Seq differential expression studies

Yu, Lianbo, Brock, Guy, Fernandez, Soledad

2017

Overview

Background Sample size calculation and power estimation are essential components of experimental designs in biomedical research. It is very challenging to estimate power for RNA-Seq differential expression under complex experimental designs. Moreover, the dependency among genes should be taken into account in order to obtain accurate results. Results In this paper, we propose a simulation based procedure for power estimation using the negative binomial distribution and assuming a generalized linear model (at the gene level) that considers the dependence between gene expression level and its variance (dispersion) and also allows equal or unequal dispersion across conditions. We compared the performance of both Wald test and likelihood ratio test under different scenarios. The null distribution of the test statistics was simulated for the desired false positive control to avoid excess false positives with the usage of an asymptotic chi-square distribution. We applied this method to the TCGA breast cancer data set. Conclusions We provide a framework for power estimation of RNA-Seq data. The proposed procedure is able to properly control the false positive error rate at the nominal level.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Algorithms / Asymptotic methods / Binomial Distribution / Bioinformatics / Biomedical and Life Sciences / Breast cancer / Breast Neoplasms - genetics / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Design / Experiments / False Positive Reactions / Gene expression / Gene Expression Profiling / Generalized linear models / Genomes / Humans / Hypotheses / Life Sciences / Likelihood ratio / Likelihood ratio test / Linear Models / Methodology / Methodology Article / Methods / Microarrays / Power / Ribonucleic acid / RNA / RNA-Seq / Sample size / Sequence Analysis, RNA / Statistical analysis / Statistical models / Statistical tests / Statistics as Topic - methods / Studies / Transcription / Transcriptome analysis / Wald test