Power analysis for RNA-Seq differential expression studies

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 234
• Main Authors: Yu, Lianbo, Fernandez, Soledad, Brock, Guy
• Format: Journal Article
• Language: English
• Published: England BioMed Central 03.05.2017; BMC
• Subjects: Asymptotic methods; Asymptotic properties; Binomial Distribution; Bioinformatics; Breast cancer; Breast Neoplasms - genetics; Cancer; Computer simulation; Design; Dispersion; Experiments; False Positive Reactions; Gene expression; Gene Expression Profiling; Generalized linear models; Genes; Genomes; Humans; Hypotheses; Likelihood ratio; Likelihood ratio test; Linear Models; Mathematical models; Methodology; Methods; Permissible error; Power; Ribonucleic acid; RNA; RNA-Seq; Sample size; Sequence Analysis, RNA; Stability; Statistical analysis; Statistical tests; Statistics; Statistics as Topic - methods; Studies; Transcription; Variance; Wald test; Wald test; RNA-Seq; Likelihood ratio test; Power
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1648-2

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. 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. 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.