Abstract
BackgroundSample 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.ResultsIn 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.ConclusionsWe 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.
Highlights
Sample size calculation and power estimation are essential components of experimental designs in biomedical research
Sample size calculations and power estimation are still some of the key issues in designing RNA-Seq experiments, but face some new challenges given the nature of RNA-Seq data
Simulations Parameter settings Count data were simulated from a negative binomial distribution under two experimental conditions with equal dispersion parameters or unequal dispersion parameters between conditions
Summary
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. Sample size calculations and power estimation are still some of the key issues in designing RNA-Seq experiments, but face some new challenges given the nature of RNA-Seq data. Several specialized software packages have been developed to model RNA-Seq data based on the negative binominal distribution. Robinson et al [7] developed the R package edgeR, which provides an exact test for two group comparisons initially and was expanded to allow multifactor designs by a generalized linear model. Love et al [8] developed the R package DESeq for differential expression analysis, which provides shrinkage estimators for both log fold change and dispersion by imposing a hierarchical model on them
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