Power analysis for RNA-Seq differential expression studies

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.

Researchers from The Ohio State University 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. They 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. The researchers applied this method to the TCGA breast cancer data set.

Mean-dispersion functional form for simulations


DESeq2 method was applied on a pilot data of unpublished canine thyroid RNA-Seq data set for setting up simulation parameters. The plot shows the estimated mean-dispersion function form (red dots) relative to the mean of the normalized counts. Black dots represent per-gene estimates of the dispersion while blue dots represent moderated estimates calculated by DESeq2. The fitted functional form and a lower and higher dependency functional forms were used in the simulation studies

Yu L, Fernandez S, Brock G. (2017) Power analysis for RNA-Seq differential expression studies. BMC Bioinformatics 18(1):234. [article]

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