| average.power.hart {FDRsamplesize2} | R Documentation |
Compute average power for RNA-seq experiments assuming Negative Binomial distribution
Description
Compute average power for RNA-seq experiments assuming Negative Binomial distribution
Usage
average.power.hart(n, alpha, log.fc, mu, sig)
Arguments
n |
per-group sample size (scalar) |
alpha |
p-value threshold (scalar) |
log.fc |
log fold-change (vector), usual null hypothesis is log.fc=0 |
mu |
read depth per gene (vector, same length as log.fc) |
sig |
coefficient of variation (CV) per gene (vector, same length as log.fc) |
Details
The power function is based on equation (1) of Hart et al (2013). It assumes a Negative Binomial model for RNA-seq read counts and equal sample size per group.
Value
Average power estimate for multiple testing procedure
References
SN Hart, TM Therneau, Y Zhang, GA Poland, and J-P Kocher (2013). Calculating Sample Size Estimates for RNA Sequencing Data. Journal of Computational Biology 20: 970-978.
See Also
power.hart for more details about power calculation of data under Negative Binomial distribution. The power calculation is based on asymptotic normal approximation.
Examples
logFC = log(rep(c(1,2),c(900,100)));
mu = rep(5,1000);
sig = rep(0.6,1000);
average.power.hart(n = 50, alpha = 0.05,log.fc = logFC, mu = mu, sig = sig)