| power.hart {FDRsamplesize2} | R Documentation |
Compute power for RNA-seq experiments assuming Negative Binomial distribution
Description
Use the formula of Hart et al (2013) to compute power for comparing RNA-seq expression across two groups assuming a Negative Binomial distribution. The power calculation is based on asymptotic normal approximation.
Usage
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
This 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
Vector of power estimates for the set of two-sided tests
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.
Examples
n.hart = 2*(qnorm(0.975)+qnorm(0.9))^2*(1/20+0.6^2)/(log(2)^2) # Equation (6) of Hart et al
power.hart(n.hart,0.05,log(2),20,0.6) # Recapitulate 90% power