power.hart: Compute Power for RNA-seq Experiments Assuming Negative...

In FDRsampsize: Compute Sample Size that Meets Requirements for Average Power and FDR

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.

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

 power.hart       # show the power function
 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  
 res=fdr.sampsize(fdr=0.1,
                  ave.pow=0.8,
                  pow.func=power.hart,
                  eff.size=rep(c(log(2),0),c(100,900)),
                  null.effect=0,mu=5,sig=1)
 res

FDRsampsize documentation built on May 2, 2019, 9:14 a.m.