average.power.hart: Compute average power for RNA-seq experiments assuming...

In FDRsamplesize2: Computing Power and Sample Size for the False Discovery Rate in Multiple Applications

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)

FDRsamplesize2 documentation built on May 29, 2024, 7:12 a.m.