average.power.li {FDRsamplesize2}

Compute average power for RNA-Seq experiments assuming Poisson distribution

Description

Use the formula of Li et al (2013) to compute power for comparing RNA-seq expression across two groups assuming the Poisson distribution.

Usage

average.power.li(n, alpha, rho, mu0, w, type)

Arguments

n

per-group sample size

alpha

p-value threshold (scalar)

rho

fold-change, usual null hypothesis is that rho=1 (vector)

mu0

average count in control group (vector)

w

ratio of the total number of reads mapped between the two groups (scalar or vector)

type

type of test: "w" for Wald, "s" for score, "lw" for log-transformed Wald, "ls" for log-transformed score

Details

This function computes the average power for a series of two-sided tests defined by the input parameters. The power is based on the sample size formulas in equations (10-13) of Li et al (2013). Also, note that the null.effect is set to 1 in the examples because the usual null hypothesis is that the fold-change = 1.

Value

Average power estimate for multiple testing procedure

References

C-I Li, P-F Su, Y Guo, and Y Shyr (2013). Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution. Int J Comput Biol Drug Des 6(4).<doi:10.1504/IJCBDD.2013.056830>

See Also

power.li for more details about power calculation of data under Poisson distribution

Examples

rho = rep(c(1,1.25),c(900,100));
mu0 = rep(5,1000);
w = rep(0.5,1000);
average.power.li(n = 50, alpha = 0.05, rho = rho, mu0 = mu0, w = w, type = "w")