power.li: Compute Power for RNA-Seq Experiments Assuming Poisson...

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

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

power.li (n, alpha, rho, mu0, w = 1, type = "w") 

Arguments

n	per-group sample size
alpha	p-value threshold
rho	fold-change, usual null hypothesis is that rho=1
mu0	average count in control group
w	ratio of total number of
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 power for each of 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

vector of power estimates for two-sided tests

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

Examples

 power.li      # show the power function
 power.li(88,0.05,1.25,5,0.5,"w")  # recapitulate 80% power in Table 1 of Li et al (2013)
 res=fdr.sampsize(fdr=0.1,
                  ave.pow=0.8,
                  pow.func=power.li,
                  eff.size=rep(c(1.5,1),c(100,900)),
                  null.effect=1,
                  mu0=5,w=1,type="w")
 res

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