power.li {FDRsamplesize2}

Compute 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

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

w

ratio of the total number of reads mapped between the two groups

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(n = 88, alpha = 0.05, rho = 1.25, mu0 = 5, w = 0.5,type = "w")
# recapitulate 80% power in Table 1 of Li et al (2013)