n.fdr.poisson {FDRsamplesize2}

Sample size calculation for Poisson data

Description

Find the sample size needed to have a desired false discovery rate and average power for a large number of two-group comparisons under Poisson distribution.

Usage

n.fdr.poisson(fdr, pwr, rho, mu0, w, type, pi0.hat = "BH")

Arguments

fdr

desired FDR (scalar numeric)

pwr

desired average power (scalar numeric)

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

type

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

pi0.hat

method to estimate proportion pi0 of tests with true null, including: "HH" (p-value histogram height) , "HM" (p-value histogram mean), "BH" (Benjamini & Hochberg 1995), "Jung" (Jung 2005)

Value

A list with the following components:

n

per-group sample size estimate

computed.avepow

average power

desired.avepow

desired average power

desired.fdr

desired FDR

input.pi0

proportion of tests with a true null hypothesis

alpha

fixed p-value threshold for multiple testing procedure

n.its

number of iteration

max.its

maximum number of iteration, default is 50

n0

lower limit for initial sample size range

n1

upper limit for initial sample size range

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

rho = rep(c(1,1.25),c(900,100));
mu0 = rep(5,1000);
w = rep(0.5,1000);
n.fdr.poisson(fdr = 0.1, pwr = 0.8, rho = rho, mu0 = mu0, w = w, type = "w", pi0.hat = "BH")