ssize.fdr (version 1.3)

ssize.twoSamp: Sample Size Calculations for Two-Sample Microarray Experiments

Description

Calculates appropriate sample sizes for two-sample microarray experiments for a desired power. Sample size calculations are performed at controlled false discovery rates, user-specified proportions of non-differentially expressed genes, effect size, and standard deviation. A graph of power versus sample size is created.

Usage

ssize.twoSamp(delta, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 35,
side = "two-sided", cex.title=1.15, cex.legend=1)

Arguments

delta

the common difference in mean expressions between the two samples for all genes

sigma

the common standard deviation of expressions for all genes

fdr

the false discovery rate to be controlled

power

the desired power to be achieved

pi0

a vector (or scalar) of proportions of non-differentially expressed genes

maxN

the maximum sample size used for power calculations

side

options are "two-sided", "upper", or "lower"

cex.title

controls size of chart titles

cex.legend

controls size of chart legend

Value

ssize

sample sizes (for each treatment) at which desired power is first reached

power

power calculations with corresponding sample sizes

crit.vals

critical value calculations of two-sample t-test with corresponding sample sizes

Details

The true difference between mean expressions of the two samples as well as the standard deviations of expressions are assumed identical for all genes. See the function ssize.twoSampVary for sample size calculations with varying differences between sample mean expressions and standard deviations among genes.

If a vector is input for pi0, sample size calculations are performed for each proportion.

References

Liu, Peng and J. T. Gene Hwang. 2007. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics 23(6): 739-746.

See Also

ssize.twoSampVary, ssize.oneSamp, ssize.oneSampVary, ssize.F, ssize.Fvary

Examples

# NOT RUN {
 ##See Figure 1.(a) of Liu & Hwang (2007)
 d<-1		##difference in differentially expressed genes to be detected
 s<-0.5  	##standard deviation
 a<-0.05	##false discovery rate to be controlled
 pwr<-0.8	##desired power
 p0<-c(0.5,0.9,0.95)	##proportions of non-differentially expressed genes
 N<-20		##maximum sample size for calculations

 ts<-ssize.twoSamp(delta=d,sigma=s,fdr=a,power=pwr,pi0=p0,maxN=N,side="two-sided")
 ts$ssize	##first sample sizes to reach desired power for each proportion of
			 ##non-differentially expressed genes
 ts$power	##calculated power for each sample size
 ts$crit.vals	##calculated critical value for each sample size

# }