ssize.fdr (version 1.3)

ssize.F: Sample Size Calculations for Multi-Sample Microarray Experiments

Description

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

Usage

ssize.F(X, beta, L = NULL, dn, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 20,
  cex.title=1.15, cex.legend=1)

Arguments

X

design matrix of experiment

beta

parameter vector

L

coefficient matrix or vector for linear contrasts of interest

dn

a function of the degrees of freedom based on the design of the experiment

sigma

the standard deviation 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

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 with corresponding sample sizes

Details

Standard deviations are assumed to be identical for all genes. See the function ssize.Fvary for sample size calculations with varying 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 {
 ##Sample size calculation for three-treatment loop design microarray experiment
 ##See Figure S2 of Liu & Hwang (2007)
 des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE)	##design matrix of loop design experiment
 b<-c(1,-0.5)			##difference between first two treatments is 1 and
  #second and third treatments is -0.5
 df<-function(n){3*n-2}		##degrees of freedom for this design is 3n-2
 s<-1				##standard deviation
 a<-0.05			##false discovery rate to be controlled
 pwr1<-0.8			##desired power
 p0<-c(0.5,0.9,0.95,0.995)	##proportions of non-differentially expressed genes
 N1<-20				##maximum sample size for calculations

 ft<-ssize.F(X=des,beta=b,dn=df,sigma=s,fdr=a,power=pwr1,pi0=p0,maxN=N1)
 ft$ssize	##first sample sizes to reach desired power for each proportion of
  #non-differentially expressed genes
 ft$power	##power for each sample size
 ft$crit.vals	##critical value for each sample size

# }