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

In ssize.fdr: Sample Size Calculations for Microarray Experiments

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

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.

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

Note

Powers calculated to be 0 may be negligibly conservative.

Critical values calculated as ‘NA’ are values >20.

Running this function with the side option of "lower" will possibly result in multiple warnings. Calculating the probability that an observation is less than the negative critical value under a t-distribution with non-centrality parameter delta/sigma (see argument section above) and the appropriate degrees of freedom is a calculation that is performed many times while the function runs. When the difference between the critical value and delta/sigma is large, this probability is virtually zero. This happens repeatedly while the function optimize finds the appropriate critical value for each sample size. Because of this, the function pt outputs a value <1e-8 in addition to a warning of “full precision not achieved”. This has no impact on the accuracy of the resulting calculations of sample size.

Author(s)

Megan Orr [e-mail], Peng Liu [e-mail]

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

 ##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

ssize.fdr documentation built on June 7, 2022, 9:06 a.m.