pow {ssize} | R Documentation |
Compute and plot power, reqired sample-size, or detectible effect size for gene expression experiment
pow(sd, n, delta, sig.level, alpha.correct = "Bonferonni") power.plot(x, xlab = "Power", ylab = "Proportion of Genes with Power >= x", marks = c(0.7, 0.8, 0.9), ...) ssize(sd, delta, sig.level, power, alpha.correct = "Bonferonni") ssize.plot(x, xlab = "Sample Size (per group)", ylab = "Proportion of Genes Needing Sample Size <= n", marks = c(2, 3, 4, 5, 6, 8, 10, 20), ...) delta(sd, n, power, sig.level, alpha.correct = "Bonferonni") delta.plot (x, xlab = "Fold Change", ylab = "Proportion of Genes with Power >= 80% at Fold Change=delta", marks = c(1.5, 2, 2.5, 3, 4, 6, 10), ...)
sd |
Vector of standard deviations for control samples |
n |
Number of observations (per group) |
delta |
Hypothetical True difference in expression. |
sig.level |
Significance level (Type I error probability) |
power |
Power |
alpha.correct |
Type of correction for multiple comparison. One of "Bonferonni" or "None". |
x |
Vector of powers generated by pow |
xlab, ylab |
x and y axis labels |
marks |
Powers at which percent of genes achieving the specified cutoff is annotated on the plot. |
... |
Additional graphical parameters |
The pow
function computes power for each element of a gene
expression experiment using an vector of estimated standard
deviations. The power is computed separately for each gene, with an
optional correction to the significance level for multiple
comparison. The power.plot
function generates a cumulative
power plot illustrating the fraction and number of genes achieve a
given power for the specified sample size, significance level, and
delta.
Periods are printed for every 10 calculations so that the user can see that the computation is proceeding.
pow
returns a vector containing the power for each standard deviation.
Gregory R. Warnes Gregory_R_Warnes@groton.pfizer.com
Warnes GR and Fasheng Li Warnes GR and Liu P, ``Sample Size Selection for Microarray Experiments'' submitted to Biometrics.
Warnes GR and Fasheng Li, ``Sample Size Selection for Microarray based Gene Expression Studies,'' Talk, "2003 FDA/Industry Statistics Workshop: From Theory to Regulatory Acceptance", American Statistical Association, Bethesda, MD, Sept 18-19, 2003. http://www.warnes.net/Research/PresentationFolder/SampleSize.pdf
ssize
,
ssize.plot
,
delta
,
delta.plot
library(gdata) # for nobs() data(exp.sd) # Histogram of the standard deviations hist(exp.sd,n=20, col="cyan", border="blue", main="", xlab="Standard Deviation (for data on the log scale)") dens <- density(exp.sd) lines(dens$x, dens$y*par("usr")[4]/max(dens$y),col="red",lwd=2) title("Histogram of Standard Deviations") # 1) What is the power if using 6 patients 3 measurements assuming # Delta=1.0, Alpha=0.05 and Observed SDs? # n=6; fold.change=2.0; power=0.8; sig.level=0.05; # all.power <- pow(sd=exp.sd, n=n, delta=log2(fold.change), sig.level=sig.level) power.plot(all.power, lwd=2, col="blue") xmax <- par("usr")[2]-0.05; ymax <- par("usr")[4]-0.05 legend(x=xmax, y=ymax, legend= strsplit( paste("n=",n,",", "fold change=",fold.change,",", "alpha=", sig.level, ",", "# genes=", nobs(sd), sep=''), "," )[[1]], xjust=1, yjust=1, cex=1.0) title("Power to Detect 2-Fold Change") # 2) What is necessary sample size for 80% power using 3 measurements/patient # assuming Delta=1.0, Alpha=0.05 and Observed SDs? # all.size <- ssize(sd=exp.sd, delta=log2(fold.change), sig.level=sig.level, power=power) ssize.plot(all.size, lwd=2, col="magenta", xlim=c(1,20)) xmax <- par("usr")[2]-1; ymin <- par("usr")[3] + 0.05 legend(x=xmax, y=ymin, legend= strsplit( paste("fold change=",fold.change,",", "alpha=", sig.level, ",", "power=",power,",", "# genes=", nobs(sd), sep=''), "," )[[1]], xjust=1, yjust=0, cex=1.0) title("Sample Size to Detect 2-Fold Change") # 3) What is necessary fold change to achieve 80% power using 3 # measurements/patient assuming n=6, Delta=1.0, Alpha=0.05 and Observed # SDs? # all.delta <- delta(sd=exp.sd, power=power, n=n, sig.level=sig.level) delta.plot(all.delta, lwd=2, col="magenta", xlim=c(1,10)) xmax <- par("usr")[2]-1; ymin <- par("usr")[3] + 0.05 legend(x=xmax, y=ymin, legend= strsplit( paste("n=",n,",", "alpha=", sig.level, ",", "power=",power,",", "# genes=", nobs(sd), sep=''), "," )[[1]], xjust=1, yjust=0, cex=1.0) title("Fold Change to Achieve 80% Power")