MSstats (version 2.4.0)

designSampleSizePlots: Visualization for sample size calculation

Description

To illustrate the relationship of desired fold change and the calculated minimal number sample size which are (1) number of biological replicates per condition, (2) number of peptides per protein, (3) number of transitions per peptide, and (4) power. The input is the result from function (designSampleSize.

Usage

designSampleSizePlots(data=data)

Arguments

data

output from function designSampleSize.

Details

Data in the example is based on the results of sample size calculation from function designSampleSize.

References

Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. "Protein significance analysis in selected reaction monitoring (SRM) measurements." Molecular & Cellular Proteomics, 11:M111.014662, 2012.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Examples

# Based on the results of sample size calculation from function designSampleSize, we generate a series of sample size plots for number of biological replicates, or peptides, or transitions or power plot.

head(QuantData) 

## plot the calculated sample sizes for future experiments:

#(1) Minimal number of biological replicates per condition

result.sample<-designSampleSize(data=QuantData,numSample=TRUE,numPep=3,numTran=4,desiredFC=c(1.25,1.75),FDR=0.05,power=0.8)
designSampleSizePlots(data=result.sample)

#(2) Minimal number of peptides per protein

result.peptide<-designSampleSize(data=QuantData,numSample=2,numPep=TRUE,numTran=4,desiredFC=c(1.25,1.75),FDR=0.05,power=0.8)	
designSampleSizePlots(data=result.peptide)

#(3) Minimal number of transitions per peptide

result.tran<-designSampleSize(data=QuantData,numSample=2,numPep=3,numTran=TRUE,desiredFC=c(1.25,1.75),FDR=0.05,power=0.8)	
designSampleSizePlots(data=result.tran)

#(4) Power

result.power<-designSampleSize(data=QuantData,numSample=2,numPep=3,numTran=4,desiredFC=c(1.25,1.75),FDR=0.05,power=TRUE)
designSampleSizePlots(data=result.power)