MSstats (version 3.4.0)

designSampleSize: Planning future experimental designs of Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiments in sample size calculation

Description

Calculate sample size for future experiments of a Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment based on intensity-based linear model. Two options of the calculation: (1) number of biological replicates per condition, (2) power.

Usage

designSampleSize(data=data,desiredFC=desiredFC,FDR=0.05,numSample=TRUE,power=0.9)

Arguments

data

'fittedmodel' in testing output from function groupComparison.

desiredFC

the range of a desired fold change which includes the lower and upper values of the desired fold change.

FDR

a pre-specified false discovery ratio (FDR) to control the overall false positive. Default is 0.05

numSample

minimal number of biological replicates per condition. TRUE represents you require to calculate the sample size for this category, else you should input the exact number of biological replicates.

power

a pre-specified statistical power which defined as the probability of detecting a true fold change. TRUE represent you require to calculate the power for this category, else you should input the average of power you expect. Default is 0.9

Value

Warning

It can only obtain either one of the categories of the sample size calculation (numSample, numPep, numTran, power) at the same time.

Details

The function fits the model and uses variance components to calculate sample size. The underlying model fitting with intensity-based linear model with technical MS run replication. Estimated sample size is rounded to 0 decimal.

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):2524-2526, 2014.

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

# Consider quantitative data (i.e. QuantData) from yeast study.
# A time course study with ten time points of interests and three biological replicates.

QuantData<-dataProcess(SRMRawData)
head(QuantData$ProcessedData)

## based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison<-rbind(comparison1,comparison2, comparison3)
row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")

testResultMultiComparisons<-groupComparison(contrast.matrix=comparison,data=QuantData)


## Calculate sample size for future experiments:

#(1) Minimal number of biological replicates per condition

designSampleSize(data=testResultMultiComparisons$fittedmodel,numSample=TRUE,
desiredFC=c(1.25,1.75),FDR=0.05,power=0.8)


#(2) Power calculation

designSampleSize(data=testResultMultiComparisons$fittedmodel,numSample=2,
desiredFC=c(1.25,1.75),FDR=0.05,power=TRUE)