designSampleSizePlots: Visualization for sample size calculation

In MSstats: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments

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.

Value

Plot for estimated sample size with assigned variable.

Author(s)

Meena Choi, Ching-Yun Chang, Olga Vitek.

Maintainer: Meena Choi ([e-mail])

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

# 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.

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)


# plot the calculated sample sizes for future experiments:

# (1) Minimal number of biological replicates per condition

result.sample<-designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=TRUE,
desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
designSampleSizePlots(data=result.sample)

# (2) Power

result.power<-designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=2,
desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
designSampleSizePlots(data=result.power)

MSstats documentation built on Feb. 28, 2021, 2:01 a.m.