designSampleSizePlots {MSstats} | R Documentation

## 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)

### 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.

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

    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)

[Package MSstats version 4.0.1 Index]