designSampleSize {MSstats} | R Documentation

## 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,
      desiredFC,
      FDR = 0.05,
      numSample = TRUE,
      power = 0.9,
      use_log_file = TRUE,
      append = FALSE,
      verbose = TRUE,
      log_file_path = NULL
    )

### 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 rate. 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
`use_log_file` | logical. If TRUE, information about data processing will be saved to a file.
`append` | logical. If TRUE, information about data processing will be added to an existing log file.
`verbose` | logical. If TRUE, information about data processing wil be printed to the console.
`log_file_path` | character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file.

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

### Value

data.frame - sample size calculation results including varibles: desiredFC, numSample, FDR, and power.

### Author(s)

Meena Choi, Ching-Yun Chang, Olga Vitek.

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

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

    ## 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)

[Package MSstats version 4.1.1 Index]