designSampleSize: Planning future experimental designs of Selected Reaction...

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

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)
                 

MeenaChoi/MSstats documentation built on Nov. 5, 2024, 11:59 a.m.