designSampleSize {MSstats} | R Documentation |
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.
designSampleSize( data, desiredFC, FDR = 0.05, numSample = TRUE, power = 0.9, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
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. |
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.
data.frame - sample size calculation results including varibles: desiredFC, numSample, FDR, and power.
Meena Choi, Ching-Yun Chang, Olga Vitek.
# 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)