designSampleSizeTMT: Planning future experimental designs of Tandem Mass Tag (TMT)...

In Vitek-Lab/MSstatsTMT: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling

Planning future experimental designs of Tandem Mass Tag (TMT) experiments acquired with Data-Dependent Acquisition (DDA or shotgun)

Description

Calculate sample size for future experiments of a TMT experiment based on intensity-based linear model. Two options of the calculation: (1) number of biological replicates per condition, (2) power.

Usage

designSampleSizeTMT(
  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 groupComparisonTMT.
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.

Examples

data(input.pd)
# use protein.summarization() to get protein abundance data
quant.pd.msstats = proteinSummarization(input.pd,
                                        method="msstats",
                                        global_norm=TRUE,
                                        reference_norm=TRUE)

test.pairwise = groupComparisonTMT(quant.pd.msstats, save_fitted_models = TRUE)
head(test.pairwise$ComparisonResult)

## Calculate sample size for future experiments:
#(1) Minimal number of biological replicates per condition
designSampleSizeTMT(data=test.pairwise$FittedModel, numSample=TRUE,
                 desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
#(2) Power calculation
designSampleSizeTMT(data=test.pairwise$FittedModel, numSample=2,
                 desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
          

Vitek-Lab/MSstatsTMT documentation built on Oct. 19, 2024, 1:14 a.m.