groupComparison: Whole plot testing

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

Whole plot testing

Description

Whole plot testing

Usage

groupComparison(
  contrast.matrix,
  data,
  save_fitted_models = TRUE,
  log_base = 2,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  numberOfCores = 1
)

Arguments

contrast.matrix	comparison between conditions of interests.
data	name of the (output of dataProcess function) data set.
save_fitted_models	logical, if TRUE, fitted models will be added to the output.
log_base	base of the logarithm used in dataProcess.
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.
numberOfCores	Number of cores for parallel processing. When > 1, a logfile named 'MSstats_groupComparison_log_progress.log' is created to track progress. Only works for Linux & Mac OS. Default is 1.

Details

contrast.matrix : comparison of interest. Based on the levels of conditions, specify 1 or -1 to the conditions of interests and 0 otherwise. The levels of conditions are sorted alphabetically. Command levels(QuantData$FeatureLevelData$GROUP_ORIGINAL) can illustrate the actual order of the levels of conditions. The underlying model fitting functions are lm and lmer for the fixed effects model and mixed effects model, respectively. The input of this function is the quantitative data from function (dataProcess).

Value

list that consists of three elements: "ComparisonResult" - data.frame with results of statistical testing, "ModelQC" - data.frame with data used to fit models for group comparison and "FittedModel" - list of fitted models.

Examples

# Consider quantitative data (i.e. QuantData) from yeast study with ten time points of interests, 
# three biological replicates, and no technical replicates. 
# It is a time-course experiment and we attempt to compare differential abundance
# between time 1 and 7 in a set of targeted proteins. 
# In this label-based SRM experiment, MSstats uses the fitted model with expanded scope of 
# Biological replication.  
QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
levels(QuantData$ProteinLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.
testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData,
                                           use_log_file = FALSE)
# table for result
testResultOneComparison$ComparisonResult

Vitek-Lab/MSstats documentation built on Nov. 4, 2024, 3:08 p.m.