groupComparisonTMT: Finding differentially abundant proteins across conditions in...

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

Finding differentially abundant proteins across conditions in TMT experiment

Description

Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in TMT experiment. Experimental design of case-control study (patients are not repeatedly measured) is automatically determined based on proper statistical model.

Usage

groupComparisonTMT(
  data,
  contrast.matrix = "pairwise",
  moderated = FALSE,
  adj.method = "BH",
  remove_norm_channel = TRUE,
  remove_empty_channel = TRUE,
  save_fitted_models = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

data	the output of proteinSummarization function. It is a list with data frames 'FeatureLevelData' and 'ProteinLevelData'
contrast.matrix	Comparison between conditions of interests. 1) default is "pairwise", which compare all possible pairs between two conditions. 2) Otherwise, users can specify the comparisons 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.
moderated	TRUE will moderate t statistic; FALSE (default) uses ordinary t statistic.
adj.method	adjusted method for multiple comparison. "BH" is default.
remove_norm_channel	TRUE(default) removes "Norm" channels from protein level data.
remove_empty_channel	TRUE(default) removes "Empty" channels from protein level data.
save_fitted_models	logical, if TRUE, fitted models will be added to
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.

Value

a list that consists of the following elements: (1) ComparisonResult: statistical testing results; (2) FittedModel: the fitted linear models

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, moderated = TRUE)
head(test.pairwise$ComparisonResult)

# Only compare condition 0.125 and 1
levels(quant.pd.msstats$ProteinLevelData$Condition)

# Compare condition 1 and 0.125
comparison=matrix(c(-1,0,0,1),nrow=1)

# Set the nafmes of each row
row.names(comparison)="1-0.125"

# Set the column names
colnames(comparison)= c("0.125", "0.5", "0.667", "1")
test.contrast = groupComparisonTMT(data = quant.pd.msstats,
contrast.matrix = comparison,
moderated = TRUE)
head(test.contrast$ComparisonResult)

Vitek-Lab/MSstatsTMT documentation built on Nov. 18, 2024, 11:30 a.m.