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groupComparison.Rd
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groupComparison.Rd
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\name{groupComparison}
\alias{groupComparison}
\title{Finding differentially abundant proteins across conditions in targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment}
\description{Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS)
experiment. It is applicable to multiple types of sample preparation, including label-free workflows, workflows that use stable isotope labeled reference proteins and peptides, and workflows that use fractionation. Experimental design of case-control study (patients are not repeatedly measured) or time course study (patients are repeatedly measured) is automatically determined based on proper statistical model.
}
\usage{
groupComparison(contrast.matrix=contrast.matrix,
data=data)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{contrast.matrix}{comparison between conditions of interests.}
\item{data}{name of the (output of dataProcess function) data set.}
}
\details{
\itemize{
\item{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$ProcessedData$GROUP_ORIGINAL) can illustrate the actual order of the levels of conditions.}
}
The underlying model fitting functions are \code{\link[stats]{lm}} and \code{\link[lme4]{lmer}} for the fixed effects model and mixed effects model, respectively.
The input of this function is the quantitative data from function (\code{\link{dataProcess}}).
}
\value{A list of data.frame \emph{ComparisonResult} is the data.frame for the result of significance analysis ; \emph{fittedModel} is the the data.frame for run-level summarized data.}
\author{Meena Choi, Ching-Yun Chang, Olga Vitek.
Maintainer: Meena Choi (\email{[email protected]})
}
\references{
Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" \emph{Bioinformatics}, 30(17):2524-2526, 2014.
Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. "Protein significance analysis in selected reaction monitoring (SRM) measurements." \emph{Molecular & Cellular Proteomics}, 11:M111.014662, 2012.
Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" \emph{BMC Bioinformatics}, 13:S16, 2012.
}
\section{Warning }{When a feature is missing completely in a condition or a MS run, a warning message is sent to the console notifying the user of the missing feature. Additional filtering or imputing process is required before model fitting.
}
\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)
head(QuantData$ProcessedData)
levels(QuantData$ProcessedData$GROUP_ORIGINAL)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.
testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData)
# table for result
testResultOneComparison$ComparisonResult
}