%\VignetteIndexEntry{Isobar Usage}
%\VignetteDepends{}
%\VignetteKeywords{Documentation}
%\VignettePackage{isobar}
\documentclass[11pt]{article}

<<init,echo=FALSE>>=
  library(distr)
@ 

\usepackage{times}
\usepackage{hyperref}
\usepackage[utf8]{inputenc}
\usepackage{xcolor}
\usepackage{listings}

\textwidth=6.2in
\textheight=8.5in
%\parskip=.3cm
\oddsidemargin=.1in
\evensidemargin=.1in
\headheight=-.3in

\newcommand{\scscst}{\scriptscriptstyle}
\newcommand{\scst}{\scriptstyle}

\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Robject}[1]{{\texttt{#1}}}
\newcommand{\Rpackage}[1]{{\texttt{#1}}}
\newcommand{\isobar}{\Rpackage{isobar}}

\SweaveOpts{keep.source=TRUE}

\author{Florian P.\ Breitwieser, Jacques Colinge}

\bibliographystyle{plainnat}

\begin{document}

\title{Isobar Vignette - iTRAQ and TMT Analysis}
\maketitle
\tableofcontents

\section{Introduction}
The \Rpackage{isobar} package is designed as an extensible and
interactive environment for data analysis and exploration of data
produced by Mass Spectrometry analysis of proteins and peptides
labelled with isobaric tags, such as iTRAQ and TMT.  \Rpackage{isobar}
implements the theory presented in Breitwieser et al., Journal of
Proteome Research 2011.

\Rpackage{isobar} allows analyzing iTRAQ 4plex and 8plex, and TMT
2plex and 6plex experiments representing them as \Robject{IBSpectra}
objects. The respective classes are \Robject{iTRAQ4plexSpectra},
\Robject{iTRAQ8plexSpectra}, \Robject{TMT2plexSpectra}, and
\Robject{TMT6plexSpectra}.

The first thing you need to do is load the package.
<<load-isobar,results=hide>>=
  library(isobar) ## load the isobar package
@ 

\section{Loading data}
\Rpackage{isobar} can read identifications and quantifications from
tab-separated and MGF files. Perl scripts are supplied to generate a
tab-separated version from the vendor formats of Mascot and Phenyx,
see appendix \ref{sec:dependencies}. The format is simple and
described in appendix \ref{app:fileformats}. Experimental support for
the mzIdentML format within R is also available - please contact the
mantainer in case of problems.

\begin{description}
\item[\small ID.CSV] tab-separated file containing peptide-spectra matches and
  spectrum meta-information such as retention time, m/z and charge. 
  Generated by parser scripts.
\item[\small MGF] contains peak lists from which quantitative
  information on reporter tags are extracted. Must be centroided.
\item[\small IBSPECTRA.CSV] tab-separated file containing the same columns as
  {\small ID.CSV} plus \emph{quantitative information} extracted from {\small MGF}
  file - that means the reporter tag masses and intensities as
  additional columns.
\end{description}

\Robject{readIBSpectra} is the primary function to generate a
\Robject{IBSpectra} object. The first argument is one of {\small
  iTRAQ4plexSpectra}, {\small iTRAQ8plexSpectra}, {\small
  TMT2plexSpectra} and {\small TMT6plexSpectra} and denotes the tag
type and therefore class. 

<<echo=TRUE,eval=FALSE>>=
  ## generating IBSpectra object from ID.CSV and MGF
  ib <- readIBSpectra("iTRAQ4plexSpectra",list.files(pattern=".id.csv"),
          list.files(pattern=".mgf"))

  ## write in tabular IBSPECTRA.CSV format to file
  write.table(as.data.frame(ib),sep="\t",row.names=F,
          file="myexperiment.ibspectra.csv")

  ## generate from saved IBSPECTRA.CSV - MGF does not have to be supplied
  ib.2 <- readIBSpectra("iTRAQ4plexSpectra","myexperiment.ibspectra.csv")
@

In case the MGF file is very big, it can be advanteguous to generate a
smaller version containing only meta- and quantitative information
before import in \texttt{R}. On Linux, the tool \texttt{grep} is
readily available.

\begin{verbatim}
egrep '^[A-Z]|^1[12][0-9]\.' BIG.mgf > SMALL.mgf
\end{verbatim}

\subsection{\Robject{ibspiked} test samples}
The examples presented are based on the dataset
\Robject{ibspiked\_set1} which has been designed to test
\Rpackage{isobar}'s functionality and searched against the Swissprot
human database with Mascot and Phenyx. \Robject{ibspiked\_set1} is an
iTRAQ 4-plex data set comprised of a complex background (albumin- and
IgG-depleted human plasma) and spiked proteins. MS analysis was
performed in ThermoFisher Scientific LTQ Orbitrap HCD instrument with
2D shotgun peptide separation (see original paper for more
details). The samples used for each iTRAQ channel are as follows:
\begin{itemize}
\item Depleted human plasma background (>150 protein detected);
\item Spiked-in proteins with the following ratios
  \begin{itemize}
    \item \texttt{CERU\_HUMAN (P00450)} at concentrations $1:1:1:1$;
    \item \texttt{CERU\_RAT (P13635)} at concentrations $1:2:5:10$;
    \item \texttt{CERU\_MOUSE (Q61147)} at concentrations $10:5:2:1$.
  \end{itemize}
\end{itemize}

A second data set with ratios 1:10:50:100 is available as
\Robject{ibspiked\_set2} from
\url{http://bininformatics.cemm.oeaw.ac.at/isobar}.

The Ceruplasmins have been selected as the share peptides. Hereafter,
we load the data pacakage and the ceru protein IDs are identified via
the \Rfunction{protein.g} function, which provides a mean to retrieve
data from \Robject{ProteinGroup} objects. \Robject{ProteinGroup} is a 
slot of \Robject{IBSpectra} objects and contains informations on proteins
and their grouping. See \ref{ss:proteingrouping}.

<<load-ibspiked>>=
  data(ibspiked_set1)
  ceru.human <- protein.g(proteinGroup(ibspiked_set1),"CERU_HUMAN")
  ceru.rat <- protein.g(proteinGroup(ibspiked_set1),"CERU_RAT")
  ceru.mouse <- protein.g(proteinGroup(ibspiked_set1),"CERU_MOUSE")
  ceru.proteins <- c(ceru.human,ceru.rat,ceru.mouse)
@ 

\subsection{Protein information and grouping in \Robject{ProteinGroup}}
\label{ss:proteingrouping}
When an \texttt{ibspectra.csv} is read, protein are grouped to
identify proteins which have unique peptides. By default, only
peptides with unique peptides are grouped.

The algorithm to infer protein groups works as follows:
\begin{enumerate}
\item Group proteins together which have been seen with exactly the
  same peptides (\Robject{indistinguishableProteins}) - these are 
  the \Robject{protein.g} identifiers.
\item Create protein groups (\Robject{proteinGroupTable}):
  \begin{enumerate}
  \item Define proteins with specific peptides as reporters
    (\Robject{reporterProteins})
  \item Get proteins which are contained \footnote{That means these
      proteins have a subset of the peptides of the reporter} by
    reporterProteins and group them below.
  \end{enumerate}
\item Create protein groups for proteins without specific peptides as
  above.
\end{enumerate}


\subsection{\Rpackage{MSnbase} integration}
\Rpackage{MSnbase} by Laurent Gatto provides data manipulation and
processing methods for MS-based proteomics data. It provides import,
representation and analysis of raw MS data stored in \texttt{mzXML},
\texttt{mzML} and \texttt{mzData} using the \Rpackage{mzR} package and
centroided and un-centroided {\small \texttt{MGF}} peak lists. It
allows to use and preprocess raw data whereas \Rpackage{isobar}
requires centroided peak lists. In the future, the \Rpackage{isobar}
class \Robject{IBSpectra} might be based on or replaced by
\Robject{MSnbase}'s class \Robject{MSnSet}.  For now, methods for
coercion are implemented:

<<eval=FALSE>>=
  as(ibspectra,"MSnSet")
  as(msnset,"IBSpectra")
@ 


\section{Data Analysis}
\subsection{Reporter mass precision}
The distribution of observed masses from the reporter tags can be used
to visualize the precision of the MS setup on the fragment level and
used to set the correct window for isolation.

The expected masses of the reporter tags are in the slot
\Rfunction{reporterTagMasses} of the implementations of the
\Robject{IBSpectra} class. The experimental masses are in the matrix
\Robject{mass} of \Robject{AssayData}; they can also be accessed by
the method \Rfunction{reporterMasses(x)}.

<<show-reporterMasses>>=
sprintf("%.4f",reporterTagMasses(ibspiked_set1))  ## expected masses
mass <- assayData(ibspiked_set1)[["mass"]] ## observerd masses
apply(mass,2,function(x) sprintf("%.4f",quantile(x,na.rm=TRUE,probs=c(0.025,0.975))))
@

\Rfunction{reporterMassPrecision} provides a plot of the distribution.

\begin{center}
\begin{figure}[htp]
<<print-reporterMasses,fig=TRUE,width=6.6,height=3>>=
  print(reporterMassPrecision(ibspiked_set1))
@ 
\caption{Reporter mass precision plot.}
\label{fig:reporterMassPrecision}
\end{figure}
\end{center}

\subsection{Normalization and isotope impurity correction}
Isotope impurity correction factors are supplied by labelling reagent
manufacturers. Default values that can be modified by the user are
available in \Rpackage{isobar} and corrections are obtained by simple
linear algebra.

Due to differences between samples it is advisable to normalize data
before further processing. By default, \Rfunction{normalize} corrects
by a factor such that the median intensities in all reporter channels
are equal.

See figure ~\ref{fig:normalization}.

<<normalize-ibspiked>>=
ib.old <- ibspiked_set1
ibspiked_set1 <- correctIsotopeImpurities(ibspiked_set1)
ibspiked_set1 <- normalize(ibspiked_set1)
@ 


\begin{center}
\begin{figure}[htp]
<<fig-maplot-normalize1,echo=FALSE>>=
png("fig_maplot_normalize.png",width=6.6,height=3,units="in",res=300,pointsize=8)
@
<<fig-maplot-normalize2>>=
par(mfrow=c(1,2))
maplot(ib.old,channel1="114",channel2="117",ylim=c(0.5,2),
       main="before normalization")
abline(h=1,col="red",lwd=2)
maplot(ibspiked_set1,channel1="114",channel2="117",ylim=c(0.5,2),
       main="after normalization")
abline(h=1,col="red",lwd=2)
@
<<fig-maplot-normalize3,echo=FALSE,results=hide>>= 
dev.off()
@
\includegraphics{fig_maplot_normalize}
\caption{Ratio versus intensity plots ('MA plots') before and after applying normalization.}
\label{fig:normalization}
\end{figure}
\end{center}

\subsection{Fitting a noise model}
A noise model is a approximation of the expected technical variation
based on signal intensity. It is stable for a certain experimental
setup and thus can be learned once. Noise is observed directly when
comparing identical samples in multiple channels (1:1 iTRAQ/TMT
sample) and we can use \Robject{ibspiked\_set1} background proteins as
a 1:1 sample. Therefore we exclude the ceruplasmins before fitting a
noise model using \Rfunction{NoiseModel}. See figure
~\ref{fig:maplots}.

<<calc-noisemodel>>=
ib.background <- subsetIBSpectra(ibspiked_set1,protein=ceru.proteins,"exclude")
noise.model <- NoiseModel(ib.background)
@ 

Though only recommended when sufficient data are available, a method
exist for the estimation of a noise model without a 1:1 dataset. It
takes longer time as it first computes all the protein ratios to shift
spectrum ratios to 1:1. To examplify this procedure, we only take rat
and mouse CERU proteins from \Robject{ibspiked\_set1}, see figure
~\ref{fig:maplots}. The resultant noise model is a rough approximation
only because of the very limitted data, see Breitwieser et
al. Supporting Information, submitted, for a real example.

<<ceru-noisemodel>>=
ib.ceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.proteins,
                            direction="exclude others",
                            specificity="reporter-specific")
nm.ceru <- NoiseModel(ib.ceru,one.to.one=FALSE,pool=TRUE)
@ 

\begin{center}
\begin{figure}[htp]
<<maplot-noisemodel1,echo=FALSE>>=
png("fig_maplot_noisemodel.png",width=3.3,height=3,units="in",res=300,pointsize=8)
@
<<maplot-noisemodel2,echo=TRUE>>=
maplot(ib.background,noise.model=c(noise.model,nm.ceru),
       channel1="114",channel2="115",ylim=c(0.2,5),
       main="95% CI noise model")
@ 
<<maplot-noisemodel3,echo=FALSE,results=hide>>=
dev.off()
@ 
\includegraphics{fig_maplot_noisemodel}

\caption{Red lines denote the 95 \% confidence interval as estimated
  by the noise model on background proteins. The blue line is
  estimated as non 1:1 noise model based on only spectra of CERU
  proteins.}
\label{fig:maplots}
\end{figure}
\end{center}

\subsection{Protein and peptide ratio calculation}
\Rfunction{estimateRatio} calculates the relative abundance of a
peptide or protein in one tag compared to another. It calculates a
weighted average (after outlier removal) of the spectrum ratios. The
weights are the inverse of the spectrum ratio variances. It requires a
\Robject{IBSpectra} and \Robject{NoiseModel} object and definitions of
channel1, channel2, and the protein or peptide. The result is
channel2/channel1.

<<estimateRatio>>=
## Calculate ratio based on all spectra of peptides specific 
##  to CERU_HUMAN, CERU_RAT or CERU_MOUSE. Returns a named
##  numeric vector.
10^estimateRatio(ibspiked_set1,noise.model,
                 channel1="114",channel2="115",
                 protein=ceru.proteins)['lratio']

## If argument 'combine=FALSE', estimateRatio returns a data.frame 
##  with one row per protein
10^estimateRatio(ibspiked_set1,noise.model,
                 channel1="114",channel2="115",
                 protein=ceru.proteins,combine=FALSE)[,'lratio']
## spiked material channel 115 vs 114: 
##                 CERU_HUMAN (P00450): 1:1
##                 CERU_RAT   (P13635): 2:1  = 2
##                 CERU_MOUSE (Q61147): 5:10 = 0.5

## Peptides shared between rat and mouse
pep.shared <- peptides(proteinGroup(ibspiked_set1),
                       c(ceru.rat,ceru.mouse),set="intersect",
                       columns=c('peptide','n.shared.groups'))
## remove those which are shared with other proteins
pep.shared <- pep.shared$peptide[pep.shared$n.shared.groups==2]

## calculate ratio: it is between the rat and mouse ratios
10^estimateRatio(ibspiked_set1,noise.model,
                 channel1="114",channel2="115",
                 peptide=pep.shared)['lratio']
@ 

When examining the global differences and differences in between
classes, \Rfunction{proteinRatios} can be used. It is also suitable to
inspect sample variability. The argument \Robject{cl} can be used to
define class labels. If \Robject{method='interclass'} or
\Robject{intraclass} and \Robject{summarize=TRUE},
\Rfunction{proteinRatios} return a single summarized ratio across and
within classes, resp..

<<protein-ratios>>=
protein.ratios <- proteinRatios(ibspiked_set1,noise.model)
str(protein.ratios)

## defined class 114 and 115 as class 'T', 116 and 117 as class 'C'
classLabels(ibspiked_set1) <- c("T","T","C","C")

proteinRatios(ibspiked_set1,noise.model,protein=ceru.proteins,
              cl=classLabels(ibspiked_set1),method="interclass",
              summarize=T)[,c("ac","lratio","variance")]
@ 

\subsection{Protein ratio distribution and selection}
Protein ratio distributions can be calculated ideally on biological
replicated. To examine differentially expressed proteins, both sample
variability information (random protein ratios) as a
\emph{fold-change} constraint, and ratio \emph{precision} can be used.
For a experimental setup with biolgical replicates in the same
experiment (but different channels), the distribution of biological
variability can be learned by computing the ratios between the
replicates. With no replicates available, one has the choice to (a)
model the actual protein ratios and just select the most extreme
ratios; (b) learn the distribution from a previous experiment; or (c)
assume a standard Cauchy distribution with location $0$ and scale
$0.1$, $0.05$, and $0.025$, which correspond with $\alpha=0.05$
roughly to fold changes of $4$, $2$, and $1.5$. 

A Cauchy distribution fits accurately this type of random protein
ratio distribution: Cauchy is displayed in red, Gaussian in blue. In
the case of \Robject{ibspiked\_set1}, the many 1:1 proteins provide us
with adequate data to learn the random protein ratio distribution,
however only of the \textit{technical} variation.


<<ratiodistribution>>=
#protein.ratios <- proteinRatios(ibspiked_set1,noise.model)
protein.ratiodistr.wn <- fitWeightedNorm(protein.ratios[,'lratio'],
                                         weights=1/protein.ratios[,'variance'])
protein.ratiodistr.cauchy <- fitCauchy(protein.ratios[,"lratio"])
@ 

\begin{center}
\begin{figure}[htp]
<<plot-ratiodistr,fig=TRUE,width=4,height=2.5>>=
library(distr) # required library
limits=seq(from=-0.5,to=0.5,by=0.001)
curve.wn <- data.frame(x=limits,y=d(protein.ratiodistr.wn)(limits))
curve.cauchy<-data.frame(x=limits,y=d(protein.ratiodistr.cauchy)(limits))

g <- ggplot(data.frame(protein.ratios),aes(x=lratio)) +
  geom_histogram(colour = "darkgreen", fill = "white",aes(y=..density..),
                 binwidth=0.02) + geom_rug() +
  geom_line(data=curve.wn,aes(x=x,y=y),colour="blue") +
  geom_line(data=curve.cauchy,aes(x=x,y=y),colour="red")
print(g)
@
\caption{Histogram of all protein ratios in
  \Robject{ibspiked\_set1}. A fit with a Gaussian and Cauchy
  probability density function is shown in blue and red,
  respectively.}
\label{fig:ratiodistr}
\end{figure}
\end{center}

Now, when supplying a \Robject{ratiodistr} parameter to
\Rfunction{estimateRatio} and \Rfunction{proteinRatios}, sample and
signal p-values are calculated, what we illustrate in the code below

<<significant-ratios>>=
rat.list <-
  estimateRatio(ibspiked_set1,noise.model=noise.model,channel1="114",channel2="115",
                protein=reporterProteins(proteinGroup(ibspiked_set1)),combine=F,
                ratiodistr=protein.ratiodistr.cauchy)
rat.list[rat.list[,"is.significant"]==1,]
@

\subsection{Detection of proteins with no specific peptides}
It is well known that MS analysis only reveals the presence of
so-called protein groups, defined as sets of proteins identified by
the same set of peptides. The protein that contains all the peptides
is the group reporter (there are possibly several group reporters) and
if it has one specific peptide at least then its presence in the
sample is certain. The status of the other proteins in the group is in
general impossible to determine.  When quantitative information is
available, there is a potential to elucidate the structure of part of
the protein groups.

In the example below, a subset \Rfunction{IBSpectra} object is created, 
   containing only peptides shared between CERU\_RAT and CERU\_MOUSE, 
   and those specific to CERU\_RAT.

<<shared-pep>>=
## peptides shared between CERU_RAT and CERU_MOUSE have been computed before
pep.shared
## peptides specific to CERU_RAT
pep.rat <- peptides(proteinGroup(ibspiked_set1),protein=ceru.rat,
                    specificity="reporter-specific")

## create an IBSpectra object with only CERU_RAT and shared peptides
ib.subset <- subsetIBSpectra(ibspiked_set1,
                             peptide=c(pep.rat,pep.shared),direction="include")

## calculate shared ratios
sr <- shared.ratios(ib.subset,noise.model,
                    channel1="114",channel2="117",
                    ratiodistr=protein.ratiodistr.cauchy)
sr

@ 
\begin{center}
\begin{figure}[htp]
<<fig=TRUE,width=6.6,height=1.8>>=
  ## plot significantly different protein groups where 90% CI does not overlap
  ## CERU_MOUSE and CERU_RAT is detected, as expected.
  shared.ratios.sign(sr,z.shared=1.282)
@ 
\caption{Peptides of spiked ceruplasmins have significantly different ratios 
    between groups. Group \textit{reporter} consists of peptides specific 
        to \texttt{CERU\_RAT (P13635)}, group \textit{member} are peptides 
        shared between \texttt{CERU\_RAT} and \texttt{CERU\_MOUSE (Q61147)}.}
\label{fig:sharedratios}
\end{figure}
\end{center}



\section{Report generation}
Analysis of reports can be analyzed with \Rpackage{isobar} by usage
of the Rscript \texttt{create\_reports.R}. This script reads command
line options and a \texttt{properties.R} file to allow a complete
analysis generating \LaTeX (and thus PDF) and Excel reports containing
all protein ratios and quality control.

The \texttt{properties.R} file in the current folder overwrite the 
settings set in the global file in the installation directory. Certain
properties must be set (such as \texttt{type} and \texttt{ibspectra}), others specify the
type of report to be generated. See appendix 
\ref{sec:properties} for the available settings.

<<eval=FALSE>>=
## execute to find the path and file location in your installation.
system.file("report",package="isobar") ## path
list.files(system.file("report",package="isobar")) ## files
@ 

\begin{description}
  \item[create\_reports.R] R script which can be used to create QC and PDF reports
    It initializes the environment, reads properties and calls \Rfunction{Sweave}
    on QC and DA Sweave files. Additionally it generates a Excel data analysis report
    by calling \texttt{tab2xls.pl}.
  \item[isobar-qc.Rnw] Sweave file with quality control plots.
  \item[isobar-analysis.Rnw] Sweave file for generating a data analysis report
    with the list of all protein ratios and list of significantly different proteins.
  \item[properties.conf] Default configuration for \texttt{create\_reports.R}.
    It is parsed as R code.
  \item[report-utils.R] Helper R functions used in Sweave documents.
  \item[report-utils.tex] Helper \LaTeX{} functions used in Sweave documents.
\end{description}

\begin{appendix}

\section{File formats}
\label{app:fileformats}
\subsection{ID CSV file format}
The Perl parsers create ID CSV files - identification information for all
matched spectra without quantitative information. You can create your own parser, 
the resulting file should be tab-delimited and contain the following columns.
Only bold columns are obligatory. The information is redundant - that means if
a peptide may stem from two different proteins the information of the identification
is repeated.

\begin{tabular}{r|l}
\textbf{accession} & Protein AC \\
\textbf{peptide} & Peptide sequence\\
modif & Peptide modification string\\
charge & Charge state\\
theo.mass & Theoretical peptide mass\\
exp.mass & Experimentally observed mass\\
parent.intens & Parent intensity\\
retention.time & Retention time\\
\textbf{spectrum} & Spectrum identifier\\
search.engine & Protein search engine and score\\
\end{tabular}

\subsection{IBSpectra CSV file format}
IBSpectra file format has the same columns as the ID CSV format and additionally 
columns containing the quantitation information, namely
\texttt{X\textit{tagname}\_mass} and \texttt{X\textit{tagname}\_ions},
for mass and intensity of each tag \textit{tagname}. Below an example of the 
further columns for an \texttt{iTRAQ 4plex} IBSpectra.


\begin{tabular}{r|l}
\textbf{X114\_mass} & reporter ion mass\\
\textbf{X115\_mass} & reporter ion mass\\
\textbf{X116\_mass} & reporter ion mass\\
\textbf{X117\_mass} & reporter ion mass\\
\textbf{X114\_ions} & reporter ion intensity\\
\textbf{X115\_ions} & reporter ion intensity\\
\textbf{X116\_ions} & reporter ion intensity\\
\textbf{X117\_ions} & reporter ion intensity\\
\end{tabular}

\section{\texttt{properties.R} for report generation}
\label{sec:properties}
\definecolor{comment}{HTML}{000065}
\definecolor{string}{HTML}{DD0000}
\definecolor{special}{HTML}{0A0AF0}
{
\lstset{ %
language=R,                % the language of the code
basicstyle={\footnotesize \tt},       % the size of the fonts that are used for the code
commentstyle=\color{comment},
stringstyle=\color{string},
linewidth=\textwidth
}
<<echo=FALSE,results=tex>>=
#p <- readLines(system.file("report","properties.R",package="isobar"))
#p <- sub("(#.*)","\\\\textcolor{comment}{\\1}",p)
#for (i in c("TRUE","FALSE","NULL"))
#  p <- sub(i,paste("\\\\textcolor{special}{",i,"}",sep=""),p)
#p <- sub('(".*?")',"\\\\textcolor{string}{\\1}",p)
#p <- gsub("#","\\#",p,fixed=TRUE)
#p <- gsub("_","\\_",p,fixed=TRUE)
#cat("\\begin{lstlisting}")
#cat(p,sep="\n")
#cat("\\end{lstlisting}")
cat(sprintf("\\lstinputlisting{%s}\n",system.file("report","properties.R",package="isobar")))
@ 

}

\section{Dependencies}\label{sec:dependencies}
\subsection{\LaTeX{} and PGF/TikZ}
\LaTeX{} is a high-quality typesetting system; it includes features designed for the production of technical and scientific documentation. It is available as free software\footnote{\url{http://www.latex-project.org}}. PGF is a \TeX{} macro package for generating graphics It comes with a user-friedly syntax layer called TikZ\footnote{\url{http://sourceforge.net/projects/pgf}}.

\LaTeX{} is used for creating PDF analysis reports, with the PGF package creating the graphics. Go to \url{http://www.latex-project.org} to get information on how to download and install a \LaTeX{} system and packages.


\subsection{Perl}\label{app:installing-perl}
Perl is a high-level, general-purpose, interpreted, dynamic programming language. Perl is required for two tasks:
\begin{itemize}
  \item Conversion of Pidres XML and Mascot DAT files to ID CSV format;
  \item Creation of Microsoft Excel format data analysis report.
\end{itemize}
Go to \url{http://www.perl.org} to download and get help on the installation of Perl on your Operating System.
For file format conversion, perl module \texttt{Statistics::Lite} is required. For Excel export \texttt{Spreadsheet::WriteExcel}. All Perl scripts are in the subdirectory \texttt{pl} of the isobar package installation.

<<eval=FALSE>>=
## execute to find the path and file location in your installation.
system.file("pl",package="isobar") ## path
list.files(system.file("pl",package="isobar")) ## files
@ 

\texttt{mascotParser2.pl} and \texttt{pidresParser2.pl} convert from respective protein search outputfiles to a XML file format, which can be converted into a CSV file readable by \textit{isobar} by using \texttt{psx2tab2.pl}.

\texttt{mascotParser2.pl} coverts from Mascot format, and requires the file \texttt{modifconv.csv} as a definition of modification names. \texttt{pidresParser2.pl} converts from Phenyx output and requires the file \texttt{parsersConfig.xml}. \texttt{tab2xls.pl} converts csv file to different sheets of an Excel spreadsheet.

\label{app:itraqmascotparser-help}
<<eval=FALSE>>=
## execute on your system
system(paste("perl",system.file("pl","mascotParser2.pl",package="isobar"),
            "--help"))

print(paste("perl",system.file("pl","pidresParser2.pl",package="isobar"),
            "--help"))
@


\section{Session Information}

The version number of R and packages loaded for generating the vignette were:

<<<echo=FALSE,results=tex>>=
  toLatex(sessionInfo())
@ 
\end{appendix}

\end{document}

