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\title{\emph{isobar} package for iTRAQ and TMT protein quantification}
\author{Florian P.\ Breitwieser, Jacques Colinge}

\begin{document}

\maketitle
\tableofcontents

\section{Introduction}
The \Rpackage{isobar} package is an extensible and interactive
environment for the data analysis and exploration of iTRAQ and TMT
data.  \Rpackage{isobar} implements the theory presented in
Breitwieser et al., Journal of Proteome Research 2011. Further
extensions for the analysis of \small{PTM} data are presented in
Breitwieser and Colinge, Journal of Proteomics 2013.

\subsection{Installation}
\Rpackage{isobar} is part of
\href{http://www.bioconductor.org/packages/release/bioc/html/isobar.html}{Bioconductor}. To
install the latest stable version available for your version of R, start R and
enter:

<<eval=FALSE>>=
source("http://bioconductor.org/biocLite.R")
biocLite("isobar")
@ 

Bioconductor has semi-annual releases, and the releases are tied to
specific to R versions. The latest development version of
\Rpackage{isobar} are always available at GitHub
(\url{https://github.com/fbreitwieser/isobar}). Use the
\Rpackage{devtools} package to install it from the source:

<<eval=FALSE>>=
library(devtools)
install_github("fbreitwieser/isobar")
@ 

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

Check the installed version of isobar:
<<eval=FALSE>>=
packageVersion("isobar")
@ 
or equivalently for older versions of R:
<<eval=FALSE>>=
packageDescription("isobar")$Version
@ 

\section{Loading data}\label{sec: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 ``ID.CSV'' format is
described in appendix \ref{app:fileformats}.  Rockerbox and MSGF+ CSV
file formats can be read, too, and are recognized by their file
extension (\texttt{peptides.csv} and \texttt{msgfp.csv}).
Experimental support for the mzIdentML format is also available -
please contact the maintainer 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}, {\small TMT6plexSpectra} and {\small TMT10plexSpectra} 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{Loading data from CID/HCD (or CID/MS3, etc) experiments}

A combined CID/HCD approach, in which for each precursor two fragmentation
spectra are acquired, has proven useful to increase the number of identified
and quantified peptide-spectrum matches. Usually, the reporter intensity 
information is taken from the HCD spectrum, and the peptide is identified 
based on the fragment ions in the CID spectrum. 

To import these experiments, a comma-separated 
\emph{mapping file} is
needed, which contains the association from the \texttt{identification} to the
\texttt{quantification} spectrum title.

\paragraph{Example mapping file (\texttt{mapping.csv}):}
\begin{verbatim}
"hcd","cid"
"spectrum 1","spectrum 2"
"spectrum 3","spectrum 4"
\end{verbatim}

By calling \Rfunction{readIBSpectra} with \texttt{mapping.file="mapping.csv"}, the spectra titles
in the \texttt{id.file} are matched to those in the \texttt{peaklist.file}. If the column names 
for the quantification and identification spectrum are not \texttt{hcd} and \texttt{cid}, resp., 
they can be set with the argument \texttt{mapping}=\texttt{c(identification.spectrum="column name 1",quantification.spectrum="column name 2")}.

\textbf{Example mapping file \texttt{mapping2.csv}:}
\begin{verbatim}
"quant-spectrum-ms3","id-spectrum-ms2"
"spectrum 1","spectrum 2"
"spectrum 3","spectrum 4"
\end{verbatim}

<<eval=FALSE>>=
readIBSpectra(...,
              mapping.file="mapping2.csv",
              mapping=c(identification.spectrum="id-spectrum-ms2",
                        quantification.spectrum="quant-spectrum-ms3")
              )
@

The argument \texttt{mapping.file} can take multiple files as argument (which are read and concatenated), or a \texttt{data.frame}.

\subsection{Loading data from CID/HCD experiments with full HCD
  spectrum}

If a full HCD spectrum was acquired, and both the HCD and CID spectrum
are searched against a protein database, the argument
\texttt{id.file.domap} to \texttt{readIBSpectra} can be used to merge
both CID and HCD identifications:

<<eval=FALSE>>=
readIBSpectra("TMT6plexSpectra",
              id.file="cid.identifications.csv",
              peaklist.file="hcd.peaklist.mgf",
              id.file.domap="hcd.identifications.csv",
              mapping.file="mapping.csv",
              ...
              )
@

Here, the CID (\texttt{cid.identifications.csv}) and HCD
identifications (\texttt{hcd.identifications.csv}) are combined and
mapped according to \texttt{mapping.csv}. Diverging identifications
are discarded.

\subsection{Integrating and thresholding precursor purity measures}
Savitzki et al., 2011 describe a measure for the precursor purity of
fragment spectra. An implementation of the algorithm for the
\texttt{multiplierz} platform is provided at
\url{http://sourceforge.net/apps/trac/multiplierz/wiki/Precursors}. The
script creates \texttt{\_precursors.txt} files for each \texttt{RAW}
file. To integrate and threshold based on the \texttt{s2i} measure,
use the following code:

<<eval=FALSE>>=
precursors <- read.delim("file1_precursors.txt",header=TRUE,sep="\t",stringsAsFactors=FALSE)
idfile <- read.delim("file1.id.csv",header=TRUE,sep="\t",stringsAsFactors=FALSE)

idfile <- merge(idfile,precursors,
                by.x=c("fixed_spectrum","scans.from"),
                by.y=c("fixed_spectrum","SCAN"),all.x=TRUE) # FIXME see /work/analysis/gwinter/raw-spectra/analysis.R
@ 

\subsection{\Rpackage{MSnbase} integration}
NOTE: It has been reported that the conversion functions are not
functional for current versions of the package. We will try to resolve
this.

\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)}.


\subsection{\Rpackage{MSnbase} integration}
NOTE: It has been reported that the conversion functions are not
functional for current versions of the package. We will try to resolve
this.

\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.

<<print-reporterMasses,fig=TRUE,width=6.6,height=3,include=FALSE>>=
  print(reporterMassPrecision(ibspiked_set1))
@ 

\begin{center}
\begin{figure}[htp]
\includegraphics{graphics/plot-print-reporterMasses}
\caption{Reporter mass precision plot.}
\label{fig:reporterMassPrecision}
\end{figure}
\end{center}

\subsection{Normalization and isotope impurity correction} \label{sec:norm-isot-impur}
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)
@ 

<<fig-maplot-normalize1,echo=FALSE>>=
png("graphics/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()
@

\begin{center}
\begin{figure}[htp]
\includegraphics{graphics/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,direction="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="include",
                            specificity="reporter-specific")
nm.ceru <- NoiseModel(ib.ceru,one.to.one=FALSE,pool=TRUE)
@ 

<<maplot-noisemodel1,echo=FALSE>>=
png("graphics/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()
@ 

\begin{center}
\begin{figure}[htp]
\includegraphics{graphics/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{combn.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,cl=c("1","0","0","0"))

## 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),combn.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}
\Rpackage{isobar} provides a rich interface for creating Excel and PDF
reports for further analysis and quality control. The main entry
function is \texttt{create.reports}. Alternatively the Rscript
\texttt{create\_reports.R} can be used. It is located in the
\texttt{report} folder of the \isobar{} installation, and reads the
properties from a file in the working directory.

The posible values are defined in the \texttt{report/properties.R} file in the
isobar installation. To generate a report with standard properties the 
following code should do the trick:

<<eval=FALSE>>=
create.reports(type="iTRAQ4plexSpectra",
               identifications="my.id.csv",peaklist="my.mgf")
@ 

The properties can also be defined in a \texttt{properties.R} file
which is located in the working directory. The properties are set in
the following order:

\begin{itemize}
  \item 'global' properties in \texttt{ISOBAR-DIRECTORY/report/properties.R}\footnote{located in \texttt{system.file('report','properties.R',package='isobar')}}
  \item 'local' properies in \texttt{WORKING-DIRECTORY/properties.R}
  \item command line arguments to \texttt{create\_reports.R} or 
        \texttt{create.reports} function
\end{itemize}

Appendix \ref{sec:properties} provides a syntax-highlighted version of
the properties file supplied with isobar, which sets the default
parameters and provides some help in the comments. The number of
paramters which can be set may seem a lot at first, however most
times only a few are needed. 

For successful completion, \LaTeX - for the PDF reports - and Perl -
for the Excel reports - need to be installed.

\subsection{Files used for report generation}
<<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.R] 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}

\bibliographystyle{plainnat}
