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\begin{document}
\SweaveOpts{concordance=TRUE}
\title{\Biocpkg{ProStaR} Tutorial}
\author{Florence Combes\footnote{firstname.lastname@cea.fr} , 
Wieczorek Samuel$^\ast$, Burger Thomas$^\ast$}
\maketitle

%% Abstract and keywords %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\vskip 0.3in minus 0.1in
\hrule
\begin{abstract}


This document is a tutorial to \Biocpkg{ProStaR}. It uses the data stored 
in the 'Exp1\_R25\_pept.txt' file, from the \Biocpkg{DAPARdata} package, to 
illustrate on a walkthrough example the use of \Biocpkg{ProStaR} to conduct a 
proteomics quantitative analysis.

% On the basis of a dataset available in  the analysis of proteomics data with 
% \Biocpkg{ProStaR}, which is the graphical interface of \Biocpkg{DAPAR} 
% (Differential Analysis of Protein Abundance with R). 
% \Biocpkg{ProStaR} being a click-button interface, no programming skill is 
% required to use it and access all the functions of \Biocpkg{DAPAR}. 
% 
% Showing a "real data" analysis as an example, this document presents the 
% analysis steps you can perform with \Biocpkg{ProStaR} : descriptive 
% statistics, missing data imputation, normalization, agregation and 
% differential analysis. 


\end{abstract}
\vskip 0.1in minus 0.05in
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newpage
\tableofcontents
\newpage


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Preambule}\label{sec:Preamb}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


This document is only a step by step illustration of the use of 
\Biocpkg{ProStaR}. For a comprehensive description of \Biocpkg{ProStaR} 
functionalities, we  refer the readers to the "\Biocpkg{DAPAR} and 
\Biocpkg{ProStaR} user manual", available on the Bioconductor webpage of 
\Biocpkg{ProStaR}. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Loading data}\label{sec:DataLoad}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


The text-format file of 'Exp1\_R25\_pept.txt' is part of the 
\Biocpkg{DAPARdata} package. For a precise understanding of the dataset, 
please refer to the document "Description of the UPS\{pep/prot\}25 datasets" 
available on the bioconductor webpage of \Biocpkg{DAPARdata}.

In a nutshell, it gathers the peptide intensities of 6 samples, containing 
each the same yeast lysate. 
In addition, several human proteins have been spiked in, with different 
concentrations: three samples theoretically contains 2.5 more humain proteins 
than the three others. At the end of the quantitative analysis, all and only 
these proteins should be selected as significantly differentially abundant.


\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/DatManager.png}}
\caption{Dataset Manager Menu}\label{fig:datman}
\end{figure}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\subsection{Text-formatted data}\label{sec:txtData}
The text file containing the data must be of tabular form, as those produced 
by MaxQuant software, for instance.
It is to be uploaded by clicking on the "Convert data" of the "Dataset manager"
submenu (see Fig.~\ref{fig:datman}). This opens the page captured on 
Fig.~\ref{fig:convdattxt} and composed of five panels: Select File, Data Id, 
Exp and Feat data, Samples and Convert. 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/ConvData.png}}
\caption{Conversion Text Data Page}\label{fig:convdattxt}
\end{figure}

In the panel \textbf{Select File}: Indicate where to find the text file 
containing the data. As in this example, the file contains peptide intensities 
that are not depicted on a logarithm scale, the corresponding options must be 
manually selected (see Fig.~\ref{fig:convdattxt}). 

In the panel \textbf{Data Id}: Indicate the column that contains the IDs of the 
peptides. These IDs have to be unique across all the rows. 
In the example dataset, it is simply the column referred to as "id". 
This is why "id" is selected in the drop down menu (see Fig.~\ref{fig:userid}). 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/DatManUserID.png}}
\caption{Using user-column 'id' as an ID in ProStaR}\label{fig:userid}
\end{figure}


In the panel \textbf{Exp. and Feat. Data}: indicate the title of the columns
that contains quantitative data of the peptides 
(See Fig.~\ref{fig:expfeatdata}). 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/ExpFeatData.png}}
\caption{Exp. and Feat. Data panel}\label{fig:expfeatdata}
\end{figure}


In the panel \textbf{Samples metadata}: Fill in the table according to the 
properties of the experimental design (see Fig.~\ref{fig:metadata}). In the 
example case, the first 3 samples correspond to 25 fmol of human proteins, 
while the others to 10fmol; the concentration is used to refer to the 
conditions. What matters here is to have different name for the two conditions,
and to have a similar name within each condition. Because of the simple 
experimental design, the biological, technical and analytical replicates do 
not need to be filled.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/MetaData.png}}
\caption{Metadata table to fill in (Samples metadata panel)}\label{fig:metadata}
\end{figure}


Finally, in the panel \textbf{Convert}, enter the name of the project and click
on "Convert data" to build an MSnSet object containing the data.  
A message indicates when the conversion is over (see fig.~\ref{fig:convdone}). 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/ConvDone.png}}
\caption{The conversion of text file to MSnSet format is done.}
\label{fig:convdone}
\end{figure}

Please also notice in the screenshot that the value in the top-right box has 
changed from 'None' to 'Original - peptide'. All along the  
\Biocpkg{ProStaR} pipeline, this box indicates the current state of the dataset.
Here "original" means that no processing  has been performed so far, and 
"peptide" means that the dataset contains peptide intensities. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Descriptive statistics}\label{sec:explo}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Once the data are loaded, their exploration/visualisation is the natural 
following step. 
By clicking on "Descriptive statistics"  in the menu, one finds a series of 9 
panels, that are helpful to understand the data:

The first one, referred to as \textbf{Overview}, shows very basics and general 
informations about your 
dataset. For our example we can see we have: 6 samples, 13919 peptides, 
19.19\% of missing values and 733 lines with only NA values (missing values).

The second one, referred to as \textbf{Missing values}, summarizes the missing 
values distributions with 3 plots (see fig.~\ref{fig:MVbarplots} from left to 
right), that are also use to monitor the imputation of missing values 
(see Section~\ref{sec:MVimput}):

\begin{itemize}
\item \textbf{'\# NA per columns'} i.e. the number of missing values per sample.
\item \textbf{'\# of lines by \# of NA'}: how many lines (peptides in our 
example) have 1, 2, .., up to 6 NA. 
The red bar bar of the barplot (6 here) corresponds to peptides that have only
missing values, i.e. that have been identified but not quantified. 
Notice that this corresponds to the 733 lines already pointed on the previous 
paragraph. 
\item \textbf{'\# of lines by \# of NA per condition'}: It is the same plot as 
the second one, yet condition-wise, rather than entire dataset-wise. Notice 
that in this third plot, the number of lines with 0 missing values is 
presented, contrarily to the second one (where it would shrink too much the 
Y-axis).
\end{itemize}

The third one, referred to as \textbf{Data explorer}, allows the practitioner 
to navigate through the MSnSet containing the data.
Several tables are available: quantitative data, peptides metadata, replicate
metadata. 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/MV-barplots.png}}
\caption{Missing Values repartition in the dataset}\label{fig:MVbarplots}
\end{figure}

The other panels produce graphical representations of the dataset: 
\textbf{Boxplot}, \textbf{Densityplot}, \textbf{Correlation matrix}, 
\textbf{Heatmap}, \textbf{Violinplot} and \textbf{CV distribution} (see some 
of them on Fig.~\ref{fig:corrmat} to~\ref{fig:densplot}).

In particular: 
\begin{itemize}
\item The correlation matrix (Fig.~\ref{fig:corrmat}) shows well the 
replicates correlate better within conditions (25fmol together or 10fmol 
together). 
This confirms the qualilty of the dataset.
\item On the density plots (Fig.~\ref{fig:densplot}) the distributions are 
Gaussian-like. It is a pre-requisite for most next coming statistical 
processing. %for data analysis, we can follow up. 
\end{itemize}

%%Corr Matrix
\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/CorrMat.png}}
\caption{Correlation Matrix (for the 6 samples)}\label{fig:corrmat}
\end{figure}

%%Boxplot
\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/Boxplot.png}}
\caption{Boxplot (raw data)}\label{fig:boxplot}
\end{figure}

%%Density plot
\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/DensPlot.png}}
\caption{Density plots of the log2 raw data}\label{fig:densplot}
\end{figure}

% %%Var dist
% \begin{figure}
% \centering
% \fbox{\includegraphics[width=0.85\textwidth]{captures/VarDist.png}}
% \caption{Variance distributions for the 6 samples}\label{fig:vardist}
% \end{figure}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Filtering}\label{sec:MVfilt}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Clicking on 'Filter data' in the drop down submenu 'Data processing' allows 
the practitioner to filter out some lines of the dataset according to 
various criteria.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/Filt.png}}
\caption{Screenshot of the missing value filtering panel. Graphics are 
interactively updated.}\label{fig:Filt}
\end{figure}

In the first panel, one filters out peptides with too many missing values. 
The same plots as those from the 'Descriptive statistics' panel are displayed 
(see Fig.~\ref{fig:Filt}). 
For this dataset, we propose to filter out peptides that have at maximum 1 
missing value among the 3 peptide intensities of each condition (but other 
filtering option would also make sense, depending on the dataset, and on the 
next coming processing). This results in the options selection that is 
illustrated on Fig.~\ref{fig:FiltPerform}, where one sees that the 3 plots 
have been automatically updated, as soon as the 'Perform filtering MV' button 
has been clicked on.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/FiltPerform.png}}
\caption{Screenshot of the missing value filtering panel. Graphics are 
interactively updated.}\label{fig:FiltPerform}
\end{figure}

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/StringFilt.png}}
\caption{Screenshot of the string based filtering panel. Graphics are 
interactively updated.}\label{fig:StringFilt}
\end{figure}


% %See fig.~\ref{fig:Filt}. 
% This threshold may be applied on the whole dataset, on each condition 
% or on at least one condition.
% \newline The barplots are updated interactively, thus allowing to easily 
% adapt the threshold if necessary.
% For our expmale we keep only the lines where there is at least 2 significant 
% values (among the 3 replicates) per condition. 

Once this filtering has been performed, it is possible to move to the 
string-based filtering panel, which proposes to remove lines on the basis of 
some meta-data (i.e. peptide sequences that are known to be contaminants ou 
reverse sequences). This panels works as the previous one and is shown on 
Fig.~\ref{fig:StringFilt}.

%Yet, with this toy 
%dataset, there is no string-based filtering to apply.

Finally, one goes on the third panel, named 'Visualise and validate', that aims
at performing the final validation of the filtering steps. To do so, one simply
clicks on "Save filtered dataset" (Fig.~\ref{fig:FiltValid}). From that point 
on, the field in the top-right box is updated to "\textbf{Filtered} - peptide",
indicating that \Biocpkg{ProStaR} will work on this filtered version of the 
dataset.  As a result, all the panels are interactively updated through 
\Biocpkg{ProStaR}. Hence, if one goes back to the 'Descriptive statistics' 
panels, all the plots will display this "\textbf{Filtered} - peptide" dataset. 
For example in the 'Overview' panel, 
it now appears that the filtered data consist of 10660 peptides 
(instead of 13919), with 3.88\% of missing values.  

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/FiltValid.png}}
\caption{Panel for filter data validation}\label{fig:FiltValid}
\end{figure}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Normalization}\label{sec:norm}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Once the data are filtered, the next step is to normalize the data (in the 
"Data processing" dropdown menu). It works as the previous processing steps, 
yet the navigation is even simpler. First, chose the adequate normalization 
method (here we have chosen "Quantile Centering - within conditions with 
quantile=0.5"); Second perfom the normalization; and finally, save it 
(see Fig.~\ref{fig:NormAfter}). As usual, notice that once the data are 
normalized, the top-right box indicates "Normalized - peptide".

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/Norm.png}}
\caption{The normalization panel - 
before performing normalization}\label{fig:Norm}
\end{figure}

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/NormAfter.png}}
\caption{The normalization panel - after the normalization has been performed}
\label{fig:NormAfter}
\end{figure}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Missing value imputation}\label{sec:MVimput}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The next step is to impute the missing values.
%To impute the data, we use the panel "Missing Values imputation" of the "Data 
%processing" submenu. 
On this panel (Fig.~\ref{fig:MV1}) two plots are of help to 
visualize the distribution of the missing values in the dataset, and to choose 
the adequate imputation method. 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/MV1.png}}
\caption{Panel Missing Values Imputation}\label{fig:MV1}
\end{figure}

On the left hand side (Fig.~\ref{fig:MV1}) is displayed the number of missing 
values as a function of the intensity of the peptides. 
% This allows to know if MV are present
% preferentially for peptides in the low intensities, or if the are spread 
% uniformly across the whole intensity-scale of the data. 
The missing values heatmap (on the right hand side) allows to see 
once-at-a-glance the possible difference of distribution of the missing values 
between the conditions. 

On this example, we impute the missing values with the imp4p method (with one
iteration, imputation of LAPALA, the upper distribution bound is set to 2.5 
and the distribution type used is beta).
Fig.~\ref{fig:MV2} represents the automatically updated plots. Once again, it 
is necessary to save the imputation before going on.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/MV2.png}}
\caption{Panel Missing Values Imputation}\label{fig:MV2}
\end{figure}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Protein-wise aggregation}
\label{sec:aggreg}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The dataset we work on is quantified at the peptide-level. Nevertheless, the 
aim of a proteomics quantitative analysis is to conclude at protein-level. 
Thus, we aggregate the peptide intensities into protein abundance values
% Several methods are available in the \Biocpkg{ProStaR} package for this
% aggregation step: sum, mean and "topn". 
(the available aggregation methods are described in the 'DAPAR and ProStaR 
user manual'). 

On the panel "Aggregation" of the "Data processing" drop-down menu 
(Fig.~\ref{fig:aggreg1}), one has first to indicate in the "Aggregate peptides" 
tab, the protein ID to consider (here 'Protein.group.IDs'). As soon as the 
protein ID is indicated, some plots and statistics provide evidences on the 
number of (specific or shared) peptides per proteins (on the right hand side 
of the panel), so as to help with the tuning of the two other parameters 
(Fig.~\ref{fig:aggreg1}).

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/aggreg1.png}}
\caption{Aggregation step panel before aggregation}\label{fig:aggreg1}
\end{figure}

After the tuning of the other parameters, namely, the aggregation method (here 
'mean'), and whether shared peptides are considered or not (here 'yes'), the 
aggregation can be performed.
A message shows up indicating the aggregation was successfully conducted 
(see Fig.~\ref{fig:aggreg2}): 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/aggreg2.png}}
\caption{Aggregation step panel after aggregation}\label{fig:aggreg2}
\end{figure}

As usual with \Biocpkg{ProStaR}, it is necessary to save the results. This is 
done on the next panel "Configure protein dataset". Here, one indicates among 
the peptide metadata of the dataset which one will be  kept and aggregated to 
define the protein metadata (here, we consider the column 
"Leading.razor.protein" - Fig.~\ref{fig:aggreg3}).
Once the aggregation is saved, please check that the top right box indicates 
now "Aggregated - proteins". From that point on, it is possible to go back to 
the "Descriptive statistics" tabs, and to investigate the new protein-wise 
dataset. For instance, in the "Overview" tab, it appears that one has the 
following information:
\begin{verbatim}
There is 6 samples in your data.
There is 2082 lines in your data.
Percentage of missing values: 0 %
\end{verbatim}

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/aggreg3.png}}
\caption{Aggregation step panel after aggregation}\label{fig:aggreg3}
\end{figure}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Differential analysis}\label{sec:DiffAna}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Differential analysis is the final step to identify differentially abundant 
proteins. This is performed on the corresponding panel of the "Data processing" 
drop-down menu. 
%It is needed that the dataset has no MV at this time (so imputation has to 
have been performed if the original dataset had MV).

The first step of the differential analysis is to build a volcano plot. This 
requires
conducting a statistical test on each protein, so as to fit them with a 
$p$-value each (which will be used as a complement to the fold-change to 
construct the plot). To do so, we advise to use Limma moderated $t$-test. 
% Limma was designed for transcriptomics data treatment but works well on 
% proteomics data for differential analysis (performed at the protein-level 
% -data not shown). 
Concretely, one has to define which are the two conditions to compare (select 
"condition 1" and "condition 2" - in the example case, it is obvious as there 
are only 2 different conditions)  and to select "Limma" on the drop down menu, 
on the left hand side of the panel. Then, the volcano plot is displayed 
(Fig.~\ref{fig:AnaDiffVolcano1}). 

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/AnaDiffVolcano1.png}}
\caption{Vocano plot panel (Differential analysis)}\label{fig:AnaDiffVolcano1}
\end{figure}

Then, the practitioner is allowed to define a threshold on the  fold-change, 
so as to filter out proteins which do not vary enough between the conditions 
(Fig.~\ref{fig:AnaDiffVolcano2}).

When the user puts the pointer of his mouse over a point of the plot, a tooltip 
window appears and shows some informations about that point. He can select the 
items to show in the Select widget where the 
different choices correspond to the columns of the feature meta-data table. 
The tooltip window is automatically updated.
When the user clicks on a point, a table is displayed above the 
volcanoplot. It shows the values of intensities for all the samples related to
the selected point. The cells colored in blue indicate that the corresponding 
value was a missing value in the original dataset and has been imputed,
The user can click and draw a rectangle on the plot to zoom in. 
By clicking on the button named "Reset zoom", the user can return to the 
entire plot.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/AnaDiffVolcano2.png}}
\caption{Vocanoplot panel (Differential analysis)}\label{fig:AnaDiffVolcano2}
\end{figure}


The \textbf{Calibrate Ana Diff} panel (fig.~\ref{fig:AnaDiffCalib}) helps the 
practitioner to check if the ditribution of the $p$-values is well-calibrated. 
This calls the cp4p R package functions, and we refer to the corresponding 
tutorial: 
\url{
https://sites.google.com/site/thomasburgerswebpage/download/
tutorial-CP4P-4.pdf}. 
Here, we simply use the default setting with "pounds" method.

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/AnaDiffCalib.png}}
\caption{Calibration panel for the differential analysis)}
\label{fig:AnaDiffCalib}
\end{figure}


On the \textbf{FDR} panel (fig.~\ref{fig:AnaDiffFDR}), one adjustes the 
$p$-value threshold (here to 0.0001), and apply the adaptive Benjamini-Hochberg 
procedure to adjust the $p$-values (corrected by pounds methods, see previous 
tab), which leads to an estimator of an upper bound of the proportion of false 
discoveries (referred to as False Discovery Rate, or FDR - in the example, it 
is estimated to 0\%).

\begin{figure}
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/AnaDiffFDR.png}}
\caption{FDR visualization (Differential Analysis)}
\label{fig:AnaDiffFDR}
\end{figure}

Finally, on the \textbf{Validate and Save} panel, one visualizes the selected 
45 proteins and save a new dataset with only the latter ones.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Export}\label{sec:export}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


At the end of the quantitative analysis, or whenever an intermediate step is 
achieved   along the processing pipeline, it is possible export the dataset. 
To do so, an "Export" panel is available in
the "Dataset Manager" drop-down menu (see Fig.~\ref{fig:Export}).

Let us note that the dataset which will be exported is the one that is 
indicated in the "Dataset versions" box. 
On the left of the panel ones specifies: the export format (MsnSet or Excel), 
and of course the name of the file exported. In the case of an Excel export, 
it is necessary to specify the protein or peptide ID that is used to map the 
data to the meta data (the file contains 3 sheets for the quantitative data, 
peptide/protein metadata and the replicate metadata). 

\begin{figure}[h]
\centering
\fbox{\includegraphics[width=0.85\textwidth]{captures/Export.png}}
\caption{Export page of \Biocpkg{ProStaR}}\label{fig:Export}
\end{figure}

\end{document}


