%\VignetteIndexEntry{IsoGeneGUI Vignette}
%\VignetteAuthor{Setia Pramana}
%\VignetteDepends{}
%\VignetteKeywords{Dose-response microarray data GUI}
%\VignettePackage{IsoGeneGUI}



\documentclass[a4paper]{article}
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\begin{document}

\title{IsoGeneGUI Package Vignette}
\author{Setia Pramana, Martin Otava, Dan Lin, Ziv Shkedy}
\date{September 25, 2014}
\maketitle

\section{Introduction}
The IsoGene Graphical User Interface (IsoGeneGUI) is a user friendly interface of the IsoGene package which is aimed to 
perform analysis of dose-response studies in microarray experiments. Compared to
original package, GUI is extended thoroughly by addition of methods from other
packages related to gene expression analysis, namely GORIC, ORCME,
ORIClust and orQA. Hence, it provides wide range of tools and covers all the
range of methods currently available in are in "non-GUI" packages. The
IsoGeneGUI is developed for the user with no or limited knowledge about R programming so he/she can perform the analysis of dose-response in microarray setting  easily. This GUI was developed using tcl/tk package.
The statistical methodologies (test statistics, etc.)  used in this package are discussed by Lin et.al (2007, 2008, 2010). 


\section{Usage}
To run the package 
<<open, eval=FALSE >>=
library(IsoGeneGUI)
IsoGeneGUI()
@


\section{Menus}

The main window of the IsoGene-UI is presented in Figure \ref{fig:Main}. The
package has five main menus: File, Analysis, Clustering, Plots and Help. In the
middle of the main window there is an info box which provides information about the data (availability and summary) and the result summary of the last performed analysis.



\begin{figure}
\includegraphics[scale=0.5]{./images/main}
\label{fig:Main}
\caption{The main window of the ISoGeneGUI.}
\end{figure}



\begin{enumerate}
\item  File:
       \begin{enumerate}
       \item Open dataset
       		 \begin{enumerate}
         		 \item R workspace (*.RData files)
          		 \item Excel or Text file (*.xls or *.txt files)
          	 \end{enumerate}
      \item  Show dataset
       \item  Exit
 		\end{enumerate}
\item Analysis:  
 	\begin{enumerate}
    \item Set seed
    \item Likelihood Ratio Test (E2): Asymptotic
    \item Permutation 
    \item Significant Analysis of Microarrays (SAM)
    	\begin{enumerate}
         \item  SAM Permutation
        \item SAM Analysis
         \end{enumerate}	
    \item Likelihood Ratio Test (E2): orQA
    \item GORIC
	\end{enumerate}
\item Clustering:  
 	\begin{enumerate}
    \item ORCME
    \item ORIClust
    \end{enumerate}
\item Plot:    
	\begin{enumerate}    
    \item IsoPlot
   	\item Permutation Plot
    \item SAM Plot
    	\begin{enumerate}   
          \item  Plot of FDR vs. Delta
          \item  Plot of number of significant genes vs. Delta
          \item Plot of number of False Positive vs. Delta
        \end{enumerate}
   	\item  User defined scatter plot
    \item  Plot ORCME clusters
	\end{enumerate}
\item Help: 
	\begin{enumerate}    
   	\item  IsoGene Help
    \item  IsoGeneGUI Help
    \item  About
  	\end{enumerate}
\end{enumerate}



\section{Reading the Data}
In the first step of the analysis, the data is uploaded to the package using the File menu.
The package can read data in R workspace file (*.RData), Microsoft Excel and
text files.
\subsection{R workspace files}
 The format of the gene expression data should be a matrix or table where the columns are the arrays 
 and the rows are the gene names. The dose information should be a vector or table contain the dose 
 levels which corresponds to the arrays in the expression matrix/table.\newline
 The package provides an R workspace example dataset called \texttt{dopamine2}. For the dopamine2 data, 
 the dose levels are given by
\small
\begin{center}
\begin{verbatim}
> dose
 [1] 0.00 0.00 0.01 0.01 0.04 0.04 0.16 0.16 0.63 0.63
[11] 2.50 2.50 0.00 0.00 0.00 0.01 0.01 0.01 0.04 0.04
[21] 0.16 0.16 0.63 0.63 2.50 2.50
\end{verbatim}

\end{center}
\normalsize
while the expression matrix has the following structure:
\small
\begin{center}

\begin{verbatim}
> dopamine[1:5, 1:6]
             X1        X2         X3        X4        X5
201_at  2.579138  2.318749  2.496895  2.456772  2.479480
202_at  2.140561  2.061804  2.131749  2.107638  2.086722
203_at  6.988566  6.620562  5.764725  6.326178  7.020716
204_at 11.081855  9.974999 10.790689 10.702516 10.544664
205_at 12.104545 12.076975 11.989770 12.151120 12.118520
\end{verbatim}
\end{center}
\normalsize



Part of the \texttt{dopamine2} can be obtained in folder "exampleData" inside the package. 


The full example data in R workspace, text and Excel files can be obtained
from: \url{http://ibiostat.be/software/isogenegui}


\subsection{Open the data set}

In order to upload the R workspace we choose in the file menu in Figure~\ref{fig:Main}a the following sequence:

\begin{center}
\begin{verbatim}
File > Open dataset > R workspace
\end{verbatim}
\end{center}

The package will automatically refer to the example data \texttt{dopamine2} if
the option to open R workspace is choosen.


Text files and Excel files can be uploaded to IsoGene-GUI as well
using following sequence in the dialogue box:

\begin{center}
\begin{verbatim}
File > Open dataset > Excel or Text files
\end{verbatim}
\end{center}

Once the the data is uploaded, information about the data is presented automatically in the info box of the main window.


\section{Exploratory Data Analysis}
\label{explore}

To explore the expression of the genes, the IsoGeneGUI provides the submenu \textbf{IsoPlot} 
from \textbf{Plots} menu. This feature displays the data points, sample means at each dose and an 
isotonic regression line. 

There are three input options to draw the isotonic regression plot, using gene name(s), row number(s) or using a range of row numbers. There are also three check boxes:
 \begin{enumerate}	
 \item Dose as ordinal.
  This option will draw the plot and treat dose as ordinal scale. The default will plot with dose as a continuous variable.
 \item Show isotonic regression curve for both direction.
 The default plot just display the isotonic trend/curve which is more likely fit the data. By checking this option, the isotonic trend for both directions will be displayed.
 \item Show summary of the data.
 This option is to provide a short summary of each selected gene.
 \end{enumerate}	



\section{Data Analysis}
The \textbf{IsoGeneGUI} package provides four options for analysis: (1) analysis
with the likelihood ratio test statistic (LRT) using its exact p-values, (2)
resampling based analysis (from IsoGene and orQA package), (3) significance
analysis of microarrays (SAM) and (4) generalized order restricted information
criterion (GORIC).

\subsection{LRT Using exact p-values}
\label{LRTAsymp}
To perform the exact LRT we choose the following sequence in the analysis menu:

\begin{verbatim}
Analysis > Likelihood Ratio Test (E2): Asymptotic
\end{verbatim}

Then the main dialog box for analysis based on the LRT using exact p-values is shown in Figure 
\ref{fig:Exact}.

\begin{figure}[!h]

\includegraphics[scale=0.5]{./images/Exact}
\caption{\label{fig:Exact}The main dialog box of Likelihood Ratio Test $E^{2}$ analysis.}
\end{figure}

Note that the users can choose to perform the analysis for all the genes on the array or on predefined subset. 
In addition, in order to adjust for multiplicity, 
we need to select from the menu the adjustment method to be used and the overall error rate.


\subsection{Resampling Based Methods}

Resampling based analysis can be performed by choosing either of following
options in analysis menu:

\begin{verbatim}
Analysis > Permutation
Analysis > Likelihood Ratio Test (E2): orQA
\end{verbatim}

first option implements function from IsoGene package and offers five different
test statistics as shown in Figure~\ref{fig:Perm} that presents the main menu
for resampling method. 
Second option implements functions from package orQA and provides virtually same
menu as in Figure~\ref{fig:Exact}. However, computation is done using
permutation test. The orQA implementation is much faster than the one from
IsoGene, but its restricted only to E2 statistics. Hence, iif it is only
interest, we recommend orQA implementation. If you are interested in other
test statistics, Permutation option is proper choice.
In both options, we need to specify the number of permutations and the
multiplicity adjustment(s). Similar to the $E^2$ menu, we can specify default graphical displays.


\begin{figure}

\includegraphics[scale=0.5]{./images/permutationMain}
\caption{\label{fig:Perm}The main dialog box for resampling-based monotone trend test.}
\end{figure}


\subsection{Significance Analysis of Microarrays (SAM)}

The \textbf{IsoGene-GUI} also provides testing for the dose-response relationship under order restricted 
alternatives using the Significance Analysis of Microarrays procedure (SAM). To perform this analysis 
there are two main steps: (1) calculating the SAM regularized test statistic using permutation and 
(2) the SAM analysis. These two steps are performed by two sub menus in the SAM menu.

\begin{verbatim}
Analysis > Significance Analysis of Microarrays > SAM Permutation
Analysis > Significance Analysis of Microarrays > SAM Analysis
\end{verbatim}


Then in the first step several options of the use fudge factor are provided. 
User can specify  to use \textbf{no Fudge Factor}, \textbf{Automatic Fudge factor} 
(fudge factor will be calculated using the methods described in the SAM manual), 
or the \textbf{fudge factor based on a specific percentile}.

After the statistics are computed or loaded from a file, we now can perform the
SAM analysis. After specifying the test statistic, the main dialog box of SAM
analysis, which shows the plot of the observed versus the expected test statistics and other options, 
will be shown (see Figure \ref{fig:SAM}). 

\begin{figure}

\includegraphics[scale=0.5]{./images/SAMAnalysisWindow}
\caption{\label{fig:SAM}The main dialog box for SAM analysis.}
\end{figure}


Note that, in this plot we can change the delta value (automatically using delta slider or 
manually using delta input box) and the FDR level. 

\subsection{Generalized Order Restricted Information Criterion (GORIC)}

The GORIC method (Kuiper et al., 2014) focuses on model selection rather then
estimates.
Its implementation in the \textbf{IsoGeneGUI} package fits all possible models under
monotonicity assumption and output their posterior probability based based on
information criterion. The information criterion used takes into account
order constraints and adjust the penalty accordingly. The posterior
probability of model is computed via simulations. Therefore, the procedure is
computationally intensive and in the pacakge, it is allowed to compute it for
one gene at the time only. Therefore, it should be applied only on genes
selected by other analyses as especiallz itneresting ones. The resulting
posterior probabilities can be used for model selection purposes.

\begin{verbatim}
Analysis > GORIC
\end{verbatim}


\section{Clustering}
The \textbf{IsoGeneGUI} package provides two methods for gene clustering
according to their profiles:
(1) $\delta$-clustering method that focus on order restricted clustering with
monotonicity assumption (ORCME), (2) information criterion based clustering
(ORIClust).

\subsection{Order Restricted Clustering for Microarray Experiments (ORCME)}

Clustering analysis performed by ORCME package is based on delta-biclustering
algorithm (Cheng and Church, 2000). It clusters genes according to their
overall profile of isotonic means. The homogeneity of selected clusters can be
influenced by appropriate parameter value. The parameter can be optimized with
respect to within cluster variability and number of discovered clusters.
Compared to ORIClust, the method can create more homogeneous clusters , while
focusing on smaller space of possible profiles (monotone ones)

\begin{verbatim}
Analysis > ORCME
\end{verbatim}


\subsection{Order Restricted Information Criterion Based Clustering Algorithm
(ORIClust)}

The ORIClust performs one-stage or two-stage clustering algorithm based on
information criterion ORICC (Liu et al., 2009). The genes are clsutered
according to their profiles, while taking into account ordering of doses and shape of dose-response
relationship. ORIClust provides wider range of possible profiles, such as
umbrella profiles, compared to ORCME. 

\begin{verbatim}
Analysis > ORIClust
\end{verbatim}


\section{Saving IsoGeneGUI Outputs}
Each plot produced from the analysis in IsoGeneGUI can be copied into clipboard which can be 
easily pasted into Microsoft Office documents, such as Microsoft Word. It can be done
by clicking the \textbf{Copy to Clipboard} button located in the bottom of the graph and then paste it directly 
to the document (or \texttt{Ctrl-V}). Furthermore, the plots can also be saved into several 
image formats (ps, png, jpeg, bmp, tiff). After the format is
selected/highlighted from the list, the user can click \textbf{Browse} button to
specify the name of the image file and also the file location.

Note that results of all genes and lists of significant genes from the three analysis above can be
shown and also saved into an \texttt{R-workspace} and/or an Excel file by
clicking \textbf{Save} button.




\section{Complete users' manual}
Users can access the IsoGeneGUI documentation  online or by installing the IsoGeneGUI
package locally. Then the following code can be typed at R prompt:

<<help, eval=FALSE >>=
if (interactive()) 
    {
	browseURL("http://ibiostat.be/software/isogenegui")
    }
	
@
or alternatively:
<<help, eval=FALSE >>=			
if (interactive()) {
library(IsoGeneGUI)
 IsoGeneGUIHelp()
}
@

Users can download example dataset and a complete Users' Manual from the site as well.


\section*{References}

\begin{enumerate} 
  
\item Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In:
\textit{Proceedings of the Eighth International Conference on Intelligent Systems
for Molecular Biology}, \textbf{1}: 93-103

\item Kuiper, R. M., Gerhard, D., and Hothorn, L. A. (2014), Identification of
the Minimum Efeective Dose for Normally Distributed Endpoints Using a Model
Selection Approach, \textit{Statistics in Biopharmaceutical Research},
\textbf{6(1)}: 55-66.

	\item Lin, D., Shkedy, Z., Yekutieli, D., Burzykowski, T., G\"{o}hlmann, H., De Bondt, A., Perera, T., Geerts, T. and Bijnens, L. (2007) 
	Testing for trends in dose-response microarray experiments: A comparison of several testing 
	procedures, multiplicity and resamplingbased inference. 
	\textit{Statistical Applications in Genetics and Molecular Biology}, \textbf{6(1)}, Article 26.
 
	\item Lin, D., Shkedy, Z., Burzykowki, R., Ion,
	T., G\"{o}hlmann, H.W.H., De Bondt, A., Perera, T., Geerts, T. and
	Bijnens, L. (2008) An investigation on performance of Significance
	Analysis of Microarray (SAM) for the comparisons of several
	treatments with one control in the presence of small variance genes.
	\textit{Biometrical Journal}, Multiple Comparison Problem, Special Issue,
	\textbf{50(5)}, 801--823.

\item Lin, D., Shkedy, Z., Yekutieli, D., Amaratunga, D. and
Bijnens, L., editors (2012) \textit{Modeling Dose-response Microarray Data in
Early Drug Development Experiments Using R}.
Springer.

\item Liu, T., Lin, N., Shi, N. and Zhang, B. (2009),
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.
\textit{BMC Bioinformatics}, \textbf{10}, 146


\end{enumerate}

\end{document}
