\section{Introduction}
{\em R/maanova} is an extensible, 
interactive environment for the analysis of microarray experiments. 
It is implemented as an add-on
package for the freely available statistical language R (www.r-project.org).
MAANOVA stands for MicroArray ANalysis Of VAriance. It provides a complete 
work flow for microarray data analysis including:
\begin{itemize}
\item Data quality checks - visualization and transformation
\item ANOVA model fitting - both fixed and mixed effects models
\item Calculating test statistics - F and Fs statistics
\item Deriving p-value - using sampling and residual shuffling permutation approach
\item Summarize the result - provide summary tables, graphics including volcano plot and cluster analysis with bootstrapping
\end{itemize}

{\em R/maanova} can be applied to any microarray data but it is
specially tailored for multiple factor experimental designs.
Mixed effects models are implemented to estimate variance components 
and perform F and T tests for differential expressions.
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% Installation section
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\newpage
\section{Installation}
\subsection{System requirements}
This package was developed under {\em R 1.8.0} and updated under {\em R 2.5.1} in the {\em Linux Redhat 8.0} operating system. The programs have been 
observed to work under {\em Windows NT/98/2000}. The memory requirement
depends on the size of the input data but a minimum of 256Mb memory
is recommended. 
\subsection{Obtaining R}
R is available in the Comprehensive R Archive Network (CRAN).
Visit \\{\tt http://cran.r-project.org} or a local mirror site.
Source code is available for UNIX/LINUX, and binaries are 
available for Windows, MacOS and many versions of Linux.  
\subsection{Installation - Windows(9x/NT/2000)}
\begin{itemize}
\item Install {\em R/maanova} from Rgui
\begin{enumerate}
\item Start Rgui
\item Select Menu {\tt Packages}, 
click {\tt Install package from local zip file}. 
Choose the file \\{\tt maanova\_*.tar.gz} and click 'OK'. 
\end{enumerate}
\item Install {\em R/maanova} outside Rgui
\begin{enumerate}
\item Unzip the {\tt maanova\_*.tar.gz} file into the directory 
{\tt \$RHOME/library} ({\tt \$RHOME} is something like 
{\tt c:/Program Files/R/rw1081}).
Note that this should create a directory \\{\tt \$RHOME/library/maanova} 
containing the R source code and the compiled dlls.
\item Start Rgui.
\item Type \Rfunction{link.html.help()} to get the help files for 
the maanova package added to the help indices.
\end{enumerate}
\end{itemize}
\subsection{Installation - Linux/Unix}
\begin{enumerate}
\item Go into the directory containing {\tt maanova\_*.tar.gz}.
\item Type \Rfunction{R CMD INSTALL maanova} to have the package installed
in the standard location such like
{\tt /usr/lib/R/library}. You will have to
be the superuser to do this. As a normal user, you can install the
package in your own local directory. To do this, type 
\Rfunction{R CMD INSTALL --library=\$LOCALRLIB maanova\_*.tar.gz}, 
where {\tt \$LOCALRLIB} is something like {\tt /home/user/Rlib/}. Then you 
will need to create a file {\tt .Renviron} in your home directory to contain
the line {\tt R\_LIBS=/home/user/Rlib} so that R will know to search for
packages in that directory.
\end{enumerate}
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% Function list
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\newpage
\section{Data and function list}
The following is a list of the available functions. 
For more information about 
the function usage use the online help by typing ``{\tt ?functionname}''
in {\em R} environment or typing {\tt help.start()} to get the html help in 
a web browser.

\begin{enumerate}
% sample data
\item Sample data available with the package. Both include subset of original
  data, which can be found at {\tt
http://www.jax.org/staff/churchill/labsite/software/}. 
\begin{description}
\item[{\tt abf1}] A 18-array Affymetrix experiment. 
\item[{\tt kidney}] A 6-array kidney data set from CAMDA (Critical Assessment of Microarray Data Analysis). 
\end{description}

% File IO
\item File I/O
\begin{description}
\item[{\tt read.madata()}] Read microarray data from TAB delimited 
text file (typical input for two-color array) or matrix type {\em R} object
(such as output of {\tt exprs()} from normalization package, such as {\em Affy}, {\em beadarray}).
\item[{\tt write.madata()}] Write microarray data to a TAB delimited text file.
\end{description}
% quality check
\item Data quality check - for two-color arrays
\begin{description}
\item[{\tt arrayview()}] View the layout of the arrays 
\item[{\tt riplot()}] Ratio intensity plot for arrays 
\item[{\tt gridcheck()}] Plot grid-by-grid data comparison for arrays
\item[{\tt dyeswapfilter()}] Flag the bad spots in dye swap experiment
\end{description}
% normalization
\item Data transformation
\begin{description}
\item[{\tt transform.madata()}] Data transformation with options to use any of
several methods
\end{description}
% model fitting
\item ANOVA model fitting 
\begin{description}
\item[{\tt fitmaanova()}] Fit ANOVA model
\item[{\tt resiplot()}] Residual plot on a given ANOVA model
\end{description}
% tests
\item Hypothesis testing
\begin{description}
\item[{\tt matest()}] F-test or T-test with permutation
\item[{\tt adjPval()}] Calculate FDR adjusted P values given the result of {\tt matest()}
\end{description}
\item Summarize the result
\begin{description}
\item[{\tt summarytable()}] Provide summary of 
{\tt matest()}, such as P-value, adjusted P-value and fold change 
\item[{\tt volcano()}] Volcano plot for summarizing F or T test results
\end{description}
% clustering
\item Clustering
\begin{description}
\item[{\tt macluster()}] Bootstrap clustering.
\item[{\tt consensus()}] Build consensus tree out of bootstrap cluster result
\item[{\tt fom()}] Use figure of merit to determine the number of groups in K-means cluster
\item[{\tt geneprofile()}] Plot the estimated relative expression for a given list of genes
\end{description}
% utility functions
\item Utility functions
\begin{description}
\item[{\tt fill.missing()}] Fill in missing data.
\item[{\tt PairContrast()}] Make all possible pairwise contrast.
\item[{\tt summary.madata()}] Summarize the data object.
\item[{\tt subset.madata()}] Subsetting the data objects.
\end{description}
\end{enumerate}
