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\begin{document}

\title{\bf Quick start guide for maigesPack -- from start to finish}
\date{\today}
\author{Gustavo H. Esteves}

\maketitle

\begin{center}
Departamento de Matem\'{a}tica e Estat\'{\i}stica - Centro de Ciencias e
Tecnologia - Universidade Estadual da Para\'{\i}ba,
Campina Grande - PB, Brasil, \url{http://www.maiges.org/en/software/}\\
\end{center}

\tableofcontents




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Overview}

This document provides a brief overview of how to use the
{\em maigesPack} package. This package integrates several R
packages in a coordinated way to do several microarray data
analysis. Some packages that were integrated are from R CRAN and the
other came from BioConductor project.

The packages from CRAN are {\em amap, class, e1071, MASS, R2HTML, rgl}
and {\em som}. From BioC we used the packages {\em annotate, convert,
  graph, limma, marray, multtest} and {\em OLIN}.

To do all data analysis process it is necessary to follow different steps. These are:
\begin{itemize}
\item Reading in data.
\item Perform simple diagnostic plots to access quality.
\item Normalization.
\item Following different methods for data analysis according with the problem.
\end{itemize}

After the two main pre-processing tasks, exploratory analysis and
normalization, the next steps in the statistical analysis depend on the
biological question for which the microarray experiment was
designed. This package integrate some statistical methods to do
several types of data analysis.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Getting started}

To load the {\tt maigesPack} package in your R session, type {\tt
library(maigesPack)}. We demonstrate the functionality of this
package using a dataset with some observations of gastro-esophageal
data. This dataset was analyzed by our group and resulted in a
publication of a research article in {\em Cancer Research}, under
volume 65(16), pages 7127--7136 (2005). Following we will show an
analysis from start to finish using a piece of this dataset, that
is included as part of this package, type {\tt data(gastro)}
to load some objects into your R session. Type {\tt ?gastro} to get more
information about this dataset.

The original data have 4800 spots (approximately 4400 unique genes) and 172 chips
with dye swap, what resulted in a dataset of 86 samples. The subdataset included
in this package has 500 spots (representing 486 unique genes) and 40 chips (from
20 observations) between the total 172.

The implementation we did use labels for samples and genes. For the
{\tt gastro} dataset we have three important labels for samples and 5
important labels for genes. To samples we have ``Sample'' that shows
an unique identification to each observation, ``Tissue'' that shows
the tissue classification for each observation (Aeso is Adenocarcinoma
from esophagus, Aest is adeno from stomach, Neso is normal esophagus and Nest is
normal stomach) and ``Type'' that shows the general type of the observations
(Col is columnar tissue and Sq is squamous tissue). For genes the important
labels are ``Name'', ``GeneId'', ``GeneName'', ``ClusterId'' and
``Annot''.

To see all sample labels available type {\tt names(gastro@Slabels)},
analogously, type {\tt names(gastro@Glabels)} to see all gene labels
available inside the dataset.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Loading the dataset}

Supposing that the user will load a new dataset, we describe how this 
can be done. To begin, users must have a gene map file giving all the 
information necessary to correctly identify genes into the chip, a 
sample file giving all the information relevant to identify the samples 
and the data tables generated by the image processing software. If data
analysis methods to be used will use gene groups and/or gene networks,
users must have this information in other one or two folder(s). The
gene map and sample file for the gastro dataset inside this package
are available at {\tt /doc/gastro/} directory in the installation folder.

It is important to verify the integrity of data tables, gene map
and sample file. Specially, we strongly recommend the user to verify
the number of lines from data tables, the layout of the chips used and
ordering of the genes. Sometimes we have seen several problems with
these characteristics. For the gastro dataset included in this
package, we didn't add the numerical data tables because they are very
large, having approximately 1.3Mb each. 

To start this tutorial open R in any working directory, load the
{\tt maigesPack} package and the gastro dataset using the
commands given below:
%code 1
<<eval=TRUE, echo=TRUE>>=
library(maigesPack)
data(gastro)
@

The object {\tt gastro} loaded above, was created using the function
{\tt loadData}.  After the check of all data tables, gene map and
sample file, the user must use this function to load numerical values
into R. The command is given below:
%code 2
<<eval=FALSE, echo=TRUE>>=
gastro = loadData(fileConf="load_gastro.conf")
@
the {\tt load\_gastro.conf} file specify the parameters needed to load the
dataset. Type {\tt ?loadData} to see the description of what this
file must contain. Pay attention, any error in the execution of this command
will be redirected into a file named {\tt load.out}. Once the {\tt gastro}
object was created, that is of class {\tt maigesPreRaw}, it is possible to load
gene groups and gene networks information using the two commands below,
respectively:
%code 3
<<eval=FALSE, echo=TRUE>>=
gastro = addGeneGrps(gastro, folder="geneGrps", ext="txt")
gastro = addPaths(gastro, folder="geneNets", ext="tgf")
@

Pay attention that the two steps above are optional. The user may skip from
loading gene groups and networks if he/she don't wanna to use these
information during the analysis process. The object generated in
this step has class {\tt maigesPreRaw}. The only methods defined to
this class of objects is {\tt print, summary} and  {\tt show}.

This class of objects is intended to serve as a lobby region into R,
where the dataset loaded may be exploratory evaluated using all
R graphics functions. Function like {\tt plot, pairs, image,} etc may
be used to search by eventual problems into dataset. Also it is
possible to do any type of calculation in any data field if the user
judge necessary.

The next step of data analysis is to convert the {\tt maigesPreRaw}
object named gastro into an object of class {\tt maigesRaw}. This is
done with the command below:
%code 4
<<eval=TRUE, echo=TRUE>>=
gastro.raw = createMaigesRaw(gastro, greenDataField="Ch1.Mean",
  greenBackDataField="Ch1.B.Mean", redDataField="Ch2.Mean",
  redBackDataField="Ch2.B.Mean", flagDataField="Flags",
  gLabelGrp="GeneName", gLabelPath="GeneName")
@



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Exploratory analysis}

From the object {\tt gastro.raw}, that is of class {\tt maigesRaw},
the user can do several exploratory analysis. The function {\tt plot}
may be used to show WA plots (that is equivalent to the MA plots, but
with W=log2[Test/Ref] instead of M=log2[Red/Green]). The command below
do this plot for the first chip and the result is represented at
Figure \ref{WAplot}:
% code 5
<<WAplot1,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
plot(gastro.raw[,1], bkgSub="none")
@
\begin{figure}[ht!]
  \centering
  \includegraphics{WAplot1}
  \caption{\label{WAplot} WA plot for the first chip of the gastro data.}
\end{figure}

Changing the parameter {\tt bkgSub}, the user change the method of background 
subtraction, for example, if the user specify {\tt bkgSub="subtract"} the 
conventional background subtraction is done. You may also do representations 
for the numerical values into the chip, using the method {\tt image}. See 
command below, that represents the W values (the W values are represented by
default, ie, without any additional parameter, if additional parameters are
added, the conventional M values are used) for the first chip that generated 
the Figure \ref{imageW}:
% code 6
<<image1,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
image(gastro.raw[,1])
@
\begin{figure}[ht!]
  \centering
  \includegraphics{image1}
  \caption{\label{imageW} Image for the first chip of the gastro data.}
\end{figure}

To change the W for another value, like A values, use {\tt image(gastro.raw[,1],
  "maA")}. Take a look at {\tt maImage} method from package {\em marray} to see
  additional parameters for this function.

Another type of exploratory graphics is the boxplot. See the commands below:
% code 7
<<eval=FALSE, echo=TRUE>>=
boxplot(gastro.raw[,2])
boxplot(gastro.raw)
@

The user may also do an hierarchical cluster to visualize the quality of the data. 
Specially when replicates of the observations are done (like the dye swap).
Use the command below to construct the hierarchical dendrogram represented at
Figure \ref{hier}.
{\small 
% code 8
<<hierRaw,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
hierM(gastro.raw, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
              sLabelID="Sample", gLabelID="Name", doHeat=FALSE)
@
}
\begin{figure}[ht!]
  \centering
  \includegraphics{hierRaw}
  \caption{\label{hier} Hierarchical cluster for all observations from
  raw gastro dataset.}
\end{figure}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Normalizing the dataset}

The first step to normalize the data is to select the spots to be used for 
estimating the normalization factor. In this package this is done by the function
{\tt selSpots}. In our example use the command:
% code 9
<<eval=TRUE, echo=TRUE>>=
gastro.raw2 = selSpots(gastro.raw, sigNoise=1, rmFlag=NULL, gLabelsID="Name",
  remove=list(c("BLANK","DAP","LYS","PHE","Q_GENE","THR","TRP")))
@

This function automatically sets the spots for each chip (column) independently
that will be used according with the criteria specified. For example, if the user 
want to remove spots with signal to noise ratio less than 1.5, specify this 
value as {\tt sigNoise=1.5}, to remove spots marked with flags 1 and 4, specify 
{\tt rmFlag=c(1,4)}.

Once this is done, the main function to do the normalization is {\tt normLoc}
that uses another function from {\em limma} package. The most usual normalization
method may be applied by the function:
% code 10
<<eval=TRUE, echo=TRUE>>=
gastro.norm = normLoc(gastro.raw2, span=0.4, method="loess")
@

There are another possibilities to do the normalization, like the method OLIN 
implemented in a package with the same name, and the method that repeats the 
loess fitting calculating the standard variation and confidence interval.
These may be done by using the commands below, but pay attention with them
because they are very time consuming. It is interesting to
look their arguments.
% code 11
<<eval=FALSE, echo=TRUE>>=
gastro.norm = normOLIN(gastro.raw2)
gastro.norm = normRepLoess(gastro.raw2)
@

After the locale normalization the user can use the functions {\tt normScaleMarray}
and {\tt normScaleLimma} to do scale adjustment. The first method use a function 
from {\em marray} package and the second use a function from {\em limma} package.
To do scale adjustment between print tips use the command below:
% code 12
<<eval=FALSE, echo=TRUE>>=
gastro.norm = normScaleMarray(gastro.norm, norm="printTipMAD")
@

To adjust the scale between chips estimating the factor for adjustment
by MAD use the command:
% code 13
<<eval=FALSE, echo=TRUE>>=
gastro.norm = normScaleMarray(gastro.norm, norm="globalMAD")
@

{\em Limma} package offers other possibilities. These were also incorporated into
this package. To do the scale adjustment for chips using an estimator slightly 
different from the MAD, use:
%code 14
<<eval=FALSE, echo=TRUE>>=
gastro.norm = normScaleLimma(gastro.norm, method="scale")
@

Another possibility is to do the stabilization of the variance along A values
for all chips. But this method must be applied directly over the raw
object. This may be done using the command below. This method is also
very time consuming.
% code 15
<<eval=FALSE, echo=TRUE>>=
gastro.norm = normScaleLimma(gastro.raw2, method="vsn")
@

The normalization functions generates objects of class {\tt maiges}. All the 
exploratory functions may also be used with this class of objects. As an example,
see the Figure \ref{WAplotN}, produced by the following command:
% code 16
<<WAplot1N,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
plot(gastro.norm[,1])
@
\begin{figure}[ht!]
  \centering
  \includegraphics{WAplot1N}
  \caption{\label{WAplotN} WA plot for the first chip of the gastro data after
    normalization.}
\end{figure}

To see the pairing between replicates in dye swap the user can do the
hierarchical grouping like what was done in the raw object. That is
done by the command below and is represented in Figure \ref{hierN}.
{\small 
% code 17
<<hierNorm,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
hierM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
              sLabelID="Sample", gLabelID="Name", doHeat=FALSE)
@
}
\begin{figure}[ht!]
  \centering
  \includegraphics{hierNorm}
  \caption{\label{hierN} Hierarchical cluster for normalized data of
  all observations  from
    gastro data.}
\end{figure}

Sometimes, the sequence (or genes) fixed onto spots may be replicated. The same 
thing may happen with the observations (the most common is dye
swap). So depending on the statistical models
used, the user must have to resume the data both for spots and biological 
observations. These may be done by using the function:
{\small 
% code 18
<<eval=TRUE, echo=TRUE, results=hide>>=
gastro.summ = summarizeReplicates(gastro.norm, gLabelID="GeneName",
  sLabelID="Sample", funcS="mean", funcG="median",
  keepEmpty=FALSE, rmBad=FALSE)
@
}

If the user needs to resume only spots or samples, simply specify {\tt funcS=NULL} or
{\tt funcG=NULL}. If both of them are {\tt NULL} no summary are done.





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Some data analysis methods}


After the normalization, or pre-processing step, the user may use
several statistical methods for data analysis. In this package we
integrated some methods that were already available into R or
BioConductor. Between these methods we have cluster algorithms,
differential gene expression and classifiers search, functional
classification of gene groups or gene networks and construction of
relevance networks.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Differentially expressed genes (DE genes)}

We implemented three main methods to search by DE genes: {\tt
  deGenes2by2Ttest}, {\tt deGenes2by2Wilcox} and {\tt
  deGenes2by2BootT}. The first one supposes that the data are normally
distributed and uses the t statistic to do the calculations. The
function may be used as in the following example:
% code 19
<<eval=TRUE, echo=TRUE>>=
gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")
gastro.ttest
@

In cases were the data don't seen to be normally distributed. The user
may use the other two methods that are non-parametric. The method {\tt
  deGenes2by2BootT} is very time consuming. See two examples of this
methods below.
% code 20
<<eval=FALSE, echo=TRUE>>=
gastro.wilcox = deGenes2by2Wilcox(gastro.summ, sLabelID="Type")
gastro.boot = deGenes2by2BootT(gastro.summ, sLabelID="Type")
@

Once the analysis was done. The user can see volcano plots and save
HTML (or CSV) tables with the results using the commands below, respectively.
% code 21
<<eval=FALSE, echo=TRUE>>=
plot(gastro.ttest)
tablesDE(gastro.ttest)
@

It is also possible to do cluster analysis selecting DE genes
according with the p-values of the tests. The functions 
{\tt hierMde}, {\tt somMde} and {\tt kmeansMde} do cluster
analysis using hierarchical, SOM and k-means algorithms, respectively.
To do the SOM cluster with 2 groups using 20 most differentially 
expressed genes (adjusting p-values by FDR) use the command below, 
the result is in the Figure \ref{SOM20}
% code 22
<<som20,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
somMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=20,
       xdim=2, ydim=1, topol="rect")
@
\begin{figure}[ht!]
  \centering
  \includegraphics{som20}
  \caption{\label{SOM20} SOM cluster with 2 groups using 20 most
  DE genes.}
\end{figure}

Similarly, it is possible to do cluster analysis directly for objects
of class {\tt maiges}, using the functions {\tt hierM}, {\tt somM} and {\tt
  kmeansM}, but without selecting differentially expressed genes.

All the methods presented above are applicable to compare two
distinct biological sample types. When there are more than two
types, it is possible to use ANOVA models. We also implemented a
method to do this kind of analysis. But first the user must run
another method to construct design and contrasts matrices. To
construct these matrices for an ANOVA model for ``Tissue'' factor (note
that this factor has 4 sample types, type {\tt getLabels(gastro.summ,
  "Type")} to see them), use the command:
%code 23
<<eval=TRUE, echo=TRUE>>=
gastro.ANOVA = designANOVA(gastro.summ, factors="Tissue")
@

And them, to fit the model and to do the F test use the command:
% code 24
<<eval=TRUE, echo=TRUE>>=
gastro.ANOVAfit = deGenesANOVA(gastro.ANOVA, retF=TRUE)
gastro.ANOVAfit
@

If the user want to do the individual t tests use (this is the default):
% code 25
<<eval=FALSE, echo=TRUE>>=
gastro.ANOVAfit = deGenesANOVA(gastro.ANOVA, retF=FALSE)
@

Another function that may be useful, specially for ANOVA analysis
is {\tt boxplot}. This type of plot may help in identify the tissues
where some gene presented alteration. For example the gene FUBP1, presents 
significantly alteration in the F test done above. Using the command
below it is possible to see in what tissue this gene is altered. 
The result is illustrated in the Figure \ref{boxplot}. The method {\tt
  boxplot} also works with objects of class {\tt maiges}.
% code 26
<<Bplot,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
boxplot(gastro.ANOVAfit, name="KLK13", gLabelID="GeneName", 
sLabelID="Tissue")
@
\begin{figure}[ht!]
  \centering
  \includegraphics{Bplot}
  \caption{\label{boxplot} Boxplot for the gene FUBP1 separating by
  tissues.}
\end{figure}

The object resulted from this fitting are also of class {\tt maigesDE}
(or {\tt maigesDEcluster}). So, the commands for plot, to save tables 
and to construct clusters, presented above also works here.
Pay attention that functions to do cluster analysis only work
for objects of classes {\tt maigesDEcluster}.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Discrimination analysis}

Another type of data analysis that were included into this package is
the discrimination (or classification) analysis. This kind of analysis
search by groups of few genes that can distinguish between different
sample types. To evaluate the quality of the groups, we use the cross
validation method. This was implemented in the functions {\tt
  classifyLDA}, {\tt classifySVM} and {\tt classifyKNN} for methods
of Fisher's linear discriminant analysis, SVM and k-neighborhood, respectively.

It is possible to search exhaustively for all genes from the
dataset. But this is very time consuming. So, we added the possibility to
search for classifiers in gene groups or networks. To search by trios
of genes that classify the two types (``Col'' and ``Sq'') using the
Fisher's method, for the seventh (cell cycle arrest, gene ontology
code GO0007050) gene group into {\tt GeneGrps} slot from
gastro.summ object use the command below:
{\small
% code 27
<<eval=TRUE, echo=TRUE>>=
gastro.class = classifyLDA(gastro.summ, sLabelID="Type",
  gNameID="GeneName", nGenes=3, geneGrp=6)
gastro.class
@
}

If the user want to find groups of three genes that discriminate
the tissues classified as normal (``Neso'' and ``Nest'') versus tumor
(``Aeso'' and ``Aest''), it is possible to use the command below:
{\small
% code 28
<<eval=FALSE, echo=TRUE>>=
gastro.class = classifyLDA(gastro.summ, sLabelID="Tissue",
  gNameID="GeneName", nGenes=3, geneGrp=6,
  facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))
@
}

To visualize or save tables (HTML or CSV) for the classifiers it is
possible to use the two commands below:
% code 29
<<eval=FALSE, echo=TRUE>>=
plot(gastro.class, idx=1)
tableClass(gastro.class)
@

As the exhaustive search is very time consuming for large groups of
genes there is another possibility implemented, the search and choose
method. The use is similar with the previous and the functions are
{\tt classifyLDAsc}, {\tt classifySVMsc} and {\tt classifyKNNsc}.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Functional classification of gene groups}

Another data analysis method implemented in this package is the
functional classification of gene groups (or modules), proposed by
Segal et al (Nature Genetics, vol: 36(10), pp: 1090-1098, 2004). This
method basically searches for groups of genes that present numbers of
differentially expressed genes greater than the expected by chance.

The user can do this kind of analysis for all gene groups loaded into
the dataset (this information is stored in the slot {\tt GeneGrps}
from objects of classes {\tt maigesPreRaw}, {\tt maigesRaw} and {\tt
  maiges}) for sample label ``Tissue'' using the command below:
%code 30
<<eval=TRUE, echo=TRUE>>=
gastro.mod = activeMod(gastro.summ, sLabelID="Tissue", cutExp=1,
  cutPhiper=0.05)
@

To plot the results, as an image (like a heatmap), for the individual
observations the user can use the command:
%code 31
<<eval=FALSE, echo=TRUE>>=
plot(gastro.mod, "S", margins=c(15,3))
@

For each tissue the user can use the command below. The result is
represented in the Figure \ref{modulo}.
%code 32
<<Mod,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
plot(gastro.mod, "C", margins=c(23,5))
@
\begin{figure}[ht!]
  \centering
  \includegraphics{Mod}
  \caption{\label{modulo} Result from functional classification of
  gene groups for ``Tissue'' label.}
\end{figure}


The function also calculate an score (with significance levels, p-values)
given the concordance of the gene and group classification. These
values can be saved, as HTML tables, for each sample type using the
command {\tt activeModScoreHTML}.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Relevance networks}

To evaluate the interaction between genes into groups it is
possible to use reverse engineering strategies. One of this techniques
was proposed by Butte et al (PNAS, vol: 97(22), pp:12182-12186, 2000),
where we estimate the correlation values between all pairs of genes
from a given gene group and test the significance of this value under
the hypothesis of null correlation. Then, we select the pairs with
correlations values significantly different from zero using some
p-value cutoff. To construct relevance networks for normal esophagus
tissue (``Neso'') in the seventh gene group, use this command:
{\small
% code 33
<<eval=FALSE, echo=TRUE>>=
gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
  samples="Neso", geneGrp=1)
@
}

To visualize the results it is possible to do an image of the
correlation values for all pairs of genes or a graph representing that
ones with p-values less than {\tt cutPval}. This can be done by the
two commands:
%code 34
<<eval=FALSE, echo=TRUE>>=
image(gastro.net)
plot(gastro.net, cutPval=0.05)
@

However, the method proposed by Butte et al don't turn possible to
compare quantitatively the results obtained in two different sample
types. So, we implemented another method that construct relevance
networks with pairs of genes presenting altered correlation values
between two sample types. This is done by a Fisher's {\em Z}
transformation. To find pairs of genes that present altered
correlation values between normal esophagus and adenocarcinoma
from esophagus, use the command:
{\small 
% code 35
<<eval=FALSE, echo=TRUE>>=
gastro.net2 = relNetworkM(gastro.summ, sLabelID="Tissue", 
  samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=7)
@
}

Also, it is possible to use the commands {\tt image} and {\tt plot} to
visualize the results. See the commands:
%code 36
<<eval=FALSE, echo=TRUE>>=
image(gastro.net2)
plot(gastro.net2, cutPval=0.05)
@

It is possible to represent the gene expression profiles in the
two sample types tested representing the regression line between
them, see the command:
% code 37
<<eval=FALSE, echo=TRUE>>=
plotGenePair(gastro.net2, "KLK13", "EVPL")
@



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Functional classification of gene networks}

Finally, we noted that any of the last two data analysis methods were
able to evaluate the activation profiles of gene networks. We proposed
and implemented an statistical method to measure the significance of
gene networks activation. This is done by a statistic that have a known
gamma distribution, and them, we test if the value of this statistic
for each sample type can happen randomly. To classify gene networks
(stored in the slot {\tt Paths} from objects of classes {\tt
  maigesPreRaw}, {\tt maigesRaw} and {\tt maiges}) for ``Tissue''
label the user can use the command:
%code 38
<<eval=TRUE, echo=TRUE>>=
gastro.net = activeNet(gastro.summ, sLabelID="Tissue")
@

To see an image representing the statistic (scores) for each tissue,
use the command below. The image is represented in Figure
\ref{score}. The color varies from black (small score) to blue (high score).
%code 39
<<Score,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
plot(gastro.net, type="score", margins=c(21,5))
@
\begin{figure}[ht!]
  \centering
  \includegraphics{Score}
  \caption{\label{score} Image representing the scores (statistics)
  from functional classification of gene networks for ``Tissue'' label.}
\end{figure}

Similarly, the user can see an image representing the p-values of the
tests for each tissue, this can be done using the command below. The
image is represented in Figure \ref{pvalues}. As in the previous
figure the color varies from black (less significance) to blue (high significance).
%code 40
<<Pvalues,fig=TRUE,prefix=FALSE,echo=TRUE,include=FALSE,eps=FALSE>>=
plot(gastro.net, type="p-value", margins=c(21,5))
@
\begin{figure}[ht!]
  \centering
  \includegraphics{Pvalues}
  \caption{\label{pvalues} Image representing the p-values
  from functional classification of gene networks for ``Tissue'' label.}
\end{figure}

It is also possible to save the score and p-values, as HTML tables,
for each sample type using the command {\tt activeNetScoreHTML}.


\end{document}
