\documentclass[a4paper,12pt,nogin]{article}
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\begin{document}

\title{\texttt{chroGPS}: visualizing the epigenome. }
\small
\author{Oscar Reina \footnote{Bioinformatics \& Biostatistics Unit,
    IRB Barcelona}  and David Rossell
  \footnotemark[1] }
\normalsize
\date{}  %comment to include current date

\maketitle


\section{Introduction}
\label{sec:intro}

The \texttt{chroGPS} package provides tools to generate intuitive maps to visualize the association between genetic elements, with emphasis on epigenetics. The approach is based on Multi-Dimensional Scaling. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds.
This manual illustrates the software functionality and highlights some ideas,
for a detailed technical description the reader is referred to the supplementary material on \citep{font:2013}.
\\\\
Many routines allow performing computations in parallel 
by specifying an argument \texttt{mc.cores}, which uses
package \newtext{\texttt{parallel}.
}
\\\\
We start by loading the package and a ChIP-chip dataset with genomic
distribution of 20 epigenetic elements from the Drosophila melanogaster
S2-DRSC cell line, coming from the modEncode project, which we will use for illustration purposes.
Even though our study and examples focuses on assessing associations
between genetic elements, this methodology can be successfully used with
any kind of multivariate data where relative distances between
elements of interest can be computed based on a given set of variables.

\section{chroGPS$^{factors}$}
\label{sec:chrogps_factors}

\footnotesize

<<import1>>=
options(width=70)
par(mar=c(2,2,2,2))
library(chroGPS)
data(s2) # Loading Dmelanogaster S2 modEncode toy example
data(toydists) # Loading precomputed distGPS objects
s2
@ 

\normalsize

\texttt{s2} is a \texttt{RangedDataList} object storing the
binding sites for 20 Drosophila melanogaster S2-DRSC sample
proteins. Data was retrieved from the modEncode website
(www.modencode.org) and belongs to the public subset of the Release 29.1 dataset. GFF files
were downloaded, read and formatted into individual RangedData
objects, stored later into a \texttt{RangedDataList} (see functions \texttt{getURL} and 
\texttt{gff2RDList} for details.) For shortening computing time for the dynamic generation of this document, some of the distances between epigenetic factors have been precomputed and stored in the \texttt{toydists} object.
  
\subsection{Building chroGPS$^{factors}$ maps}
\label{ssec:factormaps}
 
The methodology behing chroGPS$^{factors}$ is to generate a distance
matrix with all the pairwise distances between elements of interest by
means of a chosen metric. After this, a Multidimensional Scaling
representation is generated to fit the n-dimensional distances in a
lower (usually 2 or 3) k-dimensional space.
 
\footnotesize
 
<<mds1>>=
# d <- distGPS(s2, metric='avgdist')
d
mds1 <- mds(d,k=2,type='isoMDS')
mds1
mds1.3d <- mds(d,k=3,type='isoMDS')
mds1.3d
@ 
 
\normalsize
 
The R$^2$ coefficient between the original distances and their approximation in the
plot 
can be seen as an analogue to the percentage of explained variability in a PCA analysis.
For our sample data R$^2$=0.628
%(up to rounding), 
and stress=0.079 in the 2-dimensional plot, both of which
indicate a fairly good fit. A 3-dimensional plot improves these
values.
We can produce a map by using the \texttt{plot} method for \texttt{MDS} objects.
The result in shown in Figure \ref{fig:mds1}.
For 3D representations the \texttt{plot} method opens an interactive window
that allows to take full advantage of the additional dimension.
Here we commented out the code for the 3D plot and simply show a
snapshot in Figure \ref{fig:mds1}. Short names for modEncode factors
as well as colors for each chromatin domain identified
(lightgreen=transcriptionally active elements, purple=Polymerase, grey=boundary
elements, yellow=Polycomb repression, lightblue=HP1 repression) are
provided in the data frame object \texttt{s2names}, stored within \texttt{s2}.

\footnotesize
 
<<figmds1>>=
cols <- as.character(s2names$Color)
plot(mds1,drawlabels=TRUE,point.pch=20,point.cex=8,text.cex=.7,
point.col=cols,text.col='black',labels=s2names$Factor,font=2)
legend('topleft',legend=sprintf('R2=%.3f / stress=%.3f',getR2(mds1),getStress(mds1)),
bty='n',cex=1)
#plot(mds1.3d,drawlabels=TRUE,type.3d='s',point.pch=20,point.cex=.1,text.cex=.7,
#point.col=cols,text.col='black',labels=s2names$Factor)
@  

\normalsize

\setkeys{Gin}{width=0.7\textwidth} 
\begin{figure}
\begin{center}
<<label=figmds1,fig=TRUE,echo=FALSE>>=
<<figmds1>>
@
\fbox{\includegraphics{mds3d_1.png}}
\end{center}
\caption{2D map from the 20 S2 epigenetic factors and example 3D map with 76 S2 factors. Factors with more similar binding site distribution appear closer.}
\label{fig:mds1}
\end{figure}
 
 
\subsection{Integrating data sources: technical background}
\label{ssec:tecbias}
 
Currently, genomic profiling of epigenetic factors is being largely
determined through high throughput methodologies such as
ultra-sequencing (ChIP-Seq), which identifies binding sites with higher
accuracy that ChIP-chip experiments. However, there is an extensive
knowledge background based on the later. ChroGPS allows
integrating different technical sources by adjusting for
systematic biases.
\\\\
We propose two adjustment methods: Procrustes and Peak Width 
Adjustment. Procrustes finds the optimal superimposition of two sets of
points by altering their location, scale and orientation while
maintaining their relative distances. It is therefore a
general method of adjustment that can take care of several kind of
biases. However, its main limitation is that a minimal set of common
points (that is, the same factor/protein binding sites mapped in both data sources) is needed to effectively perform a valid adjustment. Due to the spatial nature of Procrustes adjustment, we strongly recommend a minimum number of 3 common points.

We illustrate the adjustments by loading {\it Drosophila melanogaster}
S2 ChIP-seq data obtained from NCBI GEO GSE19325, \url{http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19325}. We start by producing a joint map with no adjustment.


\footnotesize
 
<<figprocrustes1>>=
data(s2Seq)
s2Seq
# d2 <- distGPS(c(s2,s2Seq),metric='avgdist')
mds2 <- mds(d2,k=2,type='isoMDS')
cols <- c(as.character(s2names$Color),as.character(s2SeqNames$Color))
sampleid <- c(as.character(s2names$Factor),as.character(s2SeqNames$Factor))
pchs <- rep(c(20,17),c(length(s2),length(s2Seq)))
point.cex <- rep(c(8,5),c(length(s2),length(s2Seq)))
par(mar=c(2,2,2,2))
plot(mds2,drawlabels=TRUE,point.pch=pchs,point.cex=point.cex,text.cex=.7,
point.col=cols,text.col='black',labels=sampleid,font=2)
legend('topleft',legend=sprintf('R2=%.3f / stress=%.3f',getR2(mds2),getStress(mds2)),
bty='n',cex=1)
legend('topright',legend=c('ChIP-Chip','ChIP-Seq'),pch=c(20,17),pt.cex=c(1.5,1))
@ 
 
\normalsize
 
\setkeys{Gin}{width=0.8\textwidth} 
\begin{figure}
\begin{center}
<<label=figprocrustes1,fig=TRUE,echo=FALSE>>=
<<figprocrustes1>>
@
\end{center}
\caption{S2 ChIP-chip and ChIP-Seq data, raw integration (no adjustment). }
\label{fig:procrustes1}
\end{figure}

Figure \ref{fig:procrustes1} shows the resulting map.
While ChIP-seq elements appear close to their ChIP-chip counterparts, they form an external layer.
We now apply Procrustes to adjust these systematic biases using function \texttt{procrustesAdj}.

\footnotesize
 
<<figprocrustes2>>=
adjust <- rep(c('chip','seq'),c(length(s2),length(s2Seq)))
sampleid <- c(as.character(s2names$Factor),as.character(s2SeqNames$Factor))
mds3 <- procrustesAdj(mds2,d2,adjust=adjust,sampleid=sampleid)
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds3,drawlabels=TRUE,point.pch=pchs,point.cex=point.cex,text.cex=.7,
point.col=cols,text.col='black',labels=sampleid,font=2)
legend('topleft',legend=sprintf('R2=%.3f / stress=%.3f',getR2(mds3),getStress(mds3)),
bty='n',cex=1)
legend('topright',legend=c('ChIP-Chip','ChIP-Seq'),pch=c(20,17),pt.cex=c(1.5,1))
@
 
\normalsize
 
Peak Width Adjustment relies on the basic difference between the two
different sources of information used in our case, that is, the
resolution difference between ChIP-Seq and ChIP-chip peaks, which
translates basically in the width presented by the regions identified
as binding sites, being those peaks usually much wider in ChIP-chip
data (poorer resolution).
 
\footnotesize
 
<<figpeakwidth1>>=
s2.pAdj <- adjustPeaks(c(s2,s2Seq),adjust=adjust,sampleid=sampleid,logscale=TRUE)
# d3 <- distGPS(s2.pAdj,metric='avgdist')
mds4 <- mds(d3,k=2,type='isoMDS')
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds4,drawlabels=TRUE,point.pch=pchs,point.cex=point.cex,text.cex=.7,
point.col=cols,text.col='black',labels=sampleid,font=2)
legend('topleft',legend=sprintf('R2=%.3f / s=%.3f',getR2(mds4),getStress(mds4)),
bty='n',cex=1)
legend('topright',legend=c('ChIP-Chip','ChIP-Seq'),pch=c(20,17),pt.cex=c(1.5,1))
@

 
\normalsize
 
\setkeys{Gin}{width=0.5\textwidth} 
\begin{figure}
\begin{center}
\begin{tabular}{cc}
<<label=figprocrustes2,fig=TRUE,echo=FALSE,results=hide>>=
<<figprocrustes2>>
@ &
<<label=figpeakwidth1,fig=TRUE,echo=FALSE,results=hide>>=
<<figpeakwidth1>>
@
\end{tabular}
\end{center}
\caption{S2 ChIP-chip and ChIP-Seq data. Left: Procrustes adjustment. Right: Peak Width Adjustment. }
\label{fig:adj1}
\end{figure}

Figure \ref{fig:adj1} shows the map after Peak Width Adjustment,
where ChIP-chip and ChIP-seq elements have been adequately matched.
Whenever possible, we strongly recommend using Procrustes adjustment
due to its general nature and lack of mechanistic assumptions. This is
even more important if integrating other data sources for binding
site discovery, such as DamID, Chiapet, etc, where technical biases
are more complex than just peak location resolution and peak size.
 
\section{ChroGPS$^{genes}$}
\label{ssec:genes}
 
In addition to assessing relationship between epigenetic factors,
chroGPS also provides tools to generate chroGPS$^{genes}$ maps, useful to
visualize the relationships between genes based on their epigenetic
pattern similarities (the epigenetic marks they share).
 
\subsection{Building chroGPS$^{genes}$ maps}
\label{ssec:genemaps}
 
The proceedings are analog to those of chroGPS$^{factors}$, that is, the
definition of a metric to measure similarity between genes and using
it to generate MDS representations in k-dimensional space. The data
source of chroGPS$^{genes}$ has to be a matrix or data frame of N genes x
M factors (rows x cols), where each cell has a value of 1 if a binding
site for that protein or factor has been found in the region defined
by that gene. This annotation table can be generated by multiple
methods, in our case we annotated the genomic distribution on 76 S2
modEncode against the Drosophila melanogaster genome (Ensembl february 2012), accounting for
strict overlaps within 1000bp of gene regions, using the
\texttt{annotatePeakInBatch} function from the \texttt{ChIPpeakAnno}
package \citep{rpkg:chippeakanno}.
After that, 500 random
genes were selected randomly and this is the dataset that will be used
in all further examples.
 
\footnotesize
 
<<import2>>=
s2.tab[1:10,1:4]
@ 
 
\footnotesize
 
<<mds6>>=
d <- distGPS(s2.tab, metric='tanimoto', uniqueRows=TRUE)
d
mds1 <- mds(d,k=2,type='isoMDS')
mds1
mds2 <- mds(d,k=3,type='isoMDS')
mds2
@ 
 
\normalsize
 
%As we increase the number of points, fitting a solution in
%k-dimensional spaces gets tougher. If desired, goodness-of-fit in
%terms of R$^2$ using the 
%\texttt{boostMDS} algorithm provided in our package. As can be seen,
Increasing k \newtext{improves the R$^2$ and stress values. 
For our examples here we use non-metric isoMDS by indicating \texttt{type='isoMDS'}, which calls}
% also makes for an improved fit. During all our examples, the base 
%MDS type used for chroGPS$^{genes}$ maps will be the non-metric isoMDS one, as implemented in 
the \texttt{isoMDS} function from the
\texttt{MASS} package \citep{venables:2002}.

\footnotesize
 
<<label=figmds6>>=
par(mar=c(2,2,2,2))
plot(mds1,point.cex=1.5,point.col=densCols(getPoints(mds1)))
#plot(mds2,point.cex=1.5,type.3d='s',point.col=densCols(getPoints(mds2)))
@
 
\normalsize
 
\setkeys{Gin}{width=1.0\textwidth} 
\begin{figure}
\begin{center}
{\includegraphics{mds-2d-3d.png}}
\end{center}
\caption{2 and 3-dimensional chroGPS$^{genes}$. Genes with more similar
  epigenetic marks (binding site patterns) appear closer.}
\label{fig:mds6}
\end{figure}
 
 
\subsection{Genome-wide chroGPS$^{genes}$ maps}
\label{sec:splitMDS}
 
As mentioned, our example dataset for chroGPS$^{genes}$ maps consists in a combination
of 76 protein binding sites for 500 genes. When only unique factor
combinations are considered (all genes sharing a specific combination
of epigenetic marks are merged into a single 'epigene'), the size of
the dataset gets down to 466 genes per 76 factors.
 
\footnotesize
 
<<uniqueCount>>=
dim(s2.tab)
dim(uniqueCount(s2.tab))
@ 
 
\normalsize
 
However, when genome-wide patterns are considered, the number of epigenes can still be very high, in the order of ten
thousand unique epigenes. This poses a real challenge for
Multidimensional Scaling when trying to find an
optimal solution for k-space representation of the pairwise distances 
both in terms of accuracy and computational cost.

We start by re-running the isoMDS fit and measuring the CPU time.

\footnotesize
<<genomeGPS1>>=
system.time(mds3 <- mds(d,k=2,type='isoMDS'))
mds3
@ 
\normalsize

\newtext{We now apply our BoostMDS algorithm, which is a 2-step procedure}
(see package help for function \texttt{mds} and Supplementary Methods of \citep{font:2013} for details).
BoostMDS generates maps at much lower time and memory consumption requirements, 
while improving the R$^2$ and stress coefficients.
The first step is to obtain an initial solution by randomly splitting the original distance matrix in a number of smaller submatrices with a certain number of overlapping elements between them, so that individual MDS representations can be found for each one and later become stitched by using Procrustes with their common points. 
The second step is to formally maximize the R$^2$ coefficient by using
a gradient descent algorithm using the \texttt{boostMDS} function. The
second step also ensures that the arbitrary split used in the first
step does not have a decisive effect on the final MDS point configuration.


\footnotesize
<<genomeGPS2>>=
system.time(mds3 <- mds(d,type='isoMDS',splitMDS=TRUE,split=.5,overlap=.05,mc.cores=1))
mds3
system.time(mds4 <- mds(d,mds3,type='boostMDS',scale=TRUE))
mds4
@ 
\normalsize

Here BoostMDS provided a better solution in terms of R$^2$ and stress than isoMDS, at a lower computational time.
Our experience is that in a real example with tens of thousands of points
the advantages become more extreme.

\subsection{Annotating chroGPS$^{genes}$ maps with quantitative
  information}
\label{sec:xpr}
 
Gene expression, coming from a microarray experiment or from more
advanced RNA-Seq techniques is probably one of the first sources of
information to be used when studying a given set of
genes. Another basic source of information from epigenetic data is
the number of epigenetic marks present on a given set of genes. It is
known that some genes present more complex regulation programs that
make necessary the co-localization of several DNA binding proteins. 
\\\\
ChroGPS$^{genes}$ maps provide a straightforward way of
representing such information over a context-rich base. Basically,
coloring epigenes according to a color scale using their average gene
expression or number of epigenetic marks is sufficient to
differentiate possible regions of interest. Thus, our chroGPS$^{genes}$ map turn into a context-rich
heatmap where genes relate together due to their epigenetic similarity
and at the same time possible correlation with gene expression is
clearly visible. Furthermore, if expression data along a timeline is
available, for instance on an experiment studying time-dependant gene
expression after certain knock-out or gene activation, one can track
expression changes on specific map regions.
\\\\
In our case, we will use expression information coming from a
microarray assay involving normal Drosophila S2-DSRC cell
lines. The object \texttt{s2.wt} has normalized median expression value per gene and  
epigene ({\it i.e.}, we compute the median expression of all genes with the same combination of epigenetic marks).
The resulting plot is shown in Figure \ref{fig:mds7}
 
\footnotesize
 
<<getxpr>>=
summary(s2.wt$epigene)
summary(s2.wt$gene)
@ 
 
\footnotesize
 
<<label=figmds7>>=
plot(mds1,point.cex=1.5,scalecol=TRUE,scale=s2.wt$epigene,
     palette=rev(heat.colors(100)))
@
 
\normalsize

\setkeys{Gin}{width=0.8\textwidth}  
\begin{figure}
\begin{center}
<<label=figmds7,fig=TRUE,echo=FALSE,results=hide>>=
<<figmds7>>
@
\end{center}
\caption{2-dimensional MDS plot with chroGPS$^{genes}$ map and gene expression
  information. }
\label{fig:mds7}
\end{figure}
 
 
\subsection{Annotating chroGPS$^{genes}$ maps: clustering}
\label{sec:clusGPS}
 
A natural way to describe chroGPS$^{genes}$ maps is to highlight a set of
genes of interest, for instance those possessing an individual
epigenetic mark. One can repeat this step for several interesting gene
sets but this is cumbersome and doesn't lead to easy interpretation
unless very few sets are considered. A more advanced approach is to
analyze the whole set of epigene dissimilarities by clustering,
allowing us to detect genes with similar epigenetic patterns.
Again, using colors to represent genes in
a given cluster gives an idea of the underlying
structure, even though overlapping areas are difficult to follow,
specially as the number of considered clusters increase. 
We now use hierarchical clustering with average linkage to find gene clusters.
We will illustrate an example where we consider a partition with between
cluster distances of 0.5. 
\\\\
Clustering algorithms may deliver a large number of small clusters 
which are difficult to interpret.
To overcome this, we developed a \texttt{preMerge} step that assigns clusters below a certain size to
its closest cluster according to centroid distances. After the pre-merging step,
the number of clusters is reduced considerably, and all them have a
minimum size which allows easier map interpretation.
The function \texttt{clusGPS} integrates a clustering result into an existing map.
It also computes density estimates for each cluster in the map, which can be useful 
to assess cluster separation and further merge clusters, as we shall see later. We will first perform a hierarchical clustering over the distance matrix, which we can access with the \texttt{as.matrix} function.

\footnotesize
 
<<figclus00>>=
h <- hclust(as.dist(as.matrix(d)),method='average')
set.seed(149) # Random seed for the MCMC process within density estimation
clus <- clusGPS(d,mds1,h,ngrid=1000,densgrid=FALSE,verbose=TRUE,
preMerge=TRUE,k=max(cutree(h,h=0.5)),minpoints=20,mc.cores=1)
clus
@

\normalsize

We can represent the output of \texttt{clusGPS} graphically using the \texttt{plot} method.
The result in shown in Figure \ref{fig:clus0}. We appreciate that the
the resulting configuration presents a main central cluster (cluster 56, n=293 epigenes, colored in blue)
containing more than 50 percent of genes in all map, and is surrounded
by smaller ones that distribute along the external sections of the
map. Our functions \texttt{clusNames} and \texttt{tabClusters} provides information about the
name and size of the cluster partitions stored within a \texttt{clusGPS} object. The function \texttt{clusterID} can be used to retrieve the vector of cluster assignments for the elements of a particular clustering configuration.


\footnotesize

<<figclus0>>=
clus
clusNames(clus)
tabClusters(clus,125)
point.col <- rainbow(length(tabClusters(clus,125)))
names(point.col) <- names(tabClusters(clus,125))
point.col
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.col=point.col[as.character(clusterID(clus,125))],
point.pch=19)
@
 
\normalsize
 
Different clustering algorithms can deliver
significantly different results, thus it is important to decide how to
approach the clustering step depending on your data. Our example using hclust with average
linkage tends to divide smaller and more divergent clusters before,
while other methods may first 'attack' the most similar agglomerations.
You can use any alternative clustering algorithm by formatting its result as an \texttt{hclust} object h and passing it to the \texttt{clusGPS}
function.
% \drcomment{Is this something easy to do? What happens if I just have a cluster assignment vector? Could we add a method for that?} LET ME THINK, IT MAY OR MAY NOT BE FAST TO DO
 
\subsection{Cluster visualization with density contours}
\label{sec:clusGPS3}
 
We achieve this by using a contour representation to indicate the regions
in the map where a group of genes (ie genes with a given mark) locate
with high probability. The contour representation provides a clearer
visualization of the extent of overlap between gene sets, in an analog
way to those of the popular Venn diagrams but with the benefit of a
context-rich base providing a functional context for
interpretation.

\footnotesize
<<figclus1>>=
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.cex=1.5,point.col='grey')
for (p in c(0.95, 0.50))
plot(clus,type='contours',k=max(cutree(h,h=0.5)),lwd=5,probContour=p,
drawlabels=TRUE,labcex=2,font=2)
@ 

\normalsize

\setkeys{Gin}{width=0.5\textwidth}  
\begin{figure}
\begin{center}
\begin{tabular}{cc}
<<label=figclus0,fig=TRUE,echo=FALSE,results=hide>>=
<<figclus0>>
@&
<<label=figclus1,fig=TRUE,echo=FALSE,results=hide>>=
<<figclus1>>
@
\end{tabular}
\end{center}
\caption{2-dimensional MDS plot with chroGPS$^{genes}$ map and cluster
  identities indicated by point colors (left) and probabilistic contours drawn at 50 and 95 percent (right). }
\label{fig:clus0}
\end{figure}

%Additionally, these probability contours can be used
%as cluster robustness measurement, indicating how well clusters separate on the map. 
The \texttt{clusGPS} function computes 
Bayesian non-parametric density estimates using the \texttt{DPdensity}
function from the \texttt{DPPackage} package, but individual contours can be
generated and plotted by just calling the \texttt{contour2dDP}
function with a given set of points from the MDS object. 
Keep in mind that computation of density estimates
may \newtext{be imprecise} 
%fail 
with clusters of very few elements. Check the help of the
\texttt{clusGPS} function to get more insight on the \texttt{minpoints} parameter and how it relates to the \texttt{preMerge} step described above.

\subsection{Assessing cluster separation in chroGPS$^{genes}$ maps}
\label{sec:clusGPS2}
%%COMMENT: changed "robustness" for "separation" in the title. The notion of robustness has a specific meaning in statistics, e.g. robust to outliers, better to avoid it


Deciding the appropiate number of clusters is not an easy question.
\texttt{chroGPS} provides a method to evaluate cluster separation in
the lower dimensional representation. The cluster density estimates
can be used to compute the posterior expected correct classification rate (CCR) for each point, cluster and for the whole map,
thus not only giving an answer to how many clusters to use, but also
to show reproducible are the individual clusters in the chosen
solution. Intuitively, when two clusters share a region of high
density in the map, their miss-classification rate increases.
We can assess the CCR for each cluster using the \texttt{plot} function with the argument \texttt{type='stats'}.
Figure \ref{fig:clus2} shows the obtained plot. The dashed black line indicates the overall CCR for the map,
which is slightly lower than 0.9. All individual clusters have a CCR $\geq$ 0.8.  

\footnotesize
 
<<figclus2>>=
plot(clus,type='stats',k=max(cutree(h,h=0.5)),ylim=c(0,1),col=point.col,cex=2,pch=19,
lwd=2,ylab='CCR',xlab='Cluster ID',cut=0.75,cut.lty=3,axes=FALSE)
axis(1,at=1:length(tabClusters(clus,125)),labels=names(tabClusters(clus,125))); axis(2)
box()
@
 
\normalsize
 
\setkeys{Gin}{width=0.8\textwidth}  
\begin{figure}
\begin{center}
<<label=figclus2,fig=TRUE,echo=FALSE>>=
<<figclus2>>
@
\end{center}
\caption{Per-cluster (dots and continuous line) and global (dashed line) Correct Classification Rate. Red pointed line indicates an arbitrary threshold of 0.75 CCR. Higher values indicate more robust clusters which are better separated in space. }
\label{fig:clus2}
\end{figure}

\normalsize

\subsection{Locating genes and factors on chroGPS$^{genes}$ maps}
\label{sec:locateGPS}

%A very normal question to be addressed by chroGPS$^{genes}$ map is that of
%where a factor of interest is located, that is, 
\newtext{A natural question is}
where genes having a
given epigenetic mark tend to locate on the map. An easy solution is
just to highlight those points on a map, but that may be misleading, especially when multiple factors are considered simultaneously.
We offer tools to locate high-probability regions 
({\it i.e.} regions on the map containing a certain proportion of all the genes with a given
epigenetic mark or belonging to a specific Gene Ontology term). 
For instance, we will highlight the genes with the epigenetic factor HP1a.
The result is shown in Figure \ref{fig:loc2} (left). We see that HP1a shows a certain bimodality in its distribution, with a clear presence in the central clusters (56, 89) but also in the upper left region of the map (cluster 1 and to a lesser extent, 28).

\footnotesize

<<figloc1>>=
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.cex=1.5,point.col='grey')
for (p in c(0.5,0.95)) plot(clus,type='contours',k=max(cutree(h,h=0.5)),lwd=5,probContour=p,
drawlabels=TRUE,labcex=2,font=2)
fgenes <- uniqueCount(s2.tab)[,'HP1a_wa184.S2']==1
set.seed(149)
c1 <- contour2dDP(getPoints(mds1)[fgenes,],ngrid=1000,contour.type='none')
for (p in seq(0.1,0.9,0.1)) plotContour(c1,probContour=p,col='black')
legend('topleft',lwd=1,lty=1,col='black',legend='HP1a contours (10 to 90 percent)',bty='n')
@

\normalsize

\setkeys{Gin}{width=0.7\textwidth} 
\begin{figure}
\begin{center}
<<label=figloc1,fig=TRUE,echo=FALSE>>=
<<figloc1>>
@
\end{center}
\caption{chroGPS$^{genes}$ map with cluster contours at 50 and 95 percent the 5
  clusters presented above. In black, probability contour for HP1a 
factor.}
\label{fig:loc1}
\end{figure}

Highlighting a small set of genes on the map
({\it e.g.} canonical pathways) 
is also possible by using the \texttt{geneSetGPS} function.
\newtext{We randomly select 10 genes for illustration purposes. Figure \ref{fig:loc2} (right) shows the results.}

\footnotesize

<<figloc2>>=
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.cex=1.5,point.col='grey')
for (p in c(0.5,0.95)) plot(clus,type='contours',k=max(cutree(h,h=0.5)),lwd=5,probContour=p,
drawlabels=TRUE,labcex=2,font=2)
set.seed(149) # Random seed for random gene sampling
geneset <- sample(rownames(s2.tab),10,rep=FALSE)
mds2 <- geneSetGPS(s2.tab,mds1,geneset,uniqueCount=TRUE)
points(getPoints(mds2),col='black',cex=5,lwd=4,pch=20)
points(getPoints(mds2),col='white',cex=4,lwd=4,pch=20)
text(getPoints(mds2)[,1],getPoints(mds2)[,2],1:nrow(getPoints(mds2)),cex=1.5)
legend('bottomright',col='black',legend=paste(1:nrow(getPoints(mds2)),
geneset,sep=': '),cex=1,bty='n')
@

\normalsize

\setkeys{Gin}{width=0.7\textwidth} 
\begin{figure}
\begin{center}
<<label=figloc2,fig=TRUE,echo=FALSE>>=
<<figloc2>>
@
\end{center}
\caption{chroGPS$^{genes}$ map with cluster contours at 50 and 95 percent the 5
  clusters presented above. Left: In black, probability contour for HP1a 
factor. Right: random geneset located on the chroGPS$^{genes}$ map.}
\label{fig:loc2}
\end{figure}

\subsection{Merging overlapping clusters}
\label{sec:clusGPS3}

%Sometimes even after very small clusters are re-assigned into bigger
%ones, the number of clusters is still high and their overlapping over
%the map hampers data interpretation. 
\newtext{As discussed in Section \ref{sec:clusGPS2}, 
  for our toy example clusters obtained by setting a between-cluster distance threshold of 0.5 
  are well-separated and the CCR is high.
  When the number of points is higher or the threshold is set to a lower value, it is common
  that some clusters overlap substantially, hampering interpretation.}
Cluster density estimates offer
us an elegant way to detect significant cluster overlap over the space
defined by our MDS map, and thus allow us to merge clearly overlapping
clusters. Our approach performs this merging in an unsupervised
manner, by merging in each step the two clusters having maximum
spatial overlap, and stopping when the two next clusters to merge show
an overlap 
\newtext{substantially lower than that}
%degree changing significantly in mean to those 
from previous steps. For more details, check help for the function
\texttt{cpt.mean} in the \texttt{changepoint} package. By obtaining
clusters which better separate in space, their rate of correct
classification also improves, delivering a map configuration which is
robust, intuitive, and easy to interpret, specially with very
populated maps where the initial number of clusters may be very high.
\\\\
To illustrate the usefulness of cluster merging in some conditions, we will use a different cluster cut so that their boundaries overlap more significantly in our 2D map.
\newtext{We then merge clusters using the \texttt{mergeClusters} function.}

\footnotesize

<<figmerge0>>=
set.seed(149) # Random seed for MCMC within the density estimate process
clus2 <- clusGPS(d,mds1,h,ngrid=1000,densgrid=FALSE,verbose=TRUE,
preMerge=TRUE,k=max(cutree(h,h=0.2)),minpoints=20,mc.cores=1)
@
<<figmerge1>>=
par(mar=c(2,2,2,2))
clus3 <- mergeClusters(clus2,brake=0,mc.cores=1)
clus3
tabClusters(clus3,330)
@

\normalsize

\setkeys{Gin}{width=0.8\textwidth}  
\begin{figure}
\begin{center}
<<label=figmerge1,fig=TRUE,echo=FALSE,results=hide>>=
<<figmerge1>>
@
\end{center}
\caption{Overview of maximum cluster overlap observed in each merging step. Merging stops at 5 clusters, when the next two clusters to merge show an overlap differing significantly in mean to those from previous steps.}
\label{fig:merge1}
\end{figure}

\newtext{
  We plot the cluster contours before and after merging (Figure \ref{fig:merge2}).
  The merging step combined clusters from the central dense region of the map. These clusters had a low cluster-specific CCR,
  as shown in Figure \ref{fig:merge1}.
  After merging all cluster-specific CCR values were roughly $\geq 0.9$. 
  The code required to produce Figure \ref{fig:merge2} is provided below.
}

\footnotesize

<<figmerge21>>=
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.cex=1.5,point.col='grey')
for (p in c(0.95, 0.50)) plot(clus2,type='contours',k=max(cutree(h,h=0.2)),
lwd=5,probContour=p,drawlabels=TRUE,labcex=2,font=2)
@
<<figmerge22>>=
par(mar=c(0,0,0,0),xaxt='n',yaxt='n')
plot(mds1,point.cex=1.5,point.col='grey')
for (p in c(0.95, 0.50)) plot(clus3,type='contours',k=max(cutree(h,h=0.2)),
lwd=5,probContour=p)
@

\normalsize

\setkeys{Gin}{width=0.5\textwidth}  
\begin{figure}
\begin{center}
\begin{tabular}{cc}
<<label=figmerge21,fig=TRUE,echo=FALSE>>=
<<figmerge21>>
@&
<<label=figmerge22,fig=TRUE,echo=FALSE>>=
<<figmerge22>>
@
\end{tabular}
\end{center}
\caption{chroGPS$^{genes}$ map with clusters at between-cluster distance of 0.2, and cluster density contours at 50 and 95 percent. Left: Unmerged. Right: Merged. }
\label{fig:merge2}
\end{figure}

\normalsize

And as we did before, we can have a look at per-cluster CCR values
before and after cluster merging (\ref{fig:merge3})

\footnotesize

<<figmerge31>>=
plot(clus2,type='stats',k=max(cutree(h,h=0.2)),ylim=c(0,1),lwd=2,
ylab='CCR',xlab='Cluster ID')
@
<<figmerge32>>=
plot(clus3,type='stats',k=max(cutree(h,h=0.2)),ylim=c(0,1),lwd=2,
ylab='CCR',xlab='Cluster ID')
@

\normalsize

\setkeys{Gin}{width=0.5\textwidth} 
\begin{figure}
\begin{center}
\begin{tabular}{cc}
<<label=figmerge31,fig=TRUE,echo=FALSE,results=hide>>=
<<figmerge31>>
@ &
<<label=figmerge32,fig=TRUE,echo=FALSE,results=hide>>=
<<figmerge32>>
@
\end{tabular}
\end{center}
\caption{Per-cluster (dots and continuous line) and global (dashed line) mis-classification rate for the clusters
  shown in Figure \ref{fig:merge2}. Red dashed line indicates an arbitrary threshold of 0.7 CCR. Left: Unmerged. Right: Merged}
\label{fig:merge3}
\end{figure}


\subsection{Studying the epigenetic profile of selected clusters}
\label{sec:profileGPS}

A classical way of analyzing a group epigenes is to look at the
distribution of their epigenetic marks, that is, looking at their
epigenetic profile. A quick look into a heatmap-like plot produced with the \texttt{heatmap.2} function from the \texttt{gplots} package can
highlight specific enrichments or depletions of certain epigenetic
factors in a given cluster. As expected, this matches the 
distribution of epigenetic factors seen in Figure \ref{fig:merge2}.

\footnotesize

<<figprofile1>>=
p1 <- profileClusters(s2.tab, uniqueCount = TRUE, clus=clus3, i=max(cutree(h,h=0.2)), 
log2 = TRUE, plt = FALSE, minpoints=0)
# Requires gplots library
library(gplots)
heatmap.2(p1[,1:20],trace='none',col=bluered(100),margins=c(10,12),symbreaks=TRUE,
Rowv=FALSE,Colv=FALSE,dendrogram='none')
@

\normalsize

\setkeys{Gin}{width=1.0\textwidth}  
\begin{figure}
\begin{center}
<<label=figprofile1,fig=TRUE,echo=FALSE>>=
<<figprofile1>>
@
\end{center}
\caption{chroGPS$^{genes}$ profile heatmap of the 9 unmerged clusters presented at Figure \ref{fig:merge2} after unsupervised merging of overlapping clusters (showing 20 first factors for visualization purposes). Merged clusters get concatenated names from the original clusters. }
\label{fig:profile1}
\end{figure}

\normalsize

% \subsection{Going further, subdividing clusters}
% \label{sec:clusGPS4}
%  
% The cluster configuration after merging as shown above already explains much of the variability from our data, but 
% %one can see that some clusters are still much bigger than the other ones, 
% %and that their epigenetic profiles are somewhat 'washed out'. 
% \newtext{some clusters contain many more points than others.}
% A possible option is to just focus on these specific clusters and observe what happens when we subdivide them further.
%  
% % \drcomment{Add a method \texttt{clusterid} that extracts cluster ids from clusGPS objects. Use that method in the code below.} DONE
%  
% \footnotesize
%  
% <<figrecluster0>>=
% # First select points from bigger cluster above, cluster 3
% clus3
% tabClusters(clus3,330)
% sel=(clusterID(clus3,330)==3)
% table(sel)
% # We will subdivide at between cluster distance of 0.1
% k=max(cutree(h,h=0.1))
% set.seed(149) # Random seed for MCMC within the density estimate process
% clus4=mergeClusters(clusGPS(d,mds1,h,sel=sel,preMerge=TRUE,
%   k=k,minpoints=20,mc.cores=1),brake=3) # Use brake to keep some more clusters after merging
% @ 
%  
% \normalsize
%  
% \newtext{We visualize the sub-divided and merged cluster susing the \texttt{plot} method, as before.
% The result in shown in Figure \ref{fig:recluster1}.}
%  
% \footnotesize
%  
% <<figrecluster1>>=
% par(mar=c(2,2,2,2))
% plot(mds1,point.cex=1.5,point.col='grey',,xlim=c(-0.5,0.5),ylim=c(-0.5,0.5))
% for (p in c(0.95, 0.50)) plot(clus4,type='contours',k=k,lwd=5,probContour=p,
% drawlabels=TRUE,labcex=2,font=2)
% @
%  
% \normalsize
%  
% \setkeys{Gin}{width=0.8\textwidth}  
% \begin{figure}
% \begin{center}
% <<label=figrecluster1,fig=TRUE,echo=FALSE>>=
% <<figrecluster1>>
% @
% \end{center}
% \caption{The 4 clusters at between-cluster distance of 0.1, generated only from points from old cluster number 3 \ref{fig:merge2}.}
% \label{fig:recluster1}
% \end{figure}
%  
% And as we did previously, we may want to analyze their epigenetic
% profiles, merge them, etc.

\subsection{Beyond R: exporting chroGPS maps to Cytoscape}
\label{sec:export2Cytoscape}

No doubt R is a wonderful environment, but it has its limitations and
it may not be the most direct software to use for biologists. Having
that in mind, we developed a function for exporting any of the MDS
graphics from our chroGPS maps as an XGMML format network for the
widely used Cytoscape software \url{http://www.cytoscape.org}, \citep{shannon:2003}.
Network nodes are identified by their factor or epigene
name, so that importing external information (i.e. expression values)
or expanding the original chroGPS object with for instance external
regulation networks, Gene Ontology enrichments, etc, becomes natural
for Cytoscape users.
\\
Even if no edges are returned, the exported network keeps the relative
distribution of elements as seen in chroGPS, in order to keep the
distances between the original elements intact. For three-dimensional maps Cytoscape 3D Renderer is required.

\footnotesize

<<cyto1>>=
# For instance if mds1 contains a valid chroGPS-factors map.
# gps2xgmml(mds1, fname='chroGPS_factors.xgmml', fontSize=4,
# col=s2names$Color, cex=8)
# And use Cytoscape -> File -> Import -> Network (Multiple File Types) 
# to load the generated .xgmml file
@

\normalsize

\setkeys{Gin}{width=0.7\textwidth}  
\begin{figure}
\begin{center}
\includegraphics{chroGPS-cyto.png}
\includegraphics{chroGPS-cyto3d.png}
\end{center}
\caption{chroGPS$^{factors}$ network exported and visualized in Cytoscape. Top: 2D. Bottom: 3D.}
\label{fig:profile1}
\end{figure}

\normalsize

And this is everything, hope you enjoy using chroGPS as much as we did developing it !

\bibliographystyle{plainnat}
\bibliography{references} 

\end{document}
