%\VignetteIndexEntry{FourCSeq}
%\VignettePackage{FourCSeq}
%\VignetteEngine{knitr::knitr}
\documentclass[10pt,a4paper,oneside]{article}

\usepackage[utf8]{inputenc} 

\usepackage{calc}

%\pagestyle{empty}
<<style, eval=TRUE, echo=FALSE, results="asis">>=
BiocStyle::latex()
@

\title{\Rpackage{FourCSeq} analysis workflow}
\author{Felix A. Klein\\
European Molecular Biology Laboratory (EMBL),
\\ Heidelberg, Germany\\
\texttt{felix.klein@embl.de}}


\begin{document}

<<include=FALSE>>=
knitr::opts_chunk$set(concordance=TRUE, 
               resize.width="0.45\\textwidth", 
               fig.align='center',
               tidy = FALSE,
               message=FALSE)
@


\maketitle
\tableofcontents

\section{Introduction}

This vignette shows an example workflow of a 4C sequencing analysis. In an typical 
setting 4C sequencing data has been generated for different viewpoints 
in several replicates of multiple conditions. We focus on the analysis of a subset
of the data which was recently published \cite{Ghavi-Helm2014}. The data set 
comprises one viewpoint in replicates of 3 conditions.

  \begin{center}
    \begin{tabular}{ | l | }
      \hline 
      For further information on the underlying method and if you use \Rpackage{FourCSeq} \\
      in published research please consult and cite:  \\
      \\
      Felix A. Klein, Simon Anders, Tibor Pakozdi, Yad Ghavi-Helm, Eileen E. M. Furlong, Wolfgang Huber \\
      FourCSeq: Analysis of 4C sequencing data \\
      bioRxiv (2014). doi:10.1101/009548 \cite{Klein2014} \\
      \hline 
    \end{tabular}
  \end{center}

\section{Preprocessing}

The analysis with \Rpackage{FourCSeq} starts from  binary alignment/map (BAM)-files.
If you already have seperate bam files for each viewpoint you can skip this section, which
shows a possible way how to generate these bam files.

Usually many viewpoints are multiplexed in one sequencing lane. To demultiplex 
or just trim off the primer sequence the \Rpackage{FourCSeq} contains the python
script "demultiplex.py" in the folder "extdata/python". To run the python script
you have to install the HTSeq python package (http://www-huber.embl.de/users/anders/HTSeq/doc/install.html).

Then you can run the command:
\begin{verbatim}
python pathToScriptFile/demultiplex.py --fastq YourFASTQFile --barcode YourBarcodeFile
\end{verbatim}

The barcode file is a FASTA file containing the primer sequences that have been
used to generate the 4C library. The read starts are matched against these sequences
and if a unique match is found the primer sequence is trimmed and the remaining
read is saved in a FASTQ file with the viewpoint name attached to the original 
file name, e.g. for the FASTQ input 4c{\_}library.fastq and a primer sequence named
"viewpoint1" in the primer FASTA file, the script will generate the output file 
4c{\_}library{\_}viewpoint1.fastq for reads matching to the "viewpoint1" primer sequence.

Here is an example content of a primer FASTA file, containing one sequence for
the "testdata" viewpoint:
\begin{verbatim}
<<echo=FALSE, results='asis'>>=
primerFile = system.file("extdata/primer.fa", 
                         package="FourCSeq")
writeLines(readLines(primerFile))
@
\end{verbatim}

For additional parameters that can be passed to demulitiplex.py have a look at
the help documentation of the python script by running:
\begin{verbatim}
python pathToScriptFile/demultiplex.py --help
\end{verbatim}

If you don't know where the python script in the package is installed use the 
following command in R.
<<eval=FALSE>>=
system.file("extdata/python/demultiplex.py", package="FourCSeq")
@

After demultiplexing the files can be aligned with standard alignment software
generating bam output.


\section{Initialization of the \Rclass{FourC} object}

As first step we need to load the required libraries.
<<LoadLibraries, eval=TRUE>>=
library(FourCSeq)
@

To start the analysis we need to make a \Rclass{FourC} object. The 
\Rclass{FourC} object is created from a \Rclass{SimpleList} \Robject{exptData} 
containing information about the experiment and a \Rclass{DataFrame} \Robject{colData}
containing information about the samples. We now look at this in more detail.

For \Robject{exptData} the following information is required:
\begin{enumerate}
  \item \Robject{projectPath}, directory where the project will be saved.
  \item \Robject{fragmentDir}, subdirectory of the project directory where to
  save the information about restriction fragments.
  \item \Robject{referenceGenomePath}, path to the FASTA file of the reference 
  genome or a \Rclass{BSgenome} object.
  \item \Robject{reSequence1} and \Robject{reSequence2}, restriction enzyme 
  recognition sequence of the first and second restriction enzyme used in the 4C
  protocol, respectively. 
  \item \Robject{primerFile}, path to a FASTA file containing the primer 
  sequences of the viewpoints used for preparing the 4C libraries
  (names of the primer have to match the names of the viewpoints provided in 
  \Robject{colData}).
  \item \Robject{bamFilePath}, path to a directory where the bam files are 
  stored.
\end{enumerate}

For demonstration purposes example files of the ap viewpoint, containing only a
small region of the first 6900 bases on chromosome chr2L of the dm3 
\textit{Drosophila} genome, are saved in the \Rpackage{FourCSeq} package. Later
on we load a processed \Robject(FourC) object that contains the whole data for 
chr2L and chr2R (chr2R is the viewpoint chromosome of the ap example viewpoint).
We get the path to these files using the \Rfunction{system.file} function. 
For your own data you have to adjust the file
path to the directory where your files are stored.

%  For the analysis of the whole data on the chromosomes chr2L and chr2R see the 
%  \Rpackage{ExampleDataFourCSeq} package vignette. ???

<<filePaths, cache=TRUE>>=
referenceGenomeFile = system.file("extdata/dm3_chr2L_1-6900.fa",
                                  package="FourCSeq")
referenceGenomeFile

bamFilePath = system.file("extdata/bam", 
                          package="FourCSeq")
bamFilePath

primerFile = system.file("extdata/primer.fa", 
                         package="FourCSeq")
primerFile
@

We also take a look at the content of the primer file.
<<results='hide'>>=
writeLines(readLines(primerFile))
@
\begin{verbatim}
<<echo=FALSE,results='asis'>>=
writeLines(readLines(primerFile))
@
\end{verbatim}

The primer file contains one sequence, namely for the "ap" viewpoint.

Next we create \Robject{exptData} using "exampleData" as directory for the 
projectPath and the two restriction enzyme cutting sequences of DpnII (GATC) and
NlaIII (CATG) that were used in the experiment.
<<exptData, cache=TRUE>>=
exptData <- SimpleList(projectPath = "exampleData",
                       fragmentDir = "re_fragments",
                       referenceGenomeFile = referenceGenomeFile,                     
                       reSequence1 = "GATC",
                       reSequence2 = "CATG",
                       primerFile = primerFile,
                       bamFilePath = bamFilePath)
exptData
@

After creating \Robject{exptData} we now look at \Robject{colData}


For each library the following information has to be provided to 
\Robject{colData}:
\begin{enumerate}
  \item \Robject{viewpoint}, name of the viewpoint (has to match the viewpoint
  names in the provided primer file in \Robject{exptData}).
  \item \Robject{condition}, experimental condition.
  \item \Robject{replicate}, replicate number.
  \item \Robject{bamFile}, file name of the bam file.
  \item \Robject{sequencingPrimer}, was the 4C library sequenced from the side of the first restriction enzyme cutting site or the second. The allowed values are "first" or "second"
\end{enumerate}

<<colData, eval=TRUE, cache=TRUE>>=
colData <- DataFrame(viewpoint = "testdata",
                     condition = factor(rep(c("WE_68h", "MESO_68h", "WE_34h"), 
                                            each=2),
                                        levels = c("WE_68h", "MESO_68h", "WE_34h")),
                     replicate = rep(c(1, 2), 
                                     3),
                     bamFile = c("CRM_ap_ApME680_WE_6-8h_1_testdata.bam", 
                                 "CRM_ap_ApME680_WE_6-8h_2_testdata.bam",
                                 "CRM_ap_ApME680_MESO_6-8h_1_testdata.bam", 
                                 "CRM_ap_ApME680_MESO_6-8h_2_testdata.bam", 
                                 "CRM_ap_ApME680_WE_3-4h_1_testdata.bam",  
                                 "CRM_ap_ApME680_WE_3-4h_2_testdata.bam"),
                     sequencingPrimer="first")
colData
@

After having the necessary information in the required form, we create the
\Rclass{FourC} object.

<<ObjectInitialization, eval=TRUE, cache=TRUE, dependson=c("filePaths", "exptData", "colData")>>=
fc <- FourC(colData, exptData)
fc
@

We now have an \Rclass{FourC} object that contains all the required metadata.

\section{Fragment reference}

As the next step the provided reference genome is \textit{in-silico} digested using the 
provided restriction enzyme recognition sequences. The resulting fragment
reference is stored as \Robject{rowData} of the \Rclass{FourC} object.
<<AddingFragmentReference, eval=TRUE, cache=TRUE, dependson=c("ObjectInitialization")>>=
fc <- addFragments(fc)

fc
rowRanges(fc)
@

Now the \Rclass{FourC} object contains a \Rclass{GRanges} object in the 
\Robject{rowData} slot with the information on the fragments.

By setting \Robject{save} to \Robject{TRUE} in \Rfunction{addFragments}, the
results of the \textit{in-silico} digestion can be saved in the provided 
fragmentDir folder in the project directory, which are both defined in \Robject{exptData}. 
The first file (valid\_fragments.txt) contains the information for all fragments 
of the first restriction enzyme in the following columns:
\begin{enumerate}
    \item Chromosome
    \item Fragment start
    \item Fragment end
    \item Size of the left fragment end
    \item Size of the right fragment end
    \item Information whether the left fragment end is valid
    \item Information whether the right fragment end is valid
\end{enumerate}

The second file contains the locations of all cutting sites of the second 
restriction enzyme in the following columns:
\begin{enumerate}
    \item Chromosome
    \item Cutting site start
    \item Cutting site end
\end{enumerate}

Additionally, bedgraph files (re\_sites\_Seqeunce1/Sequence2.bed) are produced in 
the same folder for displaying the cutting sites in a genome viewer of choice 
(e.g. IGV or UCSC).

\subsection{Adding the viewpoint information}

To find the viewpoint fragment and extract the genomic position of the viewpoint,
the primers are mapped to the reference genome and fragment reference. Because
this can be time consuming for many sequences the results of 
\Rfunction{findViewpointFragments} is saved in the project directory 
provided in \Robject{exptData}. In a second step this data is loaded by 
\Rfunction{addViewpointFrags} and the \Robject{colData} of the \Rclass{FourC}
object is updated with the corresponding information of each viewpoint.
<<AddingViewpointFragmentInformation, eval=TRUE, cache=TRUE, dependson=c("ObjectInitialization")>>=
findViewpointFragments(fc) 

fc <- addViewpointFrags(fc)
@

The mapped primer fragments are also saved in the file "primerFragments.txt" in 
the provided fragmentDir folder in project directory both defined in \Robject{exptData}.
In contains one row per primer and the following columns:
\begin{enumerate}
  \item Viewpoint
  \item Chromosome
  \item Fragment start position
  \item Fragment end position
  \item Width of the whole fragment
  \item Size of the left fragment end
  \item Size of the right fragment end
  \item Information whether the left fragment end is valid
  \item Information whether the right fragment end is valid
  \item Primer start position
  \item Primer  end position
  \item Fragment side on which the primer matches
\end{enumerate}

\subsection{Adding the viewpoint information manually}

If the primer file is missing, this information can also be added manually.
The information has to contain the viewpoint chromosome name \Robject{chr}, 
the start position of the viewpoint fragment \Robject{start}, and its end position
\Robject{end}
<<AddingViewpointFragmentInformationManually, eval=FALSE>>=
colData(fc)$chr = "chr2L"
colData(fc)$start = 6027
colData(fc)$end = 6878 
@


\section{Counting reads at fragment ends}

To filter out non-informative reads, we use several criteria motivated by the
4C sequencing protocol. In the function \Rfunction{countFragmentOverlaps}, only 
reads mapping exactly to the end of a fragment with
the correct orientation are counted and assigned to the corresponding fragment 
in this step (Figure \ref{fig:frag}). The counting is strand specific, taking the
orientation of the reads into account (Figure \ref{fig:frag}). The count values are stored as matrices 
in the \Robject{assays} slot of the \Rclass{FourC} object. They are named 
\Robject{countsLeftFragEnd} and \Robject{countsRightFragEnd}.

\begin{figure}[htbp]
\begin{center}
\includegraphics[width=.7\textwidth]{readmapping.pdf}
\caption{\label{fig:frag}
If the sequencing primer starts at the first restriction enzyme
cutting site, reads that start at the fragment ends and
are oriented towards the fragment middle are kept for analysis (green arrows).
If the sequencing primer starts at the second restriction enzyme
cutting site, reads that start directly next to the
cutting site of the second restriction enzyme and are directed towards
the ends of the fragment are kept for analysis (green arrows). 
}
\end{center}
\end{figure}

If the sequence of the restriction enzyme has not been trimmed in the 
demultiplexing step (for viewpoints using a primer of the first cutting site) 
this can be done during the following step to make sure that reads start at the fragment's
end. In this example case we trim the first 4 bases of each read by setting 
\Robject{trim} to 4 to remove the GATC sequence of the first restriction enzyme.
(For the viewpoint primer starting from the second cutting site the reads can be
extended to overlap the cutting site, if the cutting site has been trimmed. In 
this case reads are counted with the \Rfunction{countFragmentOverlapsSecondCutter}
function.)

Additionally we filter out read that have a mapping quality below 30 by setting
\Robject{minMapq} to 30.


<<CountingReadOverlapsWithFragments, eval=TRUE, cache=TRUE, dependson=c("ObjectInitialization")>>=
fc <- countFragmentOverlaps(fc, trim=4, minMapq=30)
@

The counts from both fragment end are added by using the function
\Robject{combineFragEnds}.

<<combineFragEnds, eval=TRUE, cache=TRUE, dependson=c("CountingReadOverlapsWithFragments")>>=
fc <- combineFragEnds(fc)
@

We take a look at the \Rclass{FourC} object and see that it now contains 3 data 
matrices (called "assays"):
\Robject{counts}, \Robject{countsLeftFragEnd} and \Robject{countsRightFragEnd}
These matrices can be accessed by the \Rfunction{assay} or \Rfunction{assays}
functions.

% <<echo=FALSE>>=
% save(fc, 
%      file = file.path(exptData(fc)$projectPath, "fc.rda"),
%      compress="xz")
% @

<<>>=
fc
assays(fc)
head(assay(fc, "counts"))
@

For the rest of the vignette we now load the dataset of the "ap" viewpoint 
that was created for the whole chromosmes 2L and 2R of the dm3 reference genome. 
We adjust the project path and look at the \Rclass{FourC} object.

<<>>=
data(fc)
exptData(fc)$projectPath
exptData(fc)$projectPath <- "exampleData"

fc
assays(fc)
head(assay(fc, "counts"))
@

We can see, that the dimensions of the object now represent all fragments on 
chromosmes 2L and 2R of the dm3 reference genome.

The content of each assay can be saved as bigWig or bedGraph files. 
By default the \Robject{counts} assay is exported.
<<WriteTrackFiles, eval=TRUE, cache=TRUE, dependson=c("CountingReadOverlapsWithFragments")>>=
writeTrackFiles(fc)
writeTrackFiles(fc, format='bedGraph')
@

Because 4C data sometimes contains many spikes due to possible PCR artifacts,
the data can be smoothed for visualization.
<<SmoothOverFragments, eval=TRUE, cache=TRUE, dependson=c("CountingReadOverlapsWithFragments")>>=
fc <- smoothCounts(fc)
fc
@
We see that after the smoothing step there is a new assay, \Robject{counts{\_}5}, of
smoothed values.

Reproducibility between replicates can be assessed using a scatter plot of the
count values. We therefore generate such a scatter plot for two columns of the 
\Rclass{FourC} object.
<<correlation, dev.args=list(pointsize=16), dependson=c("CountingReadOverlapsWithFragments")>>=
plotScatter(fc[,c("ap_WE_68h_1", "ap_WE_68h_2")],
            xlab="Replicate1", ylab="Replicate2", asp=1)
@

They show good agreement for higher count values.


\section{Detecting interactions}

In the following step the count values are first transformed with a variance 
stabilizing transformation. After this step the variance between replicates no 
longer depends strongly on the average count value, thereby allowing
a consistent statistical treatment over a wide range of count values.
On these transformed counts, the general decay of the 4C signal with
genomic distance from the viewpoint is fitted using a symmetric monotone fit.
The residuals of the fit are used to calculate z-scores: the z-scores are the 
fit residuals divided by the median absolute deviation (MAD) of all the sample's
residuals.

This is done the \Rfunction{getZScores} function. The data is 
filtered so that only fragments with a median count of at least 40 count are kept
for the analysis. Also fragments that are close to the viewpoint, and hence show an 
extremely high count value are filtered out. If no minimum distance from the viewpoint
is defined this distance is automatically estimated by choosing the borders of
the initial signal decrease around the viewpoint. For more details and 
information about additional parameters that may be specified for the 
\Rfunction{getZScores} function type ?getZScores.
<<CalculateZScores, eval=TRUE, cache=TRUE, dependson=c("CountingReadOverlapsWithFragments"), results='hide'>>=
fcf <- getZScores(fc)
fcf
@

After calling getZScores, a new \Rclass{FourC} object is returned that has been
filtered to contain only fragments that were kept for analysis according to the 
above criteria. It also contains 
additional information added by the \Rfunction{getZScores} function 
(see ?getZScores for details).

We take a look at the distribution of z-scores, which are stored in the 
\Robject{assay} "zScores" of the \Robject{FourC} object.
<<zScoreDistribution, eval=TRUE, cache=TRUE, fig.show='hold', dependson=c("CalculateZScores")>>=
zScore <- assay(fcf, "zScore")

hist(zScore[,"ap_MESO_68h_1"], breaks=100)
hist(zScore[,"ap_MESO_68h_2"], breaks=100)
@

For the second replicate two peaks are observed in the histogram. The peak close
to -2 is due to fragments with 0 counts in the second library, which 
has a lower coverage. Since we are interested in 
finding strong interactions on the positive side of the distribution, we can 
continue and capture the strongest contacts. However, if the
influence of low values would further shift the distribution to negative values
this might lead to errors in the calculation of z-scores.
It is therefore important to check the distribution of values
after calculating the z-scores.

In the next plots we check, whether normal assumption for calculating the 
p-values is justified.
<<zScoreDistribution2, eval=TRUE, cache=TRUE, fig.show='hold', dependson=c("zScoreDistribution")>>=
qqnorm(zScore[,"ap_MESO_68h_1"], 
       main="Normal Q-Q Plot - ap_MESO_68h_1")
abline(a=0, b=1)
qqnorm(zScore[,"ap_MESO_68h_2"], 
       main="Normal Q-Q Plot - ap_MESO_68h_2")
abline(a=0, b=1)
@

As we see the approximation is satisfactory in general, even for the second replicate
for which alredy observed deviations in the histogram.

Using a conservative approach, we define interacting regions with the following 
thresholds: a fragment must have
z-scores larger than 3 for both replicates and an adjusted p-value of 0.01 
for at least one replicate. The call to \Rfunction{addPeaks} adds a new
assay to the \Rclass{FourC} object that contains booleans indicating whether an 
interaction has been called for a fragment or not.

<<addPeaks, eval=TRUE, cache=TRUE, fig.keep='none', dependson=c("CalculateZScores")>>=
fcf <- addPeaks(fcf, zScoreThresh=3, fdrThresh=0.01)
head(assay(fcf, "peaks"))
@



% <<echo=FALSE, dependson=c("CalculateZScores")>>=
% save(fcf, 
%      file = file.path(exptData(fcf)$projectPath, "fcf.rda"),
%      compress="xz")
% @

Next we take a look at the fit for the first sample.
<<fitDisplay, cache=TRUE, dependson=c("CalculateZScores"), fig.show='hold', fig.height=6, dev.args=list(pointsize=16)>>=
plotFits(fcf[,1], main="")
@

The points show the count values for the individual fragments. The red line is 
the fit and the blue dashed line is the fit plus (z-score threshold)*MAD for the
given library, where the z-score threshold has been defined by the call to 
\Rfunction{addPeaks}. If \Rfunction{addPeaks} has not been called yet, a default
z-score threshold of 2 is used. The first plot show the data left of the 
viewpoint, the second plot right of the viewpoint and for the last plot both 
sides have been combined by using the absolute distance from the viewpoint.


The \Rfunction{plotZScores} function produces plots to display the results. 
To include gene annotation, we load the \Rpackage{TxDb.Dmelanogaster.UCSC.dm3.ensGene} 
package that contains transcript information for the dm3 genome. It can be passed
as an argument to the \Rfunction{plotZScores} function.
<<plotZScores, eval=TRUE, cache=TRUE, fig.keep='all', fig.show='hold', dependson=c("CalculateZScores")>>=
library(TxDb.Dmelanogaster.UCSC.dm3.ensGene)

plotZScores(fcf[,c("ap_WE_68h_1", "ap_WE_68h_2")],
            txdb=TxDb.Dmelanogaster.UCSC.dm3.ensGene)
@

The plot shows the results for two different window sizes around the viewpoint.
The fit is shown as green line and the dashed blue lines span the interval of $\pm$
(z-score threshold)*MAD, where the z-score threshold has been defined by the call to 
\Rfunction{addPeaks}. If \Rfunction{addPeaks} has not been called yet, a default
z-score threshold of 2 is used. Red points represent fragments that have been called as
interactions.

\section{Detecting differences}

In addition to detecting interactions within a sample, one might be interested 
in finding differences of interaction frequencies between samples from different
experimental conditions.

Here we show how to detect differences between conditions. In our case the 
conditions are whole embryo tissue at 3-4~h and 6-8~h and mesoderm specific 
tissue at 6-8~h. The distance dependence, which varies between viewpoints is 
taken into account by calculating normalizationFactors.
<<FindDifferences, eval=TRUE, cache=TRUE, dependson=c("CalculateZScores")>>=
fcf <- getDifferences(fcf,
                      referenceCondition="WE_68h")
fcf
@
 
% <<echo=FALSE, dependson=c("CalculateZScores")>>=
% save(fcf, 
%      file = file.path(exptData(fcf)$projectPath, "fcfDifferences.rda"),
%      compress="xz")
% @
 
After calling getDifferences, the \Rclass{FourC} object contains additional
information about the differential test (see ?getDifferences for details.)

First we take a look at the dispersion fit calculated in the analysis to check 
if the fit worked, especially since a warning was thrown. 
As we can see the red fit nicely captures the trend of the 
black dots. The blue dots are the shrunken dispersion estimates for each 
fragment that are used in the differential test as measure for the variability 
of the data (see \Rpackage{DESeq2} vignette and \cite{Love2014} for details).
<<plotDispersion, cache=TRUE, dependson=c("FindDifferences"), dev.args=list(pointsize=16)>>=
plotDispEsts(fcf)
@

We also take a look at the estimated values of the normalization factors plotted
against the distance from the viewpoint. The horizontal lines represent the size 
factors of the different libraries.
<<normalizationFactors, cache=TRUE, dependson=c("FindDifferences"), dev.args=list(pointsize=16)>>=
plotNormalizationFactors(fcf)
@

Compared to single size factors for the library size correction these values are
shifted, especially in the region close to the viewpoint, where they span a 
range from approximately 0.3 to 3.

Next we generate an MA plot using the method from the \Rpackage{DESeq2} package 
(for details see ?plotMA and choose DESeq2). 
The MA plot shows the log fold changes between conditions plotted over the base
mean values across samples. Red dots represent fragments with an adjusted 
p-value below the significance level of 0.01.
<<maplot, cache=TRUE, dependson=c("FindDifferences"), dev.args=list(pointsize=16)>>=
plotMA(results(fcf, contrast=c("condition", "WE_68h", "MESO_68h")),
       alpha=0.01,
       xlab="Mean 4C signal",
       ylab="log2 fold change",
       ylim=c(-3.1,3.1))
@


To take a look at the results of the differential test we use the 
\Rfunction{getAllResults} function.
<<results, cache=TRUE, dependson=c("FindDifferences")>>=
results <- getAllResults(fcf)
dim(results)
head(results)[,1:6]
@

The table shows the base mean for the given fragment in the first column. Then,
for every combination of conditions, 5 columns are shown. We only look at the 
results of the first combination by selecting the first 6 columns. The second 
column shows the estimated log2 fold change between the two conditions, the 
third the estimated standard error of the log2 fold change, the fourth the 
Wald test statistic, the fifth the corresponding p-value and the sixth the 
adjusted p-value.

The results can be visualized by creating plots with the \Rfunction{plotDifferences}
function.
<<plotDifferences, eval=TRUE, cache=TRUE, dependson=c("FindDifferences"), fig.height=10, fig.width=15, resize.width="0.9\\textwidth", dev.args=list(pointsize=12)>>=
plotDifferences(fcf,
                txdb=TxDb.Dmelanogaster.UCSC.dm3.ensGene,
                plotWindows = 1e+05,
                textsize=16)
@

The plot shows the results for the comparison of the two conditions. The upper two tracks 
show the variance stabilized counts of the first condition. The fit is shown as 
green line and the dashed blue lines span the interval of $\pm$
(z-score threshold)*MAD, where the z-score threshold has been defined by the call to 
\Rfunction{addPeaks}. If \Rfunction{addPeaks} has not been called yet, a default
z-score threshold of 2 is used. Red points represent fragments that have been called as
interactions, blue points represent points that show significant changes between
conditions and orange points fulfill both criteria.
The fifth track shows a color representation of differential interactions. A green
bar means that the interaction is stronger in the first condition compared to the
second and a red bar represents the opposite case. The log2 fold changes are 
shown also on top of a gene model track (lower panel).


We now integrate the results with known gene annotation for the apterous (ap) 
gene, which is the closest gene contacted by the viewpoint. We extract the log2 
fold change of the signal at the ap promoter. The flybase gene id of the ap gene
is "FBgn0000099". The \Rfunction{genes} function return a \Robject{GRanges}
object with the genomic coordinates of the gene.
<<integrationWithAnnotation1, eval=TRUE, cache=TRUE, fig.keep='none', dependson=c("FindDifferences")>>=
apId <- "FBgn0000099"
apGene <- genes(TxDb.Dmelanogaster.UCSC.dm3.ensGene,
               vals=list(gene_id=apId))
apGene
@

The \Rfunction{promoters} function extends the transcription start site (TSS) in both 
directions and returns a \Robject{GRanges} object with the resulting genomic coordinates.
<<integrationWithAnnotation2, eval=TRUE, cache=TRUE, fig.keep='none', dependson=c("FindDifferences")>>=
apPromotor <- promoters(apGene, upstream = 500, downstream=100)
apPromotor
@

We now want to find the results for the fragment that overlaps with the ap 
promoter. Therefore we get the genomic coordinates of the fragments stored in
the \Robject{FourC} object with \Rfunction{rowRanges} and find the overlap with
\Rfunction{findOverlaps}.
<<integrationWithAnnotation3, eval=TRUE, cache=TRUE, fig.keep='none', dependson=c("FindDifferences")>>=
frags <- rowRanges(fcf) 

if(length(frags) != nrow(results))
  stop("Number of rows is not the same for the fragment data and results table.")

ov <- findOverlaps(apPromotor, frags)
ov
@
The overlap shows which fragments (subjectHits) overlaps the ap promoter 
(queryHits).

Finally we look at the results of the fragment overlapping the ap promoter by
subsetting results using the \Rfunction{subjectHits} function and look only at the
first comparison of 6-8h whole embryo and mesoderm specific tissue.
<<integrationWithAnnotation4, eval=TRUE, cache=TRUE, fig.keep='none', dependson=c("FindDifferences")>>=
results[subjectHits(ov),1:6]
@
We can see that from comparison of these conditions that there is a significant 
log2 fold change of \Sexpr{results[subjectHits(ov),2]}.

\section{Session Info}
<<sessionInfo>>=
sessionInfo()
@

\bibliography{literature}

\end{document}
