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    {\Large Carmen M. Livi} % Author name
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    \textbf{\LARGE ChIP-seq quality Control}\\[\baselineskip] % First line
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    {\textcolor{RoyalBlue}{\Huge \textbf{ChIC} }}\\[\baselineskip] 
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%\title{ChIP-seq quality Control package \textbf{ChIC}: 
%A short introduction}


\tableofcontents

\newpage



\section{Introduction}
\textbf{ChI}P-seq quality \textbf{C}ontrol package (ChIC) provides 
functions and data structures to assess the quality of ChIP-seq data. 
The tool computes three different categories of quality control (QC) 
metrics: QC-metrics designed for narrow-peak profiles and general metrics, 
QC-metrics based on the global read distribution and QC-metrics
from local signal enrichment around annotated genes. The user-friendly 
functions allow performing the analysis with a single command, 
whereas step by step functions are also available for more experienced users. 

\noindent The package comes with a large reference 
compendium of precomputed QC-metrics from public ChIP-seq samples. 
Key features are the calculation, visualisation and creation of summary 
plots for QC-metrics, tools for the comparison of metagene profiles 
against reference profiles, tools for the comparison of 
single QC-metrics against the compendium values and 
finally a random forest model to compute a single score 
summarising quality control.

\noindent ChIC provides three wrapper functions that are used to calculate 
a comprehensive set of metrics: \textcolor{airforceblue}{qualityScores\_EM()}, 
\textcolor{airforceblue}{qualityScores\_GM()} 
and \textcolor{airforceblue}{qualityScores\_LM()}.


\newpage
\section{Input}
\noindent To run ChIC the user has to provide two bam files: one for ChIP and 
one for the input. In this tutorial we illustrate ChIC functions 
using a H3K36me3 ChIP-seq dataset from ENCODE (ID: ENCFF000BFX) 
and its input (ID: ENCFF000BDQ). The data can can be downloaded from:

\noindent \url{https://www.encodeproject.org/files/ENCFF000BFX/}\\
\url{https://www.encodeproject.org/files/ENCFF000BDQ/}



<<eval=FALSE, echo=TRUE >>=

system("wget 
https://www.encodeproject.org/files/ENCFF000BFX/
    @@download/ENCFF000BFX.bam")

system("wget 
https://www.encodeproject.org/files/ENCFF000BDQ/
    @@download/ENCFF000BDQ.bam")

chipName <- "ENCFF000BFX"
inputName <- "ENCFF000BDQ"
@



\textbf{PLEASE NOTE:} For timing reasons the tutorial will 
use toy-bam files with a reduced number of chromosomes. 
Input and chip data are therefore loaded 
from our datapackage "ChIC.data":

<<eval=TRUE, echo=TRUE >>=

library(ChIC)

#load tag-list with reads aligned to a subset of chromosomes
data("chipSubset", package = "ChIC.data", 
    envir = environment())
chipBam <- chipSubset

data("inputSubset", package = "ChIC.data", 
    envir = environment())
inputBam <- inputSubset
@



<<options,echo=FALSE>>=
options(width=60)
@ 

\newpage
\section{ENCODE Metrics (EM)}

\noindent \textcolor{airforceblue}{qualityScores\_EM()} is a wrapper 
function that reads the 
provided bam files and calculates a number of QC-metrics from 
cross-correlation analysis and peak-calling. We will refer to 
the output measures as ENCODE Metrics EM, as originally proposed 
by ENCODE consortium \cite{Landt}.

<<eval=FALSE, echo=TRUE >>=

##calculating EM

mc=4 ##for parallelisation

CC_Result <- qualityScores_EM(chipName=chipName, 
    inputName=inputName, 
    annotationID="hg19",
    read_length=36, 
    mc=mc)

finalTagShift <- CC_Result$QCscores_ChIP$tag.shift
@

\noindent The function expects two bam files: one for the immunoprecipitation 
(ChIP) and one for the control (Input). The read length 
(read\_length parameter) 
can be taken from the bam file itself. The 'mc' parameter is set to 1 
by default, when changed it triggers the parallelisation and 
speeds up the calculations of a few analysis steps
that allow using multiple computing cores. 'annotationID' refers to the 
genome annotation used.  Currently hg19 and mm9 (dm3 in preparation) are 
supported. 
\textcolor{airforceblue}{qualityScores\_EM()} produces the Cross-correlation 
plot (see Figure \ref{fig:1}) and returns a number of 
QC-metrics (see: \ref{Appendix} Appendix). 
Amongst others the tag.shift value, which represents an input parameter for 
further steps (i.e. peak-calling and metagene calculation). 
\textcolor{airforceblue}{qualityScores\_EM()} executes the following single 
steps:
\begin{enumerate}
    \item Reading BAM files (readBamFile)
    \item Calculatig QC-metrics from CrossCorrelation analysis 
        (getCrossCorrelationScores) 
    \item Removing anomalies in the read distrbution (removeLocalTagAnomalies)
    \item Calculating QC-metrics from peak calls (getPeakCallingScores)
    \item Profile smoothing for further analysis steps (tagDensity)
\end{enumerate} 


\noindent \textbf{PLEASE NOTE:} The following code chunks are automatically 
executed within the \textcolor{airforceblue}{qualityScores\_EM()} wrapper 
function. 
Nevertheless each listed function is available for single 
use if the user starts from the bam file 
and wants to perform all the steps individually.

\subsection{Reading BAM files}
The first step in the \textcolor{airforceblue}{qualityScores\_EM()} function 
reads ChIP-seq data in .bam file format. The function 
expects the filename, that can also contain the pathname.

<<eval=FALSE, echo=TRUE >>=
chipBam <- readBamFile(chipName)
inputBam <- readBamFile(inputName)
@


\subsection{Calculate QC-metrics from CrossCorrelation analysis}
\textcolor{airforceblue}{getCrossCorrelationScores()} calculates QC-metrics 
from the cross-correlation analysis and other general metrics, e.g. the 
non-redundant fractions of mapped reads. An important parameter 
required by \textcolor{airforceblue}{getCrossCorrelationScores()} 
is the binding-characteristics, calculated using 
\textcolor{airforceblue}{spp::get.binding.characteristics()} function. The 
binding-characteristics structure provides information about the 
peak separation distance and the cross-correlation profile (for more details 
see \cite{Kharchenko2008}).

<<eval=TRUE, echo=FALSE >>=
mc=2
data("crossvalues_Chip", package = "ChIC.data", envir = environment())
@


<<eval=TRUE, echo=TRUE >>=

cluster <- parallel::makeCluster( mc )

## calculate binding characteristics 

chip_binding.characteristics <- get.binding.characteristics(
    chipBam, 
    srange=c(0,500), 
    bin = 5, 
    accept.all.tags = TRUE, 
    cluster = cluster)

input_binding.characteristics <- get.binding.characteristics(
    inputBam, 
    srange=c(0,500), 
    bin = 5, 
    accept.all.tags = TRUE,
    cluster = cluster)

parallel::stopCluster( cluster )
@

<<eval=FALSE, echo=TRUE >>=

## calculate cross correlation QC-metrics
crossvalues_Chip <- getCrossCorrelationScores( chipBam , 
    chip_binding.characteristics, 
    read_length = 36, 
    annotationID="hg19",
    savePlotPath = filepath, 
    mc = mc)
@

\noindent "savePlotPath" sets the path in which the Cross-Correlation 
plot (as pdf) should be saved. If nothing is provided the plot will be 
forwarded to default DISPLAY. An example of a Cross-Correlation plot 
is shown in Figure \ref{fig:1}. Along with the visual output, 
\textcolor{airforceblue}{getCrossCorrelationScores()} returns a list with EM 
(see \ref{Appendix} Appendix), from which 
we have to save tag.shift value for further steps:

<<eval=TRUE, echo=TRUE >>=
finalTagShift <- crossvalues_Chip$tag.shift
@

\begin{figure}
\centering
.\includegraphics[scale=0.60]{phantomCrossCorrelation.pdf}
\caption{Cross-correlation plot of the ChIP.}
\label{fig:1}
\end{figure}


\subsection{Remove anomalies in the read distrbution}
The data is processed further by using 
\textcolor{airforceblue}{removeLocalTagAnomalies()} 
that removes local read anomalies like regions with extremely high read 
counts compared to the neighborhood 
(for more details see \cite{Kharchenko2008}). 

<<eval=TRUE, echo=TRUE >>=

##get chromosome information and order chip and input by it
chrl_final <- intersect(names(chipBam$tags), 
    names(inputBam$tags))
chipBam$tags <- chipBam$tags[chrl_final]
chipBam$quality <- chipBam$quality[chrl_final]
inputBam$tags <- inputBam$tags[chrl_final]
inputBam$quality <- inputBam$quality[chrl_final]
@

<<eval=TRUE, echo=TRUE >>=
##remove positions with extremely high read counts with 
##respect to the neighbourhood
selectedTags <- removeLocalTagAnomalies(chipBam, 
    inputBam, 
    chip_binding.characteristics, 
    input_binding.characteristics)

inputBamSelected <- selectedTags$input.dataSelected
chipBamSelected <- selectedTags$chip.dataSelected
@


\subsection{Calculate QC-metrics from peak calls}
The last set of QC-metrics belonging to the category of EMs 
are based on the number of called peaks using 
\textcolor{airforceblue}{getPeakCallingScores()}.

<<eval=TRUE, echo=TRUE >>=
bindingScores <- getPeakCallingScores(chip = chipBam, 
    input = inputBam, 
    chip.dataSelected = chipBamSelected, 
    input.dataSelected = inputBamSelected, 
    annotationID="hg19",
    tag.shift = finalTagShift, 
    mc = mc)
@


\subsection{Profile smoothing}
The last step executed in \textcolor{airforceblue}{qualityScores\_EM()} 
is the smoothing (using a Gaussian kernel) of the read 
profile to obtain the tag density profile (for more details see 
\cite{Kharchenko2008}). 

<<eval=TRUE, echo=TRUE >>=
smoothedChip <- tagDensity(chipBamSelected,
    annotationID = "hg19", 
    tag.shift = finalTagShift, mc = mc)
smoothedInput <- tagDensity(inputBamSelected, 
    annotationID = "hg19",
    tag.shift = finalTagShift, mc = mc)
@

\noindent The read density profile is needed to 
calculate the remaining two categories of QC-metrics: 
the Global enrichment profile Metrics (GM) and the 
local enrichment profile metrics (LM) (see \ref{Appendix} Appendix). 

\newpage
\section{Global enrichment profile Metrics (GM) and Fingerprint-plot}

This category of QC-metrics is based on the global read distribution along 
the genome for ChIP and Input \cite{Diaz2012}. 
The wrapper \textcolor{airforceblue}{qualityScores\_GM()} reproduces the 
so-called 
Fingerprint plot (Figure \ref{fig:Fingerprint}), i.e. the cumulative read 
distribution plot, from which 9 quantitative QC-metrics are derived. 
Examples of these metrics are the (a) fraction of bins without 
reads for ChIP and input, (b) the point of maximum distance between 
the ChIP and input (x-coordinate, y-coordinate for ChIP and input, 
the distance calculated as absolute difference between the 
two y-coordinates, 
the sign of the difference), (c) the fraction of reads in the top 1 
percent of bins with highest coverage for ChIP and input. 

<<eval=FALSE, echo=TRUE >>=
Ch_Results <- qualityScores_GM(densityChip = smoothedChip,
    densityInput = smoothedInput, 
    savePlotPath = filepath)
str(Ch_Results)
@


<<eval=FALSE, label=Fingerprint, include=FALSE, echo=FALSE>>=
Ch_Results <- qualityScores_GM(densityChip=smoothedChip,
densityInput=smoothedInput)
@

\begin{figure}[htbp]
\centering
<<eval=TRUE, label=Fingerprint, fig=TRUE, echo=FALSE>>=
<<Fingerprint>>
@
\caption{Fingerprint plot of sample ENCFF000BFX and its input ENCFF000BDQ.}
\label{fig:Fingerprint}
\end{figure}


<<eval=TRUE, echo=FALSE >>=
str(Ch_Results)
@



\newpage

\section{Local enrichment profile metrics (LM) and metagene profiles}

\textcolor{airforceblue}{createMetageneProfile()} creates the 
unscaled single-point and 
a scaled whole gene metagene profile and returns a list with 
three items: "TSS", "TES" and "geneBody". 
Each item is again a list with the 
metagene profiles for ChIP and input.


<<eval=TRUE, echo=TRUE>>=
Meta_Result <- createMetageneProfile(
    smoothed.densityChip = smoothedChip, 
    smoothed.densityInput = smoothedInput, 
    annotationID="hg19",
    tag.shift = finalTagShift, 
    mc = mc)
@


\noindent The objects in "Meta\_Result" are needed to create the 
metagene plots and to extract the LMs for the different profiles.

\noindent Metagene profiles show the signal enrichment around a region of 
interest like the transcription start site (TSS) or over the gene body. 
ChIC creates two types of metagene profiles: an unscaled single-point profile 
for the TSS and transcription end site, and a scaled whole gene metagene 
profile 
including the gene body like in Figure \ref{fig:MetaGenePlots}. 
For the metagene profiles the tag density of the immunoprecipitation is 
taken over all RefSeq annotated human genes, 
averaged and log2 transformed. The same is done for the input.
The normalised profile (Figure \ref{fig:MetaGenePlotNorm}) 
is calculated as the signal enrichment (immunoprecipitation over the input) 
and plotted on the y-axis, whereas the genomic coordinates of the genes like 
the TSS or regions up- and downstream are shown on the x-axis. 


\subsection{Plotting an unscaled single-point metagene profile}
The "TSS" or "TES" object and the 
\textcolor{airforceblue}{qualityScores\_LMgenebody()} function 
are used to plot the unscaled profile (see Figure \ref{fig:TSS})
and return the respective LM values.

<<eval=TRUE, echo=TRUE>>=
TES_Scores=qualityScores_LM(data=Meta_Result$TES,
    tag="TES")
@

\begin{figure}[htbp]
    \centering
<<eval=TRUE, label=TSS, fig=TRUE, echo=TRUE>>=
TSS_Scores=qualityScores_LM(data=Meta_Result$TSS, 
    tag="TSS")
@
\caption{Unscaled single-point metagene profile: Signal enrichment for 
    ChIP and Input at the TSS.}
\label{fig:TSS}
\end{figure}


\subsection{Plotting a scaled whole gene metagene profile}
The "geneBody" object and the 
\textcolor{airforceblue}{qualityScores\_LMgenebody()} function 
are used to plot the scaled profile (see Figure \ref{fig:MetaGenePlots})
and return the respective LM values:

\begin{figure}[htbp]
    \centering
<<eval=TRUE, label=geneBody, fig=TRUE, echo=TRUE>>=
geneBody_Scores <- qualityScores_LMgenebody(Meta_Result$geneBody)
@
\caption{Scaled whole gene metagene profile: Signal enrichment for 
    ChIP and Input along the gene body.}
\label{fig:MetaGenePlots}
\end{figure}



\begin{figure}[htbp]
\centering
.\includegraphics{ScaledMetaGeneNormalized.pdf}
\caption{Normalized profile: signal enrichment for ChIP over 
Input along the gene body for a scaled whole gene profile.}
\label{fig:MetaGenePlotNorm}
\end{figure}

\newpage
\section{Quality assessment using the compendium of QC-metrics as reference}
The comprehensive set of QC-metrics, computed over a large set of ChIP-seq 
samples, constitutes in itself a valuable compendium that can be used as a 
reference for comparison with new samples. ChIC provides three functions 
for that:
\begin{itemize}
    \item \textit{metagenePlotsForComparison} to compare the metagene plots 
    with the compendium
    \item \textit{plotReferenceDistribution} to compare a single QC-metric 
    with the compendium values
    \item \textit{predictionScore} to obtain a single quality score from 
    the random forest models trained on the previously computed QC-metrics 
\end{itemize}


\subsection{Available chromatin marks and factors}
This useful function lists all chromatin marks and 
transcription factors that are available for the comparison analysis.
With the keywords "mark" and "TF" the respective lists with the available 
elements are listed.

<<eval=TRUE, echo=TRUE >>=
listAvailableElements(target="H3K36me3")
@



\subsection{List the sample IDs of the compendium}

Shows the IDs of all analysed ChIP-seq samples 
from ENCODE and Roadmap that have been included 
in the compendium by providing the keyword 
"ENCODE" or "Roadmap".

<<eval=TRUE, echo=TRUE >>=
head(listDatasets(dataset="ENCODE"))
@


\subsection{Comparing local enrichment profiles}
The \textcolor{airforceblue}{metagenePlotsForComparison()} 
function is used to compare the 
local enrichment profile to the reference compendium by plotting the 
metagene profile against the expected metagene for the same type of 
chromatin mark. The expected metagene profile is 
provided by the compendium mean (black line) and standard error 
(blue shadow). Examples are shown in Figures 
\ref{fig:MetaProfileComparisonGeneBody} and 
\ref{fig:MetaProfileComparisonTSS}.



\begin{figure}[htbp]
    \centering
<<eval=TRUE, label=geneBodyComparison, fig=TRUE, echo=TRUE>>=

metagenePlotsForComparison(data = Meta_Result$geneBody,
    target = "H3K4me3", 
    tag = "geneBody")
    
@

\caption{Enrichment profile plotted against the pre-computed 
profiles of the compendium. The metagene profile shows the sample signal 
(red line) for the ChIP compared to the compendium mean 
signal (black line) and the 2x standard error (blue shadow).}
\label{fig:MetaProfileComparisonGeneBody}
\end{figure}



\begin{figure}[htbp]
    \centering
<<eval=TRUE, label=TSSComparison, fig=TRUE, echo=TRUE>>=

metagenePlotsForComparison(data = Meta_Result$TSS,
    target = "H3K4me3", 
    tag = "TSS")
@

\caption{The metagene profile shows the sample signal 
for the ChIP (red line) compared to the compendium mean 
signal (black line) and the 2x standard error (blue shadow).}
\label{fig:MetaProfileComparisonTSS}
\end{figure}



%\begin{figure}[htbp]
%\centering
%.\includegraphics{Comparison.pdf}
%\caption{Enrichment profile plotted against the pre-computed 
%profiles of the compendium. The metagene profiles show the sample signal 
%(red line) for the ChIP (A) and the enrichment (chip over input) (B) 
%when compared to the compendium mean signal (black line) and its 2x standard 
%error (blue shadow).}
%\label{fig:MetaProfileComparison}
%\end{figure}



%<<eval=FALSE, label=ComparisonInput, include=FALSE, echo=FALSE>>=
%metagenePlotsForComparison(data = Meta_Result$geneBody,
%   target = "H3K4me3", tag = "geneBody")
%@

%\begin{figure}[htbp]
%\centering
%<<eval=TRUE,label=ComparisonInput, fig=TRUE, echo=FALSE>>=
%<<ComparisonInput>>
%@
%\caption{The metagene profile shows the signal (red line) 
%of the input compared to the compendium.}
%\label{fig:MetaProfileComparisonInput}
%\end{figure}


\subsection{Comparing QC-metrics to the reference values of the compendium}
Plotting a single QC-metric against the reference values from a large 
number of already published data, adds an extra level of 
information that can be easily used by less experienced users. 
An example is shown in Figure \ref{fig:ValueComparison}.

\begin{figure}[htbp]
\centering
<<eval=TRUE, label=ReferenceDistr, fig=TRUE, echo=TRUE>>=
plotReferenceDistribution(target = "H3K4me3", 
    metricToBePlotted = "RSC", 
    currentValue = crossvalues_Chip$CC_RSC )
@

\caption{The QC-metric of a newly analysed ChIP-seq sample can be 
compared to the reference values of the compendium. The density plot shows 
the QC-metric RSC (red dashed line) of the sample versus the distribution 
of the same metric in the the compendium for the respective binding 
profile or TF.}
\label{fig:ValueComparison}
\end{figure}





\subsection{Assessing data quality with machine learning}
The compendium of metrics has been used to train a random forest model 
that summarizes the sample quality in one single QC-score. 

<<eval=TRUE, echo=FALSE >>=
EM_scoresNew=NULL

EM_scoresNew$QCscores_ChIP <- crossvalues_Chip
EM_scoresNew$QCscores_binding <- bindingScores
EM_scoresNew$TagDensityInput <- list()
EM_scoresNew$TagDensityChip <-list()

CC_Result <- EM_scoresNew
@

<<eval=TRUE, echo=TRUE >>=
te <- predictionScore(target = "H3K4me3", 
    features_cc = CC_Result,
    features_global = Ch_Results,
    features_TSS = TSS_Scores, 
    features_TES = TES_Scores, 
    features_scaled = geneBody_Scores)
print(te)
@




\subsection{Summary report}
When analysing many differet ChIP-seq samples the user 
might be interested only in the visualization of the quality 
assessment. ChIC contains also a function that produces a 
summary report. The summary report is a single document (pdf 
format) that contains all the produced analysis plots. 
The function 
returns also the predicted sample QC-score.

<<eval=FALSE, echo=TRUE >>=
prediction=chicWrapper(chipName=chipName, inputName=inputName, 
    chromMarkToUse= "H3K4me3", read_length=36, mc=mc , 
    savePlotPath=filepath)

@



\newpage

\addcontentsline{toc}{section}{References}
\bibliographystyle{unsrt}

\begin{thebibliography}{10}

\bibitem{Landt}
Landt, Stephen G.
\newblock{ChIP-seq guidelines and practices of the ENCODE
and modENCODE consortia.}
\newblock {Genome Research 2012}


\bibitem{Kharchenko2008}
Kharchenko, Peter V and Tolstorukov, Michael Y and Park, Peter J.
\newblock{Design and analysis of ChIP-seq experiments for 
DNA-binding proteins.}
\newblock {Nat. Biotechnol. 2008},


\bibitem{Diaz2012}
Diaz, Aaron and Nellore, Abhinav and Song, Jun.
\newblock {CHANCE: comprehensive software for quality control and validation 
of ChIP-seq data.}
\newblock {Genome Biol. 2012}



\end{thebibliography}
\newpage

\section{Appendix} \label{Appendix}

%%\includepdf[pages=-,pagecommand={},width=\textwidth]{ListOfFeatures.pdf}
%%\includepdf[pages=-,picturecommand*={%
%%  \put (133,660) {%
%% \parbox{\textwidth}]{QC-metrics.pdf}
%\includepdf[pages=-, noautoscale=true, pagecommand={}]{QC-metrics.pdf}


\begin{figure}[htbp]
\centering
.\includegraphics[scale=1.5]{QC-metricsLM.pdf}
\end{figure}


\begin{figure}[htbp]
\centering
.\includegraphics[scale=2]{QC-metricsLM1.pdf}
\end{figure}


\begin{figure}[htbp]
\centering
.\includegraphics[scale=1.2]{QC-metricsGM_EM.pdf}
\end{figure}


\end{document}