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%%% Anuj Gupta & Luigi Marchionni
%%% March 05 2012
%%% Baltimore
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% \VignetteIndexEntry{Working with the matchBox package}
% \VignetteKeywords{Annotation, Microarray}
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%%% Title
%\title{Computing and plotting Correspondence-At-Top (CAT) curves among ranked vectors of features}
\title{Computing and plotting agreement among ranked vectors of features with matchBox.}


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%%% Authors 
\author[1]{Anuj Gupta}
\author[1]{Luigi Marchionni}

%%% Affiliations
\affil[1]{The Sidney Kimmel Comprehensive Cancer Center,
  
  Johns Hopkins University School of Medicine}


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%%% Date
\date{Modified: September 5, 2012.  Compiled: \today}


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\begin{document}

\setlength{\parskip}{0.2\baselineskip}
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\maketitle
\tableofcontents

<<start, eval=TRUE, echo=FALSE, cache=FALSE>>=
options(width=85)
options(continue=" ")
rm(list=ls())
@ 

%\newpage

\section{ {\Large \bf {Introduction}} }
The \Rpackage{matchBox} package allows to compare ranked vectors ({\it i.e.} 
by differential expression) 
of features ({\it i.e.} genes, or probe sets), using Correspondence-At-the-Top (CAT) curves. 
A CAT curve displays the overlapping proportion between two ranked vectors of identifiers 
against the number of considered features.
This techiques was originally envisaged for comparing differential gene expression 
results obtained from distinct microarray platforms in different laboratories 
(see Irizarry et al, Nat Methods (2005) \cite{Irizarry2005}), and further used
to compare differential gene expression across distinct studies
(see Ross et al, Prostate (2011) \cite{Ross:2011dm}, and Benassi et al., 
Cancer Discovery (2012) \cite{Benassi:2012wo}).


The \Rpackage{matchBox} package contains several utilities 
enabling the following:

\begin{enumerate}
  \item To filter redundant features in a \Robject{data.frame}
    ({\it i.e.} filtering multiple probe sets pointing to the same gene);
  \item To merge multiple data sets together based on a common set of identifiers;
  \item To compute the CAT curves probability intervals;
  \item To nicely plot the CAT curves.
\end{enumerate}

\vspace{5 mm}
Briefly, CAT curves enable to compare the correspondence of two vectors
ordered by a predefined ranking statistics at their top.
This is accomplished through:

\begin{enumerate}
\item Ordering the two vectors according to a desired statistics ( {\it i.e.}
  differential gene expression, significance, probability, \dots);
\item Computing the proportion of top ranking elements in common
  between the two vector for a given vector size  ({\it i.e.} top 5 features);
\item Reiterating the two steps above increasing the vector size up to all 
  common elements ({\it i.e.} the top 6 identifiers, then the top 7, 8, 9, 
  \dots, all features);
\item Plotting the proportion of common elements against the increasing vector size.
\end{enumerate}

Since CAT curves evaluate agreement at the top of ranked vectors
they are particularly useful in gene expression analysis, where only a small fraction 
of genes is expected to be differentially expressed over the large total number 
of analyzed genes.


\section{ {\Large \bf {Preparing the Data}} }

\subsection{Data structure}
Download and install the package \Rpackage{matchBox} from {\Bioc}.
<<eval=FALSE,echo=TRUE,cache=FALSE>>=
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("matchBox")
@ 

Load the library.
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
require(matchBox)
@

Load the example data contained in the \Rpackage{matchBox} package.
<<loadmatchBoxExpression,eval=TRUE,echo=TRUE,cache=FALSE>>=
data(matchBoxExpression)
@

The object \Robject{matchBoxExpression} is a list of data.frames containing 
  differential gene expression analysis resulst from three distinct experiments.
  These data.frames will be used to retrieve the identifiers 
  and the ranking statistics for computing overlapping proportions displayed
  by the CAT curves.
  Below is shown the structure of \Robject{matchBoxExpression}:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(matchBoxExpression, class)
sapply(matchBoxExpression, dim)
str(matchBoxExpression)
@


\vspace{2 mm}

\subsection{Removing redundancy}
 In order to compute the CAT curves, and then compare the results 
  obtained from the three experiments, it is necessary to identify the set 
  of {\bf non-redundant}  features in common among the three data sets.
  The \Rfunction{filterRedundant} function enables to remove the redundant 
  features within each \Robject{data.frame} prior computing the subset of  
  of common identifiers across the three data sets.
  The argument \Rfunarg{idCol} specifies the index or the name 
  of the column containing redundant identifiers, 
  while the other arguments enable to control the method
  used to remove the redundancy, as explained in the help for this function.
  
  By default  the \Rfunction{filterRedundant} function will keep the feature
  with the largest absolute ranking statistics, as defined by the
  \Rfunarg{byCol} argument. The \Rfunarg{method} argument enables to 
  control which feature to keep and which to discard.
  Currently available methods are: \Rfunarg{maxORmin}, \Rfunarg{geoMean},
  \Rfunarg{random}, \Rfunarg{mean}, and \Rfunarg{median}
  (see help for  \Rfunction{filterRedundant}).

  In the example below we are using an \Rfunction{lapply} call to remove 
  the redundant features from all the three data.frames at once. 
  To this end we will use gene symbols to identify the redundant features
  and the t-statistics to select which feature to keep. 
  
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(matchBoxExpression, function(x) any(duplicated(x[, 1])) )
allDataBySymbolAndT <- lapply(matchBoxExpression, filterRedundant, 
			      idCol="SYMBOL", byCol="t", absolute=TRUE)
@

 Each data.frame now contains only unique features.
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(allDataBySymbolAndT, dim)
sapply(allDataBySymbolAndT, function(x) any(duplicated(x[, 1])) )
@


  In the example below we are removing the redundant features
  using Enrez Gene identifiers to select the redundant features
  and the {\bf signed} log2 fold-change to select which feature to keep. 
  
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(matchBoxExpression, function(x) any(duplicated(x[, 1])) )
allDataByEGIDAndLogFC <- lapply(matchBoxExpression, filterRedundant, 
				idCol="ENTREZID", byCol="logFC", absolute=FALSE)
@

 Each of the data.frame now contains only unique features.
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(allDataByEGIDAndLogFC, dim)
sapply(allDataByEGIDAndLogFC, function(x) any(duplicated(x[, 1])) )
@


  In the example below we are removing the redundant features
  using Enrez Gene identifiers to select the redundant features,
  and the median adjusted P-value will be used  to summarize 
  the redundant features.
  
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(matchBoxExpression, function(x) any(duplicated(x[, 1])) )
allDataByEGIDAndMedianFDR <- lapply(matchBoxExpression, filterRedundant, 
				    idCol="ENTREZID", byCol="adj.P.Val", absolute=FALSE,
				    method="median")
@

 Each of the data.frame now contains only unique features.
<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(allDataByEGIDAndMedianFDR, dim)
sapply(allDataByEGIDAndMedianFDR, function(x) any(duplicated(x[, 1])) )
@


\subsection{Merging the data}
 After removing the redundant features it is now possible to obtain 
  the common unique features across all the data sets 
  using the function \Rfunction{mergeData}.
  This function finds the intersection across all the data.frames, 
  and extract the desired ranking statistics from each one.

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
data <- mergeData(allDataBySymbolAndT, idCol="SYMBOL", byCol="t")
@

 The object \Robject{data} is a \Robject{data.frame} 
  containing only the common set of features across all three 
  data.frames and the ranking statistics values of choice 
  collected from each data.frame.

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
sapply(allDataBySymbolAndT, dim)
dim(data)
str(data)
@

\section{ {\Large \bf {Correspondance at the Top Curves}}}
The \Rfunction{computeCat} {\R} function enables to compute the 
overlapping proportions of features among ranked vectors of identifiers. 
Such proportions will be subsequently used to produce the CAT curves.
When computing CAT curves a number of  paramenters must be specificied
to control the behaviour of the \Rfunction{computeCat} function.
In particular the following information will determine how the vectors of
will be ordered, and which vectors will be compaired, and 
therefore must be carefully considered:

\begin{itemize}
\item Whether or not the ranking statistics used to order the features 
  is signed ({\it i.e.} t-statistics or F-statistics like);
\item Whether the ranking statistics should be used to order the features 
  by decreasing or increasing order. For instance in the case of differential 
  gene expression between group A and B:
  \begin{itemize}
  \item Ordering by {\bf decreasing signed} t-statistics will compute the CAT 
    curve for the  genes up-regulated in group A compared to group B;
  \item Ordering by {\bf increasing signed} t-statistics will compute the CAT
    curve for  the genes down-regulated in group A compared to group B;
  \item Ordering by {\bf decreasing absolute} t-statistics will compute the CAT
    curve for the differentiallly expressed genes between the two groups;
  \item Ordering by {\bf increasing absolute} t-statistics will compute the CAT
    curve for the genes that {\bf are not differentially expressed} between 
    the two groups;
  \end{itemize}
\item Whether to compare all possible vector combinations, or to select one
  of the vectors as the reference;
\item Whether the overlapping proportion should be computed using equal ranks 
  or equal statistics;
\end{itemize}


\subsection{Computing CAT curves without  a reference ranking}

The example below shows how to compute CAT curves {\bf without} selecting 
one of the vectors  as the reference, using {\bf decreasing} ranks 
({\it i.e.} up-regulated genes are at the top of the list):

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
catHigh2LowNoRefByEqualRanks <- computeCat(data = data, idCol = 1, 
					   method="equalRank", decreasing=TRUE)
@

The example below shows to compute CAT curves {\bf without} selecting 
a vector as the reference, using {\bf increasing} ranks 
({\it i.e.} down-regulated genes are at the top of the list):

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
catLow2HighNoRefByEqualRanks <- computeCat(data = data, idCol = 1, 
					   method="equalRank", decreasing=FALSE)
@


\subsection{Computing CAT curves using a reference ranking}

The example below shows how to compute CAT curves selecting 
one of the vectors as the reference, using decreasing ranks:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
catHigh2LowWithRefByEqualRanks <- computeCat(data = data, idCol = 1, 
					     ref="dataSetA.t",  method="equalRank", decreasing=TRUE)
@

The \Robject{catHigh2LowWithRefByEqualRanks} contains the computed overlaps,
for decreasing ranks ({\it i.e.} up-regulated genes are at the top of the list):

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
str(catHigh2LowWithRefByEqualRanks)
@

The example below shows to compute CAT curves selecting one of the data set 
as reference, using decreasing t-statistics:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
catHigh2LowWithRefByEqualStats <- computeCat(data = data, idCol = 1, ref="dataSetA.t",
					     method="equalStat", decreasing=TRUE)
@

The \Robject{catHigh2LowWithRefByEqualStats} contains the computed overlaps:
for decreasing t-statistics ({\it i.e.} down-regulated genes):

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
str(catHigh2LowWithRefByEqualStats)
@

\subsection{Computing Probability Intervals}
The \Rfunction{calcHypPI} {\R} function enables to compute
the probability intervals for the CAT curves {\bf obtained using equal ranks}.
Such intervals are based on the hypergeometric distribution
(see Irizarry et al \cite{Irizarry2005} and Ross et al. \cite{Ross:2011dm}).
Briefly, the \Rfunction{calcHypPI} uses \Rfunction{qhyper} quantile function
to compute the {\bf expected} proportions of common features between 
two ordered vectors of identifiers for a set of specified quantiles 
of the hypergeometric distribution. 

\vspace{5 mm}
To understand the way such proportions are obtained
we can use the analogy of drawing a certain number of balls 
from an urn containing black and white balls, where black
represents a failure (the vectors are in different order, 
and therefore the features do not overlap),
and white represent a success (the vectors are in the same order,
and hence the features overlap).
According to this analogy the \Rfunction{calcHypPI} function 
uses the total number of features in common between the vectors
as the total number of balls in the urn, 
and the size of the vector being compared as the number of 
balls drawn from the urn.
Thus increasing vectors sizes (1, 2, 3, and so on until all features are used)
correspond to an increasing number of balls drawn from the urn at each attempt.

\vspace{5 mm}
By default the \Rfunction{calcHypPI} function assumes that the top 10\% 
of the features of the two vectors are similarly ordered. In our analogy, 
therefore, the number of white balls in the urn corresponds to 10\%
of the total common features between the vectors.
This expectation can be modified by the user with the \Rfunarg{expectedProp} 
argument. When \Rfunarg{expectedProp} is set equal to \Rfunarg{NULL}
the number of white balls in the urn
(i.e. the top ranking features in the correct order)
corresponds to the number of balls that are drawn
at each attempt (i.e. the increasing size of top features
from each vector that are being compared).

\vspace{5 mm}
The example below shows how to compute probability intervals for CAT curves,
using the default 0.1 expected proportion of top ranked features:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
PIbyRefEqualRanks <- calcHypPI(data=data)
@

The \Robject{PIbyRefEqualRanks} contains the computed
probability intervals for the CAT curves:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
head(PIbyRefEqualRanks)
@

The example below shows how to compute probability intervals for CAT curves,
setting the expected proportion of top ranked features to $0.3$:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
PIbyRefEqualRanks03 <- calcHypPI(data=data, expectedProp=0.3)
@

The \Robject{PIbyRefEqualRanks03} contains the computed
probability intervals for the CAT curves:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
head(PIbyRefEqualRanks03)
@

The example below shows how to compute probability intervals for CAT curves,
using the default 0.1 expected proportion of top ranked features, 
for a set of specific hypergeometric distribution quantiles:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
PIbyRefEqualRanksQuant <- calcHypPI(data=data, prob=c(0.75, 0.9, 0.95, 0.99) )
@

The \Robject{PIbyRefEqualRanksQuant} contains the computed
probability intervals for the CAT curves:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
head(PIbyRefEqualRanksQuant)
@

The example below shows how to compute probability intervals for CAT curves,
without specifying a proportion of expected top ranked features:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
PIbyRefEqualRanksNoExpectedProp <- calcHypPI(data=data, expectedProp=NULL)
@

The \Robject{PIbyRefEqualRanksNoExpectedProp} contains the computed
probability intervals for the CAT curves:

<<eval=TRUE,echo=TRUE,cache=FALSE>>=
head(PIbyRefEqualRanksNoExpectedProp)
@

\section{ {\Large \bf {Plotting the results}}}

The \Rfunction{plotCat} function can be used to plot the CAT curves
along with probability intervals, as derived using the \Rfunction{calcHypPI} 
function (see Figures~\ref{fig:one} and ~\ref{fig:two}, 
for CAT curves computed using a reference ranking, Figure~\ref{fig:three} 
for examples in which the reference was not used, 
and Figure~\ref{fig:four} for CAT curves for which the proportion
of expected top ranked features was not specified).

\begin{figure}[htp]
  \subsection{CAT curves based on equal ranks using a reference.}

  The example below shows how to plot CAT curves based on equal ranks,
  along with probability intervals based on a fixed expected proportion
  of similarly ranked features of 30\%, using the data set A as the reference.

  \begin{center}
  \setkeys{Gin}{width=0.75\textwidth}
<<label=fig1,eval=TRUE,echo=TRUE,cache=FALSE,fig=TRUE, width=10, height=6>>=
plotCat(catData = catHigh2LowWithRefByEqualRanks, 
	preComputedPI=PIbyRefEqualRanks03,
	cex=1.2, lwd=1.2, cexPts=1.2, spacePts=30, col=c("red", "blue"),
	main="CAT curves for decreasing t-statistics",
	where="center", legend=TRUE, legCex=1, ncol=1,
	plotLayout = layout(matrix(1:2, ncol = 2), widths = c(2,1)))
@
\end{center}
\caption{\small CAT-plot for curves based on equal ranks, using data set A as the reference.
 Lighter to darker grey shades represent probability intervals for distinct quantiles 
 of the hypergeometric distribution (0.999999, 0.999,  0.99, 0.95), assuming
 30\% of the features to be similarly ranked.}
\label{fig:one}
\end{figure}

\begin{figure}[htp]
  \subsection{CAT curves based on equal statistics using a reference.}

  The example below shows how to plot CAT curves based on equal statistics,
  using the data set A as the reference.
  When equal ranks are used each CAT curve has its own probability intervals,
  which therefore cannot be shown on the plot. 

  \begin{center}
  \setkeys{Gin}{width=0.75\textwidth}
<<label=fig2,eval=TRUE,echo=TRUE,cache=FALSE,fig=TRUE, width=10, height=6>>=
plotCat(catData = catHigh2LowWithRefByEqualStats, 
	cex=1.2, lwd=1.2, cexPts=1.2, spacePts=30, col=c("red", "blue"), 
	main="CAT curves for decreasing t-statistics",
	where="center", legend=TRUE, legCex=1, ncol=1,
	plotLayout = layout(matrix(1:2, ncol = 2), widths = c(2,1)))
@
\end{center}
\caption{\small CAT-plot for curves based on equal t-statistics, using data set A as the reference.}
\label{fig:two}
\end{figure}


\begin{figure}[htp]
  \subsection{CAT curves based on equal ranks without using a reference.}

  The example below shows how to plot CAT curves based on equal ranks,
  along with probability intervals based on a fixed expected proportion
  of similarly ranked features of 10\%, without using any data set as the 
  reference (all pair combinations are shown).

  \begin{center}
  \setkeys{Gin}{width=0.75\textwidth}
<<label=fig3,eval=TRUE,echo=TRUE,cache=FALSE,fig=TRUE, width=10, height=6>>=
plotCat(catData = catHigh2LowNoRefByEqualRanks, 
	preComputedPI=PIbyRefEqualRanks,
	cex=1.2, lwd=1.2, cexPts=1.2, spacePts=30,
	main="CAT curves for decreasing t-statistics",
	where="center", legend=TRUE, legCex=1, ncol=1,
	plotLayout = layout(matrix(1:2, ncol = 2), widths = c(2,1)))
@
\end{center}
\caption{\small CAT-plot for curves based on equal ranks for all possible combinations of vectors.
 Lighter to darker grey shades represent probability intervals for the distinct  quantiles 
 of the hypergeometric distribution (0.999999, 0.999,  0.99, 0.95), assuming
 10\% of the features to be similarly ranked.}
\label{fig:three}
\end{figure}


\begin{figure}[htp]
  \subsection{CAT curves based on equal ranks, unspecified expected 
    proportion of corresponding features.}

  The example below shows how to plot CAT curves based on equal ranks,
  along with probability intervals based on an unspecified expected proportion
  of similarly ranked features, without using any data set as the 
  reference (all pair combinations are shown).

  \begin{center}
  \setkeys{Gin}{width=0.75\textwidth}
<<label=fig4,eval=TRUE,echo=TRUE,cache=FALSE,fig=TRUE, width=10, height=6>>=
plotCat(catData = catHigh2LowNoRefByEqualRanks, 
	preComputedPI=PIbyRefEqualRanksNoExpectedProp,
	cex=1.2, lwd=1.2, cexPts=1.2, spacePts=30,
	main="CAT curves for decreasing t-statistics",
	where="center", legend=TRUE, legCex=1, ncol=1,
	plotLayout = layout(matrix(1:2, ncol = 2), widths = c(2,1)))
@
\end{center}
\caption{\small CAT-plot for curves based on equal ranks for all possible combinations of vectors.
 Lighter to darker grey shades represent probability intervals for distinct  quantiles 
 of the hypergeometric distribution (0.999999, 0.999,  0.99, 0.95). No expected proportion
 of similarly ranked features is specified.}
\label{fig:four}
\end{figure}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
\pagebreak

\section{ {\Large \bf {System Information}}}
Session information:

<<sessioInfo, echo=TRUE, eval=TRUE, cache=FALSE, results=tex>>=
toLatex(sessionInfo())
@ 

%\pagebreak
\section{ {\Large \bf {Literature Cited}} }
   \bibliographystyle{unsrt}
   \bibliography{./matchBox}

\end{document}