% rm(list=ls());library("weaver");Sweave("SuppMat.Rnw", driver=weaver)

%\VignetteIndexEntry{Reading PSI-25 XML files}
%\VignetteDepends{}
%\VignettePackage{Rintact}

\documentclass[11pt]{article}

\usepackage{times}
\usepackage{hyperref}
\usepackage{geometry}
\usepackage{longtable}
\usepackage{times}
\usepackage{underscore}
\SweaveOpts{keep.source=TRUE,eps=FALSE,pdf=TRUE,include=FALSE,prefix=FALSE,width=4,height=4} 

\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Robject}[1]{{\texttt{#1}}}
\newcommand{\Rpackage}[1]{{\textit{#1}}}
\newcommand{\Rclass}[1]{{\textit{#1}}}

\title{RpsiXML: An R programmatic interface with PSI}
\author{Jitao David Zhang and Tony Chiang}
\begin{document}
\maketitle

\begin{abstract}
We demonstrate the capabilities of the software package \Rpackage{RpsiXML}. This 
package provides a programmatic interface with those databases which adhere to 
the PSI-MI XML2.5 standardization for molecular interactions. Each experimental
dataset from any of the databases can be read into R and converted into a 
\Rclass{psimi25Graph} object upon which computatational analyses can be conducted. 
\end{abstract}

\section{Introduction}

Molecular interactions play an important role in the organizational and functional 
hierarchy of cells and tissues. Such molecular interaction data has been made publicly
available on a wide variety of public databases. Statistical and computational analysis
of these datasets necessitates the automated capability of downloading, extracting, 
parsing, and converting these data into a uniform structure.

Recently, the \textit{Protein Standardization Initiative} has developed the PSI-MI 2.5
XML schema for documenting molecular interaction data. While XML is a particularly good
format for data storage and exchange, it is less amenable to compuatational analysis. The
contents of the XML files need to be parsed and transformed in structures upon which 
computation analysis is more feasible and apropros. 

The Bioconductor software package \Rpackage{RpsiXML} serves as a programmatic interface
between the R statistical environment and the PSI-MI 2.5 XML files. This software should be
able to parse the XML files from any database which implement a valid PSI-MI 2.5 schema; 
currently, the databases that are supported by \Rpackage{RpsiXML} are:

\begin{itemize}
  \item[1.] IntAct
  \item[2.] MINT
  \item[3.] DIP
  \item[4.] HPRD
  \item[5.] BioGRID
  \item[6.] MIPS/CORUM
  \item[7.] MatrixDB
  \item[8.] MPact
\end{itemize}

\noindent We plan to support other databases which are now porting to the PSI-MI XML2.5 
schema. 

In this vignette, we demonstrate the basic functionalities of \Rpackage{RpsiXML}.

\section{Preliminaries}

\subsection{Obtaining the XML Files}
Each of the data repositories has its own FTP or download site from which the PSI-MI 2.5 
XML files can be obtained. Here we list each database as well as the location of each
FTP or download site:

\begin{table}[hb]
  \begin{tabular}{|l|l|}
    \hline
    \bf{IntAct} &
    \textit{ftp://ftp.ebi.ac.uk/pub/databases/intact/current/psi25}\\
    \hline
    \bf{Mint} &
    \textit{ftp://mint.bio.uniroma2.it/pub/release/psi/2.5/current/}\\
    \hline
    \bf{DIP} &
    \textit{http://dip.doe-mbi.ucla.edu/dip/Download.cgi}\\
    \hline
    \bf{HPRD} &
    \textit{http://www.hprd.org/download}\\
    \hline
    \bf{The BioGRID} &
    \textit{http://www.thebiogrid.org/downloads.php}\\
    \hline
    \bf{CORUM/MIPS} &
    \textit{http://mips.gsf.de/genre/proj/corum} \\
    \hline
    \bf{MatrixDB} &
    \textit{not published yet} \\
    \hline
    \bf{MPact} &
    \textit{ftp://ftpmips.gsf.de/yeast/PPI}\\
    \hline
  \end{tabular}
  \label{ta:repos}
\end{table}

\noindent The DIP repository requires one to create a login account before accessing the data.
Each of the PSI-MI XML2.5 files should be downloaded to the local file directory which is accessible 
by the R environment.

We have downloaded XML files from each of the molecular interaction data repository listed above;
we have, however, modified these datasets by truncating most of the data so as to provide the user
with helpful sample XML files that is not to large. If the user has the source package, these 
sample XML files can be found within the \textit{inst/estdata/psi25files/} directory of the package.
Otherwise, once the package has been loaded, each file can be loaded into an R session with the
following calls:

\vspace{.05in}
(First we load the package)
<<loadlibs, echo=FALSE, results=tex>>=
library(RpsiXML)
@ 
(Then we create a path to the files)
<<sampleFiles, echo=TRUE, results=tex>>=
xmlDir <- system.file("/extdata/psi25files",package="RpsiXML")
gridxml <- file.path(xmlDir, "biogrid_200804_test.xml")
@ 

\noindent The first line of code above creates a path to the desired directory while the second line
constructs the path to the \textit{biogrid} sample file in a platform independent manner.

\newpage

Table~\ref{ta:sample} gives the names of the sample XML files with the corresponding database 
repository:

\begin{table}[hb]
  \begin{tabular}{|l|l|}
    \hline
    \bf{IntAct} &
    intact$\_$2008_test.xml\\
    &
    intact$\_$complexSample.xm\\
    \hline
    \bf{Mint} &
    mint$\_$200711$\_$test.xml\\
    \hline
    \bf{DIP} &
    dip$\_$2008$\_$test.xml\\
    \hline
    \bf{HPRD} &
    hprd$\_$200709$\_$test.xml\\
    \hline
    \bf{The BioGRID} &
    biogrid$\_$200804$\_$test.xml\\
    \hline
    \bf{CORUM/MIPS} &
    mips$\_$2007$\_$test.xml \\
    \hline
    \bf{MatrixDB} &
    matrixdb$\_$20080609.xml \\
    \hline
  \end{tabular}
  \label{ta:sample}
\end{table}

\noindent It should be noted that \textit{IntAct} has two different kinds of sample XML
files: the "test" file stores the bait-prey interaction data while the "complex" file
stores manually curated protein complex membership information. For the scope of this 
vignette, we will focus on the \textit{IntAct} files as our working examples, but we 
encourage the reader to work explore the other files as well.

\subsection{XML into R}
There are two different methods for parsing the PSI-MI XML2.5 files:

\subsubsection{parsePsimi25Interaction}
The first method relies on the function \Rfunction{parsePsimi25Interaction} which systematically
searches over the XML tree structure and returns the fields (nodes) of interest. First we obtain
the XML file we wish to parse:

<<intactInteractionEX, echo=TRUE, results=tex>>=
intactxml <- file.path(xmlDir, "intact_2008_test.xml")
intactComplexxml <- file.path(xmlDir,"intact_complexSample.xml")
@ 

\noindent and then we parse the file using the \Rfunction{parsePsimi25Interaction} function:

<<parseInteraction, echo=TRUE, results=hide>>=
intactSample <- parsePsimi25Interaction(intactxml, INTACT.PSIMI25,verbose=FALSE)
intactComplexSample <- parsePsimi25Complex(intactComplexxml, INTACT.PSIMI25,verbose=FALSE)
@ 

The two arguments taken by \Rfunction{parsePsimi25Interaction} is:

\begin{itemize}
  \item[1.] A character vector with the relative path to the file of interest (can also be an URL).
  \item[2.] A supported data repository source file R object.
\end{itemize}

Because each database repository implements the PSI-MI XML2.5 standards in slightly varing ways, it 
is necessary to track these differences and to tell the parsing function which implementation 
to expect. Each of the database supported by RpsiXML has its own corresponding source
class object (\Robject{INTACT.PSIMI25, MINT.PSIMI25, DIP.PSIMI25, HPRD.PSIMI25, BIOGRID.PSIMI25,}
and \Robject{MIPS.PSIMI25}). 

The output from the \Rfunction{parsePsimi25Interaction} is an object of type 
\Robject{psimi25InteractionEntry} or \Robject{psimi25ComplexEntry} depending on the type of
input XML file. Each is a class used to carry all of the information obtained from the XML
files be it interaction or complex. From each of these classes, we can obtain various types 
of information:

\vspace{0.05in}
(\textit{From the intactSample object})
<<interactionInfo1>>=
interact <- interactions(intactSample)[1:3]
interact
organismName(intactSample)[1:3]
releaseDate(intactSample)
@ 
\vspace{0.05in}
(\textit{Looking within each interaction})
<<interactionInfo2>>=
lapply(interact, participant)[1:3]
sapply(interact, bait)[1:3]
sapply(interact, prey)[1:3]
sapply(interact, pubmedID)[1:3]
@ 

Most of the information from the extracted data is self-explanatory; we will, however, highlight 
some important pieces. The \Robject{interact} object is the output of the \Rfunction{interactions}
method which provides all the pertinent details for each interaction. This object is a list of the
\Rclass{psimi25Interaction} class. Upon each individual \Rclass{psimi25Interaction} class, there
exists methods to extract the individual pieces of information. The \Rfunction{participant} method
returns the two proteins which were involved in the interaction. If available, the \Rfunction{bait}
and \Rfunction{prey} methods returns the proteins which serve as their namesake. Each interaction
is also indexed by the pubmed ID which can also be extracted.

\vspace{0.05in}
(\textit{From the complexSample object})
<<interactionInfo1>>=
comp <- complexes(intactComplexSample)[1:2]
sapply(comp, complexName)
@

\subsubsection{psimi25XML2Graph}

While the parsing can be accomplished via the \Rfunction{parsePsimi25Interaction} function, the output
of this function is not readily accessible for computational analysis. For this case, the function
\Rfunction{psimi25XML2Graph} is a better choice. For instance we can construct the bait-prey graph
from the \textit{IntAct} sample XML file:

<<intactGraph>>=
intactGraph <- psimi25XML2Graph(intactxml, INTACT.PSIMI25, 
                                type="interaction",verbose=FALSE)
nodes(intactGraph)
degree(intactGraph)
@ 

And we can also build a protein complex membership hyper-graph from the sample complex XML
file:

<<intactHyperGraph>>=
intactHG <- psimi25XML2Graph(intactxml, INTACT.PSIMI25, type="complex",verbose=FALSE)
@ 

There is a caveat for the function \Rfunction{psimi25XML2Graph}; it does not decipher between the
data within the XML file insomuch that if it is all bait/prey, then it will generate one large 
graph. If you are sure that you would like to take all the data and create one large graphical
structure, then a call to this function is appropriate. Otherwise, if some of the data within the
XML files should be separated, a call to this function is not recommended.

\subsubsection{separateXMLDataByExpt}

A different way to transform the XML data into graphs is to call the \Rfunction{searapteXMLDataByExpt} 
function. This function will parse bait-prey data into distinct graphs indexed by the pubmed IDs. Note 
that this function cannot be called upon XML files that record manually curated protein complexes since 
there is rarely an associated pubmed ID for this type of data.

<<separateXML>>=
graphs <- separateXMLDataByExpt(xmlFiles = intactxml, psimi25source=INTACT.PSIMI25, 
                                type="indirect", directed=TRUE, abstract=TRUE,
                                verbose=FALSE) 
@ 

Now we look at the input parameters:
\begin{itemize}
  \item xmlFiles - a character vector of the relative path to the PSI-MI XML2.5 files relative to the
    R working directory.
  \item psimi25source - A supported data repository source R object
  \item type - character either "direct" or "indirect" signaling the type of interaction wanted
  \item directed - a logical to determine if the graph returned is either directed or not
  \item abstract - a logical to determine whether or not the function should also get the abstract 
    information for each dataset from NCBI
\end{itemize}

<<showGraph>>=
graphs
abstract(graphs$`18296487`)
@ 

It should be noted that if you are going to parse a large number of XML files, it is not recommended
to automatically get the abstract information since NCBI has been known to refuse and later ban 
IP addresses that consistenly demand a high volume of information. For this reason, the \Robject{abstract}
parameter has been set to FALSE as a default.

One can manually obtain the abstract information as follows:

<<abst>>=
getAbstractByPMID(names(graphs))
@ 

\section{Converting Node IDs}

The bait/prey information (when downloaded and converted into an R graph object) is encoded
by the UniProtKB identification schema. UniProtKB appears to be the most universal naming 
scheme, and so it offers consistency across databases. If there is a need to convert the names
of the nodes from the UniProtKB IDs to some other naming scheme, there is two ways of doing so:

\begin{itemize}
  \item use the R package \Rpackage{biomaRt}
  \item use the built in method \Rfunction{translateID}
\end{itemize}

The benefits of using \Rpackage{biomaRt} is that it lets you communicate with Biomart and obtain
the latest annotations and translations. The drawback is that it is a non-trivial task and is beyond
the scope of this vignette. The drawbacks of \Rfunction{translateID} is that only the naming schemes
supported (i.e. arbitrarily chosen) by each database can be supported by \Rpackage{RpsiXML}. The 
benefit is the ease and simplicity of use.

<<translate>>=
graphs1 <- translateID(graphs[[1]], to="intact")
nodes(graphs1)
@ 

If a particular node cannot be mapped to the naming schema, it will retain the UniprotKB ID. 

\section{Conclusion}
Once the XML files have been downloaded, parsed, and converted into R grpah objects, there are 
a number of applicable methods and tools available within R and Bioconductor upon which these 
data graphs can be analyzed. Some (but not all) packages include: \Rpackage{RBGL}, 
\Rpackage{ppiStats}, \Rpackage{apComplex}, etc. 

\end{document}
