%
% NOTE -- ONLY EDIT THE .Rnw FILE!!!  The .tex file is
% likely to be overwritten.
%


% \VignetteIndexEntry{HOWTO: Use the online query tools}
% \VignetteDepends{annotate, hgu95av2}
% \VignetteKeywords{Expression Analysis, Annotation}
%\VignettePackage{annotate}
\documentclass{article}

\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Rmethod}[1]{{\texttt{#1}}}

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

\usepackage{hyperref}

\usepackage[authoryear,round]{natbib}
\usepackage{times}

\bibliographystyle{plainnat}

\author{Jeff Gentry and Robert Gentleman}

\begin{document}
\title{HowTo: Querying online Data}
\maketitle{}

\section{Overview}

This article demonstrates how you can make use of the tools that have
been provided for on-line querying of data resources. These tools rely
on others (such as the NLM and NCBI) providing and documenting
appropriate web interfaces. The tools described here allow you to
either retrieve the data (usually in XML) or have it rendered in a
browser on the local machine. To do this you will need the
\Rpackage{Biobase}, \Rpackage{XML}, and \Rpackage{annotate} packages.
The functionality in this article was first described in
\citep{PubMedRnews}, although some enhancements have been
made since the writing of that article.

Assembling and using meta-data annotation is a non-trivial task. In
the Bioconductor Project we have developed tools to support two
different methods of accessing meta-data. One is based on obtaining
data from a variety of sources, of curating it and packaging it in a
form that is suitable for analysing microarray data. The second method
is to make use of on-line resources such as those provided by NLM and
NCBI. The functions described in this vignette provide infrastructure
for that second type of meta-data usage.

We first describe the functions that allow users to specify queries
and open the appropriate web page on their local machine. Then, we
investigate the much richer set of tools that are provided by NLM for
accessing and working with PubMed data.

\section{Using the Browser}

There are currently four functions that provide functionality for
accessing online data resources.
They are:
\begin{description}
\item[genbank] Users specify GenBank identifiers and
can request them to be rendered in the browser or returned in XML.
\item[pubmed] Users specify PubMed identifiers and can request them to
be rendered in the browser or returned in XML. More details on parsing
and manipulating the XML are given below.
\item[locuslinkByID] Users specify LocusLink identifiers and the
appropriate links are opened in the browser. LocusLink does not
provide XML so there is no download option, currently. The user can
request that the URL be rendered or returned.
\item[locuslinkQuery] Users specify a string that will be used as the
LocusLink query and the species of interest (there can be
several). The user can request either that the URL be rendered or returned.
\end{description}


Both \Rfunction{genbank} and \Rfunction{pubmed} can return XML
versions of the data. These returned values can subsequently be
processed using functionality provided by the \Rpackage{XML} package
\citep{XML}. Specific details and examples for PubMed are given in
Section~\ref{sec:pmq}.

The function \Rfunction{locuslinkByID} takes a set of known LocusLink
identifiers and constructs a URL that will have these rendered. The
user can either save the URL (perhaps to send to someone else or to
embed in an HTML page, see the vignette on creating HTML output for
more details).

The function \Rfunction{locuslinkQuery} takes a character string to be
used for querying PubMed. For example, this function call,
\begin{verbatim}
  locuslinkQuery("leukemia", "Hs")
\end{verbatim}
will find all Human genes that have the word leukemia associated with them
in their LocusLink records. Note that the R code is merely an
interface to the services provided by NLM and NCBI and users are
referred to those sites for complete descriptions of the algorithms
they use for searching etc.

\section{Accessing PubMed information}
\label{sec:pmq}

In this section we demonstrate how to query PubMed and how to operate
on the data that are returned. As noted above, these queries generate
XML which must then be parsed to provide the specific data items of
interest. Our example is based on the \Robject{eset} data from the
package \Rpackage{Biobase}. Users should be able to easily replace
these data with their own.


<<data>>=
library("annotate")
data(eset)
affys <- geneNames(eset)[490:500]
affys
@

Here we have selected an arbitrary set of 10 genes to be interested in
from our sample data.  However, \verb+eset+ provided us with
Affymetrix identifiers, and for the \verb+pubmed+ function, we need to
use PubMed ID values.  To obtain these, we can use the annotation
tools within \Rpackage{annotate}.

<<annotation>>=
library("hgu95av2")
ids <- getPMID(affys,"hgu95av2")
ids <- unlist(ids,use.names=FALSE)
ids <- unique(ids[!is.na(as.numeric(ids))])
ids
@

We use \Rfunction{getPMID} to obtain the PubMed identifiers that are
related to our probes of interest. Then we process these to leave out
any that have no PMIDs and we remove duplicates as well. The mapping
to PMIDs are actually based on LocusLink identifiers and since the
mapping from Affymetrix IDs to LocusLink is many to one there is some
chance of duplication. From our initial \Sexpr{length(affys)}
Affymetrix identifiers we see that there are \Sexpr{length(ids)}
unique PubMed identifiers (ie papers).

For each of these papers we can obtain information, such as the title,
the authors, the abstract, the LocusLink identifiers for genes that
are referred to in the paper and many other pieces of
information. Again, for a complete listing and description the reader
is referred to the NLM website.

We next generate the query and store the results in a variable named
\Robject{x}. This object is of class \Robject{XMLDocument} and to
manipulate it we will use functions provided by the XML package.

<<getabsts>>=
x <- pubmed(ids)
a <- xmlRoot(x)
numAbst <- length(xmlChildren(a))
numAbst
@

Our search of the \Sexpr{length(ids)} PubMed IDs (from the
\Sexpr{length(affys)} Affymetrix IDs) has
resulted in \Sexpr{numAbst} abstracts from PubMed (stored
in R using XML format).  The \Rpackage{annotate} package also provides a
\Robject{pubMedAbst} class, which will take the raw XML format from a
call to \Rfunction{pubmed} and extract the interesting sections for
easy review.

<<>>=
arts <- vector("list", length=numAbst)
absts <- rep(NA, numAbst)
for (i in 1:numAbst) {
   ## Generate the PubMedAbst object for this abstract
   arts[[i]] <- buildPubMedAbst(a[[i]])
   ## Retrieve the abstract text for this abstract
   absts[i] <- abstText(arts[[i]])
}
arts[[7]]
@

In the S language we say that the \Robject{pubMedAbst} class has a
number of different slots. They are:
\begin{description}
\item[authors] The vector of authors.
\item[pmid] The PubMed record number.
\item[abstText] The actual abstract (in text).
\item[articleTitle] The title of the article.
\item[journal] The journal it is published in.
\item[pubDate] The publication date.
\end{description}
These can all be individually extracted utilizing the provided methods,
such as \Robject{abstText} in the above example.
As you can see, the \Robject{pubMedAbst} class provides several key
pieces of information: authors, abstract text, article title, journal,
and the publication date of the journal.

Once the abstracts have been assembled you can search them using any
of the standard search techniques. Suppose for example we wanted to
know which abstracts have the term \texttt{cDNA} in them, then the
following code chunk shows how to identify these abstracts.

<<>>=
found <- grep("cDNA",absts)
goodAbsts <- arts[found]
length(goodAbsts)
@

So \Sexpr{length(goodAbsts)} of the articles relating to our genes of
interest mention the term \texttt{cDNA} in their abstracts.

Lastly, as a demonstration for how one can use the \verb+query+
toolset to cross reference several databases, we can use the same set
of PubMed IDs with another function.  In this example, the
\Rfunction{genbank} function is used with the argument
\Robject{type="uid"}.  By default, the \Rfunction{genbank} function
assumes that the id values passed in are Genbank accession numbers,
but we currently have PubMed ID values that we want to use.  The
\Robject{type="uid"} argument specifies that we are using PubMed IDs
(aka NCBI UID numbers).

<<>>=
y <- genbank(ids[1:10], type="uid")
b <- xmlRoot(x)
@

At this point the object {\tt b} can be manipulated in a manner similar
to {\tt a} from the PubMed example.

Also, note that both \Rfunction{pubmed} and \Rfunction{genbank} have an option
to display the data directly in the browser instead of XML, by
specifying {\tt disp="browser"} in the parameter listing.

\section{Generating HTML output for your abstracts}

Many users find it useful to have a web page created with links for
all of their abstracts, leading to the actual PubMed page online.
These pages can then be distributed to other people who have an
interest in the abstracts that you have found.  There are two formats
for this, the first provides for a simple HTML page which has a link
for every abstract, and the other provides for a framed HTML page with
the links on the left and the resulting PubMed page in the main
frame.  For these examples, we will be using temporary files:

<<abst2HTML>>=
fname <- tempfile()
pmAbst2HTML(goodAbsts, filename=fname)

fnameBase <- tempfile()
pmAbst2HTML(goodAbsts, filename=fnameBase, frames=TRUE)
@

\begin{figure}[htb]
        \begin{center}
          \includegraphics{noframes}
          \caption{pmAbst2HTML without frames}

          \includegraphics{frames}
          \caption{pmAbst2HTML with frames}
        \end{center}
\end{figure}

\section{Session Information}

The version number of R and packages loaded for generating the vignette were:

<<echo=FALSE>>=
sessionInfo()
@

\bibliography{annotate}

\end{document}

