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%\VignetteKeyword{Disease Pathway}
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\title{KEGGandMetacoreDzPathwaysGEO : Disease Datasets from GEO}
\author{Gaurav Bhatti}
\date{17 April 2014}
\maketitle

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\section{Overview of KEGGandMetacoreDzPathwaysGEO data package}\label{sec:overview}
\emph{KEGGandMetacoreDzPathwaysGEO} is a collection of 18 GEO datasets for which the phenotype is a disease with a 
corresponding pathway in either of the two popular gene to pathway annotation databases, KEGG and Metacore. These 
datasets were used as gold standard in comparing gene set analysis methods \cite{Tarca2013}.Details about the 
individual datasets including sample tissue, target disease pathway, etc may be obtained by typing: 
\begin{Schunk}
\begin{Sinput}
> ?KEGGandMetacoreDzPathwaysGEO
\end{Sinput}
\end{Schunk}
at the R prompt.In order to access all the datasets available in the package, type the following:
\begin{Schunk}
\begin{Sinput}
> mysets=data(package="KEGGandMetacoreDzPathwaysGEO")$results[,"Item"]
> mysets
\end{Sinput}
\end{Schunk}

The microarray data from the GEO database along with the associated metadata is stored as ExpressionSet class. "The 
ExpressionSet class is designed to combine several different sources of information into a single convenient structure.
An ExpressionSet can be manipulated (e.g., subsetted, copied) conveniently, and is the input or output from many 
Bioconductor functions." \cite{Falcon2007}.An example dataset is shown below: 
<<>>=
library(KEGGandMetacoreDzPathwaysGEO)
data(GSE1145)
show(GSE1145)
@

A similar data package, \emph{KEGGDzPathwaysGEO}, is already available for installation in Bioconductor.It contains additional 24 
GEO datasets for which the phenotype is a disease with a corresponding pathway in the KEGG database.These datasets were used to test
the performance of an in-house pathway analysis method which has also been implemented as a Bioconductor package, \emph{PADOG} \cite{Tarca2012}.

These datasets may be used to compare existing gene set pathway analysis methods or to test the performance of novel methods.

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\begin{thebibliography}{00}
\bibitem{Tarca2013} Tarca, A. L. et al. (2013) A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity. PLoS ONE 8(11): e79217.doi:10.1371/journal.pone.0079217
\bibitem{Falcon2007} Falcon, S., Morgan, M., and Gentleman, R. (2007), An Introduction to Bioconductor's ExpressionSet Class.
\bibitem{Tarca2012} Tarca, A. L. et al. (2012). Down-weighting overlapping genes improves gene set analysis. BMC Bioinformatics, 13, 136-2105-13-136. 
\end{thebibliography}
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\end{document}