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\Large Drug versus Disease data package
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\section{Introduction}

The Drug versus Disease data (DvD) package provides reference data set for the DvD package which is a pipeline for the comparison of drug and disease gene expression profiles where negatively correlated (enriched) profiles can be used to generate hypotheses of drug-repurposing and positively correlated (enriched) profiles may be used to infer side-effects of drugs. The reference data includes disease and drug profiles, where the disease profiles were manually curated from experiments available from the Gene Expression Omnibus (GEO) and the drug profiles from Connectivity Map version 2.




\subsection{Drug Signatures}

The Connectivity Map version 2.0 rank matrix was used to generate the reference set of drug profiles for DvD. All profiles for a given compound treatment were merged as described in \citep{Iorio10}, giving 1309 ranked expression profiles based on the HGU-133A platform. These profiles were then converted to gene symbols by taking the average rank where multiple probes map to the same gene and removing probes which mapped to more than one gene. These mappings were obtained from BiomaRt. Using these signatures, pairwise similarity scores were calculated using the KS running sum statistic based on the top 100 and bottom 100 genes for each profile. Affinity propagation clustering (provided by the R package apcluster) was used to create the network of drug connections. The 1309 compounds were clustered into 103 clusters, the assignments of each compound are stored in the object drugClusters. DvDdata does not contain any R code and all data objects can be accessed using the data command in R.

<<results=tex>>
data(drugClusters,package="DvDdata")
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\subsection{Disease Signatures}

Datasets included in the DvDdata reference set contained disease versus control samples that were derived from disease-relevant primary tissues. In total, 85 disease-associated microarray experiments (disease vs control) were acquired to represent and characterise 45 distinct diseases with (3766) individual microarrays. These were obtained from NCBI GEO microarray repository \href{http:// www.ncbi.nlm.nih.gov/geo/)}{http:// www.ncbi.nlm.nih.gov/geo/}.

The raw CEL files were normalised using rma and probes mapped to genes using the average ranking method. The pairwise similarity score matrix of these profiles was generated using the top and bottom 100 genes and Affinity propagation used to derive a network of disease connections, the classifications of each disease profile is given in the diseaseClusters data file. The 88 profiles resulted in a network of 12 clusters, the ranked profiles used to generate these networks are stored in the diseaseRL data object.

<<results=tex>>=
#to load the disease ranked profiles
data(diseaseRL,package="DvDdata")
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\subsection{Annotation}
The DvD data package automatically downloads and annotates Affymetrix probe sets to HUGO gene symbols using biomaRt. The annotationlist in DvDdata gives the Affymetrix platform annotation and associated database reference in BiomaRt to allow for automatic detection and calculation.
The genes which are in the intersection of the three Affymetrix platforms supported for automatic annotation by DvD are given in the genelist object.
<<>>=
data(annotationlist,package="DvDdata")
#to get the HUGO genes which are included in the reference data
data(genelist,package="DvDdata")
@
 
\subsection{GEO data}
The meta information is processed in DvD through the GEOquery package. This provides the experimental design, for DvD this information is used to identify explanatory factors which may be used in a regression model. The list of available factor values which are available on the GEO website \href{http://www.ncbi.nlm.nih.gov/geo/}{http://www.ncbi.nlm.nih.gov/geo/} are stored in the GEOfactorvalues object for use by DvD.
<<>>=
data(GEOfactorvalues,package="DvDdata")
@

\subsection{Cytoscape Information}
An associated cytoscape plug-in is available for DvD which also uses the DvDdata package. The DvDdata package contains cytodrug and cytodisease data objects which have the edges in the network along with the distance and Running sum Peak Statistic (RPS). The latter two are used as edge attributes by Cytoscape. The Running sum Peak Statistic takes values 1 or -1 where 1 indicates a positive correlation and -1 a negative correlation. The distance measure gives the strength of this correlation. This data frame is used by the DvD package to generate cytoscape sif and edge attribute files. For links out to the external web browsers, DrugBank and MeSH DvDdata also contains search compatible terms for all nodes in the reference data sets. (Note that some compounds in the connectivity map are known not to be in DrugBank).

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data(cytodrug,package="DvDdata")
#to get the compound (node) names and corresponding search terms
data(druglabels,package="DvDdata")
@



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\bibitem[Edgar \textit{et~al}., 2002]{Edgar02}
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