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% \VignetteIndexEntry{Collection and subsequent fast retrieval of identifier mapping related information from various online sources.}
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\begin{document}
\SweaveOpts{concordance=TRUE}
\title{The \Rpackage{IdMappingRetrieval} package in Bioconductor: Collecting
and caching identifier mappings from online sources.}
\author{Alex Lisovich \dag{}, Roger S. Day \dag{}\ddag{} }
\maketitle
\begin{center}
{ \dag{}Department of Biomedical Informatics, \dag{}\ddag{} Department
of Biostatistics, \\
University of Pittsburgh}
\end{center}
\section{Overview}
Research which integrates data from multiple data platforms must of
course merge on samples processed in parallel on the platforms. However,
exploiting the full biological significance of the data depends on
merging on the respective features as well. The features have platform-specific
biological identifiers, so identifier mapping is critical to this
merging. The IdMappingRetrieval package allows initial acquisition
of biological identifier mappings (ID maps) from online bioinformatics
services, with caching in localdata repositories for subsequent fast
retrieval. An ID map is a one-to-many map from one ID type (called
the primary key) to another (called the secondary key). The services
currently supported are NetAffx and Ensembl. The package employs
a unified interface for accessing these services, so that the local
repositories will be easy to create, update, and use. Aside from identifier
maps themselves, the the service's complete annotation data sets are
also accessible through the same mechanism. . Therefore, although
ID mapping is the primary goal, secondarily the package performs as
a generic annotation data collection tool if desired.
The objects produced by this package are specifically suited for use
by the package \Rpackage{IdMappingAnalysis}, currently in preparation
for Bioconductor. The purpose of \Rpackage{IdMappingAnalysis} is
to characterize and compare two or more ID maps.
Retrieval is typically subsetted by Affymetrix array.
\section{The package architecture}
\subsection{Two types of services: query-based and file-based}
The IdMappingRetrieval package retrieves data using service objects.
The package supports two types of services, query-based and file-based.
The query-based services support automated retrieval either through
a standard service API, through third-party R packages, or through
simulated interaction with service web pages. The file-based services
assist the manual download of raw data, followed by extraction of
ID maps and other information. For diverse reasons they generally
require more active user participation.
\begin{enumerate}
\item Query-based services
\begin{enumerate}
\item Affymetrix annotation file repository {[}1{]}, \\
Data source: \href{http://www.bioconductor.org/packages/2.2/bioc/html/AffyCompatible.html}{http://www.bioconductor.org/packages/2.2/bioc/html/AffyCompatible.html}.
Service class name: AnnotationAffx.
Access method: \Rpackage{AffyCompatible} Bioconductor package.
Service class name: AnnotationEnsembl.
Access method: \Rpackage{biomaRt} Bioconductor package.
\item EnVision online query system {[}4{]}, accessed using the SOAP-based
Web Services.
Data source: \textbf{LINK NEEDED}.
Service class name: AnnotationEnvision
Access method: \Rpackage{ENVISIONQuery} Bioconductor package.
\end{enumerate}
\item File-based services
\begin{enumerate}
\item Affymetrix NetAffx batch system supporting data download in chunks
{[}1{]}.
Data source: \href{http://www.affymetrix.com/analysis/index.affx}{http://www.affymetrix.com/analysis/index.affx}.
Service class name: AnnotationNetxAffx.
Access method: See Appendix A.
\item Ensembl download service {[}3,4{]}.
Data source: \href{http://uswest.ensembl.org}{http://uswest.ensembl.org}.
Service class name: AnnotationEnsemblCsv..
Access method: See Appendix C.
\end{enumerate}
\end{enumerate}
\subsection{User tasks, services, classes and objects}
The package utilizes the R.oo framework for S3 class based development
({[}5{]},
\href{http://cran.r-project.org/web/packages/R.oo/index.html}{http://cran.r-project.org/web/packages/R.oo/index.html}),
which we found to be well suited for the task. As seen in the table
above, different subclasses of the Annotation class support access
to each specific ID map retrieval service.\\
\begin{enumerate}
\item For each service of interest:
\begin{enumerate}
\item If necessary, set user credentials for login.
\item Instantiate the service object as an instance of the Annotations subclass
suitable for the service of interest,Specify the query parameters.
\item Invoke a data collection method (either getIdList or getDataFrame)
on the service object. or a set of objects each of which represents
the particular service..
\end{enumerate}
\item For a list of services:
\begin{enumerate}
\item Loop over the services as above.
\item Alternatively, for convenience, instantiate the ServiceManager and
use corresponding methods.\\
\end{enumerate}
Some parameters are common for all service object types.
\begin{itemize}
\item cacheFolderName - a subdirectory name where data for given service
object will be cashed ({}``EnVision'')
\item primaryColumn and secondaryColumn -the column names which a service
object uses to extract the Id Map from the raw data frame. In case
primaryColumn/secondaryColumn contain multiple column names, the service
object merges the corresponding raw data frame columns into one (service
specific)
\item swap - a boolean. If true, the primary/secondary columns will be swapped
at the end of the data retrieval process. Swapping reverses the 'one
to many' identifier relation
\item species - the species the collected data should be subsetted on
\end{itemize}
\end{enumerate}
The user can override default values during the service object's creation.
The default values are typically suitable. For the file-based services,
the constructor has an additional parameter:
\begin{itemize}
\item the location of the file(s) or the directory containing the input
data.
\end{itemize}
Note that micro array type is not an attribute of a service object
and is supplied as an argument to the data retrieval method, which
allows to reuse the same service object for multiple micro array types
data collection. It also allows to define micro array type interactively
in case array type argument is equal to 'menu' (see help pages for
details). \\
\begin{tabular}{|c|c|c|}
\hline
\noalign{\vskip\doublerulesep}
\textsf{\textbf{\emph{\footnotesize Class}}} & \textsf{\textbf{\emph{\footnotesize Data Source}}} & \textsf{\textbf{\emph{\footnotesize Query versus File-based}}}\tabularnewline[\doublerulesep]
\hline
{\footnotesize AnnotationAffx} & {\footnotesize Affymetrix annotation file repository} & {\footnotesize Query}\tabularnewline
{\footnotesize (based on AffyCompatible)} & & \tabularnewline
\hline
{\footnotesize AnnotationNetAffx} & {\footnotesize Affymetrix NetAffx batch query system} & {\footnotesize File (Appendix A)}\tabularnewline
\hline
\hline
{\footnotesize AnnotationEnsembl} & {\footnotesize Ensembl} & {\footnotesize Query}\tabularnewline
{\footnotesize (based on biomaRt)} & & \tabularnewline
\hline
{\footnotesize AnnotationEnsemblCsv} & {\footnotesize Ensembl front end} & {\footnotesize File (Appendix C)}\tabularnewline
\hline
{\footnotesize AnnotationEnvision} & {\footnotesize EnVision Web Services} & {\footnotesize Query}\tabularnewline
{\footnotesize (based on ENVISIONQuery)} & & \tabularnewline
\hline
\end{tabular}\\
\subsection{The caching subsystem}
Due to the large amount of data available for high density micro arrays,
the process of online data retrieval takes significant time ranging
from a few minutes for a {}``fast'' service to up to an hour (as
in case of EnVision Web Services). It would be impractical to retrieve
the same data again and again at the beginning of each analysis session
given the fact that the data in online repository typically get updated
few times a year. The IdMappingRetrieval caching mechanism allows
the system to store the retrieved data locally allowing to reuse them
in subsequent processing sessions significantly (hundreds of times)
increasing the retrieval speed. When request for data retrieval is
received, the system checks if the data requested already stored in
caching directory and initiates the online retrieval process only
when data of interest is not available. The system can be forced to
retrieve the data online even if present in caching subsystem thus
allowing to update the cached data if necessary. The caching subsystem
is implemented as a hierarchical set of folders within a single root
directory which is created if necessary during the session setup (see
Getting Started).
Under the root directory, there is a set of folders each corresponding
to the data collected by a particular service. The service folder
name is assigned during the particular service object creation and
takes a default name if not specified ({}``Ensembl'' for AnnotationEnsembl
object etc.). It is possible
to create multiple folders for a given service object type in case
the folder name specified explicitly thus allowing to cache multiple
instances of data from the same online resource. This feature allows
to store data available from the same service at different moments
allowing to investigate how the given data source was evolving over
time (see Use Case 2).
For a given service folder, there is a set of subfolders each of which
corresponds to a particular micro array name ({}``HG-U133\_Plus\_2''
etc.). Each micro array type related subfolder contains a set of files
storing the R data objects (.RData files). There are two types of
objects which are stored under the micro array folder. The first is
a (single) data frame containing the raw data retrieved from a particular
service, possibly containing multiple columns each for a particular
data attribute. The second is a set of data frames representing the
mapping between the particular attribute pairs (Uniprot Accession
to probeset ID mapping as an example) extracted from a raw data frame
during the last step of the ID mapping information retrieval process.
The {}``two tier'' caching allows to further speed up the data retrieval
process for a particular attribute pair eliminating the need to read
the entire raw data frame, whose size can easily exceed few dozen
MB.
\subsection{The output data formats}
\subsubsection{Identifier mapping data format}
The identifier mapping related information (ID Map) is represented
by a data frame consisting of two columns in character format. The
first (primary) column contains a unique set of identifiers of a particular
type (Uniprot accessions being an example) which represent the identifier
mapping source. The second (secondary) column contains the results
of mapping of a given (primary) identifier to the target identifier
type (Affymetrix probeset ID as an example) obtained as a result of
a query to the particular online service. As a single source identifier
can map to multiple target identifiers, the secondary column elements
are represented by comma separated strings.
The column names can be set during the service object initialization
or can be deduced from the raw data if this information is not provided.
In later case, the ID Map column names will depend on the particular
service in use. If a further comparison of ID mapping performance
for various online services is desirable, it is recommended to set
the unified column names during the service objects initialization
so to the nature of ID Map data ({}``Uniprot'' and {}``Affy''
for Uniprot accessions to Affymetrix probeset ID mapping as an example).
In further writing, we will refer to the names of a first and second
columns as primary and secondary keys correspondingly.
\subsubsection{Complete raw data set format}
The system always returns the complete raw data set as a data frame.
As each particular online service uses it's own data format, the data
frame format will be different for each particular service and the
query type. If the detailed information on the data frame content
is desirable, we refer user to the documentation from a particular
online service provider.
\section{Examples}
\subsection{Session initialization and setup}
The data retrieval session always starts with the call to the package
initialization function which tells the system which directory should
be used for data caching. If specified directory does not exist yet,
it will be created during the initialization process. As some online
services require credentials (user ID and password) to grant user
an access to the data, the next step of initialization process for
a newly created caching directory should be setting the credentials
for a service which requires them. At the moment, the only service
directly requiring credentials is an Affymetrix query-based service
(AnnotationNetAffx), however the rest of implemented services also
utilizes some of the AnnotationAffx functionality (micro array name
and/or probeset ID list retrieval) and therefore requires setting
the same credentials during initialization process. Note that setting
credentials is mandatory only for newly created caching directory
and can be omitted in subsequent data retrieval sessions. The final
step in initialization process is setting the verbosity level, defining
the micro array type, mnemonic names for source and target identifier
types (primary and secondary key values) as well as location of files
and/or folders for file-based services. A typical initialization script
for a data retrieval session is given below:
\begin{singlespace}
<<chunk1,eval=FALSE,keep.source=TRUE>>=
library(IdMappingRetrieval)
# setup verbosity level,array type and DB keys
arrayType <- "HG-U133_Plus_2";
primaryKey <- "Uniprot";
secondaryKey <- "Affy";
#initialize caching directory structure
Annotation$init(directory="./annotationData",verbose=TRUE);
#set Affymetrix credentials
AnnotationAffx$setCredentials(
user="alex.lisovich@gmail.com",password="125438",verbose=TRUE);
@
\end{singlespace}
\subsection{Service object initialization}
A few examples of different service objects initialization are given
below.
\begin{singlespace}
<<chunk2,eval=FALSE,keep.source=TRUE>>=
#create Affymetrix service object
annAffx_Q <- tryCatch(
AnnotationAffx(cacheFolderName="Affymetrix",
primaryColumn="Probe.Set.ID",
secondaryColumn="SwissProt",
swap=TRUE) );
#create Ensembl file-based service object
annEnsemblCsv <- tryCatch(
AnnotationEnsemblCsv(
cacheFolderName="EnsemblCsv",
primaryColumn=c("UniProt.SwissProt.Accession",
"UniProt.TrEMBL.Accession"),
secondaryColumn=NA,
swap=FALSE, full.merge=TRUE,
df_filename="ENSEMBL_biomart_export.txt") );
#create Envision service object
annEnvision <- tryCatch(
AnnotationEnvision(
cacheFolderName="EnVision",
primaryColumn=c("UniProt.SwissProt.Accession",
"UniProt.TrEMBL.Accession"),
secondaryColumn=NA,
swap=TRUE, species="Homo sapiens", full.merge=TRUE) );
errorSummary = sapply(c('annAffx_Q', 'annEnsemblCsv', ''), function(service)
inherits(service, "error") )
errorMesg = ifelse(any(errorSummary),
paste("(In this vignette build, the service(s) ",
names(which(errorSummary)), "timed out.)"),
errorMesg = "(All services were successfully retrieved.)"
)
@
\end{singlespace}
\subsection{Collecting ID maps from multiple services }
Typically, the IdMappingRetrieval package is used in conjunction with
the IdMappingAnalysis package which accepts the set of collected identifier
mapping data in the form of IdMap object list . The steps involved
in the process include:
\begin{itemize}
\item Defining array type and primary/secondary key values for a resulting
IdMap object set $^{\text{*}}$.
\item Defining the location of a file or directory containing preloaded
data for file-based services
\item Creating a set of service retrieval objects, one per each particular
service
\item Retrieving the Id Map through the getIdMap() method or a complete
raw data frame through the getDataFrame() method.
\end{itemize}
$^{\text{*}}$The need to define the primary/secondary column names
for resulting IdMap object set on top of primaryColumn/secondaryColumn
arguments used during the service object creation arises from the
fact that there is no standard naming convention for the raw data
column names among the services. Therefore for a subsequent analysis
of identifier mapping performance among the services we provide the
arguments primaryKey and secondaryKey to getIdMap() and getIdMapList()
methods for Annotation and ServiceManager objects respectively.
The script below illustrates the process of collecting such data from
all services implemented within the IdMappingRetrieval package.
<<chunk3a,eval=FALSE,keep.source=TRUE>>=
# create service objects
services <- list();
services$NetAffx_F <- AnnotationAffx("Affymetrix");
#collect Id Map data
idMapList <- lapply(services, getIdMap,
arrayType=arrayType,
primaryKey=primaryKey,
secondaryKey=secondaryKey,
force=FALSE, verbose=TRUE);
@
This initial retrieval takes some time. But repeating the retrieval is fast due to caching.
<<chunk3b,eval=FALSE,keep.source=TRUE>>=
idMapList <- lapply(services, getIdMap,
arrayType=arrayType,
primaryKey=primaryKey,
secondaryKey=secondaryKey,
force=FALSE, verbose=TRUE);
names(idMapList);
idMapList$NetAffx_F[100:110,];
@
Instead of map objects, the results can be stored as data frames.
<<chunk3c,eval=FALSE,keep.source=TRUE>>=
#collect complete data frames
dfList <- lapply(services, getDataFrame,
arrayType=arrayType,
force=FALSE, verbose=TRUE);
names(dfList);
names(dfList$NetAffx_F);
@
\subsection{Collecting data from various services using service manager}
An alternative way of achieving the same results is to use the \Rclass{ServiceManager}
object, a container for service objects. The \Rclass{ServiceManager}
class generates the full set of default services automatically. The
following script repeats the work of the previous code, more compactly.
<<chunk4,eval=FALSE,keep.source=TRUE>>=
#create service manager object containing set of default services
svm <- ServiceManager(ServiceManager$getDefaultServices());
names(getServices(svm));
idMapList <- getIdMapList(svm,
arrayType=arrayType,
selection=c("NetAffx_Q"),
primaryKey=primaryKey,
secondaryKey=secondaryKey, verbose=TRUE);
#collect complete data frames
dfList <- getDataFrameList(svm,
arrayType=arrayType,
selection=c("NetAffx_Q"), verbose=TRUE);
@
\subsection{Collecting data from various services interactively using service
manager}
If micro array type and/or the paths for file-based services are not
defined (set to NULL), ServiceManager present user with dialog(s)
allowing selecting it interactively. Note that in a code snippet below
the set of default services is generated internally by invoking a
static version of getIdMapList() and getDataFrameList().
<<chunk5, eval=FALSE,keep.source=TRUE>>=
#collect complete IdMap data interactively
#selecting array type and services to collect data from
idMapList <- ServiceManager$getIdMapList(
primaryKey= primaryKey,
secondaryKey=secondaryKey,verbose=TRUE);
#collect complete data frames interactively
#selecting array type and services to collect data from
dfList <- ServiceManager$getDataFrameList(
arrayType="menu",
selection="menu", verbose=TRUE);
@
<<chunk6,eval=FALSE,results=hide,echo=FALSE,keep.source=FALSE>>=
#remove caching directory to prevent including it into tar.gz during build
unlink("./annotationData",recursive=TRUE);
@
\section{Session information }
This version of IdMappingRetrieval has been developed with R 2.11.0.
R session information:
<<sessionInfo, results=tex>>=
toLatex(sessionInfo())
@
\section{Acknowledgments}
We would like to thank Rafael Jimenez from the Enfin EnCore team for
an enjoyable discussion and the great effort he has put into expanding
the ID mapping related EnCore web services to better suite our needs.
We also would like to thank Himanshu Grover, a graduate student in
Biomedical Informatics at the University of Pittsburgh, for the great
effort and useful suggestions he has made during the package preparation
and testing.
\section{References}
\begin{description}
\item [{{[}1{]}}] Liu, G., NetAffx: Affymetrix probesets and annotations.
Nucleic Acids Res. 2003, 31(1):82-6.
\item [{{[}2{]}}] Xos? M. Fern?ndez-Su?rez and Michael K. Schuster Using
the Ensembl Genome Server to Browse Genomic Sequence Data. UNIT 1.15
in Current Protocols in Bioinformatics, Jun 2010. \\
www.ncbi.nlm.nih.gov/pubmed/20521244
\item [{{[}3{]}}] Giulietta M Spudich and Xos? M Fern?ndez-Su?rez Touring
Ensembl: A practical guide to genome browsing BMC Genomics 2010, 11:295
(11 May 2010)
\item [{{[}4{]}}] Florian Reisinger, Manuel Corpas, John Hancock, Henning
Hermjakob, Ewan Birney and Pascal Kahlem. ENFIN - An Integrative Structure
for Systems Biology. Data Integration in the Life Sciences Lecture
Notes in Computer Science, 2008, Volume 5109/2008, 132-143, DOI: 10.1007/978-3-540-69828-9\_13
\item [{{[}5{]}}] Henrik Bengtsson, The R.oo package - Object-oriented
programming with references using standard R code, Talk at the DSC
2003 conference, Wienna, March 22, 2003.
\end{description}
\section*{Appendix A}
\subsection*{Obtaining the Affymetrix ID Mapping data files using the NetAffx
batch query system and HG-U133\_Plus\_2 array as an example.}
\begin{enumerate}
\item Create a set of the files for upload containing the set of Affymetrix
Probe Set IDs for a given array ensuring the number of IDs for each
file does not exceed 10000 (NetAffx batch query limit): \\
\\
AnnotationNetAffx\$createSubmission(\\
chunk=10000,folder=\textquotedbl{}./NetAffx\_submission\textquotedbl{},verbose=TRUE);
\item Register on the Affymetrix Web site (\href{http://www.affymetrix.com}{www.affymetrix.com})
if you do not have an account yet.
\item Select \textquotedblleft{}NetAffx\textquotedblright{} item in the
menu at the top of the page and then select \textquotedblleft{}Batch
Query\textquotedblright{} item in \textquotedblleft{} 3` IVT Expression\textquotedblright{}
tab on the NetAffx page. The \textquotedblleft{}Batch Query\textquotedblright{}
page will popup.
\item Create a custom annotation view which should include Probe Set ID
as primary (first) identifier and SwissProt as a secondary if not
done yet as follows: - Select \textquotedblleft{}Custom Annotation
Views in the leftmost panel of the page (NetAffx Analysis Center).
The \textquotedblleft{}Create/Edit Custom Views\textquotedblright{}
page will popup. - Enter the custom view name (\textquotedblleft{}Affy2Uniprot\textquotedblright{}
for example) and select Probe Set ID and SwissProt items in \textquotedblleft{}Select
Fields for your view\textquotedblright{} list. Click Submit. - Go
back to the \textquotedblleft{}Batch Query\textquotedblright{} page.
Select \textquotedblleft{}Human Genome U133 Plus 2.0 array\textquotedblright{}
and \textquotedblleft{}Probe Set ID\textquotedblright{} in \textquotedblleft{}Select
a GeneChip Array\textquotedblright{} and \textquotedblleft{}Select
the search type\textquotedblright{} list boxes correspondingly.
\item Select the first upload file from the set created at step 1 using
\textquotedblleft{}Browse\textquotedblright{} button and click \textquotedblleft{}Submit\textquotedblright{}.
The \textquotedblleft{}Show Results\textquotedblright{} page will
popup.
\item Click on \textquotedblleft{}Export Results\textquotedblright{} item.
The \textquotedblleft{}Export Center\textquotedblright{} page will
popup. Select \textquotedblleft{}Tab Separated Value\textquotedblright{}
and \textquotedblleft{}Affy2Uniprot\textquotedblright{} in \textquotedblleft{}Select
File Format\textquotedblright{} and \textquotedblleft{}Select a View\textquotedblright{}
list boxes correspondingly. Click \textquotedblleft{}Submit\textquotedblright{}
and then save the exported file when prompted.
\item Repeat steps 3, 5 and 6 for files created at step 1. Copy the set
of resulting .tsv files into directory of your choice and use them
when working with AnnotationNetAffx service retrieval object.
\item After downloading the file, the ID Map data can be retrieved by creating
the AnnotationNetAffx object with df\_filename slot containing the
path to the downloaded file directory (or the character vector of
paths to file set) and then calling Annotation.getIdMap() on this
object. If df\_filename is not specified (NULL by default) the File
Open dialog will popup during the data retrieval allowing to specify
the file set interactively.
\end{enumerate}
\section*{Appendix B}
\subsection*{Obtaining the Ensembl ID Mapping data file using the HG-U133\_Plus\_2
array as an example.}
\begin{enumerate}
\item Open the Ensembl main page (\href{http://www.ensembl.org}{www.ensembl.org})
and then select \textquotedblleft{}BioMart\textquotedblright{} item
from the menu at the top.
\item Choose \textquotedblleft{}Ensembl Genes 60\textquotedblright{} as
a database and select \textquotedblleft{}Homo sapience genes (GRCh37.p2)\textquotedblright{}
as a data set.
\item Define the attributes by clicking on the \textquotedblleft{}Attributes\textquotedblright{}
item in the left panel and then check the following items in the right
panel: - \textquotedblleft{}GENE/ Ensembl Gene ID\textquotedblright{},
- \textquotedblleft{}External References/ UniProt/TrEMBL Accession\textquotedblright{}
, - \textquotedblleft{}External References/ UniProt/SwissProt Accession\textquotedblright{}
and - \textquotedblleft{}Microarray/ Affy HG U133-PLUS-2\textquotedblright{}
Click on \textquotedblleft{}Results\textquotedblright{} button above
the left panel. The Results will be displayed in the right panel.
\item In the Results panel, set \textquotedblleft{}Export all results to\textquotedblright{}
as \textquotedblleft{}File\textquotedblright{} and \textquotedblleft{}CSV\textquotedblright{}
and check the box \textquotedblleft{}Unique Results only\textquotedblright{}.
Press \textquotedblleft{}GO\textquotedblright{} and then save the
file when prompted.
\item After downloading the file, the ID Map data can be retrieved by creating
the AnnotationEnsembleCsv object with df\_filename slot containing
the path to the downloaded file and then calling Annotation.getIdMap()
on this object. If df\_filename is not specified (NULL by default)
the File Open dialog will pop up during the data retrieval allowing
to specify the file location interactively.
\end{enumerate}
\end{document}