affyPara - a Bioconductor Package for Parallelized Preprocessing Algorithms of Affymetrix Microarray Data

Markus Schmidberger, Esmeralda Vicedo and Ulrich MansmannDivision of Biometrics and Bioinformatics, IBE, University of Munich, 81377 Munich, Germany. AbstractMicroarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one...

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Published inBioinformatics and biology insights Vol. 2009; no. 3; pp. BBI.S3060 - 87
Main Authors Schmidberger, Markus, Vicedo, Esmeralda, Mansmann, Ulrich
Format Journal Article
LanguageEnglish
Published London, England SAGE Publishing 01.01.2009
SAGE Publications
Sage Publications Ltd. (UK)
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ISSN1177-9322
1177-9322
DOI10.4137/BBI.S3060

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Abstract Markus Schmidberger, Esmeralda Vicedo and Ulrich MansmannDivision of Biometrics and Bioinformatics, IBE, University of Munich, 81377 Munich, Germany. AbstractMicroarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly. This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays. affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
AbstractList Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly.This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly. This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays. affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly.This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly.This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly. This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays. affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package. Keywords: parallel computing, R, microarray, preprocessing, normalization
Markus Schmidberger, Esmeralda Vicedo and Ulrich MansmannDivision of Biometrics and Bioinformatics, IBE, University of Munich, 81377 Munich, Germany. AbstractMicroarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly. This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays. affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
Audience Academic
Author Esmeralda Vicedo
Markus Schmidberger
Ulrich Mansmann
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crossref_primary_10_1093_nar_gkt229
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10.1371/journal.pcbi.0040022
10.1198/106186007X178979
10.2202/1544-6115.1078
10.1093/bioinformatics/18.suppl_1.S96
10.1007/0-387-29362-0
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Keywords R
microarray
preprocessing
normalization
parallel computing
Language English
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Snippet Markus Schmidberger, Esmeralda Vicedo and Ulrich MansmannDivision of Biometrics and Bioinformatics, IBE, University of Munich, 81377 Munich, Germany....
Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The...
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SourceType Open Website
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StartPage BBI.S3060
SubjectTerms Algorithms
Arrays
Computation
Licenses
Packages
Parallel processing
Partitions
Preprocessing
Short Report
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Title affyPara - a Bioconductor Package for Parallelized Preprocessing Algorithms of Affymetrix Microarray Data
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