MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration

Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor’s methods and classes implemented in different packages manage individual experiments, there i...

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Published inBMC bioinformatics Vol. 18; no. 1; p. 36
Main Authors Hernandez-Ferrer, Carles, Ruiz-Arenas, Carlos, Beltran-Gomila, Alba, González, Juan R.
Format Journal Article
LanguageEnglish
Published London BioMed Central 17.01.2017
BioMed Central Ltd
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ISSN1471-2105
1471-2105
DOI10.1186/s12859-016-1455-1

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Abstract Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor’s methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. Results To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. Conclusions MultiDataSet is a suitable class for data integration under R and Bioconductor framework.
AbstractList Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor’s methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. Results To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. Conclusions MultiDataSet is a suitable class for data integration under R and Bioconductor framework.
Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. MultiDataSet is a suitable class for data integration under R and Bioconductor framework.
BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. RESULTS: To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. CONCLUSIONS: MultiDataSet is a suitable class for data integration under R and Bioconductor framework.
Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. Results To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. Conclusions MultiDataSet is a suitable class for data integration under R and Bioconductor framework.
BACKGROUNDReduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples.RESULTSTo cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment.CONCLUSIONSMultiDataSet is a suitable class for data integration under R and Bioconductor framework.
ArticleNumber 36
Audience Academic
Author Beltran-Gomila, Alba
González, Juan R.
Ruiz-Arenas, Carlos
Hernandez-Ferrer, Carles
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Issue 1
Keywords Data infrastructure
R
Data integration
Data organization
Omics data
Language English
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Snippet Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple...
Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in...
Background Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple...
BACKGROUNDReduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple...
BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple...
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StartPage 36
SubjectTerms Algorithms
Big data
Bioinformatics
Biological assay
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Dades massives
Data processing
DNA Methylation
Gene Expression
Genomics
Genomics - methods
Humans
Life Sciences
Methods
Microarrays
Multivariate Analysis
Processament de dades
Results and data
Software
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Title MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration
URI https://link.springer.com/article/10.1186/s12859-016-1455-1
https://www.ncbi.nlm.nih.gov/pubmed/28095799
https://www.proquest.com/docview/1863565594
https://www.proquest.com/docview/1861566963
https://recercat.cat/handle/2072/272691
https://pubmed.ncbi.nlm.nih.gov/PMC5240259
Volume 18
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