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 in | BMC bioinformatics Vol. 18; no. 1; p. 36 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
BioMed Central
17.01.2017
BioMed Central Ltd |
Subjects | |
Online Access | Get full text |
ISSN | 1471-2105 1471-2105 |
DOI | 10.1186/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. |
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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 |
Author_xml | – sequence: 1 givenname: Carles surname: Hernandez-Ferrer fullname: Hernandez-Ferrer, Carles organization: Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP) – sequence: 2 givenname: Carlos surname: Ruiz-Arenas fullname: Ruiz-Arenas, Carlos organization: Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP) – sequence: 3 givenname: Alba surname: Beltran-Gomila fullname: Beltran-Gomila, Alba organization: Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP) – sequence: 4 givenname: Juan R. surname: González fullname: González, Juan R. email: juanr.gonzalez@isglobal.org organization: Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP) |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28095799$$D View this record in MEDLINE/PubMed |
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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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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 |
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