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hernandez-ferrer2017_2366.pdf (614.76 KB)

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2017-01-17

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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/106469

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

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Recurs relacionat

http://dx.doi.org/10.1186/s12859-016-1455-1

Resum

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.

Matèries

Processament de dades, Dades massives

Matèries (anglès)

Data processing, Big data

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Articles publicats en revistes (ISGlobal)

Citació

HERNÁNDEZ FERRER, Carles, et al. MultiDataSet: an R package for encapsulating multiple data sets
                with application to omic data integration. BMC Bioinformatics. 2017. Vol. 18, num. 36. ISSN 1471-2105. [consulted: 4 of August of 2026]. Available at: https://hdl.handle.net/2445/106469

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