Hernández Ferrer, CarlesRuiz Arenas, CarlosBeltran Gomila, AlbaGonzález, Juan Ramón2017-02-032017-02-032017-01-171471-2105https://hdl.handle.net/2445/106469BACKGROUND: 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.7 p.application/pdfeng(c) Hernández Ferrer et al., 2017Processament de dadesDades massivesData processingBig datainfo:eu-repo/semantics/article2017-02-01info:eu-repo/semantics/openAccess28095799