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dc.contributor.authorRamos, M
dc.contributor.authorSchiffer, L
dc.contributor.authorRe, A
dc.contributor.authorAzhar, R
dc.contributor.authorBasunia, A
dc.contributor.authorRodriguez, C
dc.contributor.authorChan, T
dc.contributor.authorChapman, Phil
dc.contributor.authorDavis, S
dc.contributor.authorGomez-Cabrero, D
dc.contributor.authorCulhane, A
dc.contributor.authorHaibe-Kains, B
dc.contributor.authorHansen, K
dc.contributor.authorKodali, H
dc.contributor.authorLouis, M
dc.contributor.authorMer, A
dc.contributor.authorRiester, M
dc.contributor.authorMorgan, M
dc.contributor.authorCarey, V
dc.contributor.authorWaldron, L
dc.date.accessioned2017-12-14T15:10:57Z
dc.date.available2017-12-14T15:10:57Z
dc.date.issued2017-11-01
dc.identifier.citationSoftware for the integration of multiomics experiments in Bioconductor. 2017, 77 (21):e39-e42 Cancer Res.en
dc.identifier.issn1538-7445
dc.identifier.pmid29092936
dc.identifier.doi10.1158/0008-5472.CAN-17-0344
dc.identifier.urihttp://hdl.handle.net/10541/620717
dc.description.abstractMultiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.
dc.language.isoenen
dc.rightsArchived with thanks to Cancer researchen
dc.subject.meshComputational Biology
dc.subject.meshDatasets as Topic
dc.subject.meshGenome, Human
dc.subject.meshGenomics
dc.subject.meshHumans
dc.subject.meshNeoplasms
dc.subject.meshSoftware
dc.titleSoftware for the integration of multiomics experiments in Bioconductor.en
dc.typeArticleen
dc.contributor.departmentGraduate School of Public Health & Health Policy, City University of New York, New York, New Yorken
dc.identifier.journalCancer Researchen
refterms.dateFOA2020-04-20T14:47:06Z
html.description.abstractMultiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.


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