TY - JOUR
T1 - Software for the integration of multiomics experiments in bioconductor
AU - Ramos, Marcel
AU - Schiffer, Lucas
AU - Re, Angela
AU - Azhar, Rimsha
AU - Basunia, Azfar
AU - Rodriguez, Carmen
AU - Chan, Tiffany
AU - Chapman, Phil
AU - Davis, Sean R.
AU - Gomez-Cabrero, David
AU - Culhane, Aedin C.
AU - Haibe-Kains, Benjamin
AU - Hansen, Kasper D.
AU - Kodali, Hanish
AU - Louis, Marie S.
AU - Mer, Arvind S.
AU - Riester, Markus
AU - Morgan, Martin
AU - Carey, Vince
AU - Waldron, Levi
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Multiomics 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.
AB - Multiomics 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.
UR - http://cancerres.aacrjournals.org/lookup/doi/10.1158/0008-5472.CAN-17-0344
UR - http://www.scopus.com/inward/record.url?scp=85034074199&partnerID=8YFLogxK
U2 - 10.1158/0008-5472.CAN-17-0344
DO - 10.1158/0008-5472.CAN-17-0344
M3 - Article
SN - 1538-7445
VL - 77
SP - e39-e42
JO - Cancer Research
JF - Cancer Research
IS - 21
ER -
