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Cancer Res. 2017 Nov 1;77(21):e39-e42. doi: 10.1158/0008-5472.CAN-17-0344.

Software for the Integration of Multiomics Experiments in Bioconductor.

Author information

1
Graduate School of Public Health & Health Policy, City University of New York, New York, New York.
2
Institute for Implementation Science in Population Health, City University of New York, New York, New York.
3
Roswell Park Cancer Institute, University of Buffalo, Buffalo, New York.
4
Centre for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Corso Trento, Torino, Italy.
5
Harvard TH Chan School of Public Health, Boston, Massachusetts.
6
Computational Biology Support Team, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, United Kingdom.
7
Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
8
Mucosal and Salivary Biology Division, King's College London Dental Institute, London, United Kingdom.
9
Dana-Farber Cancer Institute, Boston, Massachusetts.
10
Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
11
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
12
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
13
Ontario Institute of Cancer Research, Toronto, Ontario, Canada.
14
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
15
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland.
16
Novartis Institutes for BioMedical Research, Cambridge, Massachusetts.
17
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
18
Graduate School of Public Health & Health Policy, City University of New York, New York, New York. levi.waldron@sph.cuny.edu.

Abstract

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. Cancer Res; 77(21); e39-42. ©2017 AACR.

PMID:
29092936
PMCID:
PMC5679241
DOI:
10.1158/0008-5472.CAN-17-0344
[Indexed for MEDLINE]
Free PMC Article

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