RT Journal Article
SR Electronic
T1 Software for the integration of multi-omics experiments in Bioconductor
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 144774
DO 10.1101/144774
A1 Marcel Ramos
A1 Lucas Schiffer
A1 Angela Re
A1 Rimsha Azhar
A1 Azfar Basunia
A1 Carmen Rodriguez Cabrera
A1 Tiffany Chan
A1 Philip Chapman
A1 Sean Davis
A1 David Gomez-Cabrero
A1 Aedin C. Culhane
A1 Benjamin Haibe-Kains
A1 Kasper D. Hansen
A1 Hanish Kodali
A1 Marie Stephie Louis
A1 Arvind Singh Mer
A1 Markus Riester
A1 Martin Morgan
A1 Vincent Carey
A1 Levi Waldron
YR 2017
UL http://biorxiv.org/content/early/2017/06/01/144774.abstract
AB Multi-omics 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 multi-omics 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 all of the multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) 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 multi-omics data and enhances data science applications of multiple omics datasets.