Software for the integration of multi-omics experiments in Bioconductor

Background Cancer research has been transformed by the general availability of high-throughput molecular assays that can measure multiple complementary biological processes, leading to complex experiments, analysis designs, and data integration challenges. New software classes and methods that deal with the complexity of multiple assays applied across overlapping sample sets are needed to enable efficient analysis of these complex multi-omics datasets. Methods 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. Results Example applications that integrate omics data types showcase the software and documentation. We also provide ready-to-analyze MultiAssayExperiment objects for each cancer type in The Cancer Genome Atlas (TCGA). Conclusions The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets