Supplemental materials from Software for the Integration of Multiomics Experiments in Bioconductor

Ramos, Marcel; Schiffer, Lucas; Re, Angela; Azhar, Rimsha; Basunia, Azfar; Rodriguez, Carmen; Chan, Tiffany; Chapman, Phil; Davis, Sean R.; Gomez-Cabrero, David; Culhane, Aedin C.; Haibe-Kains, Benjamin; Hansen, Kasper D.; Kodali, Hanish; Louis, Marie S.; Mer, Arvind S.; Riester, Markus; Morgan, Martin; Carey, Vince; Waldron, Levi

doi:10.1158/0008-5472.22418229.v1

Supplemental materials from Software for the Integration of Multiomics Experiments in Bioconductor

journal contribution

posted on 2023-03-31, 01:40 authored by Marcel Ramos, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez, Tiffany Chan, Phil Chapman, Sean R. Davis, David Gomez-Cabrero, Aedin C. Culhane, Benjamin Haibe-Kains, Kasper D. Hansen, Hanish Kodali, Marie S. Louis, Arvind S. Mer, Markus Riester, Martin Morgan, Vince Carey, Levi Waldron

Supplementary Methods and Example Analyses. This file provides additional detail on the MultiAssayExperiment data class, methods of preparation of the TCGA objects, methods and additional results for the example analyses. It includes Supplemental Figures 1-2 (Mogsa enrichment scores for Hallmark gene sets), Supplemental Figure 3 (correlation between copy number, mRNA, and protein profiles for the NCI-60 cell lines), Supplemental Table 1 (comparison between the features of MultiAssayExperiment and MultiDataSet), Supplemental Table 2 (summary table of TCGA datasets), and Supplemental Table 3 (elements of the MultiAssayExperiment data class).

History

ARTICLE 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.