%0 Journal Article %1 Ramos:2017:Cancer-Res:29092936 %A Ramos, M %A Schiffer, L %A Re, A %A Azhar, R %A Basunia, A %A Rodriguez, C %A Chan, T %A Chapman, P %A Davis, S R %A Gomez-Cabrero, D %A Culhane, A C %A Haibe-Kains, B %A Hansen, K D %A Kodali, H %A Louis, M S %A Mer, A S %A Riester, M %A Morgan, M %A Carey, V %A Waldron, L %D 2017 %J Cancer Res %K MUSTREAD bioconductor bioinformatics fulltext software %N 21 %P 39-39 %R 10.1158/0008-5472.CAN-17-0344 %T Software for the Integration of Multiomics Experiments in Bioconductor %U https://www.ncbi.nlm.nih.gov/pubmed/29092936 %V 77 %X 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.

@article{Ramos:2017:Cancer-Res:29092936, 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.}, added-at = {2018-02-17T14:27:05.000+0100}, author = {Ramos, M and Schiffer, L and Re, A and Azhar, R and Basunia, A and Rodriguez, C and Chan, T and Chapman, P and Davis, S R and Gomez-Cabrero, D and Culhane, A C and Haibe-Kains, B and Hansen, K D and Kodali, H and Louis, M S and Mer, A S and Riester, M and Morgan, M and Carey, V and Waldron, L}, biburl = {https://www.bibsonomy.org/bibtex/2d58e1070af523beb8d515ed4799d9ee0/marcsaric}, description = {Software for the Integration of Multiomics Experiments in Bioconductor. - PubMed - NCBI}, doi = {10.1158/0008-5472.CAN-17-0344}, interhash = {c197565b5dbc45e1b2978e5c3ae3e4eb}, intrahash = {d58e1070af523beb8d515ed4799d9ee0}, journal = {Cancer Res}, keywords = {MUSTREAD bioconductor bioinformatics fulltext software}, month = nov, number = 21, pages = {39-39}, pmid = {29092936}, timestamp = {2018-02-17T14:27:05.000+0100}, title = {Software for the Integration of Multiomics Experiments in Bioconductor}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29092936}, volume = 77, year = 2017 }