text: The Bioconductor project curates a variety of R packages aimed at integrating omic data:
  *[http://bioconductor.org/packages/release/bioc/html/omicade4.html omicade4], for multiple co-inertia analysis of multi omic datasets
  *[http://bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html MultiAssayExperiment], offering a bioconductor interface for overlapping samples
  *[http://bioconductor.org/packages/release/bioc/html/IMAS.html IMAS], a package focused on using multi omic data for evaluating alternative splicing
  *[http://bioconductor.org/packages/release/bioc/html/bioCancer.html bioCancer], a package for visualization of multiomic cancer data
  *[http://bioconductor.org/packages/release/bioc/html/mixOmics.html mixOmics], a suite of multivariate methods for data integration
  *[http://bioconductor.org/packages/release/bioc/html/MultiDataSet.html MultiDataSet], a package for encapsulating multiple data sets
  The [https://cran.r-project.org/web/packages/RGCCA/ RGCCA package] implements a versatile framework for data integration. This package is freely available on the [https://cran.r-project.org/ Comprehensive R Archive Network (CRAN)].
  The OmicTools  database further highlights R packages and othertools for multi omic data analysis:
  * [http://www.paintomics.org/ PaintOmics], a web resource for visualization of multi-omics datasets
  * SIGMA, a Java program focused on integrated analysis of cancer datasets
  * iOmicsPASS, a tool in C++ for multiomic-based phenotype prediction
  * [https://github.com/mkanai/grimon Grimon], an R graphical interface for visualization of multiomic data
  * [https://pypi.org/project/omics_pipe/ Omics Pipe], a framework in Python for reproducibly automating multiomic data analysis