Risa allows to access metadata/data in ISA-tab format and builds Bioconductor data structures. Apart from parsing ISA-tab files, the package also provides functionality to save the ISA-tab dataset, or each of its individual files. Additionally, it is also possible to update assay files. Currently, metadata associated to proteomics and metabolomi…
R
Switch branches/tags
Latest commit cecebad Oct 12, 2016 @agbeltran agbeltran Merge pull request #37 from agbeltran/master
Adding BugReports link and bumping minor version
Permalink
Failed to load latest commit information.
R no visible binding for global variable Mar 10, 2015
inst Move the vignette, closes #28 Mar 10, 2015
man Addressing warnings for undocumented S4 methods - closes #35 Oct 10, 2016
vignettes Move the vignette, closes #28 Mar 10, 2015
DESCRIPTION
NAMESPACE Packages in Depends field not imported from. These packages need to b… Mar 10, 2015
NEWS Updating NEWS and URL and version in DESCRIPTION file Mar 15, 2015
README.md
TODO

README.md

Risa

Risa is an R package that is part of the ISA tools suite (http://isa-tools.org). Risa supports parsing, saving and updating ISA-tab datasets. It also builds bridges from the ISA-Tab syntax to analysis pipelines for specific assay types, such as mass spectrometry and DNA microarray assays, by building R objects from the metadata required for other packages downstream, such as xcms and affy, respectively. In addition, Risa includes functionality to suggest packages in BioConductor that might be relevant for the assay types in the ISA-TAB dataset being considered. This recommentation functionality relies on the BioCViews annotations provided by each BioConductor package.

The Risa package is available in Bioconductor:

For more information about the ISA tools, consider:

Read the Paper in BMC Bioinformatics!

Access the Open Access BMC Bioinformatics article on Risa here.

Alejandra González-Beltrán, Steffen Neumann, Eamonn Maguire, Susanna-Assunta Sansone and Philippe Rocca-Serra.
The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again BMC Bioinformatics 2014, 15(Suppl 1):S11 doi:10.1186/1471-2105-15-S1-S11

Development

If you have feature requests or find any issues when using Risa, please let us know through the issues tracker at [https://github.com/ISA-tools/Risa/issues].

Contributing

You should read this article about Git Flow: http://scottchacon.com/2011/08/31/github-flow.html. It's a really useful tutorial on how to use Git for collaborative development.

  1. Fork it.
  2. Clone your forked repository to your machine
  3. Create a branch (git checkout -b myRisa)
  4. Make your changes
  5. Run the tests (mvn clean test)
  6. Commit your changes (git commit -am "Added something useful")
  7. Push to the branch (git push origin myRisa)
  8. Create a Pull Request from your branch.
  9. Promote it. Get others to drop in and +1 it.