- recipe r-jetset
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
- Homepage:
- License:
OTHER / Artistic-2.0
- Recipe:
- package r-jetset¶
- Versions:
3.4.0-3,3.4.0-2,3.4.0-1,3.4.0-0- Depends:
on bioconductor-annotationdbi
on bioconductor-org.hs.eg.db
on r-base
>=4.4,<4.5.0a0
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install r-jetset
to add into an existing workspace instead, run:
pixi add r-jetset
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install r-jetset
Alternatively, to install into a new environment, run:
conda create -n envname r-jetset
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/r-jetset:<tag>
(see r-jetset/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/r-jetset/README.html)
