Package Recipe 'bioconductor-proloc'

recipe bioconductor-proloc

A unifying bioinformatics framework for spatial proteomics

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/pRoloc.html
License:: GPL-2
Recipe:: /bioconductor-proloc/meta.yaml
Links:: biotools: proloc

The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.

package bioconductor-proloc¶

Versions:

1.50.0-0, 1.46.0-0, 1.42.0-0, 1.40.1-0, 1.38.0-1, 1.38.0-0, 1.34.0-2, 1.34.0-1, 1.34.0-0,

1.50.0-0, 1.46.0-0, 1.42.0-0, 1.40.1-0, 1.38.0-1, 1.38.0-0, 1.34.0-2, 1.34.0-1, 1.34.0-0, 1.32.0-0, 1.30.0-1, 1.30.0-0, 1.28.0-0, 1.26.0-0, 1.24.0-1, 1.22.1-0, 1.22.0-0, 1.18.0-0

Depends:

on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-biobase >=2.70.0,<2.71.0a0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0a0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-biomart >=2.66.0,<2.67.0
on bioconductor-biomart >=2.66.0,<2.67.0a0
on bioconductor-mlinterfaces >=1.90.0,<1.91.0
on bioconductor-mlinterfaces >=1.90.0,<1.91.0a0
on bioconductor-msnbase >=2.36.0,<2.37.0
on bioconductor-msnbase >=2.36.0,<2.37.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-caret
on r-class
on r-coda
on r-colorspace
on r-dendextend
on r-e1071
on r-fnn
on r-ggplot2
on r-gtools
on r-hexbin
on r-kernlab
on r-knitr
on r-laplacesdemon
on r-lattice
on r-mass
on r-mclust >=4.3
on r-mixtools
on r-mvtnorm
on r-nnet
on r-plyr
on r-proxy
on r-randomforest
on r-rcolorbrewer
on r-rcpp >=0.10.3
on r-rcpparmadillo
on r-sampling
on r-scales

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 bioconductor-proloc

to add into an existing workspace instead, run:

pixi add bioconductor-proloc

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 bioconductor-proloc

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-proloc

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/bioconductor-proloc:<tag>

(see bioconductor-proloc/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:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-proloc/README.html)