Package Recipe 'bioconductor-diffustats'

recipe bioconductor-diffustats

Diffusion scores on biological networks

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/diffuStats.html
License:: GPL-3
Recipe:: /bioconductor-diffustats/meta.yaml
Links:: biotools: diffuStats, doi: 10.1093/bioinformatics/btx632

Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking.

package bioconductor-diffustats¶

Versions:

1.30.0-0, 1.26.0-1, 1.26.0-0, 1.22.0-0, 1.20.0-0, 1.18.0-2, 1.18.0-1, 1.18.0-0, 1.14.0-2,

1.30.0-0, 1.26.0-1, 1.26.0-0, 1.22.0-0, 1.20.0-0, 1.18.0-2, 1.18.0-1, 1.18.0-0, 1.14.0-2, 1.14.0-1, 1.14.0-0, 1.12.0-0, 1.10.2-0, 1.10.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-1, 1.4.0-0, 1.2.0-0, 0.104.0-0, 0.102.0-0

Depends:

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-checkmate
on r-expm
on r-igraph
on r-mass
on r-matrix
on r-plyr
on r-precrec
on r-rcpp
on r-rcpparmadillo
on r-rcppparallel
on tbb-devel >=2022.3.0,<2022.4.0a0

Additional platforms:

linux-aarch64

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-diffustats

to add into an existing workspace instead, run:

pixi add bioconductor-diffustats

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-diffustats

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-diffustats

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-diffustats:<tag>

(see bioconductor-diffustats/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-diffustats/README.html)