Package Recipe 'bioconductor-glmsparsenet'

recipe bioconductor-glmsparsenet

Network Centrality Metrics for Elastic-Net Regularized Models

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/glmSparseNet.html
License:: GPL-3
Recipe:: /bioconductor-glmsparsenet/meta.yaml

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

package bioconductor-glmsparsenet¶

Versions:

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0,

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-1, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-biomart >=2.66.0,<2.67.0
on bioconductor-multiassayexperiment >=1.36.0,<1.37.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-tcgautils >=1.30.0,<1.31.0
on r-base >=4.5,<4.6.0a0
on r-checkmate
on r-dplyr
on r-forcats
on r-futile.logger
on r-ggplot2
on r-glmnet
on r-glue
on r-httr
on r-lifecycle
on r-matrix
on r-readr
on r-rlang
on r-survminer

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-glmsparsenet

to add into an existing workspace instead, run:

pixi add bioconductor-glmsparsenet

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-glmsparsenet

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-glmsparsenet

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-glmsparsenet:<tag>

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