Package Recipe 'r-sigminer'

recipe r-sigminer

Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2020) <DOI:10.1101/2020.04.27.20082404> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Macintyre, Geoff, et al. (2018) <DOI:10.1038/s41588-018-0179-8>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

Homepage:: https://github.com/ShixiangWang/sigminer
License:: MIT / MIT
Recipe:: /r-sigminer/meta.yaml

package r-sigminer¶

Versions:

2.3.1-2, 2.3.1-1, 2.3.1-0, 2.3.0-0, 2.2.2-0, 2.2.0-2, 2.2.0-1, 2.2.0-0, 2.1.9-2,

2.3.1-2, 2.3.1-1, 2.3.1-0, 2.3.0-0, 2.2.2-0, 2.2.0-2, 2.2.0-1, 2.2.0-0, 2.1.9-2, 2.1.9-1, 2.1.9-0, 2.1.8-1, 2.1.8-0, 2.1.7-1, 2.1.7-0, 2.1.6-1, 2.1.6-0, 2.1.5-1, 2.1.5-0, 2.1.4-0, 2.1.3-1, 2.1.3-0, 2.1.2-1, 2.1.2-0, 2.1.1-0, 2.1.0-0, 2.0.5-0, 2.0.4-0, 2.0.3-0, 2.0.2-0, 2.0.1-1, 2.0.1-0, 2.0.0-1, 2.0.0-0, 1.2.5-1, 1.2.5-0, 1.2.4-0, 1.2.1-0

Depends:

on bioconductor-maftools >=2.26.0,<2.27.0a0
on libgcc >=14
on libstdcxx >=14
on r-base >=4.5,<4.6.0a0
on r-cli >=2.0.0
on r-cowplot
on r-data.table
on r-dplyr
on r-furrr >=0.2.0
on r-future
on r-ggplot2 >=3.3.0
on r-ggpubr
on r-magrittr
on r-nmf
on r-purrr
on r-rcpp
on r-rlang >=0.1.2
on r-tidyr

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 r-sigminer

to add into an existing workspace instead, run:

pixi add r-sigminer

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-sigminer

Alternatively, to install into a new environment, run:

conda create -n envname r-sigminer

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-sigminer:<tag>

(see r-sigminer/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/r-sigminer/README.html)