Package Recipe 'bioconductor-sparsesignatures'

recipe bioconductor-sparsesignatures

SparseSignatures

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/SparseSignatures.html
License:: file LICENSE
Recipe:: /bioconductor-sparsesignatures/meta.yaml

Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients.

package bioconductor-sparsesignatures¶

Versions:

2.20.0-0, 2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0,

2.20.0-0, 2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-1, 1.2.0-0

Depends:

on bioconductor-biostrings >=2.78.0,<2.79.0
on bioconductor-bsgenome >=1.78.0,<1.79.0
on bioconductor-genomeinfodb >=1.46.0,<1.47.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on r-base >=4.5,<4.6.0a0
on r-data.table
on r-ggplot2
on r-gridextra
on r-nmf
on r-nnlasso
on r-nnls
on r-reshape2
on r-rhpcblasctl

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-sparsesignatures

to add into an existing workspace instead, run:

pixi add bioconductor-sparsesignatures

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-sparsesignatures

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-sparsesignatures

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-sparsesignatures:<tag>

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