Package Recipe 'bioconductor-amaretto'

recipe bioconductor-amaretto

Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/AMARETTO.html
License:: Apache License (== 2.0) + file LICENSE
Recipe:: /bioconductor-amaretto/meta.yaml

Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

package bioconductor-amaretto¶

Versions:

1.26.0-0, 1.22.0-0, 1.18.0-0, 1.16.0-0, 1.13.0-0, 1.10.0-2, 1.10.0-1, 1.10.0-0, 1.8.0-0,

1.26.0-0, 1.22.0-0, 1.18.0-0, 1.16.0-0, 1.13.0-0, 1.10.0-2, 1.10.0-1, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0, 1.4.0-0, 1.1.1-1, 1.0.0-1

Depends:

on bioconductor-biocfilecache >=3.0.0,<3.1.0
on bioconductor-biocfilecache >=3.0.0,<3.1.0a0
on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-complexheatmap >=2.26.1,<2.27.0a0
on bioconductor-curatedtcgadata >=1.32.0,<1.33.0
on bioconductor-curatedtcgadata >=1.32.1,<1.33.0a0
on bioconductor-impute >=1.84.0,<1.85.0
on bioconductor-impute >=1.84.0,<1.85.0a0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-limma >=3.66.0,<3.67.0a0
on bioconductor-multiassayexperiment >=1.36.0,<1.37.0
on bioconductor-multiassayexperiment >=1.36.1,<1.37.0a0
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-callr >=3.0.0.9001
on r-circlize
on r-doparallel
on r-dplyr
on r-dt
on r-foreach
on r-ggplot2
on r-glmnet
on r-gridextra
on r-httr
on r-knitr
on r-matrix
on r-matrixstats
on r-rcpp
on r-readr
on r-reshape2
on r-rmarkdown
on r-tibble

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-amaretto

to add into an existing workspace instead, run:

pixi add bioconductor-amaretto

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-amaretto

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-amaretto

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-amaretto:<tag>

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