recipe bioconductor-pigengene

Infers biological signatures from gene expression data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/Pigengene.html

License:

GPL (>=2)

Recipe:

/bioconductor-pigengene/meta.yaml

Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes.

package bioconductor-pigengene

versions:

1.28.0-0,  1.26.0-0,  1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.16.0-0,  1.14.0-0,  1.12.0-0, 

1.28.0-0,  1.26.0-0,  1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.16.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.8.0-0

depends bioconductor-biocstyle:

>=2.30.0,<2.31.0

depends bioconductor-clusterprofiler:

>=4.10.0,<4.11.0

depends bioconductor-dose:

>=3.28.0,<3.29.0

depends bioconductor-go.db:

>=3.18.0,<3.19.0

depends bioconductor-graph:

>=1.80.0,<1.81.0

depends bioconductor-impute:

>=1.76.0,<1.77.0

depends bioconductor-preprocesscore:

>=1.64.0,<1.65.0

depends bioconductor-reactomepa:

>=1.46.0,<1.47.0

depends bioconductor-rgraphviz:

>=2.46.0,<2.47.0

depends r-base:

>=4.3,<4.4.0a0

depends r-bnlearn:

>=4.7

depends r-c50:

>=0.1.2

depends r-dbi:

depends r-dplyr:

depends r-gdata:

depends r-ggplot2:

depends r-mass:

depends r-matrixstats:

depends r-openxlsx:

depends r-partykit:

depends r-pheatmap:

>=1.0.8

depends r-wgcna:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-pigengene

and update with::

   mamba update bioconductor-pigengene

To create a new environment, run:

mamba create --name myenvname bioconductor-pigengene

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-pigengene:<tag>

(see `bioconductor-pigengene/tags`_ for valid values for ``<tag>``)

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-pigengene/README.html)