recipe bioconductor-gostag

A tool to use GO Subtrees to Tag and Annotate Genes within a set

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-gostag/meta.yaml

Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.

package bioconductor-gostag

(downloads) docker_bioconductor-gostag

versions:
1.26.0-0,  1.24.0-0,  1.22.0-0,  1.18.0-0,  1.16.0-0,  1.14.2-0,  1.14.1-0,  1.13.1-0,  1.12.0-0, 

1.26.0-0,  1.24.0-0,  1.22.0-0,  1.18.0-0,  1.16.0-0,  1.14.2-0,  1.14.1-0,  1.13.1-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.6.1-0

depends bioconductor-annotationdbi:

>=1.64.0,<1.65.0

depends bioconductor-biomart:

>=2.58.0,<2.59.0

depends bioconductor-go.db:

>=3.18.0,<3.19.0

depends r-base:

>=4.3,<4.4.0a0

depends r-memoise:

requirements:

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-gostag

and update with::

   mamba update bioconductor-gostag

To create a new environment, run:

mamba create --name myenvname bioconductor-gostag

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-gostag:<tag>

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

Download stats

Link to this page

Render an install-with-bioconda 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-gostag/README.html)