Package Recipe 'bioconductor-poma'

recipe bioconductor-poma

Tools for Omics Data Analysis

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/POMA.html
License:: GPL-3
Recipe:: /bioconductor-poma/meta.yaml

The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.

package bioconductor-poma¶

Versions:

1.20.0-0, 1.16.0-0, 1.12.0-0, 1.10.0-0, 1.8.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-2, 1.0.0-1

Depends:

on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-deseq2 >=1.50.0,<1.51.0
on bioconductor-fgsea >=1.36.0,<1.37.0
on bioconductor-impute >=1.84.0,<1.85.0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-mixomics >=6.34.0,<6.35.0
on bioconductor-rankprod >=3.36.0,<3.37.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-sva >=3.58.0,<3.59.0
on r-base >=4.5,<4.6.0a0
on r-broom
on r-caret
on r-dbscan
on r-dplyr
on r-fsa
on r-ggcorrplot
on r-ggplot2
on r-ggrepel
on r-glmnet
on r-janitor
on r-lme4
on r-magrittr
on r-mass
on r-msigdbr
on r-multcomp
on r-purrr
on r-randomforest
on r-rlang
on r-tibble
on r-tidyr
on r-uwot
on r-vegan

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-poma

to add into an existing workspace instead, run:

pixi add bioconductor-poma

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-poma

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-poma

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-poma:<tag>

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