Package Recipe 'bioconductor-benchdamic'

recipe bioconductor-benchdamic

Benchmark of differential abundance methods on microbiome data

Homepage:: https://bioconductor.org/packages/3.18/bioc/html/benchdamic.html
License:: Artistic-2.0
Recipe:: /bioconductor-benchdamic/meta.yaml

Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.

package bioconductor-benchdamic¶

Versions:

1.6.0-0, 1.4.0-0, 1.0.0-0

Depends:

on bioconductor-aldex2 >=1.32.0,<1.33.0
on bioconductor-ancombc >=2.2.0,<2.3.0
on bioconductor-biocparallel >=1.34.0,<1.35.0
on bioconductor-dearseq >=1.12.0,<1.13.0
on bioconductor-deseq2 >=1.40.0,<1.41.0
on bioconductor-edger >=3.42.0,<3.43.0
on bioconductor-limma >=3.56.0,<3.57.0
on bioconductor-mast >=1.26.0,<1.27.0
on bioconductor-metagenomeseq >=1.42.0,<1.43.0
on bioconductor-noiseq >=2.44.0,<2.45.0
on bioconductor-phyloseq >=1.44.0,<1.45.0
on bioconductor-summarizedexperiment >=1.30.0,<1.31.0
on bioconductor-treesummarizedexperiment >=2.8.0,<2.9.0
on bioconductor-zinbwave >=1.22.0,<1.23.0
on r-base >=4.3,<4.4.0a0
on r-corncob
on r-cowplot
on r-ggdendro
on r-ggplot2
on r-ggridges
on r-lme4
on r-mglm
on r-plyr
on r-rcolorbrewer
on r-reshape2
on r-seurat
on r-tidytext

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-benchdamic

to add into an existing workspace instead, run:

pixi add bioconductor-benchdamic

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-benchdamic

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-benchdamic

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-benchdamic:<tag>

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