recipe bioconductor-benchdamic

Benchmark of differential abundance methods on microbiome data






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

(downloads) docker_bioconductor-benchdamic



depends bioconductor-aldex2:


depends bioconductor-ancombc:


depends bioconductor-biocparallel:


depends bioconductor-dearseq:


depends bioconductor-deseq2:


depends bioconductor-edger:


depends bioconductor-limma:


depends bioconductor-mast:


depends bioconductor-metagenomeseq:


depends bioconductor-noiseq:


depends bioconductor-phyloseq:


depends bioconductor-summarizedexperiment:


depends bioconductor-treesummarizedexperiment:


depends bioconductor-zinbwave:


depends r-base:


depends r-corncob:

depends r-cowplot:

depends r-ggdendro:

depends r-ggplot2:

depends r-ggridges:

depends r-lme4:

depends r-mglm:

depends r-plyr:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-seurat:

depends r-tidytext:



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-benchdamic

and update with::

   mamba update bioconductor-benchdamic

To create a new environment, run:

mamba create --name myenvname bioconductor-benchdamic

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<tag>

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

Download stats