recipe bioconductor-microbiomemarker

microbiome biomarker analysis toolkit






To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers.

package bioconductor-microbiomemarker

(downloads) docker_bioconductor-microbiomemarker



depends bioconductor-aldex2:


depends bioconductor-ancombc:


depends bioconductor-biobase:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-biomformat:


depends bioconductor-biostrings:


depends bioconductor-complexheatmap:


depends bioconductor-deseq2:


depends bioconductor-edger:


depends bioconductor-ggtree:


depends bioconductor-iranges:


depends bioconductor-limma:


depends bioconductor-metagenomeseq:


depends bioconductor-multtest:


depends bioconductor-phyloseq:


depends bioconductor-s4vectors:


depends r-base:


depends r-caret:

depends r-coin:

depends r-dplyr:

depends r-ggplot2:

depends r-ggsignif:

depends r-magrittr:

depends r-mass:

depends r-patchwork:

depends r-plotroc:

depends r-proc:

depends r-purrr:

depends r-rlang:

depends r-tibble:

depends r-tidyr:

depends r-tidytree:

depends r-vegan:

depends r-yaml:



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-microbiomemarker

and update with::

   mamba update bioconductor-microbiomemarker

To create a new environment, run:

mamba create --name myenvname bioconductor-microbiomemarker

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-microbiomemarker/tags`_ for valid values for ``<tag>``)

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