recipe bioconductor-maaslin2

"Multivariable Association Discovery in Population-scale Meta-omics Studies"






MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin.

package bioconductor-maaslin2

(downloads) docker_bioconductor-maaslin2



depends bioconductor-edger:


depends bioconductor-metagenomeseq:


depends r-base:


depends r-biglm:

depends r-car:

depends r-chemometrics:

depends r-cplm:

depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-glmmtmb:

depends r-hash:

depends r-lme4:

depends r-lmertest:

depends r-logging:

depends r-mass:

depends r-optparse:

depends r-pbapply:

depends r-pcapp:

depends r-pheatmap:

depends r-pscl:

depends r-robustbase:

depends r-tibble:

depends r-vegan:



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-maaslin2

and update with::

   mamba update bioconductor-maaslin2

To create a new environment, run:

mamba create --name myenvname bioconductor-maaslin2

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-maaslin2/tags`_ for valid values for ``<tag>``)

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