recipe bioconductor-dar

Differential Abundance Analysis by Consensus

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/dar.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-dar/meta.yaml

Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.

package bioconductor-dar

versions:

1.2.0-0

depends bioconductor-complexheatmap:

>=2.22.0,<2.23.0

depends bioconductor-mia:

>=1.14.0,<1.15.0

depends bioconductor-phyloseq:

>=1.50.0,<1.51.0

depends r-base:

>=4.4,<4.5.0a0

depends r-cli:

depends r-crayon:

depends r-dplyr:

depends r-generics:

depends r-ggplot2:

depends r-glue:

depends r-gplots:

depends r-heatmaply:

depends r-magrittr:

depends r-purrr:

depends r-readr:

depends r-rlang:

>=0.4.11

depends r-scales:

depends r-stringr:

depends r-tibble:

depends r-tidyr:

depends r-upsetr:

depends r-waldo:

requirements:

additional platforms:

Installation

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-dar

and update with::

   mamba update bioconductor-dar

To create a new environment, run:

mamba create --name myenvname bioconductor-dar

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 quay.io/biocontainers/bioconductor-dar:<tag>

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

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Link to this page

Render an badge with the following MarkDown:

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