:orphan: .. only available via index, not via toctree .. title:: Package Recipe 'bioconductor-dar' .. highlight: bash bioconductor-dar ================ .. conda:recipe:: bioconductor-dar :replaces_section_title: :noindex: 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. .. conda:package:: bioconductor-dar |downloads_bioconductor-dar| |docker_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: .. rubric:: Installation You need a conda-compatible package manager (currently either `micromamba `_, `mamba `_, or `conda `_) and the Bioconda channel already activated (see :ref:`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: (see `bioconductor-dar/tags`_ for valid values for ````) .. |downloads_bioconductor-dar| image:: https://img.shields.io/conda/dn/bioconda/bioconductor-dar.svg?style=flat :target: https://anaconda.org/bioconda/bioconductor-dar :alt: (downloads) .. |docker_bioconductor-dar| image:: https://quay.io/repository/biocontainers/bioconductor-dar/status :target: https://quay.io/repository/biocontainers/bioconductor-dar .. _`bioconductor-dar/tags`: https://quay.io/repository/biocontainers/bioconductor-dar?tab=tags .. raw:: html Download stats ----------------- .. raw:: html :file: ../../templates/package_dashboard.html Link to this page ----------------- Render an |install-with-bioconda| 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-dar/README.html) .. |install-with-bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat :target: http://bioconda.github.io/recipes/bioconductor-dar/README.html