recipe bioconductor-autonomics

Unified statistal Modeling of Omics Data

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-autonomics/meta.yaml

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.

package bioconductor-autonomics

versions:

1.10.2-0,  1.8.0-0,  1.6.0-0,  1.2.0-0,  1.0.0-0

depends bioconductor-biocfilecache:

>=2.10.0,<2.11.0

depends bioconductor-biocgenerics:

>=0.48.0,<0.49.0

depends bioconductor-edger:

>=4.0.0,<4.1.0

depends bioconductor-limma:

>=3.58.0,<3.59.0

depends bioconductor-multiassayexperiment:

>=1.28.0,<1.29.0

depends bioconductor-pcamethods:

>=1.94.0,<1.95.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-abind:

depends r-assertive.base:

depends r-assertive.files:

depends r-assertive.numbers:

depends r-assertive.sets:

depends r-base:

>=4.3,<4.4.0a0

depends r-bit64:

depends r-colorspace:

depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-magrittr:

depends r-matrixstats:

depends r-r.utils:

depends r-rappdirs:

depends r-readxl:

depends r-rlang:

depends r-scales:

depends r-stringi:

depends r-tidyr:

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-autonomics

and update with::

   mamba update bioconductor-autonomics

To create a new environment, run:

mamba create --name myenvname bioconductor-autonomics

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-autonomics:<tag>

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

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