recipe bioconductor-scannotatr.models

Pretrained models for scAnnotatR package

Homepage:

https://bioconductor.org/packages/3.18/data/annotation/html/scAnnotatR.models.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-scannotatr.models/meta.yaml

Pretrained models for scAnnotatR package. These models can be used to automatically classify several (immune) cell types in human scRNA-seq data.

package bioconductor-scannotatr.models

(downloads) docker_bioconductor-scannotatr.models

versions:

0.99.10-4,  0.99.10-3,  0.99.10-2,  0.99.10-1,  0.99.10-0

depends bioconductor-data-packages:

>=20231203

depends curl:

depends r-base:

>=4.3,<4.4.0a0

requirements:

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-scannotatr.models

and update with::

   mamba update bioconductor-scannotatr.models

To create a new environment, run:

mamba create --name myenvname bioconductor-scannotatr.models

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-scannotatr.models:<tag>

(see `bioconductor-scannotatr.models/tags`_ for valid values for ``<tag>``)

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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-scannotatr.models/README.html)