Package Recipe 'r-clustermole'

recipe r-clustermole

Unbiased Single-Cell Transcriptomic Data Cell Type Identification.

Homepage:: https://igordot.github.io/clustermole/
License:: MIT / MIT
Recipe:: /r-clustermole/meta.yaml

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

package r-clustermole¶

Versions:

1.1.1-0

Depends:

on bioconductor-gseabase
on bioconductor-gsva >=1.50.0
on bioconductor-singscore
on r-base >=4.5,<4.6.0a0
on r-dplyr
on r-magrittr
on r-rlang
on r-tibble
on r-tidyr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install r-clustermole

to add into an existing workspace instead, run:

pixi add r-clustermole

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install r-clustermole

Alternatively, to install into a new environment, run:

conda create -n envname r-clustermole

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/r-clustermole:<tag>

(see r-clustermole/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an 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/r-clustermole/README.html)