recipe r-scdc

SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the batch-effect confounding.

Homepage:

https://github.com/omnideconv/SCDC

License:

MIT / MIT

Recipe:

/r-scdc/meta.yaml

Links:

doi: 10.1093/bib/bbz166

package r-scdc

versions:

0-8,  0-7,  0-6,  0-5,  0-4,  0-3,  0-2,  0-1,  0-0

depends bioconductor-biobase:

>=2.50.0

depends libgcc-ng:

>=12

depends libstdcxx-ng:

>=12

depends r-base:

>=4.0,<4.1

depends r-cowplot:

>=1.1.1

depends r-ggplot2:

>=3.3.5

depends r-l1pack:

>=0.38.196

depends r-nnls:

>=1.4

depends r-pheatmap:

>=1.0.12

depends r-rcpp:

>=1.0.7

depends r-reshape:

>=0.8.8

depends xbioc:

>=0.1.19

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 r-scdc

and update with::

   mamba update r-scdc

To create a new environment, run:

mamba create --name myenvname r-scdc

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/r-scdc:<tag>

(see `r-scdc/tags`_ for valid values for ``<tag>``)

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