recipe bioconductor-scddboost

A compositional model to assess expression changes from single-cell rna-seq data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/scDDboost.html

License:

GPL (>= 2)

Recipe:

/bioconductor-scddboost/meta.yaml

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

package bioconductor-scddboost

versions:

1.4.0-0,  1.2.0-0,  1.0.0-1,  1.0.0-0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0a0

depends bioconductor-ebseq:

>=2.0.0,<2.1.0

depends bioconductor-ebseq:

>=2.0.0,<2.1.0a0

depends bioconductor-oscope:

>=1.32.0,<1.33.0

depends bioconductor-oscope:

>=1.32.0,<1.33.0a0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0a0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-bh:

depends r-cluster:

depends r-ggplot2:

depends r-mclust:

depends r-rcpp:

>=0.12.11

depends r-rcppeigen:

>=0.3.2.9.0

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-scddboost

and update with::

   mamba update bioconductor-scddboost

To create a new environment, run:

mamba create --name myenvname bioconductor-scddboost

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-scddboost:<tag>

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

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Link to this page

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