recipe bioconductor-mnem

Mixture Nested Effects Models

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-mnem/meta.yaml

Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, … and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

package bioconductor-mnem

versions:

1.18.0-0,  1.16.0-0,  1.14.0-1,  1.14.0-0,  1.10.0-2,  1.10.0-1,  1.10.0-0,  1.8.0-0,  1.6.5-0, 

1.18.0-0,  1.16.0-0,  1.14.0-1,  1.14.0-0,  1.10.0-2,  1.10.0-1,  1.10.0-0,  1.8.0-0,  1.6.5-0,  1.6.1-0,  1.4.0-0

depends bioconductor-graph:

>=1.80.0,<1.81.0

depends bioconductor-graph:

>=1.80.0,<1.81.0a0

depends bioconductor-linnorm:

>=2.26.0,<2.27.0

depends bioconductor-linnorm:

>=2.26.0,<2.27.0a0

depends bioconductor-rgraphviz:

>=2.46.0,<2.47.0

depends bioconductor-rgraphviz:

>=2.46.0,<2.47.0a0

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-cluster:

depends r-data.table:

depends r-e1071:

depends r-flexclust:

depends r-ggplot2:

depends r-lattice:

depends r-matrixstats:

depends r-naturalsort:

depends r-rcpp:

depends r-rcppeigen:

depends r-snowfall:

depends r-tsne:

depends r-wesanderson:

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-mnem

and update with::

   mamba update bioconductor-mnem

To create a new environment, run:

mamba create --name myenvname bioconductor-mnem

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-mnem:<tag>

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

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