recipe bioconductor-mnem

Mixture Nested Effects Models

Homepage

https://bioconductor.org/packages/3.11/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

1.4.0-0

Depends

• bioconductor-graph >=1.66.0,<1.67.0

• bioconductor-linnorm >=2.12.0,<2.13.0

• bioconductor-rgraphviz >=2.32.0,<2.33.0

• libblas >=3.8.0,<4.0a0

• libgcc-ng >=7.3.0

• liblapack >=3.8.0,<3.9.0a0

• libstdcxx-ng >=7.3.0

• r-base >=4.0,<4.1.0a0

• r-cluster

• r-data.table

• r-e1071

• r-flexclust

• r-lattice

• r-matrixstats

• r-naturalsort

• r-rcpp

• r-rcppeigen

• r-snowfall

• r-tsne

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-mnem

and update with:

conda update bioconductor-mnem

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-mnem:<tag>

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

Link to this page

Render an badge with the following MarkDown:

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