recipe bioconductor-mnem

Mixture Nested Effects Models






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

(downloads) docker_bioconductor-mnem



Required By:


With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-mnem

and update with:

conda update bioconductor-mnem

or use the docker container:

docker pull<tag>

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

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