Package Recipe 'bioconductor-mnem'

recipe bioconductor-mnem

Mixture Nested Effects Models

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/mnem.html
License:: GPL-3
Recipe:: /bioconductor-mnem/meta.yaml
Links:: biotools: mnem

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:

1.26.0-0, 1.22.0-0, 1.18.0-1, 1.18.0-0, 1.16.0-0, 1.14.0-1, 1.14.0-0, 1.10.0-2, 1.10.0-1,

1.26.0-0, 1.22.0-0, 1.18.0-1, 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:

on bioconductor-graph >=1.88.0,<1.89.0
on bioconductor-graph >=1.88.1,<1.89.0a0
on bioconductor-linnorm >=2.34.0,<2.35.0
on bioconductor-linnorm >=2.34.0,<2.35.0a0
on bioconductor-rgraphviz >=2.54.0,<2.55.0
on bioconductor-rgraphviz >=2.54.0,<2.55.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-cluster
on r-data.table
on r-e1071
on r-flexclust
on r-ggplot2
on r-lattice
on r-matrixstats
on r-naturalsort
on r-rcpp
on r-rcppeigen
on r-snowfall
on r-tsne
on r-wesanderson

Additional platforms:

linux-aarch64

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-mnem

to add into an existing workspace instead, run:

pixi add bioconductor-mnem

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-mnem

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-mnem

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-mnem/README.html)