mnem

This is the development version of mnem; for the stable release version, see mnem.

Mixture Nested Effects Models

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DOI: 10.18129/B9.bioc.mnem

Bioconductor version: Development (3.23)

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.

Author: Martin Pirkl [aut, cre]

Maintainer: Martin Pirkl <martinpirkl at yahoo.de>

Citation (from within R, enter citation("mnem")):

Installation

To install this package, start R (version "4.6") and enter:


if (!require("BiocManager", quietly = TRUE))
 install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("mnem")

For older versions of R, please refer to the appropriate Bioconductor release.