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.
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pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
6.03 score 4 stars 3 dependents 520 downloadsepiNEM - epiNEM
epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens.
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pathwayssystemsbiologynetworkinferencenetwork
5.48 score 1 stars 2 dependents 1 scripts 546 downloadsclustNet - Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
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bayesian-networkbayesian-networksclusteringdaggenomicsmixture-modelnetwork-clustering
5.23 score 7 stars 48 scripts 315 downloadsnempi - Inferring unobserved perturbations from gene expression data
Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
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softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
4.60 score 2 stars 2 scripts 406 downloads