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bioc
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.
Maintained by Martin Pirkl. Last updated 7 days ago.
pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
4 stars 6.81 score 15 scripts 4 dependentsbioc
epiNEM: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.
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetwork
1 stars 5.83 score 1 scripts 3 dependentsbioc
bnem:Training of logical models from indirect measurements of perturbation experiments
bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetworkgeneexpressiongeneregulationpreprocessing
2 stars 4.60 score 5 scriptsbioc
nempi: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.
Maintained by Martin Pirkl. Last updated 5 months ago.
softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
2 stars 4.60 score 2 scriptsbioc
dce:Pathway Enrichment Based on Differential Causal Effects
Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
Maintained by Kim Philipp Jablonski. Last updated 4 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 scripts