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.
Last updated 5 months ago
pathwayssystemsbiologynetworkinferencenetwork
5.83 score 1 stars 3 dependents 1 scripts 372 downloadsmnem - 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.
Last updated 4 months ago
pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
5.64 score 4 stars 4 dependents 15 scripts 300 downloadsclustNet - Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Last updated 1 years ago
bayesian-networkbayesian-networksclusteringdaggenomicsmixture-modelnetwork-clustering
5.16 score 7 stars 41 scripts 230 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.
Last updated 5 months ago
softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
4.60 score 2 stars 2 scripts 210 downloads
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.
Last updated 3 months ago
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
4.59 score 13 stars 4 scripts 198 downloads