No articles match
| Nested Effects Models-based perturbation inference: | Inference of unobserved perturbations from gene expression profiles.4 years ago
Introduction | Installation and loading | Small example | Data simulation | Perturbation inference | Prior matrix | Final perturbation matrix | Session information | References:
| Epistatic Nested Effects Models: | Inferring mixed epistasis from indirect measurements of knock-out screens6 years ago
Introduction | Loading epiNEM | Quick start | Simulations | Yeast knock-out screens | Identification of significant modulators for pairs of double knock-outs | Validation of identified interactions | String-db interactions | Graph based GO similarity scores | Enrichment analysis | Enrichment of identified sets of modulators for each double knock-out | Enrichment of effect reporters for each identified modulator | Creation of data objects | Session information | References
| Mixture Nested Effects Models: | Simultaneous inference of causal networks and corresponding subpopulations from single cell perturbation data6 years ago
Introduction | Installation and loading | Small example | Data simulation | Network inference w.r.t. complexity | Cell responsibilities | Discrete data | Multiple perturbations | Application to pooled CRISPR screens | Cell cycle regulators (Perturb-seq) | Session information | Discrete data model | Expectation of the complete data log likelihood for large data sets | References:
| Boolean Nested Effects Models: | Inferring the logical signalling of pathways from indirect measurements and biological perturbations.6 years ago
Introduction | Installing and loading B-NEM | Toy example for a DAG | Stimulated and inhibited S-genes can overlap | Pre-attach E-genes | Visualizing network residuals | B-Cell receptor signalling