ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of high-throughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the time-dependent propagation of active and passive states depending on a network structure. Optimal network structures given the experimental data are reconstructed. Two network inference algorithms can be used: inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA, a Genetic Algorithm network optimisation. Inclusion of prior biological knowledge can be done using different network prior models.

Package details

AuthorChristian Bender
MaintainerChristian Bender <[email protected]>
LicenseGPL (>=2)
Package repositoryView on R-Forge
Installation Install the latest version of this package by entering the following in R:
install.packages("ddepn", repos="http://R-Forge.R-project.org")

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ddepn documentation built on May 2, 2019, 4:42 p.m.