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.

Version: 2.2
Depends: R (≥ 2.14), lattice, coda, igraph, graph
Imports: genefilter, gam, gplots
Suggests: parallel, Rgraphviz, BoolNet
Published: 2013-08-13
Author: Christian Bender
Maintainer: Christian Bender <christian.bender at tron-mainz.de>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: ddepn results


Reference manual: ddepn.pdf
Vignettes: Dynamic Deterministic Effects Propagation Networks - exemplary workflow
Package source: ddepn_2.2.tar.gz
OS X binary: ddepn_2.2.tgz
Windows binary: ddepn_2.2.zip
Old sources: ddepn archive