Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: Development (3.0)

q-order partial correlation graphs, or qp-graphs for short, are undirected Gaussian graphical Markov models built from q-order partial correlations. They are useful for learning undirected graphical Gaussian Markov models from data sets where the number of random variables p exceeds the available sample size n as, for instance, in the case of microarray data where they can be employed to reverse engineer a molecular regulatory network.

Author: R. Castelo and A. Roverato

Maintainer: Robert Castelo <robert.castelo at upf.edu>

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PDF BasicUsersGuide.pdf
PDF R Script Reverse-engineer transcriptional regulatory networks using qpgraph
PDF R Script Simulating molecular regulatory networks using qpgraph
PDF   Reference Manual
Text   NEWS


biocViews GeneExpression, GeneRegulation, GraphAndNetwork, Microarray, NetworkInference, Pathways, Software, Transcription
Version 1.21.0
In Bioconductor since BioC 2.4 (R-2.9)
License GPL (>= 2)
Depends R (>= 3.0.0)
Imports methods, parallel, Matrix (>= 1.0), annotate, graph(>= 1.41.2), Biobase, GGBase, AnnotationDbi, mvtnorm, qtl, Rgraphviz
Suggests BiocStyle, genefilter, org.EcK12.eg.db
System Requirements
URL http://functionalgenomics.upf.edu/qpgraph
Depends On Me
Imports Me clipper
Suggests Me

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Package Source qpgraph_1.21.0.tar.gz
Windows Binary qpgraph_1.21.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) qpgraph_1.21.0.tgz
Mac OS X 10.9 (Mavericks)
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