Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: 2.9

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   Reference Manual
Text   NEWS


biocViews Microarray, GeneExpression, Transcription, Pathways, Bioinformatics, GraphsAndNetworks
Depends methods
Imports methods, annotate, Matrix, graph, Biobase, AnnotationDbi
Suggests Matrix, mvtnorm, graph, genefilter, Category, org.EcK12.eg.db, GOstats
System Requirements
License GPL (>= 2)
URL http://functionalgenomics.upf.edu/qpgraph
Depends On Me
Imports Me
Suggests Me GSVA
Version 1.10.1
Since Bioconductor 2.4 (R-2.9)

Package Downloads

Package Source qpgraph_1.10.1.tar.gz
Windows Binary qpgraph_1.10.1.zip (32- & 64-bit)
MacOS 10.5 (Leopard) binary qpgraph_1.10.1.tgz
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Fred Hutchinson Cancer Research Center