qpgraph

Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: 2.10

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>

To install this package, start R and enter:

    source("http://bioconductor.org/biocLite.R")
    biocLite("qpgraph")

To cite this package in a publication, start R and enter:

    citation("qpgraph")

Documentation

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

Details

biocViews Bioinformatics, GeneExpression, GraphsAndNetworks, Microarray, Pathways, Software, Transcription
Depends R (>= 2.10), methods
Imports methods, annotate, Matrix, graph, Biobase, GGBase, 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 1.12.2
Since Bioconductor 2.4 (R-2.9)

Package Downloads

Package Source qpgraph_1.12.2.tar.gz
Windows Binary qpgraph_1.12.2.zip (32- & 64-bit)
MacOS 10.5 (Leopard) binary qpgraph_1.12.2.tgz
Package Downloads Report Download Stats

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Fred Hutchinson Cancer Research Center