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

qpgraph

Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: 2.13

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>

Installation

To install this package, start R and enter:

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

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("qpgraph")

 

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

Details

biocViews	GeneExpression, GeneRegulation, GraphsAndNetworks, Microarray, NetworkInference, Pathways, Software, Transcription
Version	1.18.9
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(>= 1.40.1), 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

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source	qpgraph_1.18.9.tar.gz
Windows Binary	qpgraph_1.18.9.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard)	qpgraph_1.18.9.tgz
Browse/checkout source	(username/password: readonly)
Package Downloads Report	Download Stats