qpgraph

Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: 2.5

q-order partial correlation graphs, or qp-graphs for short, are undirected Gaussian graphical Markov models that represent 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

To install this package, start R and enter:

source("//www.anjoumacpherson.com/biocLite.R") biocLite("qpgraph")

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

citation("qpgraph")

Documentation

PDF		qpPCCdistbyTF.pdf
PDF		qpPreRecComparison.pdf
PDF		qpPreRecComparisonFullRecall.pdf
PDF		qpTRnet50pctpre.pdf
PDF	R Script	Reverse-engineer transcriptional regulatory networks using qpgraph
PDF		Reference Manual

Details

biocViews	Microarray,GeneExpression,Transcription,Pathways,Bioinformatics,GraphsAndNetworks
Depends	methods,Biobase(>= 2.5.5),AnnotationDbi
Imports	methods,Biobase(>= 2.5.5),AnnotationDbi
Suggests	mvtnorm,graph,Rgraphviz,annotate,genefilter,Category(>= 2.9.7),org.EcK12.eg.db(>= 2.2.6),GOstats
System Requirements
License	GPL (>= 2)
URL	http://functionalgenomics.upf.edu/qpgraph
Depends On Me
Imports Me
Suggests Me
Version	1.2.0
Since	Bioconductor 2.4 (R-2.9)

Package Downloads

Package Source	qpgraph_1.2.0.tar.gz
Windows Binary	qpgraph_1.2.0.zip(32- & 64-bit)
MacOS 10.5 (Leopard) binary	qpgraph_1.2.0.tgz
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