qpgraph

Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: Development (2.8)

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

To install this package, start R and enter:

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

Documentation

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

Reference Manual

Details

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
Version	1.7.16

Package Downloads

Package Source	qpgraph_1.7.16.tar.gz
Windows Binary	qpgraph_1.7.15.zip (32- & 64-bit)
MacOS 10.5 (Leopard) binary	qpgraph_1.7.15.tgz
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