Reverse engineering of molecular regulatory networks with qp-graphs

Bioconductor version: Release (2.11)

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
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biocViews GeneExpression, GeneRegulation, GraphsAndNetworks, Microarray, NetworkInference, Pathways, Software, Transcription
Version 1.14.4
In Bioconductor since BioC 2.4 (R-2.9)
License GPL (>= 2)
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
URL http://functionalgenomics.upf.edu/qpgraph
Depends On Me
Imports Me
Suggests Me

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Package Source qpgraph_1.14.4.tar.gz
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