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biocLite("qpgraph")
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qpgraph
Reverse engineering of molecular regulatory networks with qp-graphs
Bioconductor version: 2.14
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>
Citation (from within R,
enter citation("qpgraph")):
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")
| BasicUsersGuide.pdf | ||
| R Script | Reverse-engineer transcriptional regulatory networks using qpgraph | |
| R Script | Simulating molecular regulatory networks using qpgraph | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneExpression, GeneRegulation, GraphAndNetwork, Microarray, NetworkInference, Pathways, Software, Transcription |
| Version | 1.20.2 |
| 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(>= 1.41.2), 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_1.20.2.tar.gz |
| Windows Binary | qpgraph_1.20.2.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | qpgraph_1.20.2.tgz |
| Mac OS X 10.9 (Mavericks) | qpgraph_1.20.2.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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