Estimates gene and eQTL networks from high-throughput expression and genotyping assays. qpgraph is based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data. qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions.

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qpgraph specifications

Software type:
Package
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0, GNU General Public License version 2.0
Version:
2.8.2
Requirements:
R
Source code URL:
https://bioconductor.org/packages/release/bioc/src/contrib/qpgraph_2.8.2.tar.gz

Publications

  • (Tur et al., 2014) Mapping eQTL networks with mixed graphical Markov models. Genetics.
    PMID: 25271303
  • (Castelo and Roverato, 2009) Reverse engineering molecular regulatory networks from microarray data with qp-graphs. Journal of Computational Biology.
    PMID: 19178140

qpgraph support

Documentation

Additional informations

http://functionalgenomics.upf.edu/qpgraph/

Credits

Institution(s)

Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain; Research Program on Biomedical Informatics, Institut Municipal d’Investigació Mèdica, Barcelona, Spain; Department of Statistical Science, Università di Bologna, Bologna, Italy

Funding source(s)

This work was supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) (grant TIN2008-00556/TIN), by the ISCIII COMBIOMED Network (grant RD07/0067/0001), by a research fellowship of the Ramon y Cajal program from the Spanish MICINN (RYC-2006-000932) and by the Ministero dell’Università e della Ricerca (grant PRIN-2007AYHZWC, FISR MITICA).

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