[BioC] New Bioconductor package: qpgraph

[Robert Castelo]

Dear list,

a new package called 'qpgraph' has recently become part of the current
development version of Bioconductor (version 2.4).

'qpraph' aids in reverse engineering molecular regulatory networks from
microarray data using a formalism called qp-graphs.

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.

You can find an example on how to use it to build a network model of a
transcriptional regulatory network in the accompanying vignette
(qpTxRegNet.pdf). The description of the approach and further examples
can be found in the main text and web supplementary material of the
recently published article:

R. Castelo and A. Roverato. Reverse engineering molecular regulatory
networks from microarray data with qp-graphs, Journal of Computational
Biology, 16(2):213-227, 2009.
[preprint: http://functionalgenomics.upf.edu/CasRovJCB09.pdf]
[supplement: http://functionalgenomics.upf.edu/supplements/qpgraph]

We will try to address any question you may have about the package and
its methodology and look forward to your comments and suggestions.


Best wishes,

Robert Castelo.