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Bioinformatics Advance Access originally published online on December 10, 2008
Bioinformatics 2009 25(3):417-418; doi:10.1093/bioinformatics/btn637
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SIMoNe: Statistical Inference for MOdular NEtworks

Julien Chiquet *, Alexander Smith , Gilles Grasseau , Catherine Matias and Christophe Ambroise

UMR CNRS 8071 Statistique et Génome, 523, place des Terrasses, F-91000 Évry, France

*To whom correspondence should be addressed.


  Abstract

Summary: The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and possibly high-dimensional setting. Its originality lies in the fact that it searches for a latent modular structure to drive the inference procedure through adaptive penalization of the concentration matrix.

Availability: Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/

Contact: julien.chiquet{at}genopole.cnrs.fr

Associate Editor: John Quackenbush

Received on October 23, 2008; revised on December 4, 2008; accepted on December 6, 2008

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