SIMoNe: Statistical Inference for MOdular NEtworks.
CONCLUSIONS
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.


BACKGROUND
Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/
Chiquet
Julien
J
UMR CNRS 8071 Statistique et Génome, 523, place des Terrasses, F-91000 Evry, France. julien.chiquet@genopole.cnrs.fr
Smith
Alexander
A
Grasseau
Gilles
G
Matias
Catherine
C
Ambroise
Christophe
C
D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D012984:Software