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1ères Rencontres R, Bordeaux : France (2012)

Comparison of network inference packages and methods for multiple network inference

Nathalie Villa-Vialaneix 1, Nicolas A. Edwards 2, Laurence Liaubet 2, Nathalie Viguerie 3, Magali Sancristobal 2

(02/07/2012)

Integrative and systems biology is a very promising tool for deciphering the biological and genetic mechanisms underlying complex traits. In particular, gene networks are used to model interactions between genes of interest. They can be de ned in various ways, but a standard approach is to infer a co-expression network from genes expression measured by means of sequencing techniques (for example, microarrays). Among methods used to perform the inference, Gaussian graphical models (GGM) are based on the assumption that the gene expressions are distributed as Gaussian variables, and Σ is their covariance matrix. This article is a comparison between different packages and approaches for network inference in the case where several samples are used to infer different network with an underlying common biological functionning.

1 :	Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM)
	Université Paris I - Panthéon-Sorbonne
2 :	Laboratoire de Génétique Cellulaire (LGC)
	Institut national de la recherche agronomique (INRA) : UR0444 – Ecole Nationale Vétérinaire de Toulouse
3 :	Institut des maladies métaboliques et cardiovasculaires (I2MC)
	INSERM : U1048 – Université Paul Sabatier [UPS] - Toulouse III

Domaine

:

Mathématiques/Statistiques Statistiques/Théorie

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Contributeur : Nathalie Villa-Vialaneix <>

Soumis le : Mercredi 4 Juillet 2012, 15:35:02

Dernière modification le : Mercredi 4 Juillet 2012, 16:52:11