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 San Cristobal (2)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 LGC - Laboratoire de Génétique Cellulaire
3 I2MC - Institut des Maladies Métaboliques et Cardiovasculaires

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.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Nathalie Villa-Vialaneix, Nicolas A. Edwards, Laurence Liaubet, Nathalie Viguerie, Magali San Cristobal. Comparison of network inference packages and methods for multiple network inference. 1ères Rencontres R, Jul 2012, Bordeaux, France. pp.93-94. ⟨hal-00714444⟩