A new gene co-expression network analysis based on Core Structure Detection (CSD)

Anne-Claire Brunet (1) , Jean-Marc Azaïs (1) , Jean-Michel Loubes (1) , J Amar (2) , R Burcelin (2)

1 IMT - Institut de Mathématiques de Toulouse UMR5219
2 I2MC - Institut des Maladies Métaboliques et Cardiovasculaires

We propose a novel method to cluster gene networks. Based on a dissimilarity built using correlation structures, we consider networks that connect all the genes based on the strength of their dissimilarity. The large number of genes require the use of the threshold to find sparse structures in the graph. in this work, using the notion of graph coreness, we identify clusters of genes which are central in the network. Then we estimate a network that has these genes as main hubs. We use this new representation to identify biologically meaningful clusters, and to highlight the importance of the nodes that compose the core structures based on biological interpretations.

Mots clés: gene network; clustering; coreness

Domaines: Statistiques [math.ST]; Bio-informatique [q-bio.QM]

Anne-Claire Brunet, Jean-Marc Azaïs, Jean-Michel Loubes, J Amar, R Burcelin. A new gene co-expression network analysis based on Core Structure Detection (CSD). 2016. ⟨hal-01341529⟩