Affiliations: LIMOS, UMR CNRS 6158, Ensemble des Cézeaux, 63173
Aubière cedex, France. E-mail: {kaba,pinet,sigayret,berry}@isima.fr | Equipe de Bioinformatique Génomique et
Moléculaire, INSERM U726, Université Paris 7, case 7113, 2 Place
Jussieu, 75251 Paris cedex 05, France. E-mail: [email protected]
Abstract: Recent work has used graphs to modelize expression data from
microarray experiments, in view of partitioning the genes into clusters. In
this paper, we introduce the use of a decomposition by clique separators. Our
aim is to improve the classical clustering methods in two ways: first we want
to allow an overlap between clusters, as this seems biologically sound, and
second we want to be guided by the structure of the graph to define the number
of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae).
Our results are good, as the expression profiles of
the clusters we find are very coherent. Moreover, we are able to organize into
another graph the clusters we find, and order them in a fashion which turns out
to respect the chronological order defined by the the sporulation process.
Keywords: Clustering method, microarray, graph decomposition, threshold family of graphs, expression profile
