Clustering gene expression data using graph separators

Bangaly Kaba, Nicolas Pinet, Gaëlle Lelandais, Alain Sigayret, Anne Berry

Research output: Contribution to journalJournal Articlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)433-452
Number of pages20
JournalIn Silico Biology
Issue number4-5
Publication statusPublished - 2007


  • Clustering method
  • Expression profile
  • Graph decomposition
  • Microarray
  • Threshold family of graphs


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