Bioinformatics Vol. 18 no. 90001 2002
Pages S145-S154
© 2002 Oxford University Press
Co-clustering of biological networks and gene expression data
1 Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft,
Schloss Birlinghoven, Sankt Augustin, 53754, Germany
2 Institut für Informatik, Ludwig-Maximilians-Universität
München, Theresienstraße 39, München, 80333, Germany
3 Max-Planck-Institut für Informatik,
Stuhlsatzenhausweg 85, Saarbrücken, 66123, Germany
Received on January 24, 2002
; revised on March 28, 2002
; accepted on March 28, 2002
Motivation: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.
Results: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.
Contact: Daniel.Hanisch{at}scai.fhg.de
Keywords: gene expression; biological networks; metabolic networks; co-clustering; clustering.
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