Hanisch, Daniel;Zien, Alexander;Zimmer, Ralf - Co-clustering of Biological Networks and Gene Expression Data

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Title: Co-clustering of Biological Networks and Gene Expression Data	P55
Hanisch, Daniel; Zien, Alexander; Zimmer, Ralf Daniel.Hanisch@scai.fhg.de Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, St. Augustin, 53754, Germany

Large scale gene expression data are often analyzed by clustering genes based on gene expression data alone, though a-priori knowledge in the form of biological networks may be available.
We propose to construct a distance function which combines information from expression data and biological networks and subsequently compute a joint clustering of genes and vertices of the network. The resulting clusters contain subsets of genes and vertices which are compact according to both measures, i.e. are related with respect to the conducted expression assays and embedded in the same functional network context. The clusters can be visualized in form of an annotated biological network and, thus, are well suited for analysis by biological experts.
This general approach is elaborated here 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 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.

[1] D. Hanisch, A. Zien, R. Zimmer, T. Lengauer (2002). Co-clustering of biological networks and gene expression data. Bioinformatics, Vol 18 Suppl 1, S145-S154
[2] M.B. Eisen, P.T. Spellman, P.O. Brown, D. Botstein (1998). Cluster analysis and disply of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the USA, 95, 14863-14868. Genetics.
[3] J.L. DeRisi, V.R. Iyer, P.O. Brown (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale, Science, 278, 680-685.