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Co-Clustering of Biological Networks and Gene Expression Data




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.

Author(s): Hanisch, D. and Zien, A. and Zimmer, R. and Lengauer, T.
Journal: Bioinformatics
Number (issue): Suppl 1
Pages: 145S-154S
Year: 2002
Month: July
Day: 0
Series: 18

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Institution: Fraunhofer Institute SCAI
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Co-Clustering of Biological Networks and Gene Expression Data},
  author = {Hanisch, D. and Zien, A. and Zimmer, R. and Lengauer, T.},
  journal = {Bioinformatics},
  number = {Suppl 1},
  pages = {145S-154S},
  series = {18},
  organization = {Max-Planck-Gesellschaft},
  institution = {Fraunhofer Institute SCAI},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2002},
  month_numeric = {7}