Spectral Clustering Gene Ontology Terms to Group Genes by Function

  • Nora Speer
  • Christian Spieth
  • Andreas Zell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3692)


With the invention of biotechnological high throughput methods like DNA microarrays, biologists are capable of producing huge amounts of data. During the analysis of such data the need for a grouping of the genes according to their biological function arises. In this paper, we propose a method that provides such a grouping. As functional information, we use Gene Ontology terms. Our method clusters all GO terms present in a data set using a Spectral Clustering method. Then, mapping the genes back to their annotation, genes can be associated to one or more clusters of defined biological processes. We show that our Spectral Clustering method is capable of finding clusters with high inner cluster similarity.


Gene Ontology Directed Acyclic Graph Semantic Similarity Spectral Cluster Spectral Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adryan, B., Schuh, R.: Gene Ontology-based clustering of gene expression data. Bioinformatics 20(16), 2851–2852 (2004)CrossRefGoogle Scholar
  2. 2.
    Beißbarth, T., Speed, T.: GOstat: find statistically overexpressed Gene Ontologies within groups of genes. Bioinformatics 20(9), 1464–1465 (2004)CrossRefGoogle Scholar
  3. 3.
    Flmer, A., Joslyn, C.A., Mniszewski, S.M., Heaton, G.: The gene ontology categorizer. Bioinformatics 20(Suppl. 1), i169–i177 (2004)Google Scholar
  4. 4.
    Cho, R.J., Huang, M., Campbell, M.J., Dong, H., Steinmetz, L., Sapinoso, L., Hampton, G., Elledge, S.J., Davis, R.W., Lockhart, D.J.: Transcriptional regulation and function during the human cell cycle. Nature Genetics 27(1), 48–54 (2001)CrossRefGoogle Scholar
  5. 5.
    Davies, J.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)CrossRefGoogle Scholar
  6. 6.
    Doniger, S.W., Salomonis, N., Dahlqusi, K.D., Vranizan, K., Lawlor, S.C., Conklin, B.R.: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biology 4(1), R7 (2003)Google Scholar
  7. 7.
    Gat-Viks, I., Sharan, R., Shamir, R.: Scoring clustering solutions by their biological relevance. Bioinformatics 19(18), 2381–2389 (2003)CrossRefGoogle Scholar
  8. 8.
    Gene Lynx (2004),
  9. 9.
    Hvidsten, T.R., Laegreid, A., Komorowski, J.: Learning rule-based models of biological process from gene expression time profiles using Gene Ontology. Bioinformatics 19(9), 1116–1123 (2003)CrossRefGoogle Scholar
  10. 10.
    Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C.F., Trent, J.M., Staudt, L.M., Hudson Jr., J., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O.: The transcriptional program in response of human fibroblasts to serum. Science 283, 83–87 (1999)CrossRefGoogle Scholar
  11. 11.
    Lee, S.G., Hur, J.U., Kim, Y.S.: A graph-theoretic modeling on go space for biological interpretation on gene clusters. Bioinformatics 20(3), 381–388 (2004)CrossRefGoogle Scholar
  12. 12.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, vol. 1, pp. 296–304. Morgan Kaufmann, San Francisco (1998)Google Scholar
  13. 13.
    Lord, P.W., Stevens, R.D., Brass, A., Goble, C.A.: Semantic similarity measures as tools for exploring the gene ontology. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 601–612 (2003)Google Scholar
  14. 14.
    Meila, M., Shi, J.: Learning segmantation by random walks. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, vol. 13, pp. 873–879 (2001)Google Scholar
  15. 15.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2002)Google Scholar
  16. 16.
    Perona, P., Freeman, W.: A factorization approach to grouping. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 655–670. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  17. 17.
    Robinson, P.N., Wollstein, A., Böhme, U., Beattie, B.: Ontologizing gene-expression microarray data: characterizing clusters with gene ontology. Bioinformatics 20(6), 979–981 (2003)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Speer, N., Fröhlich, H., Spieth, C., Zell, A.: Functional grouping of genes using spectral clustering and gene ontology. In: To appear in Proceedings of the IEEE International Joint Conference on Neural Networks (2005)Google Scholar
  20. 20.
    Speer, N., Spieth, C., Zell, A.: A memetic clustering algorithm for the functional partition of genes based on the Gene Ontology. In: Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 252–259 (2004)Google Scholar
  21. 21.
    The Gene Ontology Consortium. The gene ontology (GO) database and informatics resource. Nucleic Acids Research 32, D258–D261 (2004)Google Scholar
  22. 22.
    Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 975–982 (1999)Google Scholar
  23. 23.
    Zeeberg, B.R., Feng, W., Wang, G., Fojo, A.T., et al.: GOminer: a resource for biological interpretation of genomic and proteomic data. Genome Biology 4(R28) (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nora Speer
    • 1
  • Christian Spieth
    • 1
  • Andreas Zell
    • 1
  1. 1.Centre for Bioinformatics Tübingen (ZBIT)University of TübingenTübingenGermany

Personalised recommendations