Skip to main content

Spectral Clustering Gene Ontology Terms to Group Genes by Function

  • Conference paper
Book cover Algorithms in Bioinformatics (WABI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3692))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adryan, B., Schuh, R.: Gene Ontology-based clustering of gene expression data. Bioinformatics 20(16), 2851–2852 (2004)

    Article  Google Scholar 

  2. Beißbarth, T., Speed, T.: GOstat: find statistically overexpressed Gene Ontologies within groups of genes. Bioinformatics 20(9), 1464–1465 (2004)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  5. Davies, J.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)

    Article  Google Scholar 

  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. Gat-Viks, I., Sharan, R., Shamir, R.: Scoring clustering solutions by their biological relevance. Bioinformatics 19(18), 2381–2389 (2003)

    Article  Google Scholar 

  8. Gene Lynx (2004), http://www.genelynx.org

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  18. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  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. 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. The Gene Ontology Consortium. The gene ontology (GO) database and informatics resource. Nucleic Acids Research 32, D258–D261 (2004)

    Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Speer, N., Spieth, C., Zell, A. (2005). Spectral Clustering Gene Ontology Terms to Group Genes by Function. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_1

Download citation

  • DOI: https://doi.org/10.1007/11557067_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29008-7

  • Online ISBN: 978-3-540-31812-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics