Comparative Analysis of Spatial Patterns of Gene Expression in Drosophila melanogaster Imaginal Discs

  • Cyrus L. Harmon
  • Parvez Ahammad
  • Ann Hammonds
  • Richard Weiszmann
  • Susan E. Celniker
  • S. Shankar Sastry
  • Gerald M. Rubin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4453)

Abstract

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

Primary keyphrases: Genomic imaging, Gene expression analysis,Clustering.

Secondary keyphrases: Microarray data analysis, Imaginal discs.

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References

  1. 1.
    Adams, M., et al.: Science 287, 2185 (2000)Google Scholar
  2. 2.
    Misra, S., et al.: Genome Biol 3, 1 (2002)Google Scholar
  3. 3.
    Arbeitman, M.N., et al.: Science 297, 2270 (2002)Google Scholar
  4. 4.
    Tomancak, P., et al.: Genome Biology 3, 1 (2002)Google Scholar
  5. 5.
    Lipshutz, R., Fodor, S., Gingeras, T., Lockhart, D.: Nat. Genet. 21, 20 (1999)Google Scholar
  6. 6.
    Eisen, M.B., Brown, P.O.: Methods Enzymol. 303, 179 (1999)Google Scholar
  7. 7.
    Kamberova, G., Shah, S.: DNA Array Image Analysis – Nuts and Bolts. DNA Press LLC (2002)Google Scholar
  8. 8.
    Jain, A.N., et al.: Genome Res. 12, 325 (2002)Google Scholar
  9. 9.
    White, K.P., Rifkin, S.A., Hurban, P., Hogness, D.S.: Science 286, 2179 (1999)Google Scholar
  10. 10.
    Klebes, A., Biehs, B., Cifuentes, F., Kornberg, T.: Genome Biol. 3, RESEARCH0038 (2002)Google Scholar
  11. 11.
    Butler, M.J., et al.: Development 130, 659 (2003)Google Scholar
  12. 12.
    Eugene, O., Yund, A., Fristrom, J.W.: Tissue Culture Association Manual 5, 1055 (1979)Google Scholar
  13. 13.
    Miller, E., Matsakis, N., Viola, P.: IEEE Conference on Computer Vision and Pattern Recognition, pp. 464–471 (2000)Google Scholar
  14. 14.
    Miller, E.: Learning from One Example in Machine Vision by Sharing Probability Densities, Ph.D. thesis, Massachusetts Institute of Technology (2002)Google Scholar
  15. 15.
    Ahammad, P., Harmon, C.L., Hammonds, A., Sastry, S.S., Rubin, G.M.: CVPR (2) IEEE Computer Society, Washington, DC, USA, pp. 755–760 (2005)Google Scholar
  16. 16.
    Barrow, H.G., Tenenbaum, J.M., Boles, R.C., Wolf, H.C.: IJCAI, pp. 659–663 (1977)Google Scholar
  17. 17.
    Borgefors, G.: IEEE Transactions on Pattern Analysis and Machine Intelligience 10, 849, Published (1988)Google Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  19. 19.
    Soille, P.: Morphological Image Analysis. Springer, Heidelberg (1999)MATHGoogle Scholar
  20. 20.
    Lewis, J.: Fast normalized cross-correlation (1995)Google Scholar
  21. 21.
    O’Neill, J.W., Bier, E.: Biotechniques 17, 870, 874 (1994)Google Scholar
  22. 22.
    Hauptmann, G.: Methods 23, 359 (2001)Google Scholar
  23. 23.
    Kosman, D., et al.: Science 305, 846 (2004)Google Scholar
  24. 24.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley and Sons, Chichester (2001)MATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Cyrus L. Harmon
    • 1
  • Parvez Ahammad
    • 2
  • Ann Hammonds
    • 1
  • Richard Weiszmann
    • 3
  • Susan E. Celniker
    • 3
  • S. Shankar Sastry
    • 2
  • Gerald M. Rubin
    • 1
  1. 1.Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720 
  2. 2.Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720 
  3. 3.Department of Genome Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 

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