Binary Pattern Dictionary Learning for Gene Expression Representation in Drosophila Imaginal Discs

  • Jiří BorovecEmail author
  • Jan Kybic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10117)


We present an image processing pipeline which accepts a large number of images, containing spatial expression information for thousands of genes in Drosophila imaginal discs. We assume that the gene activations are binary and can be expressed as a union of a small set of non-overlapping spatial patterns, yielding a compact representation of the spatial activation of each gene. This lends itself well to further automatic analysis, with the hope of discovering new biological relationships. Traditionally, the images were labeled manually, which was very time consuming. The key part of our work is a binary pattern dictionary learning algorithm, that takes a set of binary images and determines a set of patterns, which can be used to represent the input images with a small error. We also describe the preprocessing phase, where input images are segmented to recover the activation images and spatially aligned to a common reference. We compare binary pattern dictionary learning to existing alternative methods on synthetic data and also show results of the algorithm on real microscopy images of the Drosophila imaginal discs.



This work was supported by the Czech Science Foundation project 14-21421S and by the Grant Agency of the Czech Technical University in Prague under the grant SGS15/154/OHK3/2T/13.


  1. 1.
    Medzhitov, R., Preston-Hurlburt, P., Janeway Jr., C.A.: A human homologue ofthe Drosophila Toll protein signals activation of adaptive immunity. Nature 388, 394–397 (1997)CrossRefGoogle Scholar
  2. 2.
    Tomancak, P., Berman, B.P., Beaton, A., Weiszmann, R., Kwan, E., Hartenstein, V., Celniker, S.E., Rubin, G.M.: Global analysis of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 8, R145 (2007)CrossRefGoogle Scholar
  3. 3.
    Hammonds, A.A.S., Bristow, C.A., Fisher, W.W., Weiszmann, R., Wu, S., Hartenstein, V., Kellis, M., Yu, B., Frise, E., Celniker, S.E.: Spatial expression of transcription factors in Drosophila embryonic organ development. Genome Biol. 14, R140 (2013)CrossRefGoogle Scholar
  4. 4.
    Brower, D.L.: Engrailed gene expression in Drosophila imaginal discs. EMBO J. 5, 2649–2656 (1986)Google Scholar
  5. 5.
    Ahammad, P., Harmon, C.L., Hammonds, A., Sastry, S.S., Rubin, G.M.: Joint nonparametric alignment for analizing spatial gene expression patterns in Drosophila imaginal discs. In: Proceedings of CVPR (2005)Google Scholar
  6. 6.
    Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 16, 1370–1386 (2004)CrossRefGoogle Scholar
  7. 7.
    Kim, J., Kim, K., Kim, J.H.: Semantic signature: comparative interpretation of gene expression on a semantic space. Comput. Math. Methods Med. 2016, 1–10 (2016)Google Scholar
  8. 8.
    Klema, J., Malinka, F., Zelezny, F.: Semantic biclustering: a new way to analyze and interpret gene expression data. In: Bourgeois, A., Skums, P., Wan, X., Zelikovsky, A. (eds.) Bioinformatics Research and Applications, pp. 332–333. Springer, Heidelberg (2016)Google Scholar
  9. 9.
    Tweedie, S., Ashburner, M., Falls, K., Leyland, P., McQuilton, P., et al.: FlyBase: enhancing Drosophila gene ontology annotations. Nucleic Acids Res. 37, 555–559 (2009)CrossRefGoogle Scholar
  10. 10.
    Tomancak, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis, S.E., Richards, S., Ashburner, M., Hartenstein, V., Celniker, S.E., Rubin, G.M.: Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 3 (2002). RESEARCH0088,
  11. 11.
    Pruteanu-Malinici, I., Mace, D.L., Ohler, U.: Automatic annotation of spatial expression patterns via sparse Bayesian factor models. PLOS Comput. Biol. 7, e1002098 (2011)CrossRefGoogle Scholar
  12. 12.
    Wu, S., Joseph, A., Hammonds, A.S., Celniker, S.E., Yu, B., Frise, E.: Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks. Proc. Natl. Acad. Sci. 113, 201521171 (2016)Google Scholar
  13. 13.
    Zou, H., Hastie, T., Tibshirani, R., Johnstone, I., Lu, A.: Sparse principal component analysis. J. Comput. Graph. Stat. 15, 1–29 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hyvarinen, A.: Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)CrossRefGoogle Scholar
  15. 15.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 1–8 (2009)Google Scholar
  16. 16.
    Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19, 2756–2779 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Belohlavek, R., Vychodil, V.: Discovery of optimal factors in binary data via a novel method of matrix decomposition. J. Comput. Syst. Sci. 76, 3–20 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Zhang, Z.Y., Li, T., Ding, C., Ren, X.W., Zhang, X.S.: Binary matrix factorization for analyzing gene expression data. Data Mining Knowl. Discov. 20, 28–52 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression, vol. 159. Kluwer Academic Press, Dordrecht (1992). 760CrossRefzbMATHGoogle Scholar
  20. 20.
    Borovec, J.: Fully automatic segmentation of stained histological cuts. In: Husník, L. (ed.) 17th International Student Conference on Electrical Engineering, pp. 1–7. CTU in Prague, Prague (2013)Google Scholar
  21. 21.
    Achanta, R., Shaji, A.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)CrossRefGoogle Scholar
  22. 22.
    Boykov, Y., Veksler, O.: Fast approximate energy minimization via graph cuts. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)CrossRefGoogle Scholar
  23. 23.
    Kybic, J., Dolejsi, M., Borovec, J.: Fast registration of segmented images by normal sampling. In: Bio Image Computing (BIC) Workshop at CVPR, pp. 11–19 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Machine Perception, Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

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