Abstract
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.
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References
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)
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)
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)
Brower, D.L.: Engrailed gene expression in Drosophila imaginal discs. EMBO J. 5, 2649–2656 (1986)
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)
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 16, 1370–1386 (2004)
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)
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)
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)
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, https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-12-research0088
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)
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)
Zou, H., Hastie, T., Tibshirani, R., Johnstone, I., Lu, A.: Sparse principal component analysis. J. Comput. Graph. Stat. 15, 1–29 (2006)
Hyvarinen, A.: Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)
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)
Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19, 2756–2779 (2007)
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)
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)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression, vol. 159. Kluwer Academic Press, Dordrecht (1992). 760
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)
Achanta, R., Shaji, A.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)
Boykov, Y., Veksler, O.: Fast approximate energy minimization via graph cuts. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)
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)
Acknowledgement
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.
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Borovec, J., Kybic, J. (2017). Binary Pattern Dictionary Learning for Gene Expression Representation in Drosophila Imaginal Discs. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_40
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DOI: https://doi.org/10.1007/978-3-319-54427-4_40
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