Segmentation of cDNA Microarray Images using Parallel Spectral Clustering
Abstract Microarray technology generates large amounts of expression level of genes to be analyzed simultaneously. This analysis implies microarray image segmentation to extract the quantitative information from spots. Spectral clustering is one of the most relevant unsupervised methods able to gather data without a priori information on shapes or locality. We propose and test on microarray images a parallel strategy for the Spectral Clustering method based on domain decomposition with a criterion to determine the number of clusters.
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A. Greenbaum, S. Hammarling, A. McKenney, et al. LAPACK Users’ guide. Society for Industrial Mathematics, 1999.
M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embed-
ding and clustering. Advances in Neural Information Processing Systems, 2002.
W.-Y. Chen, Y. Song, S. Yangqiu, H. Bai, C.-J. Lin, and E. Y. Chang. Parallel Spectral Clustering in Distributed Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
C. Fowlkes, S. Belongie, F. Chung, and J. Malik. Spectral grouping using the Nytrom method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004.
N. Giannakeas and D. Fotiadis. Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images. Handbook of research on advanced techniques in diagnostic images and biomedical application, 2008.
D. Glez-Peña; F. Díaz, J.M. Hernández; J.M. Corchado and F. Fdez-Riverola. geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinformatics, 2009.
R. Lehoucq, D. Sorensen, and C. Yang. ARPACK users’ guide: solution of large-
scale eigenvalue problems with implicitly restarted Arnoldi methods. SIAM, 1998.
A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: analysis and an
algorithm. Proceedings in Advance Neural Information Processing Systems, 2002.
S. Mouysset, J. Noailles, and D. Ruiz. Using a Global Parameter for Gaussian Affinity Matrix in Spectral Clustering, Lecture Notes in Computer Science,
Springer-Verlag, 2008.
S. Mouysset, J. Noailles, and D. Ruiz. On an Interpretation of Spectral Clustering via Heat Equation and Finite Elements Theory. Lecture Notes in Engineering and Computer Science, 2010.
S. Mouysset and R. Guivarch. Sparsification on Parallel Spectral Clustering. Lecture Notes in Computer Science, Springer-Verlag, 2013 (to appear).
L. Rueda and L. Qin. A new method for DNA microarray image segmentation.
Image Analysis and Recognition, 2005.
L. Rueda and J. Rojas. A Pattern Classification Approach to DNA Microarray Image Segmentation. Pattern Recognition in Bioinformatics, pages 319–330, 2009.
Y. Song, W.-Y. Chen, H. Bai, C. Lin, and E. Chang. Parallel spectral clustering. Proceedings of European Conference on Machine Learning and Pattern Knowledge Discovery. Springer, 2008.
V. Uslan, O. Bucak, and B. Cekmece. Microarray image segmentation using clustering methods. Mathematical and Computational Applications, 2010.
Y. Yang,M. Buckley, and T. Speed. Analysis of cDNA microarray images. Briefings in bioinformatics, 2001.
Mouysset, S., Guivarch, R., Noailles, J., & Ruiz, D. (2013). Segmentation of cDNA Microarray Images using Parallel Spectral Clustering. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2(1), 1–8. https://doi.org/10.14201/ADCAIJ20132418
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