Abstract
In cancer diagnosis and treatment, clustering based on gene expression data has been shown to be a powerful method in cancer class discovery. In this paper, we discuss the use of nonnegative matrix factorization with sparseness constraints (NMFSC), a method which can be used to learn a parts representation of the data, to analysis gene expression data. We illustrate how to choose appropriate sparseness factors in the algorithm and demonstrate the improvement of NMFSC by direct comparison with the nonnegative matrix factorization (NMF). In addition, when using it on the two well-studied datasets, we obtain pretty much the same results with the sparse non-negative matrix factorization (SNMF).
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References
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Boston (1990)
Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 401, 788–791 (1999)
Bicciato, S., Luchini, A., Di, B.C.: PCA Disjoint Models for Multiclass Cancer Analysis Using Gene Expression Data. Bioinformatics 19, 571–578 (2003)
Comon, P.: Independent Component Analysis -a New Concept? Signal Processing 36, 287–314 (1994)
Hyvä rinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Interscience, Chichester (2001)
Bell, A.J., Sejnowski, T.J.: The ’Independent Components’ of Natural Scenes are Edge Filters. Vision Research 37, 3327–3338 (1997)
Li, Z., Hou, X., Zhang, H., Cheng, Q.: Learning Spatially Localized Parts-based Representations. Proceedings of the CVPR’01 Conference I, 207–212 (2001)
Wang, Y., Jia, Y., Hu, C., Turk, M.: Fisher Non-negative Matrix Factorization for Learning Local Features. In: Asian Conference on Computer Vision, Jeju, Korea, January 27-30, pp. 27–30 (2004)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster Analysis and Display of Genome-wide Expression Patterns. Proc. Natl. Acad. Sci. 95, 14863–14868 (1998)
Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C.: Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling. Nature 403, 503–511 (2000)
Perou, C.M., Sorlie, T., Eisen, M.B.: Molecular Portraits of Human Breast Tumours. Nature 406, 747–752 (2000)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting Patterns of Gene Expression with Self-organizing Maps: Methods and Application to Hematopoietic Differentiation. Proc. Natl. Acad. Sci. 96, 2907–2912 (1999)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Brunet, J.P., Tamayo, P., Golun, T.R., Mesirov, J.P.: Metagenes and Molecular Pattern Discovery Using Matrix Factorization. Proc. Natl. Acad. Sci. USA 101(12), 4164–4169 (2004)
Yuan, G., George, C.: Improving Molecular Cancer Class Discovery Through Sparse Non-negative Matrix Factorization. Bioinformations 21, 3970–3975 (2005)
Hoyer, P.O.: Non-Negative Matrix Factorization with Sparseness Constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)
Hoyer, P.O.: Non-negative Sparsecoding Neural Networks for Signal Processing? In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland, pp. 557–565 (2002)
Shahnaz, F., Berry, M., Pauca, P., Plemmons, R.: Document Clustering Using Nonnegative Matrix Factorization. Journal on Information Processing & Management, 2004 (in Press)
Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus Clustering: a Resampling-based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Mach., Learn 52, 91–118 (2003)
Lee, D.D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. In Advances in Neural Information Processing 13, 556–562 (2001)
Xu, W., Liu, X., Gong, Y.: Document-clustering Based on Non-negative Matrix Factorization. In: Proceedings of SIGIR’03, July 28-August 1, Toronto, CA, pp. 267–273 (2003)
Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y., Goumnerova, L.C., Lack, B., Lau, P.M.: Prediction of Central Nerous System Embryonal Tumour Outcome Based on Gene Expression. Nature 415, 436–442 (2002)
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Kong, X., Zheng, C., Wu, Y., Shang, L. (2007). Molecular Cancer Class Discovery Using Non-negative Matrix Factorization with Sparseness Constraint. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_79
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DOI: https://doi.org/10.1007/978-3-540-74171-8_79
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