Abstract
In this paper, we have explored the use of improved non – negative matrix factorization (INMF) to analyze gene expression data. Firstly, the mathematical principle of INMF algorithm is analyzed; Secondly, we proposed an INMF - based method for clustering periodic genes, which can provide valuable information for gene network research. Using simulated data, our approach is able to extract periodic genes subsets even when the signal-to-noise ratio is low. Subsequently, our approach is tested by real gene expression datasets from Yeast and is compared with the related other approaches. Our results showed that our scheme is feasible and effective.
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Cho, R.J., Campbell, M.J., et al.: A Genome–Wide Transcriptional Analysis of the Mitotic Cell Cycle. Mol. Cell 2, 65–73 (1998)
Spellman, P.T., Sherlock, G., Zhang, M.Q., et al.: Comprehensive Identification of Cell Cycle–Regulated Gene of the Yeast Saccharomyces Cerevisiae by Micro-Array Hybridation. Mol. Biol. Cell 9, 3273–3297 (1998)
Cho, R.J., Huang, M.X., Dong, H., et al.: Transcriptional Regulation and Function During the Human Cell Cycle. Nature Genetics 27, 48–54 (2001)
Lockhart, D.J., et al.: Expression Monitoring by Hybridization to High – Density Oligonucleotide Arrays. Nature Biotechnology 14, 1675–1680 (1996)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster Analysis and Display of Genome–wide Expression Patterns. Proc. Nat. Acad. Science USA. 95, 14863–14868 (1998)
Lukashin, A.V., Fuchs, R.: Analysis of Temporal Gene Expression Profiles: Clustering by Simulated Annealing and Determining the Optimal Number of Clusters. Bioinformatics 17, 405–414 (2001)
Mclachlan, G.J., Bean, R.W.: A Mixture Model–Based Approach to the Clustering of Micro-Array Expression Data. Bioinformatics 18, 413–422 (2002)
Deutsch, J.M.: Evolutionary Algorithms for Finding Optimal Gene Sets in Microarray Prediction. Bioinformatics 19, 45–52 (2003)
Raychaudhuri, S., Stuart, J.M., et al.: Principal Components Analysis to Summarize Micro-array Experiments: Application to Sporulation Time Series. In: Pacific Symposium on Biocomputing, pp. 455–466 (2000)
Wall, M.E., Rechtsteiner, A., et al.: Singular Value Decomposition and Principal Component Analysis. In: Berrar, D.P. (ed.) A Practical Approach to Micro-array Data Analysis, ch. 5, pp. 91–109. Kluwer, Dordrecht (2003)
Holter, N.S., Mitra, M., Maritan, A., et al.: Fundamental Patterns Underlying Gene Expression Profiles: Simplicity From Complexity. Proc. Nat Acad. Science USA 97, 8409–8414 (2000)
Alter, O., Brown, P.O., Botstein, D.: Singular Value Decomposition for Genome–Wide Expression Data Processing and Modelling. Proc. Nat. Acad. Science USA 97, 10101–10106 (2000)
Wall, M.E., Dyck, P.A., Brettin, T.S.: SVDMAN - Singular Value Decomposition Analysis of Micro-array Data. Bioinformatics 17, 566–568 (2001)
Liebermeister, W.: Linear Modes of Gene Expression Determined by Independent Component Analysis. Bioinformatics 18(1), 51–60 (2002)
Yamanishi, Y., Itoh, M., Kanehisa, M.: Extraction of Organism Groups from Phylogenetic Profiled using Independent Component Analysis. Genome Informatics 13, 60–70 (2002)
Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annu. Eugenics, pt. II 7, 179–188 (1936)
Brown, M., Grundy, W.N., Campbell, M.J., et al.: Knowledge - based Analysis of Microarray Data by Using Support Vector Machines. Proc. Nat. Acad. Science USA 97, 262–267 (2000)
Duda, R.O., Hart, P.E., Stork, D.G.: Patten Classification, 2nd edn. Wiley, New York (2001)
Whitfield, M.L., Sherlock, G., Saldanha, A.J., et al.: Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumor. Mol. Biol. Cell 13, 1977–2000 (2002)
Wichert, S., Fokianos, K., Strimmer, K.: Identifying Periodically Expressed Transcripts in Microarray Time Data. Bioinformatics 20(1), 5–20 (2004)
Zhu, Q., Cui, H., Cao, K., Chan, W.C.: Algorithm Fusion of Gene Expression Profiling for Diffuse Large B – Cell Lymphoma Outcome Prediction. IEEE Trans. On Information Tech. In Biomedical. 8(2), 79–88 (2004)
Shedden, K., Cooper, S.: Analysis of Cell – Cycle Gene Expression in Saccharonization Cerevisiae using Microarrays and Multiple Synchronization Methods. Nucleic Acids Res. 30, 2920–2929 (2002)
Pascual, A.D., et al.: Non – negative Matrix Factorization for Gene Expression and Scientific Text Analysis. In: Proceedings of 11th International Conference on Intelligent Systems for Molecular Biology, Australia (2003)
Rao, N., Shepherd, S.J.: Extracting Characteristic Patterns from Genome – Wide Expression by NMF. In: Proceedings of IEEE Internaltional Conference on Computational Systems Bioinformatics, Stanford, California, pp. 570–571 (2004)
Neumann, V.J.: Functional Operators. Annals of Mathematics No. 22, vol. II. Princeton University Press, Princeton (1950)
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Rao, N., Shepherd, S.J. (2006). Clustering Gene Expression Data for Periodic Genes Based on INMF. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_45
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DOI: https://doi.org/10.1007/11816102_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
Online ISBN: 978-3-540-37282-0
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