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Int. J. Mol. Sci. 2014, 15(6), 10835-10854;

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

Pattern Recognition and Intelligent System Lab.,Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road,Beijing 100876, China
Computational Systems Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute,University College London, 72 Huntley Street, London WC1E 6BT, UK
Communication Theory Lab., KTH - Royal Institute of Technology, Osquldas väg 10,10044 Stockholm, Sweden
Author to whom correspondence should be addressed.
Received: 24 March 2014 / Revised: 12 May 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
(This article belongs to the Special Issue Identification and Roles of the Structure of DNA)
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As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance. View Full-Text
Keywords: non-Gaussian statistical models; dimension reduction; unsupervised learning; feature selection; DNA methylation analysis non-Gaussian statistical models; dimension reduction; unsupervised learning; feature selection; DNA methylation analysis

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Ma, Z.; Teschendorff, A.E.; Yu, H.; Taghia, J.; Guo, J. Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis. Int. J. Mol. Sci. 2014, 15, 10835-10854.

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