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Open AccessArticle

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

1
Pattern Recognition and Intelligent System Lab.,Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road,Beijing 100876, China
2
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
3
Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute,University College London, 72 Huntley Street, London WC1E 6BT, UK
4
Communication Theory Lab., KTH - Royal Institute of Technology, Osquldas väg 10,10044 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2014, 15(6), 10835-10854; https://doi.org/10.3390/ijms150610835
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)
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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MDPI and ACS Style

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. https://doi.org/10.3390/ijms150610835

AMA Style

Ma Z, Teschendorff AE, Yu H, Taghia J, Guo J. Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis. International Journal of Molecular Sciences. 2014; 15(6):10835-10854. https://doi.org/10.3390/ijms150610835

Chicago/Turabian Style

Ma, Zhanyu; Teschendorff, Andrew E.; Yu, Hong; Taghia, Jalil; Guo, Jun. 2014. "Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis" Int. J. Mol. Sci. 15, no. 6: 10835-10854. https://doi.org/10.3390/ijms150610835

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