Int. J. Mol. Sci. 2014, 15(6), 10835-10854; doi:10.3390/ijms150610835

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

1,* email, 2,3email, 1email, 4email and 1email
Received: 24 March 2014; in revised form: 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)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: 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.

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.

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.

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