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Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis
KTH, School of Electrical Engineering (EES), Communication Theory.
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2014 (English)In: International Journal of Molecular Sciences, ISSN 1422-0067, E-ISSN 1422-0067, Vol. 15, no 6, 10835-10854 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 15, no 6, 10835-10854 p.
Keyword [en]
non-Gaussian statistical models, dimension reduction, unsupervised learning, feature selection, DNA methylation analysis
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-148635DOI: 10.3390/ijms150610835ISI: 000338639000097Scopus ID: 2-s2.0-84902578435OAI: oai:DiVA.org:kth-148635DiVA: diva2:736910
Funder
EU, FP7, Seventh Framework Programme, 612212
Note

QC 20140811

Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2017-12-05Bibliographically approved

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Taghia, Jalil
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  • de-DE
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  • Other locale
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