Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis
2014 (English)In: International Journal of Molecular Sciences, ISSN 1422-0067, Vol. 15, no 6, 10835-10854 p.Article in journal (Refereed) Published
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.
non-Gaussian statistical models, dimension reduction, unsupervised learning, feature selection, DNA methylation analysis
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-148635DOI: 10.3390/ijms150610835ISI: 000338639000097ScopusID: 2-s2.0-84902578435OAI: oai:DiVA.org:kth-148635DiVA: diva2:736910
FunderEU, FP7, Seventh Framework Programme, 612212
QC 201408112014-08-112014-08-112014-08-11Bibliographically approved