International Journal of Molecular Sciences | |
Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis | |
Zhanyu Ma1  Andrew E. Teschendorff2  Hong Yu1  Jalil Taghia3  | |
[1] Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China; E-Mails:;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; E-Mail:;Communication Theory Lab., KTH - Royal Institute of Technology, Osquldas väg 10, 10044 Stockholm, Sweden; E-Mail: | |
关键词: non-Gaussian statistical models; dimension reduction; unsupervised learning; feature selection; DNA methylation analysis; | |
DOI : 10.3390/ijms150610835 | |
来源: mdpi | |
【 摘 要 】
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.
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland
【 预 览 】
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