Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

International Journal of Molecular Sciences

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