Comparison of clustering methods for investigation of genome-wide methylation array data.

Frontiers in Genetics

Satish ePendurthi¹  Harry eClifford²  Richard D Emes²  Frank eWessely² 

[1] University of Abertay Dundee;University of Nottingham;

关键词: Epigenomics;    epigenetics;    clustering;    Hierarchical;    Illumina;    Infinium;   

DOI  :  10.3389/fgene.2011.00088

来源: DOAJ

【 摘 要 】

The use of genome-wide methylation arrays has proved very informative to investigate both clinical and biological questions in human epigenomics. The use of clustering methods either for exploration of these data or to compare to an a priori grouping e.g normal versus disease allows assessment of groupings of data without user bias. However no consensus on the methods to use for clustering of methylation array approaches has been reached. To determine the most appropriate clustering method for analysis of Illumina array methylation data, a collection of data sets was simulated and used to compare clustering methods. Both Hierarchical clustering and Non-Hierarchical clustering methods (k-means, k-medoids and fuzzy clustering algorithms) were compared using a range of distance and linkage methods. As no single method consistently outperformed others across different simulations, we propose a method to capture the best clustering outcome based on an additional measure, the silhouette width. This approach produced a consistently higher cluster accuracy compared to using any one method in isolation.

【 授权许可】

Unknown