Cluster analysis for DNA methylation profiles having a detection threshold

Background: DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data. Results: We compare performance of existing methodology (such as k-means) with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Paul Marjoram, Jing Chang, Peter W. Laird, Kimberly D. Siegmund
• Keywords: BMCBI 2006, DNA Methylation, MethyLight Technology, Molecular Feature