A Beta-Mixture Model for Dimensionality Reduction, Sample Classification and Analysis

Background: Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model. Results: Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) ...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Kirsti Laurila, Bodil Oster, Claus L. Andersen, Philippe Lamy, Torben F. Ørntoft, Olli Yli-Harja, Carsten Wiuf
• Keywords: Biometrics, BMCBI 2011, DNA Methylation, Methylation State, Mixture Model