Abstract
BackgroundPatterns 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.ResultsBased 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) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.ConclusionsWe have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.
Highlights
Patterns of genome-wide methylation vary between tissue types
The Methylation Array and the Number of CpGs in Probes in Different Genomic Regions The Illumina human methylation 27k array consists of 27,578 probes that measure the methylation status of CpGs in the human genome at single nucleotide resolution
Colon cancer can be divided into different types and here we study patients with microsatellite instable (MSI) and microsatellite stable (MSS) tumors
Summary
Patterns of genome-wide methylation vary between tissue types. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. We used methylation microarray data from 42 colon cancer samples to assess the model. The methylation patterns of genes may change and these alterations have been shown to be related to complex diseases, such as heart diseases [1], schizophrenia [2] and different cancers [3,4]. Other methylation alterations have been shown to be specific to cancer type and stage [7,8]. High-throughput technologies, such as microarrays and large-scale sequencing, allow genome-wide methylation can be categorized into three different broad categories or states, low, medium and high methylation. The model facilitates (i) reduction of the dimensionality of a methylation profile (ii) for each probe, computation of the posterior probability of a methylation state, (iii) computation of a posterior probability that the latter state was correctly predicted
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