ArticleBMC bioinformatics2011
A beta-mixture model for dimensionality reduction, sample classification and analysis.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer.PloS one · 2024Article
- Compound hierarchical correlated beta mixture with an application to cluster mouse transcription factor DNA binding data.Biostatistics (Oxford, England) · 2015Article
- Comparisons of non-Gaussian statistical models in DNA methylation analysis.International journal of molecular sciences · 2014Article
- Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures.Statistical applications in genetics and molecular biology · 2013Article
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Authors and funding
7 authors.
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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.
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