A Hybrid Parameter Estimation Algorithm for Beta Mixtures and Applications to Methylation State Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9838)

Abstract

Mixtures of beta distributions have previously been shown to be a flexible tool for modeling data with values on the unit interval, such as methylation levels. However, maximum likelihood parameter estimation with beta distributions suffers from problems because of singularities in the log-likelihood function if some observations take the values 0 or 1. While ad-hoc corrections have been proposed to mitigate this problem, we propose a different approach to parameter estimation for beta mixtures where such problems do not arise in the first place. Our algorithm has computational advantages over the maximum-likelihood-based EM algorithm. As an application, we demonstrate that methylation state classification is more accurate when using adaptive thresholds from beta mixtures than non-adaptive thresholds on observed methylation levels.

Keywords

Mixture model Beta distribution Maximum likelihood Method of moments EM algorithm Differential methylation Classification 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Genome Informatics, Institute of Human Genetics, University Hospital EssenUniversity of Duisburg-EssenEssenGermany

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