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A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification

Overview of attention for article published in Algorithms for Molecular Biology, August 2017
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Title
A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification
Published in
Algorithms for Molecular Biology, August 2017
DOI 10.1186/s13015-017-0112-1
Pubmed ID
Authors

Christopher Schröder, Sven Rahmann

Abstract

Mixtures of beta distributions are 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 combines latent variables with the method of moments instead of maximum likelihood, which has computational advantages over the popular 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. We also demonstrate that we can accurately infer the number of mixture components. The hybrid algorithm between likelihood-based component un-mixing and moment-based parameter estimation is a robust and efficient method for beta mixture estimation. We provide an implementation of the method ("betamix") as open source software under the MIT license.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Student > Master 2 33%
Student > Doctoral Student 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 2 33%
Physics and Astronomy 1 17%
Medicine and Dentistry 1 17%
Engineering 1 17%
Unknown 1 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 August 2017.
All research outputs
#10,313,348
of 11,627,913 outputs
Outputs from Algorithms for Molecular Biology
#143
of 179 outputs
Outputs of similar age
#223,048
of 264,127 outputs
Outputs of similar age from Algorithms for Molecular Biology
#8
of 8 outputs
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