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Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures

Recursively partitioned mixture model clustering of DNA methylation data using biologically... Abstract DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and non-parametric, have been proposed for clustering large-scale DNA methylation data. However, most of these approaches do not incorporate known biological relationships of measured features, and in some cases, rely on unrealistic assumptions regarding the nature of DNA methylation. Here, we propose a modified version of a recursively partitioned mixture model (RPMM) that integrates information related to the proximity of CpG loci within the genome to inform correlation structures from which subsequent clustering analysis is based. Using simulations and four methylation data sets, we demonstrate that integrating biologically informative correlation structures within RPMM resulted in improved goodness-of-fit, clustering consistency, and the ability to detect biologically meaningful clusters compared to methods which ignore such correlation. Integrating biologically-informed correlation structures to enhance modeling techniques is motivated by the rapid increase in resolution of DNA methylation microarrays and the increasing understanding of the biology of this epigenetic mechanism. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Applications in Genetics and Molecular Biology de Gruyter

Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures

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Publisher
de Gruyter
Copyright
Copyright © 2013 by the
ISSN
2194-6302
eISSN
1544-6115
DOI
10.1515/sagmb-2012-0068
pmid
23468465
Publisher site
See Article on Publisher Site

Abstract

Abstract DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and non-parametric, have been proposed for clustering large-scale DNA methylation data. However, most of these approaches do not incorporate known biological relationships of measured features, and in some cases, rely on unrealistic assumptions regarding the nature of DNA methylation. Here, we propose a modified version of a recursively partitioned mixture model (RPMM) that integrates information related to the proximity of CpG loci within the genome to inform correlation structures from which subsequent clustering analysis is based. Using simulations and four methylation data sets, we demonstrate that integrating biologically informative correlation structures within RPMM resulted in improved goodness-of-fit, clustering consistency, and the ability to detect biologically meaningful clusters compared to methods which ignore such correlation. Integrating biologically-informed correlation structures to enhance modeling techniques is motivated by the rapid increase in resolution of DNA methylation microarrays and the increasing understanding of the biology of this epigenetic mechanism.

Journal

Statistical Applications in Genetics and Molecular Biology – de Gruyter

Published: Mar 5, 2013

References

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    Marsit
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    Grigoriu
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    Chen
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    Christensen
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    Koestler
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    Lindsay
  • Approximate inference in generalized linear mixed models
    Breslow
  • Mixed and mixture regression models for continuous bounded responses using the beta distribution
    Verkuilen
  • Copy number variation has little impact on bead - arraybased measures of dna methylation dx org
    Houseman
  • Model - based clustering of dna methylation array data : a recursive - partitioning algorithm for high - dimensional data arising as a mixture of beta distributions dx org
    Houseman
  • Rocke On the beta transformation family
  • Semiparametric estimation in the rasch model and related exponential response models including a simple latent class model for item analysis
    Lindsay
  • High - throughput method for analyzing methylation of cpgs in targeted genomic regions dx org
    Nautiyal
  • epigenome - wide scan identifies differential dna methylation in newborns related to maternal smoking during pregnancy dx org
    Joubert
  • Model based clustering discriminant analysis and density estimation
    Fraley
  • Applications of beta - mixture models in bioinformatics dx org
    Wu
  • Objective criteria for the evaluation of clustering methods
    Rand
  • - scale analysis of aberrant dna methylation in colorectal cancer dx org
    Hinoue
  • Cholesky residuals for assessing normal errors in a linear model with correlated outcomes
    Houseman
  • The power and the promise of dna methylation markers dx org
    Laird
  • Infant growth restriction is associated with distinct patterns of dna methylation in human placentas dx org
    Banister
  • den Boom methylation profiling of cancer cell lines dx org
    Ehrich
  • beta - mixture model for dimensionality reduction sample classification and analysis dx org
    Laurila
  • Integrating prior knowledge in multiple testing under dependence with applications to detecting differential dna methylation dx org
    Kuan