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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Latent Variable Models for Analyzing DNA Methylation

Latent Variable Models for Analyzing DNA Methylation

Chapter:
(p.387) Chapter 15 Latent Variable Models for Analyzing DNA Methylation
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

E. Andrés Houseman

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0015

Deoxyribonucleic acid (DNA) methylation is tightly linked with cellular differentiation. For instance, it has been observed that DNA methylation in tumor cells encodes phenotypic information about the tumor. Thus, understanding of tumor biology is fruitfully enhanced by the study of the multivariate structure of DNA methylation data. To the extent that such data possess discrete latent structure, it can be viewed as encoding different tumor subtypes (in cancer studies) or tissue types (more generally). However, in some cases there may be more evidence of continuous latent structure reflecting a continuous range of variation. This chapter discusses several specific latent variable models that have been used in the last decade to analyze DNA methylation data, including approaches for modeling DNA methylation data in low-dimensional settings such as in candidate gene studies and recursively partitioned mixture model approaches for modeling DNA methylation in high-dimensional settings.

Keywords:   DNA methylation, latent variable models, recursively partitioned mixture models

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