PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models

Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely, the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and nondifferentially methylated subjects in the cancer group, and capture the differences...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2017-10, Vol.112 (520), p.1393-1404
Main Authors: Hong, Chuan, Ning, Yang, Wang, Shuang, Wu, Hao, Carroll, Raymond J., Chen, Yong
Format: Article
Language:English
Subjects:
Applications and Case Studies
Asymptotics
Between-subjects design
Cancer
Chi-square test
Computer simulation
Conditional likelihood
data collection
Deoxyribonucleic acid
DNA
DNA methylation
Epigenetics
Nonregular problem
Ovarian cancer
ovarian neoplasms
Penalized likelihood
Penalty function
Permutations
Power
Regression analysis
Semiparametric mixture model
Simulation
Statistical methods
statistical models
Statistical tests
Statistics
variance
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Summary:Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely, the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and nondifferentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g., mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and nonidentifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, the test with simple asymptotic distribution has computational advantages compared with permutation-based test for high-dimensional genetic or epigenetic data. We propose a pseudolikelihood-based expectation-maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. In particular, the proposed test outperforms the commonly used tests under all simulation settings considered, especially when there are variance differences between two groups. The proposed test is applied to a real dataset to identify differentially methylated sites between ovarian cancer subjects and normal subjects. Supplementary materials for this article are available online.
ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2017.1280405