A composite likelihood approach to gaussian network differentiation with application to epigenetics
A composite likelihood approach to gaussian network differentiation with application to epigenetics
For networks originated from dependent populations, methods to test network differentiation between the two populations are generally designed incorporating the nature of dependence. Doing so potentially complicates the inferencing process with heavy computing burden. Through simulations, we assess the value of using composite likelihood to carry out network comparisons under different statuses of population dependency. We apply the method to real-life epigenetic data and assess epigenetic network stability over time.
Bayesian methods, DNA methylation, MCMC, Undirected network, composite likelihood, network differentiation
1-19
Adefisoye, James O.
be9024dd-9e95-4f69-b882-b02fba90b7ef
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Adefisoye, James O.
be9024dd-9e95-4f69-b882-b02fba90b7ef
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Adefisoye, James O., Arshad, S. Hasan and Zhang, Hongmei
(2025)
A composite likelihood approach to gaussian network differentiation with application to epigenetics.
Journal of Applied Statistics, .
(doi:10.1080/02664763.2025.2596185).
Abstract
For networks originated from dependent populations, methods to test network differentiation between the two populations are generally designed incorporating the nature of dependence. Doing so potentially complicates the inferencing process with heavy computing burden. Through simulations, we assess the value of using composite likelihood to carry out network comparisons under different statuses of population dependency. We apply the method to real-life epigenetic data and assess epigenetic network stability over time.
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Composite_Likelihood_2025.10.10
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Accepted/In Press date: 22 November 2025
e-pub ahead of print date: 5 December 2025
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Publisher Copyright:
© 2025 Informa UK Limited, trading as Taylor & Francis Group.
Keywords:
Bayesian methods, DNA methylation, MCMC, Undirected network, composite likelihood, network differentiation
Identifiers
Local EPrints ID: 508830
URI: http://eprints.soton.ac.uk/id/eprint/508830
ISSN: 0266-4763
PURE UUID: 40b2d48b-7035-482e-b1f5-d986e0ad7ed4
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Date deposited: 04 Feb 2026 17:43
Last modified: 04 Feb 2026 17:44
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Contributors
Author:
James O. Adefisoye
Author:
Hongmei Zhang
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