Zhang, Jian (2013) Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters. Journal of the American Statistical Association, 108 (504). pp. 1366-1384. ISSN 0162-1459. (doi:10.1080/01621459.2013.835661) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:41701)
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Official URL: http://dx.doi.org/10.1080/01621459.2013.835661 |
Abstract
Most clustering methods assume that the data can be represented by mutually exclusive clusters, although this assumption may not be the
case in practice. For example, in gene expression microarray studies, investigators have often found that a gene can play multiple functions
in a cell and may, therefore, belong to more than one cluster simultaneously, and that gene clusters can be linked to each other in certain
pathways. This article examines the effect of the above assumption on the likelihood of finding latent clusters using theoretical calculations
and simulation studies, for which the epistatic structures were known in advance, and on real data analyses. To explore potential links
between clusters, we introduce an epistatic mixture model which extends the Gaussian mixture by including epistatic terms. A generalized
expectation-maximization (EM) algorithm is developed to compute the related maximum likelihood estimators. The Bayesian information
criterion is then used to determine the order of the proposed model. A bootstrap test is proposed for testing whether the epistatic mixture
model is a significantly better fit to the data than a standard mixture model in which each data point belongs to one cluster. The asymptotic
properties of the proposed estimators are also investigated when the number of analysis units is large. The results demonstrate that the
epistatic links between clusters do have a serious effect on the accuracy of clustering and that our epistatic approach can substantially reduce
such an effect and improve the fit.
Item Type: | Article |
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DOI/Identification number: | 10.1080/01621459.2013.835661 |
Uncontrolled keywords: | Asymptotic property; Bootstrap test; Epistatic link; Finite epistatic mixture; Generalized EM algorithm; Model-based epistatic clustering. |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Jian Zhang |
Date Deposited: | 07 Jul 2014 14:01 UTC |
Last Modified: | 16 Nov 2021 10:16 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/41701 (The current URI for this page, for reference purposes) |
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