Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model

Handle URI:
http://hdl.handle.net/10754/597651
Title:
Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model
Authors:
Mo, Qianxing; Liang, Faming
Abstract:
ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.
Citation:
Mo Q, Liang F (2010) Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model. Biometrics 66: 1284–1294. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01379.x.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
29-Jan-2010
DOI:
10.1111/j.1541-0420.2009.01379.x
PubMed ID:
20128774
Type:
Article
ISSN:
0006-341X
Sponsors:
The authors thank Hongkai Ji for helpful discussion about Tilemap HMM, and the editor, the associate editor, and the referees for their comments, which have led to significant improvement of this article. FL's research was partially supported by grants from the National Science Foundation (DMS-0607755) and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST).
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMo, Qianxingen
dc.contributor.authorLiang, Famingen
dc.date.accessioned2016-02-25T12:43:44Zen
dc.date.available2016-02-25T12:43:44Zen
dc.date.issued2010-01-29en
dc.identifier.citationMo Q, Liang F (2010) Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model. Biometrics 66: 1284–1294. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01379.x.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid20128774en
dc.identifier.doi10.1111/j.1541-0420.2009.01379.xen
dc.identifier.urihttp://hdl.handle.net/10754/597651en
dc.description.abstractChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.en
dc.description.sponsorshipThe authors thank Hongkai Ji for helpful discussion about Tilemap HMM, and the editor, the associate editor, and the referees for their comments, which have led to significant improvement of this article. FL's research was partially supported by grants from the National Science Foundation (DMS-0607755) and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherWiley-Blackwellen
dc.subjectAffymetrix tiling arraysen
dc.subjectAgilent promoter arraysen
dc.subjectBayesian hierarchicalen
dc.subjectChIP-chipen
dc.subjectGibbs sampleren
dc.subjectHidden Markov random fielden
dc.subjectIsing modelen
dc.subjectSpatial statisticsen
dc.titleBayesian Modeling of ChIP-chip Data Through a High-Order Ising Modelen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.contributor.institutionMemorial Sloan-Kettering Cancer Center, New York, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en

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