Show simple item record

dc.contributor.authorMo, Qianxing
dc.contributor.authorLiang, Faming
dc.date.accessioned2016-02-25T12:43:44Z
dc.date.available2016-02-25T12:43:44Z
dc.date.issued2010-01-29
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.
dc.identifier.issn0006-341X
dc.identifier.pmid20128774
dc.identifier.doi10.1111/j.1541-0420.2009.01379.x
dc.identifier.urihttp://hdl.handle.net/10754/597651
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.
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).
dc.publisherWiley
dc.subjectAffymetrix tiling arrays
dc.subjectAgilent promoter arrays
dc.subjectBayesian hierarchical
dc.subjectChIP-chip
dc.subjectGibbs sampler
dc.subjectHidden Markov random field
dc.subjectIsing model
dc.subjectSpatial statistics
dc.titleBayesian Modeling of ChIP-chip Data Through a High-Order Ising Model
dc.typeArticle
dc.identifier.journalBiometrics
dc.contributor.institutionMemorial Sloan-Kettering Cancer Center, New York, United States
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2010-01-29
dc.date.published-print2010-12


This item appears in the following Collection(s)

Show simple item record