Bayesian modeling of ChIP-chip data through a high-order Ising model

Biometrics
Qianxing Mo, Faming Liang

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 sign...Continue Reading

References

Jul 28, 2005·Bioinformatics·Hongkai Ji, Wing Hung Wong
Sep 13, 2005·Cell·Laurie A BoyerRichard A Young
May 2, 2006·Statistical Applications in Genetics and Molecular Biology·Gordon K Smyth
May 19, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Sündüz KeleşSimon E Cawley
Sep 25, 2007·Biometrics·Raphael GottardoX Shirley Liu
Sep 9, 2009·Nature Reviews. Genetics·Peter J Park

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Citations

Jun 13, 2012·Journal of Toxicology and Environmental Health. Part a·Martin SchäferKatja Ickstadt
Jun 6, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Worapan KusakunniranHongdong Li
Mar 19, 2013·Statistical Applications in Genetics and Molecular Biology·Xiao GuanghuaYang Xie

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