Handle URI:
http://hdl.handle.net/10754/597282
Title:
A hidden Ising model for ChIP-chip data analysis
Authors:
Mo, Q.; Liang, F.
Abstract:
Motivation: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. Availability: A software called iChip is freely available at http://www.bioconductor.org/. Contact: moq@mskcc.org. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.
Citation:
Mo Q, Liang F (2010) A hidden Ising model for ChIP-chip data analysis. Bioinformatics 26: 777–783. Available: http://dx.doi.org/10.1093/bioinformatics/btq032.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
28-Jan-2010
DOI:
10.1093/bioinformatics/btq032
PubMed ID:
20110277
Type:
Article
ISSN:
1367-4803; 1460-2059
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.Funding: National Science Foundation (grant DMS-0607755 to L.F., partially); award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST to L.F.).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMo, Q.en
dc.contributor.authorLiang, F.en
dc.date.accessioned2016-02-25T12:29:45Zen
dc.date.available2016-02-25T12:29:45Zen
dc.date.issued2010-01-28en
dc.identifier.citationMo Q, Liang F (2010) A hidden Ising model for ChIP-chip data analysis. Bioinformatics 26: 777–783. Available: http://dx.doi.org/10.1093/bioinformatics/btq032.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid20110277en
dc.identifier.doi10.1093/bioinformatics/btq032en
dc.identifier.urihttp://hdl.handle.net/10754/597282en
dc.description.abstractMotivation: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. Availability: A software called iChip is freely available at http://www.bioconductor.org/. Contact: moq@mskcc.org. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.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.Funding: National Science Foundation (grant DMS-0607755 to L.F., partially); award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST to L.F.).en
dc.publisherOxford University Press (OUP)en
dc.titleA hidden Ising model for ChIP-chip data analysisen
dc.typeArticleen
dc.identifier.journalBioinformaticsen
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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