Bayesian modeling of ChIP-chip data using latent variables.

Handle URI:
http://hdl.handle.net/10754/596770
Title:
Bayesian modeling of ChIP-chip data using latent variables.
Authors:
Wu, Mingqi; Liang, Faming; Tian, Yanan
Abstract:
BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.
Citation:
Wu M, Liang F, Tian Y (2009) Bayesian modeling of ChIP-chip data using latent variables. BMC Bioinformatics 10: 352. Available: http://dx.doi.org/10.1186/1471-2105-10-352.
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
26-Oct-2009
DOI:
10.1186/1471-2105-10-352
PubMed ID:
19857265
PubMed Central ID:
PMC2779819
Type:
Article
ISSN:
1471-2105
Sponsors:
Liang's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). Tian's research is supported in part by ESO9859. The authors thank the editor, the associate editor, and the referee for their comments which have led to significant improvement of this paper.
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Full metadata record

DC FieldValue Language
dc.contributor.authorWu, Mingqien
dc.contributor.authorLiang, Famingen
dc.contributor.authorTian, Yananen
dc.date.accessioned2016-02-21T08:50:19Zen
dc.date.available2016-02-21T08:50:19Zen
dc.date.issued2009-10-26en
dc.identifier.citationWu M, Liang F, Tian Y (2009) Bayesian modeling of ChIP-chip data using latent variables. BMC Bioinformatics 10: 352. Available: http://dx.doi.org/10.1186/1471-2105-10-352.en
dc.identifier.issn1471-2105en
dc.identifier.pmid19857265en
dc.identifier.doi10.1186/1471-2105-10-352en
dc.identifier.urihttp://hdl.handle.net/10754/596770en
dc.description.abstractBACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.en
dc.description.sponsorshipLiang's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). Tian's research is supported in part by ESO9859. The authors thank the editor, the associate editor, and the referee for their comments which have led to significant improvement of this paper.en
dc.publisherSpringer Natureen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subject.meshBayes Theoremen
dc.subject.meshChromatin Immunoprecipitationen
dc.titleBayesian modeling of ChIP-chip data using latent variables.en
dc.typeArticleen
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC2779819en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en

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