Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach

Detalhes bibliográficos
Autor(a) principal: Nascimento, Moysés
Data de Publicação: 2012
Outros Autores: Sáfadi, Thelma, Silva, Fabyano Fonseca e, Nascimento, Ana Carolina C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: https://doi.org/10.1093/bioinformatics/bts322
http://www.locus.ufv.br/handle/123456789/13263
Resumo: In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web http://www.det.ufv.br/∼moyses/links.php.
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spelling Nascimento, MoysésSáfadi, ThelmaSilva, Fabyano Fonseca eNascimento, Ana Carolina C.2017-11-17T18:45:24Z2017-11-17T18:45:24Z2012-06-041460-2059https://doi.org/10.1093/bioinformatics/bts322http://www.locus.ufv.br/handle/123456789/13263In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web http://www.det.ufv.br/∼moyses/links.php.engBioinformaticsVolume 28, Issue 15, Pages 2004–2007, August 2012Bayesian model-basedClustering of temporalGene expressionAutoregressive panelBayesian model-based clustering of temporal gene expression using autoregressive panel data approachinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALbts322.pdfbts322.pdftexto completoapplication/pdf352452https://locus.ufv.br//bitstream/123456789/13263/1/bts322.pdfe7b4fda546c0af3126121db7f6f034b2MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/13263/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILbts322.pdf.jpgbts322.pdf.jpgIM Thumbnailimage/jpeg1725https://locus.ufv.br//bitstream/123456789/13263/3/bts322.pdf.jpg983399eb75bfe9eb9f7ebd3e906259beMD53123456789/132632017-11-17 22:00:38.722oai:locus.ufv.br:123456789/13263Tk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452017-11-18T01:00:38LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
title Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
spellingShingle Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
Nascimento, Moysés
Bayesian model-based
Clustering of temporal
Gene expression
Autoregressive panel
title_short Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
title_full Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
title_fullStr Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
title_full_unstemmed Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
title_sort Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
author Nascimento, Moysés
author_facet Nascimento, Moysés
Sáfadi, Thelma
Silva, Fabyano Fonseca e
Nascimento, Ana Carolina C.
author_role author
author2 Sáfadi, Thelma
Silva, Fabyano Fonseca e
Nascimento, Ana Carolina C.
author2_role author
author
author
dc.contributor.author.fl_str_mv Nascimento, Moysés
Sáfadi, Thelma
Silva, Fabyano Fonseca e
Nascimento, Ana Carolina C.
dc.subject.pt-BR.fl_str_mv Bayesian model-based
Clustering of temporal
Gene expression
Autoregressive panel
topic Bayesian model-based
Clustering of temporal
Gene expression
Autoregressive panel
description In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web http://www.det.ufv.br/∼moyses/links.php.
publishDate 2012
dc.date.issued.fl_str_mv 2012-06-04
dc.date.accessioned.fl_str_mv 2017-11-17T18:45:24Z
dc.date.available.fl_str_mv 2017-11-17T18:45:24Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://doi.org/10.1093/bioinformatics/bts322
http://www.locus.ufv.br/handle/123456789/13263
dc.identifier.issn.none.fl_str_mv 1460-2059
identifier_str_mv 1460-2059
url https://doi.org/10.1093/bioinformatics/bts322
http://www.locus.ufv.br/handle/123456789/13263
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv Volume 28, Issue 15, Pages 2004–2007, August 2012
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dc.publisher.none.fl_str_mv Bioinformatics
publisher.none.fl_str_mv Bioinformatics
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