Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
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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LOCUS Repositório Institucional da UFV |
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2145 |
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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: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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 |
format |
article |
status_str |
publishedVersion |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Bioinformatics |
publisher.none.fl_str_mv |
Bioinformatics |
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reponame:LOCUS Repositório Institucional da UFV instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
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LOCUS Repositório Institucional da UFV |
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LOCUS Repositório Institucional da UFV |
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