Biclustering of gene expression data by non-smooth non-negative matrix factorization

18 pages, 1 table, 5 figures, 1 additional file.

Detalles Bibliográficos
Autores: Carmona-Sáez, Pedro, Pascual-Marqui, Roberto D., Tirado, Francisco, Carazo, José M., Pascual-Montano, Alberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2006
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/17470
Acceso en línea:http://hdl.handle.net/10261/17470
Access Level:acceso abierto
Palabra clave:Datasets
nsNMF
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Biclustering of gene expression data by non-smooth non-negative matrix factorizationCarmona-Sáez, PedroPascual-Marqui, Roberto D.Tirado, FranciscoCarazo, José M.Pascual-Montano, AlbertoDatasetsnsNMF18 pages, 1 table, 5 figures, 1 additional file.[Background] The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states.[Results] In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions.[Conclusion] The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.This work has been supported by the Spanish grants GR/SAL/0653/2004, CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between the Spanish Research Council and the National Research Council of Canada (CSIC-050402040003). The authors also thank the KEY Foundation for Brain-Mind Research in Zurich for partial economical support of this work. P.C.S. is the recipient of a fellowship from Comunidad de Madrid (CAM). A.P.M. acknowledges the support of the Spanish Ramón y Cajal program.Peer reviewedBioMed Central200920092006info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersion639533 bytesapplication/pdfhttp://hdl.handle.net/10261/17470reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1186/1471-2105-7-78info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/174702026-05-22T06:33:51Z
dc.title.none.fl_str_mv Biclustering of gene expression data by non-smooth non-negative matrix factorization
title Biclustering of gene expression data by non-smooth non-negative matrix factorization
spellingShingle Biclustering of gene expression data by non-smooth non-negative matrix factorization
Carmona-Sáez, Pedro
Datasets
nsNMF
title_short Biclustering of gene expression data by non-smooth non-negative matrix factorization
title_full Biclustering of gene expression data by non-smooth non-negative matrix factorization
title_fullStr Biclustering of gene expression data by non-smooth non-negative matrix factorization
title_full_unstemmed Biclustering of gene expression data by non-smooth non-negative matrix factorization
title_sort Biclustering of gene expression data by non-smooth non-negative matrix factorization
dc.creator.none.fl_str_mv Carmona-Sáez, Pedro
Pascual-Marqui, Roberto D.
Tirado, Francisco
Carazo, José M.
Pascual-Montano, Alberto
author Carmona-Sáez, Pedro
author_facet Carmona-Sáez, Pedro
Pascual-Marqui, Roberto D.
Tirado, Francisco
Carazo, José M.
Pascual-Montano, Alberto
author_role author
author2 Pascual-Marqui, Roberto D.
Tirado, Francisco
Carazo, José M.
Pascual-Montano, Alberto
author2_role author
author
author
author
dc.subject.none.fl_str_mv Datasets
nsNMF
topic Datasets
nsNMF
description 18 pages, 1 table, 5 figures, 1 additional file.
publishDate 2006
dc.date.none.fl_str_mv 2006
2009
2009
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/17470
url http://hdl.handle.net/10261/17470
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1186/1471-2105-7-78
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 639533 bytes
application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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