Clustering gene expression data with a penalized graph-based metric

Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes...

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Detalles Bibliográficos
Autores: Baya, Ariel Emilio, Granitto, Pablo Miguel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2011
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/15184
Acceso en línea:http://hdl.handle.net/11336/15184
Access Level:acceso abierto
Palabra clave:CLUSTERING
ISOMAP
GENE EXPRESSION
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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spelling Clustering gene expression data with a penalized graph-based metricBaya, Ariel EmilioGranitto, Pablo MiguelCLUSTERINGISOMAPGENE EXPRESSIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a highdimensional space, as could be the case of some gene expression datasets. Results: In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric. Conclusions: In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.Fil: Baya, Ariel Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaBiomed Central2011-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15184Baya, Ariel Emilio; Granitto, Pablo Miguel; Clustering gene expression data with a penalized graph-based metric; Biomed Central; Bmc Bioinformatics; 12; 2; 1-2011; 1-181471-2105enginfo:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2105-12-2info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-2info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023695/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:46:34Zoai:ri.conicet.gov.ar:11336/15184instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 13:46:34.943CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Clustering gene expression data with a penalized graph-based metric
title Clustering gene expression data with a penalized graph-based metric
spellingShingle Clustering gene expression data with a penalized graph-based metric
Baya, Ariel Emilio
CLUSTERING
ISOMAP
GENE EXPRESSION
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
title_short Clustering gene expression data with a penalized graph-based metric
title_full Clustering gene expression data with a penalized graph-based metric
title_fullStr Clustering gene expression data with a penalized graph-based metric
title_full_unstemmed Clustering gene expression data with a penalized graph-based metric
title_sort Clustering gene expression data with a penalized graph-based metric
dc.creator.none.fl_str_mv Baya, Ariel Emilio
Granitto, Pablo Miguel
author Baya, Ariel Emilio
author_facet Baya, Ariel Emilio
Granitto, Pablo Miguel
author_role author
author2 Granitto, Pablo Miguel
author2_role author
dc.subject.none.fl_str_mv CLUSTERING
ISOMAP
GENE EXPRESSION
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
topic CLUSTERING
ISOMAP
GENE EXPRESSION
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
description Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a highdimensional space, as could be the case of some gene expression datasets. Results: In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric. Conclusions: In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.
publishDate 2011
dc.date.none.fl_str_mv 2011-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/15184
Baya, Ariel Emilio; Granitto, Pablo Miguel; Clustering gene expression data with a penalized graph-based metric; Biomed Central; Bmc Bioinformatics; 12; 2; 1-2011; 1-18
1471-2105
url http://hdl.handle.net/11336/15184
identifier_str_mv Baya, Ariel Emilio; Granitto, Pablo Miguel; Clustering gene expression data with a penalized graph-based metric; Biomed Central; Bmc Bioinformatics; 12; 2; 1-2011; 1-18
1471-2105
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/1471-2105-12-2
info:eu-repo/semantics/altIdentifier/url/https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-2
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023695/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Biomed Central
publisher.none.fl_str_mv Biomed Central
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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