lmdme: Linear Models on Designed Multivariate Experiments in R

Autores
Fresno Rodríguez, Cristóbal; Balzarini, Monica Graciela; Fernandez, Elmer Andres
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Thelmdmepackage decomposes analysis of variance (ANOVA) through linear mod-els on designed multivariate experiments, allowing ANOVA-principal component analysis(APCA) and ANOVA-simultaneous component analysis (ASCA) inR. It also extends bothmethods with the application of partial least squares (PLS) through the specification ofa desired output matrix. The package is freely available fromBioconductorand licensedunder the GNU General Public License.ANOVA decomposition methods for designed multivariate experiments are becomingpopular in “omics” experiments (transcriptomics, metabolomics, etc.), where measure-ments are performed according to a predefined experimental design, with several exper-imental factors or including subject-specific clinical covariates, such as those present incurrent clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods forstudying interaction patterns on multidimensional datasets. However, currently anRimplementation of APCA is only available forSpectradata in theChemoSpecpackage,whereas ASCA is based on average calculations on the indices of up to three design ma-trices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA isnot available in anRpackage.Here, we present anRimplementation for ANOVA decomposition with PCA/PLSanalysis that allows the user to specify (through a flexibleformulainterface), almostany linear model with the associated inference on the estimated effects, as well as todisplay functions to explore results both of PCA and PLS. We describe the model, itsimplementation and two high-throughputmicroarrayexamples: one applied to interactionpattern analysis and the other to quality assessment.
Fil: Fresno Rodríguez, Cristóbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica de Córdoba; Argentina
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; Argentina
Materia
LINEAR MODEL
ANOVA DESCOMPOSITION
PCA
PLS
DESIGNED EXPERIMENTS
R
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33951
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/33951
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling lmdme: Linear Models on Designed Multivariate Experiments in RFresno Rodríguez, CristóbalBalzarini, Monica GracielaFernandez, Elmer AndresLINEAR MODELANOVA DESCOMPOSITIONPCAPLSDESIGNED EXPERIMENTSRhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Thelmdmepackage decomposes analysis of variance (ANOVA) through linear mod-els on designed multivariate experiments, allowing ANOVA-principal component analysis(APCA) and ANOVA-simultaneous component analysis (ASCA) inR. It also extends bothmethods with the application of partial least squares (PLS) through the specification ofa desired output matrix. The package is freely available fromBioconductorand licensedunder the GNU General Public License.ANOVA decomposition methods for designed multivariate experiments are becomingpopular in “omics” experiments (transcriptomics, metabolomics, etc.), where measure-ments are performed according to a predefined experimental design, with several exper-imental factors or including subject-specific clinical covariates, such as those present incurrent clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods forstudying interaction patterns on multidimensional datasets. However, currently anRimplementation of APCA is only available forSpectradata in theChemoSpecpackage,whereas ASCA is based on average calculations on the indices of up to three design ma-trices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA isnot available in anRpackage.Here, we present anRimplementation for ANOVA decomposition with PCA/PLSanalysis that allows the user to specify (through a flexibleformulainterface), almostany linear model with the associated inference on the estimated effects, as well as todisplay functions to explore results both of PCA and PLS. We describe the model, itsimplementation and two high-throughputmicroarrayexamples: one applied to interactionpattern analysis and the other to quality assessment.Fil: Fresno Rodríguez, Cristóbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica de Córdoba; ArgentinaFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; ArgentinaJournal Statistical Software2014-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33951Fresno Rodríguez, Cristóbal; Balzarini, Monica Graciela; Fernandez, Elmer Andres; lmdme: Linear Models on Designed Multivariate Experiments in R; Journal Statistical Software; Journal Of Statistical Software; 56; 7; 4-2014; 1-161548-7660CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v056i07info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:43:17Zoai:ri.conicet.gov.ar:11336/33951instacron: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:34982025-09-29 10:43:17.689CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv lmdme: Linear Models on Designed Multivariate Experiments in R
title lmdme: Linear Models on Designed Multivariate Experiments in R
spellingShingle lmdme: Linear Models on Designed Multivariate Experiments in R
Fresno Rodríguez, Cristóbal
LINEAR MODEL
ANOVA DESCOMPOSITION
PCA
PLS
DESIGNED EXPERIMENTS
R
title_short lmdme: Linear Models on Designed Multivariate Experiments in R
title_full lmdme: Linear Models on Designed Multivariate Experiments in R
title_fullStr lmdme: Linear Models on Designed Multivariate Experiments in R
title_full_unstemmed lmdme: Linear Models on Designed Multivariate Experiments in R
title_sort lmdme: Linear Models on Designed Multivariate Experiments in R
dc.creator.none.fl_str_mv Fresno Rodríguez, Cristóbal
Balzarini, Monica Graciela
Fernandez, Elmer Andres
author Fresno Rodríguez, Cristóbal
author_facet Fresno Rodríguez, Cristóbal
Balzarini, Monica Graciela
Fernandez, Elmer Andres
author_role author
author2 Balzarini, Monica Graciela
Fernandez, Elmer Andres
author2_role author
author
dc.subject.none.fl_str_mv LINEAR MODEL
ANOVA DESCOMPOSITION
PCA
PLS
DESIGNED EXPERIMENTS
R
topic LINEAR MODEL
ANOVA DESCOMPOSITION
PCA
PLS
DESIGNED EXPERIMENTS
R
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Thelmdmepackage decomposes analysis of variance (ANOVA) through linear mod-els on designed multivariate experiments, allowing ANOVA-principal component analysis(APCA) and ANOVA-simultaneous component analysis (ASCA) inR. It also extends bothmethods with the application of partial least squares (PLS) through the specification ofa desired output matrix. The package is freely available fromBioconductorand licensedunder the GNU General Public License.ANOVA decomposition methods for designed multivariate experiments are becomingpopular in “omics” experiments (transcriptomics, metabolomics, etc.), where measure-ments are performed according to a predefined experimental design, with several exper-imental factors or including subject-specific clinical covariates, such as those present incurrent clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods forstudying interaction patterns on multidimensional datasets. However, currently anRimplementation of APCA is only available forSpectradata in theChemoSpecpackage,whereas ASCA is based on average calculations on the indices of up to three design ma-trices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA isnot available in anRpackage.Here, we present anRimplementation for ANOVA decomposition with PCA/PLSanalysis that allows the user to specify (through a flexibleformulainterface), almostany linear model with the associated inference on the estimated effects, as well as todisplay functions to explore results both of PCA and PLS. We describe the model, itsimplementation and two high-throughputmicroarrayexamples: one applied to interactionpattern analysis and the other to quality assessment.
Fil: Fresno Rodríguez, Cristóbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica de Córdoba; Argentina
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; Argentina
description Thelmdmepackage decomposes analysis of variance (ANOVA) through linear mod-els on designed multivariate experiments, allowing ANOVA-principal component analysis(APCA) and ANOVA-simultaneous component analysis (ASCA) inR. It also extends bothmethods with the application of partial least squares (PLS) through the specification ofa desired output matrix. The package is freely available fromBioconductorand licensedunder the GNU General Public License.ANOVA decomposition methods for designed multivariate experiments are becomingpopular in “omics” experiments (transcriptomics, metabolomics, etc.), where measure-ments are performed according to a predefined experimental design, with several exper-imental factors or including subject-specific clinical covariates, such as those present incurrent clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods forstudying interaction patterns on multidimensional datasets. However, currently anRimplementation of APCA is only available forSpectradata in theChemoSpecpackage,whereas ASCA is based on average calculations on the indices of up to three design ma-trices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA isnot available in anRpackage.Here, we present anRimplementation for ANOVA decomposition with PCA/PLSanalysis that allows the user to specify (through a flexibleformulainterface), almostany linear model with the associated inference on the estimated effects, as well as todisplay functions to explore results both of PCA and PLS. We describe the model, itsimplementation and two high-throughputmicroarrayexamples: one applied to interactionpattern analysis and the other to quality assessment.
publishDate 2014
dc.date.none.fl_str_mv 2014-04
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/11336/33951
Fresno Rodríguez, Cristóbal; Balzarini, Monica Graciela; Fernandez, Elmer Andres; lmdme: Linear Models on Designed Multivariate Experiments in R; Journal Statistical Software; Journal Of Statistical Software; 56; 7; 4-2014; 1-16
1548-7660
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33951
identifier_str_mv Fresno Rodríguez, Cristóbal; Balzarini, Monica Graciela; Fernandez, Elmer Andres; lmdme: Linear Models on Designed Multivariate Experiments in R; Journal Statistical Software; Journal Of Statistical Software; 56; 7; 4-2014; 1-16
1548-7660
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v056i07
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
dc.publisher.none.fl_str_mv Journal Statistical Software
publisher.none.fl_str_mv Journal Statistical Software
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
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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score 13.070432

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