Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study

Autores
Fernandez, Elmer Andres; Girotti, Maria Romina; López del Olmo, Juan A.; Llera, Andrea Sabina; Podhajcer, Osvaldo Luis; Cantet, Rodolfo Juan Carlos; Balzarini, Monica Graciela
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Motivation: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. Results: We propose to identify DIGEDE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.
Fil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Girotti, Maria Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: López del Olmo, Juan A.. Fundación Centro Nacional de Investigaciones Cardiovasculares; España
Fil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Podhajcer, Osvaldo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Melanoma
2d Dige
Bioinformatics
Protein Expression
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:11336/36959
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/36959
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell studyFernandez, Elmer AndresGirotti, Maria RominaLópez del Olmo, Juan A.Llera, Andrea SabinaPodhajcer, Osvaldo LuisCantet, Rodolfo Juan CarlosBalzarini, Monica GracielaMelanoma2d DigeBioinformaticsProtein Expressionhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Motivation: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. Results: We propose to identify DIGEDE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.Fil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Girotti, Maria Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: López del Olmo, Juan A.. Fundación Centro Nacional de Investigaciones Cardiovasculares; EspañaFil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Podhajcer, Osvaldo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Press2008-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/36959Fernandez, Elmer Andres; Girotti, Maria Romina; López del Olmo, Juan A. ; Llera, Andrea Sabina; Podhajcer, Osvaldo Luis; et al.; Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study; Oxford University Press; Bioinformatics (Oxford, England); 24; 23; 12-2008; 2706-27121367-4803CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/24/23/2706/179880info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btn508info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:24:06Zoai:ri.conicet.gov.ar:11336/36959instacron: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 10:24:06.525CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
title Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
spellingShingle Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
Fernandez, Elmer Andres
Melanoma
2d Dige
Bioinformatics
Protein Expression
title_short Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
title_full Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
title_fullStr Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
title_full_unstemmed Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
title_sort Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study
dc.creator.none.fl_str_mv Fernandez, Elmer Andres
Girotti, Maria Romina
López del Olmo, Juan A.
Llera, Andrea Sabina
Podhajcer, Osvaldo Luis
Cantet, Rodolfo Juan Carlos
Balzarini, Monica Graciela
author Fernandez, Elmer Andres
author_facet Fernandez, Elmer Andres
Girotti, Maria Romina
López del Olmo, Juan A.
Llera, Andrea Sabina
Podhajcer, Osvaldo Luis
Cantet, Rodolfo Juan Carlos
Balzarini, Monica Graciela
author_role author
author2 Girotti, Maria Romina
López del Olmo, Juan A.
Llera, Andrea Sabina
Podhajcer, Osvaldo Luis
Cantet, Rodolfo Juan Carlos
Balzarini, Monica Graciela
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Melanoma
2d Dige
Bioinformatics
Protein Expression
topic Melanoma
2d Dige
Bioinformatics
Protein Expression
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Motivation: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. Results: We propose to identify DIGEDE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.
Fil: Fernandez, Elmer Andres. Universidad Católica de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Girotti, Maria Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: López del Olmo, Juan A.. Fundación Centro Nacional de Investigaciones Cardiovasculares; España
Fil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Podhajcer, Osvaldo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Motivation: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. Results: We propose to identify DIGEDE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.
publishDate 2008
dc.date.none.fl_str_mv 2008-12
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/11336/36959
Fernandez, Elmer Andres; Girotti, Maria Romina; López del Olmo, Juan A. ; Llera, Andrea Sabina; Podhajcer, Osvaldo Luis; et al.; Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study; Oxford University Press; Bioinformatics (Oxford, England); 24; 23; 12-2008; 2706-2712
1367-4803
CONICET Digital
CONICET
url http://hdl.handle.net/11336/36959
identifier_str_mv Fernandez, Elmer Andres; Girotti, Maria Romina; López del Olmo, Juan A. ; Llera, Andrea Sabina; Podhajcer, Osvaldo Luis; et al.; Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study; Oxford University Press; Bioinformatics (Oxford, England); 24; 23; 12-2008; 2706-2712
1367-4803
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://academic.oup.com/bioinformatics/article/24/23/2706/179880
info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btn508
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
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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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