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

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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 ana...

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Autores principales: Fernández, Elmer Andrés, Girotti, María R., López del Olmo, Juan A., Llera, Andrea S., Podhajcer, Osvaldo, Cantet, Rodolfo J. C., Balzarini, Mónica
Formato: Artículo acceptedVersion
Lenguaje:Español
Publicado: 2008
Materias:
TA Ingeniería de asistencia técnica (General). Ingeniería Civil (General)
Acceso en línea:http://pa.bibdigital.ucc.edu.ar/4179/1/A_Fern%C3%A1ndez_Girotti_L%C3%B3pezdelOlmo_Llera_Podhajcer_Cantet_Balzarini.pdf
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Producción Académica Universidad Católica de Córdoba (UCCor) de Universidad Católica de Córdoba
id I38-R144-4179
record_format dspace
spelling I38-R144-41792025-04-28T14:47:44Z http://pa.bibdigital.ucc.edu.ar/4179/ Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study Fernández, Elmer Andrés Girotti, María R. López del Olmo, Juan A. Llera, Andrea S. Podhajcer, Osvaldo Cantet, Rodolfo J. C. Balzarini, Mónica TA Ingeniería de asistencia técnica (General). Ingeniería Civil (General) 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 foldchanges 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 DIGE DE 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. 2008-12-31 application/pdf spa http://pa.bibdigital.ucc.edu.ar/4179/1/A_Fern%C3%A1ndez_Girotti_L%C3%B3pezdelOlmo_Llera_Podhajcer_Cantet_Balzarini.pdf Fernández, Elmer Andrés ORCID: https://orcid.org/0000-0002-4711-8634 <https://orcid.org/0000-0002-4711-8634>, Girotti, María R., López del Olmo, Juan A., Llera, Andrea S., Podhajcer, Osvaldo ORCID: https://orcid.org/0000-0002-6512-8553 <https://orcid.org/0000-0002-6512-8553>, Cantet, Rodolfo J. C. and Balzarini, Mónica (2008) Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study. Bioinformatics, 24 (23). pp. 2706-2712. ISSN 1460-2059 info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btn508 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/acceptedVersion Fil: Fernández, Elmer Andrés. Universidad Católica de Córdoba. Facultad de Ingeniería; Argentina Fil: Girotti, María R. Laboratory of Molecular and Cellular Therapy; Argentina Fil: López del Olmo, Juan A. 4Unidad de Proteómica, Centro Nacional de Investigaciones Cardiovasculares; España Fil: Llera, Andrea S. Laboratory of Molecular and Cellular Therapy; Argentina Fil: Podhajcer, Osvaldo. Laboratory of Molecular and Cellular Therapy; Argentina Fil: Cantet, Rodolfo J. C. Laboratory of Molecular and Cellular Therapy; Argentina Fil: Balzarini, Mónica. Laboratory of Molecular and Cellular Therapy; Argentina info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
institution Universidad Católica de Córdoba
institution_str I-38
repository_str R-144
collection Producción Académica Universidad Católica de Córdoba (UCCor)
language Español
orig_language_str_mv spa
topic TA Ingeniería de asistencia técnica (General). Ingeniería Civil (General)
spellingShingle TA Ingeniería de asistencia técnica (General). Ingeniería Civil (General)
Fernández, Elmer Andrés
Girotti, María R.
López del Olmo, Juan A.
Llera, Andrea S.
Podhajcer, Osvaldo
Cantet, Rodolfo J. C.
Balzarini, Mónica
Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study
topic_facet TA Ingeniería de asistencia técnica (General). Ingeniería Civil (General)
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 foldchanges 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 DIGE DE 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.
format Artículo
Artículo
acceptedVersion
author Fernández, Elmer Andrés
Girotti, María R.
López del Olmo, Juan A.
Llera, Andrea S.
Podhajcer, Osvaldo
Cantet, Rodolfo J. C.
Balzarini, Mónica
author_facet Fernández, Elmer Andrés
Girotti, María R.
López del Olmo, Juan A.
Llera, Andrea S.
Podhajcer, Osvaldo
Cantet, Rodolfo J. C.
Balzarini, Mónica
author_sort Fernández, Elmer Andrés
title Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study
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
publishDate 2008
url http://pa.bibdigital.ucc.edu.ar/4179/1/A_Fern%C3%A1ndez_Girotti_L%C3%B3pezdelOlmo_Llera_Podhajcer_Cantet_Balzarini.pdf
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