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GEAMM v.1.4: a versatile program for mixed model analysis of gene expression data

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GEAMM v.1.4: a versatile program for mixed model analysis of gene expression data

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Nombre: Casellas;Ibáñez-E ...
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dc.contributor.author Casellas, J. es_ES
dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.contributor.author Martinez-Giner, M. es_ES
dc.contributor.author Varona, L. es_ES
dc.date.accessioned 2020-04-17T12:52:41Z
dc.date.available 2020-04-17T12:52:41Z
dc.date.issued 2008-02 es_ES
dc.identifier.issn 0268-9146 es_ES
dc.identifier.uri http://hdl.handle.net/10251/140990
dc.description.abstract [EN] This note presents the program geamm v.1.4 (Gene Expression Analysis with Mixed Models), a versatile software program to analyse appropriately normalized gene expression data by a heteroskedastic mixed model, and allowing for both discrete and continuous effects in model. Although the analysis of gene expression data is an area of intensive software development, available programs are focused on the comparison between discrete¿type effects1 (e.g., health vs. cancerous cells2), whereas continuous effects like age3 cannot be easily accommodated. To our best knowledge, this is the first microarray¿specific program allowing for an easy and flexible modelling of continuous effects on gene expression analysis. Mixed model equations are implemented under Bayesian inference and solved through Gibbs sampling. The program, user's guide and example files can be downloaded free of charge from http://www.bdporc.irta.es/Publicacions/GEAMM.zip. A user's guide is also available as Appendix S1. es_ES
dc.description.sponsorship The research contract of J. Casellas was partially financed by Spain s Ministerio de Educación y Ciencia (programa Juan de la Cierva). The authors are indebted to J. L. Noguera, R. Quintanilla, R. N. Pena and A. Cánovas for their helpful comments. This software has been developed within the frame of research projects AGL2004-08368-C03/GAN, AGL2002-04271-C03 and GEN2003-20658-C05-05. GEAMM v.1.4 has been extensively tested on several data sets and results were checked for consistency with alternative software when possible.7 The authors would appreciate to be informed of any detected bug. es_ES
dc.language Inglés es_ES
dc.publisher Blackwell Publishing es_ES
dc.relation.ispartof Animal Genetics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title GEAMM v.1.4: a versatile program for mixed model analysis of gene expression data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1111/j.1365-2052.2007.01670.x es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MEC//AGL2004-08368-C03/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICYT//AGL2002-04271-C03/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICYT//GEN2003-20658-C05-05/ES/Análisis genómico mediante microarrays de la arquitectura genética de fenotipos complejos en porcino/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.description.bibliographicCitation Casellas, J.; Ibáñez-Escriche, N.; Martinez-Giner, M.; Varona, L. (2008). GEAMM v.1.4: a versatile program for mixed model analysis of gene expression data. Animal Genetics. 39:89-90. https://doi.org/10.1111/j.1365-2052.2007.01670.x es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1111/j.1365-2052.2007.01670.x es_ES
dc.description.upvformatpinicio 89 es_ES
dc.description.upvformatpfin 90 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 39 es_ES
dc.relation.pasarela S\392558 es_ES
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.contributor.funder Ministerio de Ciencia y Tecnología es_ES
dc.description.references Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-25. doi:10.2202/1544-6115.1027 es_ES
dc.description.references Quackenbush, J. (2006). Microarray Analysis and Tumor Classification. New England Journal of Medicine, 354(23), 2463-2472. doi:10.1056/nejmra042342 es_ES
dc.description.references Ghosh, D., & Chinnaiyan, A. M. (2004). Covariate adjustment in the analysis of microarray data from clinical studies. Functional & Integrative Genomics, 5(1), 18-27. doi:10.1007/s10142-004-0120-3 es_ES
dc.description.references Wolfinger, R. D., Gibson, G., Wolfinger, E. D., Bennett, L., Hamadeh, H., Bushel, P., … Paules, R. S. (2001). Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models. Journal of Computational Biology, 8(6), 625-637. doi:10.1089/106652701753307520 es_ES
dc.description.references Patterson, T. A., Lobenhofer, E. K., Fulmer-Smentek, S. B., Collins, P. J., Chu, T.-M., Bao, W., … Wolfinger, R. D. (2006). Performance comparison of one-color and two-color platforms within the Microarray Quality Control (MAQC) project. Nature Biotechnology, 24(9), 1140-1150. doi:10.1038/nbt1242 es_ES


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