A new variance stabilizing transformation for gene expression data analysis

In this paper, we introduce a new family of power transformations, which has the generalized logarithm as one of its members, in the same manner as the usual logarithm belongs to the family of Box-Cox power transformations. Although the new family has been developed for analyzing gene expression dat...

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Autor principal: Kelmansky, D.M
Otros Autores: Martínez, E.J, Leiva, V.
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2013
Acceso en línea:Registro en Scopus
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100 1 |a Kelmansky, D.M. 
245 1 2 |a A new variance stabilizing transformation for gene expression data analysis 
260 |c 2013 
270 1 0 |m Leiva, V.; Departamento de Estadística, Universidad de Valparaíso, Avda. Gran Bretaña 1111, Playa Ancha, Valparaíso, Chile; email: victor.leiva@uv.cl 
506 |2 openaire  |e Política editorial 
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504 |a Kotz, S., Leiva, V., Sanhueza, A., Two new mixture models related to the inverse Gaussian distribution (2010) Meth. Comp. App. Prob, 12, pp. 199-212 
504 |a Leiva, V., Hernández, H., Riquelme, A., A new package for the Birnbaum-Saunders distribution (2006) R J, 6, pp. 35-40 
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504 |a Leiva, V., Sanhueza, A., Kelmansky, D.M., Martínez, E.J., On the glog-normal distribution and its association with the gene expression problem (2009) Comp. Stat. Data Anal, 53, pp. 1613-1621 
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520 3 |a In this paper, we introduce a new family of power transformations, which has the generalized logarithm as one of its members, in the same manner as the usual logarithm belongs to the family of Box-Cox power transformations. Although the new family has been developed for analyzing gene expression data, it allows a wider scope of mean-variance related data to be reached. We study the analytical properties of the new family of transformations, as well as the mean-variance relationships that are stabilized by using its members. We propose a methodology based on this new family, which includes a simple strategy for selecting the family member adequate for a data set. We evaluate the finite sample behavior of different classical and robust estimators based on this strategy by Monte Carlo simulations. We analyze real genomic data by using the proposed transformation to empirically show how the new methodology allows the variance of these data to be stabilized.  |l eng 
536 |a Detalles de la financiación: Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, 1120879 
536 |a Detalles de la financiación: Comisión Nacional de Investigación Científica y Tecnológica, CONICYT 
536 |a Detalles de la financiación: Universidad de Buenos Aires 
536 |a Detalles de la financiación: Comisión Nacional de Investigación Científica y Tecnológica, CONICYT 
536 |a Detalles de la financiación: Acknowledgements: The authors wish to thank the Editor-in-Chief, Prof. Michael Stumpf, and anonymous referees for their constructive comments on an earlier version of this manuscript, which resulted in this improved version. The research work of Dr. D.M. Kelmansky and Dr. E.J. Martínez was partially supported by the X-018 grant of the Universidad de Buenos Aires, and the PICT PRH 01-01 grant of the Agencia Nacional de Promoción Científica y Tecnológica, from Argentina. The research work of Dr. Víctor Leiva was partially supported by FONDECYT 1120879 grant of the Comisión Nacional de Investigación Científica y Tecnológica, from Chile. 
593 |a Departamento de Estadística, Universidad de Valparaíso, Avda. Gran Bretaña 1111, Playa Ancha, Valparaíso, Chile 
593 |a Instituto de Cálculo, FCEN, Universidad de Buenos Aires, Argentina 
690 1 0 |a CLASSICAL AND ROBUST ESTIMATORS 
690 1 0 |a LINEAR MODELS 
690 1 0 |a MICROARRAYS 
690 1 0 |a MONTE CARLO METHOD 
690 1 0 |a POWER TRANSFORMATIONS 
690 1 0 |a R SOFTWARE 
690 1 0 |a REGRESSION METHODS 
690 1 0 |a ARTICLE 
690 1 0 |a CONTAMINATION 
690 1 0 |a DATA ANALYSIS 
690 1 0 |a FAMILY 
690 1 0 |a GENE EXPRESSION 
690 1 0 |a GENETIC TRANSFORMATION 
690 1 0 |a GENOMICS 
690 1 0 |a HUMAN 
690 1 0 |a HUMAN GENOME 
690 1 0 |a METHODOLOGY 
690 1 0 |a MICROARRAY ANALYSIS 
690 1 0 |a MONTE CARLO METHOD 
690 1 0 |a STATISTICAL MODEL 
690 1 0 |a VARIANCE 
690 1 0 |a ALGORITHMS 
690 1 0 |a COMPUTER SIMULATION 
690 1 0 |a DATA INTERPRETATION, STATISTICAL 
690 1 0 |a GENE EXPRESSION PROFILING 
690 1 0 |a HUMANS 
690 1 0 |a LINEAR MODELS 
690 1 0 |a MODELS, GENETIC 
690 1 0 |a MONTE CARLO METHOD 
690 1 0 |a OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS 
690 1 0 |a SOFTWARE 
700 1 |a Martínez, E.J. 
700 1 |a Leiva, V. 
773 0 |d 2013  |g v. 12  |h pp. 653-666  |k n. 6  |p Stat. Appl. Genet. Mol. Biol.  |x 15446115  |t Statistical Applications in Genetics and Molecular Biology 
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