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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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2013
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 09640caa a22011177a 4500 | ||
|---|---|---|---|
| 001 | PAPER-11096 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204112.0 | ||
| 008 | 190411s2013 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84888174557 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 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 Durbin, B.P., Hardin, J.S., Hawkins, D.M., Rocke, D.M., A variance-stabilizing transformation for gene-expression microarray data (2002) Bioinformatics, 18, pp. S105-S110 | ||
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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 | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888174557&doi=10.1515%2fsagmb-2012-0030&partnerID=40&md5=d6cc54e8acbd1cf50b967311bbc96967 |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.1515/sagmb-2012-0030 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15446115_v12_n6_p653_Kelmansky |y Registro en la Biblioteca Digital |
| 961 | |a paper_15446115_v12_n6_p653_Kelmansky |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||
| 999 | |c 72049 | ||