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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Detalles Bibliográficos
Autores principales: Kelmansky, D.M., Martínez, E.J., Leiva, V.
Formato: JOUR
Materias:
Classical and robust estimators
Linear models
Microarrays
Monte Carlo method
Power transformations
R software
Regression methods
article
contamination
data analysis
family
gene expression
genetic transformation
genomics
human
human genome
methodology
microarray analysis
statistical model
variance
Algorithms
Computer Simulation
Data Interpretation, Statistical
Gene Expression Profiling
Humans
Linear Models
Models, Genetic
Monte Carlo Method
Oligonucleotide Array Sequence Analysis
Software
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_15446115_v12_n6_p653_Kelmansky
record_format dspace
spelling todo:paper_15446115_v12_n6_p653_Kelmansky2023-10-03T16:22:59Z A new variance stabilizing transformation for gene expression data analysis Kelmansky, D.M. Martínez, E.J. Leiva, V. Classical and robust estimators Linear models Microarrays Monte Carlo method Power transformations R software Regression methods article contamination data analysis family gene expression genetic transformation genomics human human genome methodology microarray analysis Monte Carlo method statistical model variance Algorithms Computer Simulation Data Interpretation, Statistical Gene Expression Profiling Humans Linear Models Models, Genetic Monte Carlo Method Oligonucleotide Array Sequence Analysis Software 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. Fil:Kelmansky, D.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Martínez, E.J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Classical and robust estimators
Linear models
Microarrays
Monte Carlo method
Power transformations
R software
Regression methods
article
contamination
data analysis
family
gene expression
genetic transformation
genomics
human
human genome
methodology
microarray analysis
Monte Carlo method
statistical model
variance
Algorithms
Computer Simulation
Data Interpretation, Statistical
Gene Expression Profiling
Humans
Linear Models
Models, Genetic
Monte Carlo Method
Oligonucleotide Array Sequence Analysis
Software
spellingShingle Classical and robust estimators
Linear models
Microarrays
Monte Carlo method
Power transformations
R software
Regression methods
article
contamination
data analysis
family
gene expression
genetic transformation
genomics
human
human genome
methodology
microarray analysis
Monte Carlo method
statistical model
variance
Algorithms
Computer Simulation
Data Interpretation, Statistical
Gene Expression Profiling
Humans
Linear Models
Models, Genetic
Monte Carlo Method
Oligonucleotide Array Sequence Analysis
Software
Kelmansky, D.M.
Martínez, E.J.
Leiva, V.
A new variance stabilizing transformation for gene expression data analysis
topic_facet Classical and robust estimators
Linear models
Microarrays
Monte Carlo method
Power transformations
R software
Regression methods
article
contamination
data analysis
family
gene expression
genetic transformation
genomics
human
human genome
methodology
microarray analysis
Monte Carlo method
statistical model
variance
Algorithms
Computer Simulation
Data Interpretation, Statistical
Gene Expression Profiling
Humans
Linear Models
Models, Genetic
Monte Carlo Method
Oligonucleotide Array Sequence Analysis
Software
description 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.
format JOUR
author Kelmansky, D.M.
Martínez, E.J.
Leiva, V.
author_facet Kelmansky, D.M.
Martínez, E.J.
Leiva, V.
author_sort Kelmansky, D.M.
title A new variance stabilizing transformation for gene expression data analysis
title_short A new variance stabilizing transformation for gene expression data analysis
title_full A new variance stabilizing transformation for gene expression data analysis
title_fullStr A new variance stabilizing transformation for gene expression data analysis
title_full_unstemmed A new variance stabilizing transformation for gene expression data analysis
title_sort new variance stabilizing transformation for gene expression data analysis
url http://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky
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AT kelmanskydm newvariancestabilizingtransformationforgeneexpressiondataanalysis
AT martinezej newvariancestabilizingtransformationforgeneexpressiondataanalysis
AT leivav newvariancestabilizingtransformationforgeneexpressiondataanalysis
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