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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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15446115_v12_n6_p653_Kelmansky http://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky |
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paper:paper_15446115_v12_n6_p653_Kelmansky2023-06-08T16:21:09Z A new variance stabilizing transformation for gene expression data analysis Kelmansky, Diana M. Martínez, Elena Julia 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. 2013 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15446115_v12_n6_p653_Kelmansky 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, Diana M. Martínez, Elena Julia 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. |
author |
Kelmansky, Diana M. Martínez, Elena Julia |
author_facet |
Kelmansky, Diana M. Martínez, Elena Julia |
author_sort |
Kelmansky, Diana 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 |
publishDate |
2013 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15446115_v12_n6_p653_Kelmansky http://hdl.handle.net/20.500.12110/paper_15446115_v12_n6_p653_Kelmansky |
work_keys_str_mv |
AT kelmanskydianam anewvariancestabilizingtransformationforgeneexpressiondataanalysis AT martinezelenajulia anewvariancestabilizingtransformationforgeneexpressiondataanalysis AT kelmanskydianam newvariancestabilizingtransformationforgeneexpressiondataanalysis AT martinezelenajulia newvariancestabilizingtransformationforgeneexpressiondataanalysis |
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1768543770440105984 |