A new distribution family for microarray data

The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative standpoint taken here is to search for models that fit the data, characterized by the presence of negative values, preser...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kelmansky, D.M., Ricci, L.
Formato:	JOUR
Materias:	Combined maximum likelihood estimators Data analysis Gpower-normal Microarrays Pseudo-dispersion models Truncated normal
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_20763905_v6_n1_p_Kelmansky
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_20763905_v6_n1_p_Kelmansky
record_format	dspace
spelling	todo:paper_20763905_v6_n1_p_Kelmansky2023-10-03T16:39:17Z A new distribution family for microarray data Kelmansky, D.M. Ricci, L. Combined maximum likelihood estimators Data analysis Gpower-normal Microarrays Pseudo-dispersion models Truncated normal The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative standpoint taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by p ∈ R is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. R codes are available from the authors upon request. © 2017 by the authors; licensee MDPI, Basel, Switzerland. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_20763905_v6_n1_p_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	Combined maximum likelihood estimators Data analysis Gpower-normal Microarrays Pseudo-dispersion models Truncated normal
spellingShingle	Combined maximum likelihood estimators Data analysis Gpower-normal Microarrays Pseudo-dispersion models Truncated normal Kelmansky, D.M. Ricci, L. A new distribution family for microarray data
topic_facet	Combined maximum likelihood estimators Data analysis Gpower-normal Microarrays Pseudo-dispersion models Truncated normal
description	The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative standpoint taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by p ∈ R is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. R codes are available from the authors upon request. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
format	JOUR
author	Kelmansky, D.M. Ricci, L.
author_facet	Kelmansky, D.M. Ricci, L.
author_sort	Kelmansky, D.M.
title	A new distribution family for microarray data
title_short	A new distribution family for microarray data
title_full	A new distribution family for microarray data
title_fullStr	A new distribution family for microarray data
title_full_unstemmed	A new distribution family for microarray data
title_sort	new distribution family for microarray data
url	http://hdl.handle.net/20.500.12110/paper_20763905_v6_n1_p_Kelmansky
work_keys_str_mv	AT kelmanskydm anewdistributionfamilyformicroarraydata AT riccil anewdistributionfamilyformicroarraydata AT kelmanskydm newdistributionfamilyformicroarraydata AT riccil newdistributionfamilyformicroarraydata
_version_	1782026320782295040

A new distribution family for microarray data

Ejemplares similares