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rox: A Statistical Model for Regression with Missing Values

High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two co...

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Detalles Bibliográficos
Autores principales: Buyukozkan, Mustafa, Benedetti, Elisa, Krumsiek, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861384/
https://www.ncbi.nlm.nih.gov/pubmed/36677052
http://dx.doi.org/10.3390/metabo13010127
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author Buyukozkan, Mustafa
Benedetti, Elisa
Krumsiek, Jan
author_facet Buyukozkan, Mustafa
Benedetti, Elisa
Krumsiek, Jan
author_sort Buyukozkan, Mustafa
collection PubMed
description High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to deal with this issue include the removal of samples with missing values and imputation approaches that substitute the missing measurements with reasonable estimates. Both approaches, however, suffer from various shortcomings and pitfalls. In this paper, we present “rox”, a novel statistical model for the analysis of omics data with missing values without the need for imputation. The model directly incorporates missing values as “low” concentrations into the calculation. We show the superiority of rox over common approaches on simulated data and on six metabolomics datasets. Fully leveraging the information contained in LOD-based missing values, rox provides a powerful tool for the statistical analysis of omics data.
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spelling pubmed-98613842023-01-22 rox: A Statistical Model for Regression with Missing Values Buyukozkan, Mustafa Benedetti, Elisa Krumsiek, Jan Metabolites Article High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to deal with this issue include the removal of samples with missing values and imputation approaches that substitute the missing measurements with reasonable estimates. Both approaches, however, suffer from various shortcomings and pitfalls. In this paper, we present “rox”, a novel statistical model for the analysis of omics data with missing values without the need for imputation. The model directly incorporates missing values as “low” concentrations into the calculation. We show the superiority of rox over common approaches on simulated data and on six metabolomics datasets. Fully leveraging the information contained in LOD-based missing values, rox provides a powerful tool for the statistical analysis of omics data. MDPI 2023-01-13 /pmc/articles/PMC9861384/ /pubmed/36677052 http://dx.doi.org/10.3390/metabo13010127 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Buyukozkan, Mustafa
Benedetti, Elisa
Krumsiek, Jan
rox: A Statistical Model for Regression with Missing Values
title rox: A Statistical Model for Regression with Missing Values
title_full rox: A Statistical Model for Regression with Missing Values
title_fullStr rox: A Statistical Model for Regression with Missing Values
title_full_unstemmed rox: A Statistical Model for Regression with Missing Values
title_short rox: A Statistical Model for Regression with Missing Values
title_sort rox: a statistical model for regression with missing values
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861384/
https://www.ncbi.nlm.nih.gov/pubmed/36677052
http://dx.doi.org/10.3390/metabo13010127
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